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Sample records for biomolecular reaction networks

  1. Stochastic Simulation of Biomolecular Reaction Networks Using the Biomolecular Network Simulator Software

    Science.gov (United States)

    2008-02-01

    investigate the simulation of a biomolecular reaction network with BNS, a simple model of a generic self-assembling catalytic ligation reaction in a...Amino Acid Pools Nucleotide Triphosphate Pools Nucleotide Monophosphate Pools Ligation Reaction 1551 517 7 RESULTS Simulation of exemplar...and reaction r8 is the catalytic ligation reaction . In figures 5(B) through 5(F), both the time-averaged event rate for a single simulation run

  2. Stochastic Simulation and Analysis of Biomolecular Reaction Networks

    Science.gov (United States)

    2009-01-01

    a discrete stochastic system, a hypothetical model of a generic two gene, self- assembling catalytic ligation reaction in a cell-free tran- scription...ligation reactions and the tRNA charging reactions terminate. Third, the first metabolic ligation reaction terminated when Sub_1 was depleted at about...2900 sec and subsequently, the second metabolic ligation reaction would have terminated when all of Prod_A formed by the first ligation reaction was

  3. Stochastic Simulation of Biomolecular Networks in Dynamic Environments.

    Directory of Open Access Journals (Sweden)

    Margaritis Voliotis

    2016-06-01

    Full Text Available Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate-using decision-making by a large population of quorum sensing bacteria-that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.

  4. The fidelity of dynamic signaling by noisy biomolecular networks.

    Directory of Open Access Journals (Sweden)

    Clive G Bowsher

    Full Text Available Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.

  5. Biomolecular Network-Based Synergistic Drug Combination Discovery

    Directory of Open Access Journals (Sweden)

    Xiangyi Li

    2016-01-01

    Full Text Available Drug combination is a powerful and promising approach for complex disease therapy such as cancer and cardiovascular disease. However, the number of synergistic drug combinations approved by the Food and Drug Administration is very small. To bridge the gap between urgent need and low yield, researchers have constructed various models to identify synergistic drug combinations. Among these models, biomolecular network-based model is outstanding because of its ability to reflect and illustrate the relationships among drugs, disease-related genes, therapeutic targets, and disease-specific signaling pathways as a system. In this review, we analyzed and classified models for synergistic drug combination prediction in recent decade according to their respective algorithms. Besides, we collected useful resources including databases and analysis tools for synergistic drug combination prediction. It should provide a quick resource for computational biologists who work with network medicine or synergistic drug combination designing.

  6. The interplay of intrinsic and extrinsic bounded noises in biomolecular networks.

    Directory of Open Access Journals (Sweden)

    Giulio Caravagna

    Full Text Available After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a biomolecular network. The influence of intrinsic and extrinsic noises on biomolecular networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: (i the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, (ii a model of enzymatic futile cycle and (iii a genetic toggle switch. In (ii and (iii we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possible functional role of bounded noises.

  7. Mining, modeling, and evaluation of subnetworks from large biomolecular networks and its comparison study.

    Science.gov (United States)

    Hu, Xiaohua; Ng, Michael; Wu, Fang-Xiang; Sokhansanj, Bahrad A

    2009-03-01

    In this paper, we present a novel method to mine, model, and evaluate a regulatory system executing cellular functions that can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to such a biomolecular network to obtain various subnetworks. Second, computational models are generated for the subnetworks and simulated to predict their behavior in the cellular context. We discuss and evaluate some of the advanced computational modeling approaches, in particular, state-space modeling, probabilistic Boolean network modeling, and fuzzy logic modeling. The modeling and simulation results represent hypotheses that are tested against high-throughput biological datasets (microarrays and/or genetic screens) under normal and perturbation conditions. Experimental results on time-series gene expression data for the human cell cycle indicate that our approach is promising for subnetwork mining and simulation from large biomolecular networks.

  8. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

    DEFF Research Database (Denmark)

    Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens

    2008-01-01

    Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem...... is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology...... with transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...

  9. Accelerated search for biomolecular network models to interpret high-throughput experimental data

    Directory of Open Access Journals (Sweden)

    Sokhansanj Bahrad A

    2007-07-01

    Full Text Available Abstract Background The functions of human cells are carried out by biomolecular networks, which include proteins, genes, and regulatory sites within DNA that encode and control protein expression. Models of biomolecular network structure and dynamics can be inferred from high-throughput measurements of gene and protein expression. We build on our previously developed fuzzy logic method for bridging quantitative and qualitative biological data to address the challenges of noisy, low resolution high-throughput measurements, i.e., from gene expression microarrays. We employ an evolutionary search algorithm to accelerate the search for hypothetical fuzzy biomolecular network models consistent with a biological data set. We also develop a method to estimate the probability of a potential network model fitting a set of data by chance. The resulting metric provides an estimate of both model quality and dataset quality, identifying data that are too noisy to identify meaningful correlations between the measured variables. Results Optimal parameters for the evolutionary search were identified based on artificial data, and the algorithm showed scalable and consistent performance for as many as 150 variables. The method was tested on previously published human cell cycle gene expression microarray data sets. The evolutionary search method was found to converge to the results of exhaustive search. The randomized evolutionary search was able to converge on a set of similar best-fitting network models on different training data sets after 30 generations running 30 models per generation. Consistent results were found regardless of which of the published data sets were used to train or verify the quantitative predictions of the best-fitting models for cell cycle gene dynamics. Conclusion Our results demonstrate the capability of scalable evolutionary search for fuzzy network models to address the problem of inferring models based on complex, noisy biomolecular

  10. Architecture of transcriptional regulatory circuits is knitted over the topology of bio-molecular interaction networks

    Directory of Open Access Journals (Sweden)

    Nielsen Jens

    2008-02-01

    Full Text Available Abstract Background Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with transcriptome data, thereby allowing the identification of key biological features (Reporter Features around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes. Conclusion Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that are significantly affected between or across conditions. Results of the Reporter Feature analysis not only provide a snapshot of the transcriptional regulatory program but also are biologically easy to interpret and provide a powerful way to generate new hypotheses. Our Reporter Features analyses of yeast glucose repression and human diabetes data brings hints towards the understanding of the principles of transcriptional regulation controlling these two important and potentially closely related systems.

  11. Translated chemical reaction networks.

    Science.gov (United States)

    Johnston, Matthew D

    2014-05-01

    Many biochemical and industrial applications involve complicated networks of simultaneously occurring chemical reactions. Under the assumption of mass action kinetics, the dynamics of these chemical reaction networks are governed by systems of polynomial ordinary differential equations. The steady states of these mass action systems have been analyzed via a variety of techniques, including stoichiometric network analysis, deficiency theory, and algebraic techniques (e.g., Gröbner bases). In this paper, we present a novel method for characterizing the steady states of mass action systems. Our method explicitly links a network's capacity to permit a particular class of steady states, called toric steady states, to topological properties of a generalized network called a translated chemical reaction network. These networks share their reaction vectors with their source network but are permitted to have different complex stoichiometries and different network topologies. We apply the results to examples drawn from the biochemical literature.

  12. Autocatalysis in reaction networks.

    Science.gov (United States)

    Deshpande, Abhishek; Gopalkrishnan, Manoj

    2014-10-01

    The persistence conjecture is a long-standing open problem in chemical reaction network theory. It concerns the behavior of solutions to coupled ODE systems that arise from applying mass-action kinetics to a network of chemical reactions. The idea is that if all reactions are reversible in a weak sense, then no species can go extinct. A notion that has been found useful in thinking about persistence is that of "critical siphon." We explore the combinatorics of critical siphons, with a view toward the persistence conjecture. We introduce the notions of "drainable" and "self-replicable" (or autocatalytic) siphons. We show that: Every minimal critical siphon is either drainable or self-replicable; reaction networks without drainable siphons are persistent; and nonautocatalytic weakly reversible networks are persistent. Our results clarify that the difficulties in proving the persistence conjecture are essentially due to competition between drainable and self-replicable siphons.

  13. A Theoretical Study of Distribution of First Passage Times of Biomolecular Folding and Reactions with Application to Single Molecules

    Science.gov (United States)

    Wang, Jin; Leite, Vitor; Stell, George; Lee, Chi-Lun

    2002-03-01

    We study the distribution of first passage times of biomolecular folding and reactions through the general framework of energy landscape theory. Both the analytical and lattice simulation results show above cirtain specific temperature, the distribution of first passage time is log-normal, while under the same temperature, the distribution starts to develop fatty tails and deviate from the log-normal distribution, indicating intermittency whereas rare events might dominate the whole process. A power law distribution of first passage time was found analytically in this situation. Applications and connections to experiments on single molecule reaction dynamics are studied.

  14. Modeling stochasticity in biochemical reaction networks

    Science.gov (United States)

    Constantino, P. H.; Vlysidis, M.; Smadbeck, P.; Kaznessis, Y. N.

    2016-03-01

    Small biomolecular systems are inherently stochastic. Indeed, fluctuations of molecular species are substantial in living organisms and may result in significant variation in cellular phenotypes. The chemical master equation (CME) is the most detailed mathematical model that can describe stochastic behaviors. However, because of its complexity the CME has been solved for only few, very small reaction networks. As a result, the contribution of CME-based approaches to biology has been very limited. In this review we discuss the approach of solving CME by a set of differential equations of probability moments, called moment equations. We present different approaches to produce and to solve these equations, emphasizing the use of factorial moments and the zero information entropy closure scheme. We also provide information on the stability analysis of stochastic systems. Finally, we speculate on the utility of CME-based modeling formalisms, especially in the context of synthetic biology efforts.

  15. iGNM 2.0: the Gaussian network model database for biomolecular structural dynamics.

    Science.gov (United States)

    Li, Hongchun; Chang, Yuan-Yu; Yang, Lee-Wei; Bahar, Ivet

    2016-01-04

    Gaussian network model (GNM) is a simple yet powerful model for investigating the dynamics of proteins and their complexes. GNM analysis became a broadly used method for assessing the conformational dynamics of biomolecular structures with the development of a user-friendly interface and database, iGNM, in 2005. We present here an updated version, iGNM 2.0 http://gnmdb.csb.pitt.edu/, which covers more than 95% of the structures currently available in the Protein Data Bank (PDB). Advanced search and visualization capabilities, both 2D and 3D, permit users to retrieve information on inter-residue and inter-domain cross-correlations, cooperative modes of motion, the location of hinge sites and energy localization spots. The ability of iGNM 2.0 to provide structural dynamics data on the large majority of PDB structures and, in particular, on their biological assemblies makes it a useful resource for establishing the bridge between structure, dynamics and function.

  16. Thermodynamics of random reaction networks.

    Directory of Open Access Journals (Sweden)

    Jakob Fischer

    Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.

  17. Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications.

    Science.gov (United States)

    Verma, Arjun; Fratto, Brian E; Privman, Vladimir; Katz, Evgeny

    2016-07-05

    We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s) as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.

  18. Design of Flow Systems for Improved Networking and Reduced Noise in Biomolecular Signal Processing in Biocomputing and Biosensing Applications

    Directory of Open Access Journals (Sweden)

    Arjun Verma

    2016-07-01

    Full Text Available We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.

  19. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular networks

    NARCIS (Netherlands)

    Colak, R.; Moser, F.; Shu, J.; Schoenhuth, A.; Chen, N.; Ester, M.

    2010-01-01

    Background Computational prediction of functionally related groups of genes (functional modules) from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustive

  20. Module discovery by exhaustive search for densely connected, co-expressed regions in biomolecular interaction networks.

    Directory of Open Access Journals (Sweden)

    Recep Colak

    Full Text Available BACKGROUND: Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. METHODOLOGY/PRINCIPAL FINDINGS: We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. CONCLUSION/SIGNIFICANCE: We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze

  1. Modeling and Analysis of Abnormality Detection in Biomolecular Nano-Networks

    CERN Document Server

    Ghavami, Siavash; Masoudi-Nejad, Ali

    2012-01-01

    A scheme for detection of abnormality in molecular nano-networks is proposed. This is motivated by the fact that early diagnosis, classification and detection of diseases such as cancer play a crucial role in their successful treatment. The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells as abnormality that is captured by variations in parameters of a nano-communications channel. In the second step, the SNMs transmit micro-scale messages over a noisy micro communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals. The detection performance of each SNM is analyzed by setting up a Neyman-Pearson test. Next, taking into account the effect of the MCC, the overall performance of the proposed NADS is quantified in terms of probabilities of misdetection and false alarm. A design problem is formulated, ...

  2. A Networks Approach to Modeling Enzymatic Reactions.

    Science.gov (United States)

    Imhof, P

    2016-01-01

    Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes.

  3. Atoms of multistationarity in chemical reaction networks

    CERN Document Server

    Joshi, Badal

    2011-01-01

    Chemical reaction networks taken with mass-action kinetics are dynamical systems that arise in chemical engineering and systems biology. Deciding whether a chemical reaction network admits multiple positive steady states is to determine existence of multiple positive solutions to a system of polynomials with unknown coefficients. In this work, we consider the question of whether the minimal (in a precise sense) networks, which we propose to call `atoms of multistationarity,' characterize the entire set of multistationary networks. We show that if a subnetwork admits multiple nondegenerate positive steady states, then these steady states can be extended to establish multistationarity of a larger network, provided that the two networks share the same stoichiometric subspace. Our result provides the mathematical foundation for a technique used by Siegal-Gaskins et al. of establishing bistability by way of `network ancestry.' Here, our main application is for enumerating small multistationary continuous-flow stir...

  4. Limits for Stochastic Reaction Networks

    DEFF Research Database (Denmark)

    Cappelletti, Daniele

    at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...... of the stochastic reaction systems. Specically, we build a theory for stochastic reaction systems that is parallel to the deciency zero theory for deterministic systems, which dates back to the 70s. A deciency theory for stochastic reaction systems was missing, and few results connecting deciency and stochastic....... Such species, in the deterministic modelling regime, assume always the same value at any positive steady state. In the stochastic setting, we prove that, if the initial condition is a point in the basin of attraction of a positive steady state of the corresponding deterministic model and tends to innity...

  5. Multilayer Network Analysis of Nuclear Reactions

    Science.gov (United States)

    Zhu, Liang; Ma, Yu-Gang; Chen, Qu; Han, Ding-Ding

    2016-08-01

    The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, 4He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the β-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart.

  6. Multilayer network analysis of nuclear reactions

    CERN Document Server

    Zhu, Liang; Chen, Qu; Han, Ding-Ding

    2016-01-01

    The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, $^4$He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the $\\beta$-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart.

  7. Programming in biomolecular computation

    DEFF Research Database (Denmark)

    Hartmann, Lars Røeboe; Jones, Neil; Simonsen, Jakob Grue

    2010-01-01

    executable, but are also compilable and interpretable. It is universal: all computable functions can be computed (in natural ways and without arcane encodings of data and algorithm); it is also uniform: new “hardware” is not needed to solve new problems; and (last but not least) it is Turing complete......Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We introduce a model of computation that is evidently programmable......, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only...

  8. Programming in Biomolecular Computation

    DEFF Research Database (Denmark)

    Hartmann, Lars; Jones, Neil; Simonsen, Jakob Grue

    2010-01-01

    Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We introduce a model of computation that is evidently programmable......, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only...... in a strong sense: a universal algorithm exists, that is able to execute any program, and is not asymptotically inefficient. A prototype model has been implemented (for now in silico on a conventional computer). This work opens new perspectives on just how computation may be specified at the biological level....

  9. Law of localization in chemical reaction networks

    CERN Document Server

    Okada, Takashi

    2016-01-01

    In living cells, chemical reactions are connected by sharing their products and substrates, and form complex networks, e.g. metabolic pathways. Here we developed a theory to predict the sensitivity, i.e. the responses of concentrations and fluxes to perturbations of enzymes, from network structure alone. Responses turn out to exhibit two characteristic patterns, $localization$ and $hierarchy$. We present a general theorem connecting sensitivity with network topology that explains these characteristic patterns. Our results imply that network topology is an origin of biological robustness. Finally, we suggest a strategy to determine real networks from experimental measurements.

  10. Concordant chemical reaction networks and the Species-Reaction Graph.

    Science.gov (United States)

    Shinar, Guy; Feinberg, Martin

    2013-01-01

    In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network's Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams.

  11. Markovian Dynamics on Complex Reaction Networks

    CERN Document Server

    Goutsias, John

    2012-01-01

    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underling population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions, the computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating...

  12. Applications of dynamic nuclear polarization to the study of reactions and reagents in organic and biomolecular chemistry.

    Science.gov (United States)

    Hilty, Christian; Bowen, Sean

    2010-08-07

    Nuclear Magnetic Resonance (NMR) is an important spectroscopic tool for the identification and structural characterization of molecules in chemistry and biochemistry. The most significant limitation of NMR compared to other spectroscopies is its relatively low sensitivity, which thus often requires long measurement times or large amounts of sample. A way of increasing sensitivity of single scan NMR spectra by several orders of magnitude is through hyperpolarization of nuclear spins. Dynamic nuclear polarization allows hyperpolarization of most spins in small molecules encountered in chemistry and biochemistry. NMR spectra of small amounts of samples from natural source, or from chemical synthesis can readily be acquired. Perhaps more interestingly, the availability of the entire hyperpolarized NMR signal in one single scan allows the measurement of transient processes in real time, if applied together with a stopped-flow technique. Through observation of chemical shift, different reactant and product species can be distinguished, and kinetics and mechanisms, for example in enzyme catalyzed reactions, can be elucidated. Real-time hyperpolarization-enhanced NMR is uniquely amenable to correlating atomic positions not only through space, but also over time between reactant and product species. Such correlations carry mechanistic information about a reaction, and can prove reaction pathways. Applications of this technique are emerging in different areas of chemistry concerned with rapid reactions, including not only enzymatic processes, but also chemical catalysis and protein folding.

  13. Characterizing multistationarity regimes in biochemical reaction networks.

    Directory of Open Access Journals (Sweden)

    Irene Otero-Muras

    Full Text Available Switch like responses appear as common strategies in the regulation of cellular systems. Here we present a method to characterize bistable regimes in biochemical reaction networks that can be of use to both direct and reverse engineering of biological switches. In the design of a synthetic biological switch, it is important to study the capability for bistability of the underlying biochemical network structure. Chemical Reaction Network Theory (CRNT may help at this level to decide whether a given network has the capacity for multiple positive equilibria, based on their structural properties. However, in order to build a working switch, we also need to ensure that the bistability property is robust, by studying the conditions leading to the existence of two different steady states. In the reverse engineering of biological switches, knowledge collected about the bistable regimes of the underlying potential model structures can contribute at the model identification stage to a drastic reduction of the feasible region in the parameter space of search. In this work, we make use and extend previous results of the CRNT, aiming not only to discriminate whether a biochemical reaction network can exhibit multiple steady states, but also to determine the regions within the whole space of parameters capable of producing multistationarity. To that purpose we present and justify a condition on the parameters of biochemical networks for the appearance of multistationarity, and propose an efficient and reliable computational method to check its satisfaction through the parameter space.

  14. Retroactivity in the Context of Modularly Structured Biomolecular Systems

    Science.gov (United States)

    Pantoja-Hernández, Libertad; Martínez-García, Juan Carlos

    2015-01-01

    Synthetic biology has intensively promoted the technical implementation of modular strategies in the fabrication of biological devices. Modules are considered as networks of reactions. The behavior displayed by biomolecular systems results from the information processes carried out by the interconnection of the involved modules. However, in natural systems, module wiring is not a free-of-charge process; as a consequence of interconnection, a reactive phenomenon called retroactivity emerges. This phenomenon is characterized by signals that propagate from downstream modules (the modules that receive the incoming signals upon interconnection) to upstream ones (the modules that send the signals upon interconnection). Such retroactivity signals, depending of their strength, may change and sometimes even disrupt the behavior of modular biomolecular systems. Thus, analysis of retroactivity effects in natural biological and biosynthetic systems is crucial to achieve a deeper understanding of how this interconnection between functionally characterized modules takes place and how it impacts the overall behavior of the involved cell. By discussing the modules interconnection in natural and synthetic biomolecular systems, we propose that such systems should be considered as quasi-modular. PMID:26137457

  15. Mean field interaction in biochemical reaction networks

    KAUST Repository

    Tembine, Hamidou

    2011-09-01

    In this paper we establish a relationship between chemical dynamics and mean field game dynamics. We show that chemical reaction networks can be studied using noisy mean field limits. We provide deterministic, noisy and switching mean field limits and illustrate them with numerical examples. © 2011 IEEE.

  16. Hamiltonian perspective on compartmental reaction-diffusion networks

    NARCIS (Netherlands)

    Seslija, Marko; van der Schaft, Arjan; Scherpen, Jacquelien M. A.

    2014-01-01

    Inspired by the recent developments in modeling and analysis of reaction networks, we provide a geometric formulation of the reversible reaction networks under the influence of diffusion. Using the graph knowledge of the underlying reaction network, the obtained reaction diffusion system is a distri

  17. Combinatorics of reaction-network posets.

    Science.gov (United States)

    Klein, Douglas J; Ivanciuc, Teodora; Ryzhov, Anton; Ivanciuc, Ovidiu

    2008-11-01

    Reaction networks are viewed as derived from ordinary molecular structures related in reactant-product pairs so as to manifest a chemical super-structure. Such super-structures then are candidates for applications in a general combinatoric chemistry. Notable additional characterization of a reaction super-structure occurs when such reaction graphs are directed, as for example when there is progressive substitution (or addition) on a fixed molecular skeleton. Such a set of partially ordered entities is in mathematics termed a poset, which further manifests a number of special properties, as then might be utilized in different applications. Focus on the overall "super-structural" poset goes beyond ordinary molecular structure in attending to how a structure fits into a (reaction) network, and thereby brings an extra "dimension" to conventional stereochemical theory. The possibility that different molecular properties vary smoothly along chains of interconnections in such a super-structure is a natural assumption for a novel approach to molecular property and bioactivity correlations. Different manners to interpolate/extrapolate on a poset network yield quantitative super-structure/activity relationships (QSSARs), with some numerical fits, e.g., for properties of polychlorinated biphenyls (PCBs) seemingly being quite reasonable. There seems to be promise for combinatoric posetic ideas.

  18. Neural Networks in Chemical Reaction Dynamics

    CERN Document Server

    Raff, Lionel; Hagan, Martin

    2011-01-01

    This monograph presents recent advances in neural network (NN) approaches and applications to chemical reaction dynamics. Topics covered include: (i) the development of ab initio potential-energy surfaces (PES) for complex multichannel systems using modified novelty sampling and feedforward NNs; (ii) methods for sampling the configuration space of critical importance, such as trajectory and novelty sampling methods and gradient fitting methods; (iii) parametrization of interatomic potential functions using a genetic algorithm accelerated with a NN; (iv) parametrization of analytic interatomic

  19. Elimination of intermediate species in multiscale stochastic reaction networks

    DEFF Research Database (Denmark)

    Cappelletti, Daniele; Wiuf, Carsten

    2016-01-01

    We study networks of biochemical reactions modelled by continuoustime Markov processes. Such networks typically contain many molecular species and reactions and are hard to study analytically as well as by simulation. Particularly, we are interested in reaction networks with intermediate species ...

  20. Chemical Reaction Networks for Computing Polynomials.

    Science.gov (United States)

    Salehi, Sayed Ahmad; Parhi, Keshab K; Riedel, Marc D

    2017-01-20

    Chemical reaction networks (CRNs) provide a fundamental model in the study of molecular systems. Widely used as formalism for the analysis of chemical and biochemical systems, CRNs have received renewed attention as a model for molecular computation. This paper demonstrates that, with a new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these functions must map the unit interval to itself. These polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. In the proposed encoding approach, each variable is represented using two molecular types: a type-0 and a type-1. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of type-0 and type-1 molecules. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. Molecular encoders for converting any input in a standard representation to the fractional representation as well as decoders for converting the computed output from the fractional to a standard representation are presented. The method is illustrated first for generic CRNs; then chemical reactions designed for an example are mapped to DNA strand-displacement reactions.

  1. Programming in biomolecular computation

    DEFF Research Database (Denmark)

    Hartmann, Lars Røeboe; Jones, Neil; Simonsen, Jakob Grue

    2011-01-01

    Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We identify a number of common features in programming that seem conspicu...

  2. Structural simplification of chemical reaction networks in partial steady states.

    Science.gov (United States)

    Madelaine, Guillaume; Lhoussaine, Cédric; Niehren, Joachim; Tonello, Elisa

    2016-11-01

    We study the structural simplification of chemical reaction networks with partial steady state semantics assuming that the concentrations of some but not all species are constant. We present a simplification rule that can eliminate intermediate species that are in partial steady state, while preserving the dynamics of all other species. Our simplification rule can be applied to general reaction networks with some but few restrictions on the possible kinetic laws. We can also simplify reaction networks subject to conservation laws. We prove that our simplification rule is correct when applied to a module of a reaction network, as long as the partial steady state is assumed with respect to the complete network. Michaelis-Menten's simplification rule for enzymatic reactions falls out as a special case. We have implemented an algorithm that applies our simplification rules repeatedly and applied it to reaction networks from systems biology.

  3. Safe design of cooled tubular reactors for exothermic multiple reactions: Multiple-reaction networks

    NARCIS (Netherlands)

    Westerink, E.J.; Westerterp, K.R.

    1988-01-01

    The model of the pseudo-homogeneous, one-dimensional cooled tubular reactor is applied to a multiple-reaction network. It is demonstrated for a network which consists of two parallel and two consecutive reactions. Three criteria are developed to obtain an integral yield which does not deviate more t

  4. Modular verification of chemical reaction network encodings via serializability analysis.

    Science.gov (United States)

    Lakin, Matthew R; Stefanovic, Darko; Phillips, Andrew

    2016-06-13

    Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a "commit reaction" that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of "extra tolerance", which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited.

  5. Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions

    Science.gov (United States)

    Semenov, Sergey N.; Kraft, Lewis J.; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E.; Kang, Kyungtae; Fox, Jerome M.; Whitesides, George M.

    2016-09-01

    Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving

  6. A statistical mechanical description of biomolecular hydration

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-02-01

    We present an efficient and accurate theoretical description of the structural hydration of biological macromolecules. The hydration of molecules of almost arbitrary size (tRNA, antibody-antigen complexes, photosynthetic reaction centre) can be studied in solution and in the crystal environment. The biomolecular structure obtained from x-ray crystallography, NMR, or modeling is required as input information. The structural arrangement of water molecules near a biomolecular surface is represented by the local water density analogous to the corresponding electron density in an x-ray diffraction experiment. The water-density distribution is approximated in terms of two- and three-particle correlation functions of solute atoms with water using a potentials-of-mean-force expansion.

  7. Biomolecular EPR spectroscopy

    CERN Document Server

    Hagen, Wilfred Raymond

    2008-01-01

    Comprehensive, Up-to-Date Coverage of Spectroscopy Theory and its Applications to Biological SystemsAlthough a multitude of books have been published about spectroscopy, most of them only occasionally refer to biological systems and the specific problems of biomolecular EPR (bioEPR). Biomolecular EPR Spectroscopy provides a practical introduction to bioEPR and demonstrates how this remarkable tool allows researchers to delve into the structural, functional, and analytical analysis of paramagnetic molecules found in the biochemistry of all species on the planet. A Must-Have Reference in an Intrinsically Multidisciplinary FieldThis authoritative reference seamlessly covers all important bioEPR applications, including low-spin and high-spin metalloproteins, spin traps and spin lables, interaction between active sites, and redox systems. It is loaded with practical tricks as well as do's and don'ts that are based on the author's 30 years of experience in the field. The book also comes with an unprecedented set of...

  8. On the Complexity of Reconstructing Chemical Reaction Networks

    DEFF Research Database (Denmark)

    Fagerberg, Rolf; Flamm, Christoph; Merkle, Daniel

    2013-01-01

    The analysis of the structure of chemical reaction networks is crucial for a better understanding of chemical processes. Such networks are well described as hypergraphs. However, due to the available methods, analyses regarding network properties are typically made on standard graphs derived from...... the full hypergraph description, e.g. on the so-called species and reaction graphs. However, a reconstruction of the underlying hypergraph from these graphs is not necessarily unique. In this paper, we address the problem of reconstructing a hypergraph from its species and reaction graph and show NP...

  9. Neural networks for the prediction organic chemistry reactions

    CERN Document Server

    Wei, Jennifer N; Aspuru-Guzik, Alán

    2016-01-01

    Reaction prediction remains one of the great challenges for organic chemistry. Solving this problem computationally requires the programming of a vast amount of knowledge and intuition of the rules of organic chemistry and the development of algorithms for their application. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. In this work, we introduce a novel algorithm for predicting the products of organic chemistry reactions using machine learning to first identify the reaction type. In particular, we trained deep convolutional neural networks to predict the outcome of reactions based example reactions, using a new reaction fingerprint model. Due to the flexibility of neural networks, the system can attempt to predict reactions outside the domain where it was trained. We test this capability on problems from a popular organic chemistry textbook.

  10. A Reaction-Based River/Stream Water Quality Model: Reaction Network Decomposition and Model Application

    OpenAIRE

    2012-01-01

    This paper describes details of an automatic matrix decomposition approach for a reaction-based stream water quality model. The method yields a set of equilibrium equations, a set of kinetic-variable transport equations involving kinetic reactions only, and a set of component transport equations involving no reactions. Partial decomposition of the system of water quality constituent transport equations is performed via Gauss-Jordan column reduction of the reaction network by pivoting on equil...

  11. Research on Nuclear Reaction Network Equation for Fission Product Nuclides

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    Nuclear Reaction Network Equation calculation system for fission product nuclides was developed. With the system, the number of the fission product nuclides at different time can be calculated in the different neutron field intensity and neutron energy spectra

  12. Stochastic Generator of Chemical Structure. 3. Reaction Network Generation

    Energy Technology Data Exchange (ETDEWEB)

    FAULON,JEAN-LOUP; SAULT,ALLEN G.

    2000-07-15

    A new method to generate chemical reaction network is proposed. The particularity of the method is that network generation and mechanism reduction are performed simultaneously using sampling techniques. Our method is tested for hydrocarbon thermal cracking. Results and theoretical arguments demonstrate that our method scales in polynomial time while other deterministic network generator scale in exponential time. This finding offers the possibility to investigate complex reacting systems such as those studied in petroleum refining and combustion.

  13. Neural networks for the prediction organic chemistry reactions

    OpenAIRE

    Wei, Jennifer N.; Duvenaud, David; Aspuru-Guzik, Alán

    2016-01-01

    Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reag...

  14. Output-input ratio in thermally fluctuating biomolecular machines.

    Science.gov (United States)

    Kurzynski, Michal; Torchala, Mieczyslaw; Chelminiak, Przemyslaw

    2014-01-01

    Biological molecular machines are proteins that operate under isothermal conditions and hence are referred to as free energy transducers. They can be formally considered as enzymes that simultaneously catalyze two chemical reactions: the free energy-donating (input) reaction and the free energy-accepting (output) one. Most if not all biologically active proteins display a slow stochastic dynamics of transitions between a variety of conformational substates composing their native state. This makes the description of the enzymatic reaction kinetics in terms of conventional rate constants insufficient. In the steady state, upon taking advantage of the assumption that each reaction proceeds through a single pair (the gate) of transition conformational substates of the enzyme-substrates complex, the degree of coupling between the output and the input reaction fluxes has been expressed in terms of the mean first-passage times on a conformational transition network between the distinguished substates. The theory is confronted with the results of random-walk simulations on the five-dimensional hypercube. The formal proof is given that, for single input and output gates, the output-input degree of coupling cannot exceed unity. As some experiments suggest such exceeding, looking for the conditions for increasing the degree of coupling value over unity challenges the theory. Performed simulations of random walks on several model networks involving more extended gates indicate that the case of the degree of coupling value higher than 1 is realized in a natural way on critical branching trees extended by long-range shortcuts. Such networks are scale-free and display the property of the small world. For short-range shortcuts, the networks are scale-free and fractal, representing a reasonable model for biomolecular machines displaying tight coupling, i.e., the degree of coupling equal exactly to unity. A hypothesis is stated that the protein conformational transition networks, as

  15. DNA reaction networks: Providing a panoramic view

    Science.gov (United States)

    Wang, Fei; Fan, Chunhai

    2016-08-01

    A quantitative understanding of the functional landscape of a biochemical circuit can reveal the design rules required to optimize the circuit. Now, a high-throughput droplet-based microfluidic platform has been developed which enables high-resolution mapping of bifurcation diagrams for two nonlinear DNA networks.

  16. SABRE: A Tool for Stochastic Analysis of Biochemical Reaction Networks

    CERN Document Server

    Didier, Frederic; Mateescu, Maria; Wolf, Verena

    2010-01-01

    The importance of stochasticity within biological systems has been shown repeatedly during the last years and has raised the need for efficient stochastic tools. We present SABRE, a tool for stochastic analysis of biochemical reaction networks. SABRE implements fast adaptive uniformization (FAU), a direct numerical approximation algorithm for computing transient solutions of biochemical reaction networks. Biochemical reactions networks represent biological systems studied at a molecular level and these reactions can be modeled as transitions of a Markov chain. SABRE accepts as input the formalism of guarded commands, which it interprets either as continuous-time or as discrete-time Markov chains. Besides operating in a stochastic mode, SABRE may also perform a deterministic analysis by directly computing a mean-field approximation of the system under study. We illustrate the different functionalities of SABRE by means of biological case studies.

  17. Uncertainty quantification for quantum chemical models of complex reaction networks.

    Science.gov (United States)

    Proppe, Jonny; Husch, Tamara; Simm, Gregor N; Reiher, Markus

    2016-12-22

    For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.

  18. Sensitivity of chemical reaction networks: a structural approach. 1. Examples and the carbon metabolic network.

    Science.gov (United States)

    Mochizuki, Atsushi; Fiedler, Bernold

    2015-02-21

    In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle.

  19. Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.

    Science.gov (United States)

    Chen, Xueying; Zhao, Min; Qu, Hong

    2015-01-01

    T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis.

  20. Programming the dynamics of biochemical reaction networks.

    Science.gov (United States)

    Simmel, Friedrich C

    2013-01-22

    The development of complex self-organizing molecular systems for future nanotechnology requires not only robust formation of molecular structures by self-assembly but also precise control over their temporal dynamics. As an exquisite example of such control, in this issue of ACS Nano, Fujii and Rondelez demonstrate a particularly compact realization of a molecular "predator-prey" ecosystem consisting of only three DNA species and three enzymes. The system displays pronounced oscillatory dynamics, in good agreement with the predictions of a simple theoretical model. Moreover, its considerable modularity also allows for ecological studies of competition and cooperation within molecular networks.

  1. Turing instability in reaction-diffusion models on complex networks

    Science.gov (United States)

    Ide, Yusuke; Izuhara, Hirofumi; Machida, Takuya

    2016-09-01

    In this paper, the Turing instability in reaction-diffusion models defined on complex networks is studied. Here, we focus on three types of models which generate complex networks, i.e. the Erdős-Rényi, the Watts-Strogatz, and the threshold network models. From analysis of the Laplacian matrices of graphs generated by these models, we numerically reveal that stable and unstable regions of a homogeneous steady state on the parameter space of two diffusion coefficients completely differ, depending on the network architecture. In addition, we theoretically discuss the stable and unstable regions in the cases of regular enhanced ring lattices which include regular circles, and networks generated by the threshold network model when the number of vertices is large enough.

  2. A Reaction-Based River/Stream Water Quality Model: Reaction Network Decomposition and Model Application

    Directory of Open Access Journals (Sweden)

    Fan Zhang

    2012-01-01

    Full Text Available This paper describes details of an automatic matrix decomposition approach for a reaction-based stream water quality model. The method yields a set of equilibrium equations, a set of kinetic-variable transport equations involving kinetic reactions only, and a set of component transport equations involving no reactions. Partial decomposition of the system of water quality constituent transport equations is performed via Gauss-Jordan column reduction of the reaction network by pivoting on equilibrium reactions to decouple equilibrium and kinetic reactions. This approach minimizes the number of partial differential advective-dispersive transport equations and enables robust numerical integration. Complete matrix decomposition by further pivoting on linearly independent kinetic reactions allows some rate equations to be formulated individually and explicitly enforces conservation of component species when component transport equations are solved. The methodology is demonstrated for a case study involving eutrophication reactions in the Des Moines River in Iowa, USA and for two hypothetical examples to illustrate the ability of the model to simulate sediment and chemical transport with both mobile and immobile water phases and with complex reaction networks involving both kinetic and equilibrium reactions.

  3. Efficient parameter sensitivity computation for spatially extended reaction networks

    Science.gov (United States)

    Lester, C.; Yates, C. A.; Baker, R. E.

    2017-01-01

    Reaction-diffusion models are widely used to study spatially extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on the stochastic models of spatially extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.

  4. Neural Networks for the Prediction of Organic Chemistry Reactions

    Science.gov (United States)

    2016-01-01

    Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, “learn” from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook. PMID:27800555

  5. Neural Networks for the Prediction of Organic Chemistry Reactions.

    Science.gov (United States)

    Wei, Jennifer N; Duvenaud, David; Aspuru-Guzik, Alán

    2016-10-26

    Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.

  6. Variational Methods for Biomolecular Modeling

    CERN Document Server

    Wei, Guo-Wei

    2016-01-01

    Structure, function and dynamics of many biomolecular systems can be characterized by the energetic variational principle and the corresponding systems of partial differential equations (PDEs). This principle allows us to focus on the identification of essential energetic components, the optimal parametrization of energies, and the efficient computational implementation of energy variation or minimization. Given the fact that complex biomolecular systems are structurally non-uniform and their interactions occur through contact interfaces, their free energies are associated with various interfaces as well, such as solute-solvent interface, molecular binding interface, lipid domain interface, and membrane surfaces. This fact motivates the inclusion of interface geometry, particular its curvatures, to the parametrization of free energies. Applications of such interface geometry based energetic variational principles are illustrated through three concrete topics: the multiscale modeling of biomolecular electrosta...

  7. ARWAR: A network approach for predicting Adverse Drug Reactions.

    Science.gov (United States)

    Rahmani, Hossein; Weiss, Gerhard; Méndez-Lucio, Oscar; Bender, Andreas

    2016-01-01

    Predicting novel drug side-effects, or Adverse Drug Reactions (ADRs), plays an important role in the drug discovery process. Existing methods consider mainly the chemical and biological characteristics of each drug individually, thereby neglecting information hidden in the relationships among drugs. Complementary to the existing individual methods, in this paper, we propose a novel network approach for ADR prediction that is called Augmented Random-WAlk with Restarts (ARWAR). ARWAR, first, applies an existing method to build a network of highly related drugs. Then, it augments the original drug network by adding new nodes and new edges to the network and finally, it applies Random Walks with Restarts to predict novel ADRs. Empirical results show that the ARWAR method presented here outperforms the existing network approach by 20% with respect to average Fmeasure. Furthermore, ARWAR is capable of generating novel hypotheses about drugs with respect to novel and biologically meaningful ADR.

  8. Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.

    Science.gov (United States)

    Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán

    2014-03-11

    While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.

  9. Chemical and genomic evolution of enzyme-catalyzed reaction networks.

    Science.gov (United States)

    Kanehisa, Minoru

    2013-09-02

    There is a tendency that a unit of enzyme genes in an operon-like structure in the prokaryotic genome encodes enzymes that catalyze a series of consecutive reactions in a metabolic pathway. Our recent analysis shows that this and other genomic units correspond to chemical units reflecting chemical logic of organic reactions. From all known metabolic pathways in the KEGG database we identified chemical units, called reaction modules, as the conserved sequences of chemical structure transformation patterns of small molecules. The extracted patterns suggest co-evolution of genomic units and chemical units. While the core of the metabolic network may have evolved with mechanisms involving individual enzymes and reactions, its extension may have been driven by modular units of enzymes and reactions.

  10. Molecular codes in biological and chemical reaction networks.

    Science.gov (United States)

    Görlich, Dennis; Dittrich, Peter

    2013-01-01

    Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.

  11. Model-order reduction of biochemical reaction networks

    NARCIS (Netherlands)

    Rao, Shodhan; Schaft, Arjan van der; Eunen, Karen van; Bakker, Barbara M.; Jayawardhana, Bayu

    2013-01-01

    In this paper we propose a model-order reduction method for chemical reaction networks governed by general enzyme kinetics, including the mass-action and Michaelis-Menten kinetics. The model-order reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the gr

  12. Grid computing and biomolecular simulation.

    Science.gov (United States)

    Woods, Christopher J; Ng, Muan Hong; Johnston, Steven; Murdock, Stuart E; Wu, Bing; Tai, Kaihsu; Fangohr, Hans; Jeffreys, Paul; Cox, Simon; Frey, Jeremy G; Sansom, Mark S P; Essex, Jonathan W

    2005-08-15

    Biomolecular computer simulations are now widely used not only in an academic setting to understand the fundamental role of molecular dynamics on biological function, but also in the industrial context to assist in drug design. In this paper, two applications of Grid computing to this area will be outlined. The first, involving the coupling of distributed computing resources to dedicated Beowulf clusters, is targeted at simulating protein conformational change using the Replica Exchange methodology. In the second, the rationale and design of a database of biomolecular simulation trajectories is described. Both applications illustrate the increasingly important role modern computational methods are playing in the life sciences.

  13. Chemical reaction network approaches to Biochemical Systems Theory.

    Science.gov (United States)

    Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R

    2015-11-01

    This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed.

  14. Switching Dynamics in Reaction Networks Induced by Molecular Discreteness

    CERN Document Server

    Togashi, Y; Kaneko, Kunihiko; Togashi, Yuichi

    2006-01-01

    To study the fluctuations and dynamics in chemical reaction processes, stochastic differential equations based on the rate equation involving chemical concentrations are often adopted. When the number of molecules is very small, however, the discreteness in the number of molecules cannot be neglected since the number of molecules must be an integer. This discreteness can be important in biochemical reactions, where the total number of molecules is not significantly larger than the number of chemical species. To elucidate the effects of such discreteness, we study autocatalytic reaction systems comprising several chemical species through stochastic particle simulations. The generation of novel states is observed; it is caused by the extinction of some molecular species due to the discreteness in their number. We demonstrate that the reaction dynamics are switched by a single molecule, which leads to the reconstruction of the acting network structure. We also show the strong dependence of the chemical concentra...

  15. Stochastic analysis of complex reaction networks using binomial moment equations.

    Science.gov (United States)

    Barzel, Baruch; Biham, Ofer

    2012-09-01

    The stochastic analysis of complex reaction networks is a difficult problem because the number of microscopic states in such systems increases exponentially with the number of reactive species. Direct integration of the master equation is thus infeasible and is most often replaced by Monte Carlo simulations. While Monte Carlo simulations are a highly effective tool, equation-based formulations are more amenable to analytical treatment and may provide deeper insight into the dynamics of the network. Here, we present a highly efficient equation-based method for the analysis of stochastic reaction networks. The method is based on the recently introduced binomial moment equations [Barzel and Biham, Phys. Rev. Lett. 106, 150602 (2011)]. The binomial moments are linear combinations of the ordinary moments of the probability distribution function of the population sizes of the interacting species. They capture the essential combinatorics of the reaction processes reflecting their stoichiometric structure. This leads to a simple and transparent form of the equations, and allows a highly efficient and surprisingly simple truncation scheme. Unlike ordinary moment equations, in which the inclusion of high order moments is prohibitively complicated, the binomial moment equations can be easily constructed up to any desired order. The result is a set of equations that enables the stochastic analysis of complex reaction networks under a broad range of conditions. The number of equations is dramatically reduced from the exponential proliferation of the master equation to a polynomial (and often quadratic) dependence on the number of reactive species in the binomial moment equations. The aim of this paper is twofold: to present a complete derivation of the binomial moment equations; to demonstrate the applicability of the moment equations for a representative set of example networks, in which stochastic effects play an important role.

  16. Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Ziaul Huque

    2007-08-31

    This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.

  17. Reachability bounds for chemical reaction networks and strand displacement systems.

    Science.gov (United States)

    Condon, Anne; Kirkpatrick, Bonnie; Maňuch, Ján

    2014-01-01

    Chemical reaction networks (CRNs) and DNA strand displacement systems (DSDs) are widely-studied and useful models of molecular programming. However, in order for some DSDs in the literature to behave in an expected manner, the initial number of copies of some reagents is required to be fixed. In this paper we show that, when multiple copies of all initial molecules are present, general types of CRNs and DSDs fail to work correctly if the length of the shortest sequence of reactions needed to produce any given molecule exceeds a threshold that grows polynomially with attributes of the system.

  18. Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry

    OpenAIRE

    Rappoport, Dmitrij; Galvin, Cooper J.; Zubarev, Dmitry; Aspuru-Guzik, Alan

    2014-01-01

    While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reacti...

  19. Cumulative signal transmission in nonlinear reaction-diffusion networks.

    Directory of Open Access Journals (Sweden)

    Diego A Oyarzún

    Full Text Available Quantifying signal transmission in biochemical systems is key to uncover the mechanisms that cells use to control their responses to environmental stimuli. In this work we use the time-integral of chemical species as a measure of a network's ability to cumulatively transmit signals encoded in spatiotemporal concentrations. We identify a class of nonlinear reaction-diffusion networks in which the time-integrals of some species can be computed analytically. The derived time-integrals do not require knowledge of the solution of the reaction-diffusion equation, and we provide a simple graphical test to check if a given network belongs to the proposed class. The formulae for the time-integrals reveal how the kinetic parameters shape signal transmission in a network under spatiotemporal stimuli. We use these to show that a canonical complex-formation mechanism behaves as a spatial low-pass filter, the bandwidth of which is inversely proportional to the diffusion length of the ligand.

  20. Propagation of kinetic uncertainties through a canonical topology of the TLR4 signaling network in different regions of biochemical reaction space

    Directory of Open Access Journals (Sweden)

    St Laurent Georges

    2010-03-01

    signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology, but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes. Conclusions Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.

  1. Matrix trace operators: from spectral moments of molecular graphs and complex networks to perturbations in synthetic reactions, micelle nanoparticles, and drug ADME processes.

    Science.gov (United States)

    Gonzalez-Diaz, Humberto; Arrasate, Sonia; Juan, Asier Gomez-San; Sotomayor, Nuria; Lete, Esther; Speck-Planche, Alejandro; Ruso, Juan M; Luan, Feng; Cordeiro, Maria Natalia Dias Soeiro

    2014-01-01

    The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the

  2. Nonequilibrium phase transitions in biomolecular signal transduction

    Science.gov (United States)

    Smith, Eric; Krishnamurthy, Supriya; Fontana, Walter; Krakauer, David

    2011-11-01

    We study a mechanism for reliable switching in biomolecular signal-transduction cascades. Steady bistable states are created by system-size cooperative effects in populations of proteins, in spite of the fact that the phosphorylation-state transitions of any molecule, by means of which the switch is implemented, are highly stochastic. The emergence of switching is a nonequilibrium phase transition in an energetically driven, dissipative system described by a master equation. We use operator and functional integral methods from reaction-diffusion theory to solve for the phase structure, noise spectrum, and escape trajectories and first-passage times of a class of minimal models of switches, showing how all critical properties for switch behavior can be computed within a unified framework.

  3. Quantum dynamics of bio-molecular systems in noisy environments

    OpenAIRE

    Huelga S.F.; Plenio M.B.

    2012-01-01

    We discuss three different aspects of the quantum dynamics of bio-molecular systems and more generally complex networks in the presence of strongly coupled environments. Firstly, we make a case for the systematic study of fundamental structural elements underlying the quantum dynamics of these systems, identify such elements and explore the resulting interplay of quantum dynamics and environmental decoherence. Secondly, we critically examine some existing approaches to the numerical descripti...

  4. COEL: A Cloud-based Reaction Network Simulator

    Directory of Open Access Journals (Sweden)

    Peter eBanda

    2016-04-01

    Full Text Available Chemical Reaction Networks (CRNs are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework that does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL's key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants.COEL is an open-source project hosted on GitHub (http://dx.doi.org/10.5281/zenodo.46544, which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at http://coel-sim.org. The framework is ideally suited for a collaborative use in both research and education.

  5. Global parameter identification of stochastic reaction networks from single trajectories.

    Science.gov (United States)

    Müller, Christian L; Ramaswamy, Rajesh; Sbalzarini, Ivo F

    2012-01-01

    We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy (FCS) provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell-cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation (GaA), and efficient exact stochastic simulation algorithms (SSA) that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.

  6. A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks

    KAUST Repository

    Moraes, Alvaro

    2016-07-07

    In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.

  7. Integrative NMR for biomolecular research.

    Science.gov (United States)

    Lee, Woonghee; Cornilescu, Gabriel; Dashti, Hesam; Eghbalnia, Hamid R; Tonelli, Marco; Westler, William M; Butcher, Samuel E; Henzler-Wildman, Katherine A; Markley, John L

    2016-04-01

    NMR spectroscopy is a powerful technique for determining structural and functional features of biomolecules in physiological solution as well as for observing their intermolecular interactions in real-time. However, complex steps associated with its practice have made the approach daunting for non-specialists. We introduce an NMR platform that makes biomolecular NMR spectroscopy much more accessible by integrating tools, databases, web services, and video tutorials that can be launched by simple installation of NMRFAM software packages or using a cross-platform virtual machine that can be run on any standard laptop or desktop computer. The software package can be downloaded freely from the NMRFAM software download page ( http://pine.nmrfam.wisc.edu/download_packages.html ), and detailed instructions are available from the Integrative NMR Video Tutorial page ( http://pine.nmrfam.wisc.edu/integrative.html ).

  8. Exact probability distributions of selected species in stochastic chemical reaction networks.

    Science.gov (United States)

    López-Caamal, Fernando; Marquez-Lago, Tatiana T

    2014-09-01

    Chemical reactions are discrete, stochastic events. As such, the species' molecular numbers can be described by an associated master equation. However, handling such an equation may become difficult due to the large size of reaction networks. A commonly used approach to forecast the behaviour of reaction networks is to perform computational simulations of such systems and analyse their outcome statistically. This approach, however, might require high computational costs to provide accurate results. In this paper we opt for an analytical approach to obtain the time-dependent solution of the Chemical Master Equation for selected species in a general reaction network. When the reaction networks are composed exclusively of zeroth and first-order reactions, this analytical approach significantly alleviates the computational burden required by simulation-based methods. By building upon these analytical solutions, we analyse a general monomolecular reaction network with an arbitrary number of species to obtain the exact marginal probability distribution for selected species. Additionally, we study two particular topologies of monomolecular reaction networks, namely (i) an unbranched chain of monomolecular reactions with and without synthesis and degradation reactions and (ii) a circular chain of monomolecular reactions. We illustrate our methodology and alternative ways to use it for non-linear systems by analysing a protein autoactivation mechanism. Later, we compare the computational load required for the implementation of our results and a pure computational approach to analyse an unbranched chain of monomolecular reactions. Finally, we study calcium ions gates in the sarco/endoplasmic reticulum mediated by ryanodine receptors.

  9. Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.

    Science.gov (United States)

    Arampatzis, Georgios; Katsoulakis, Markos A; Pantazis, Yannis

    2015-01-01

    Existing sensitivity analysis approaches are not able to handle efficiently stochastic reaction networks with a large number of parameters and species, which are typical in the modeling and simulation of complex biochemical phenomena. In this paper, a two-step strategy for parametric sensitivity analysis for such systems is proposed, exploiting advantages and synergies between two recently proposed sensitivity analysis methodologies for stochastic dynamics. The first method performs sensitivity analysis of the stochastic dynamics by means of the Fisher Information Matrix on the underlying distribution of the trajectories; the second method is a reduced-variance, finite-difference, gradient-type sensitivity approach relying on stochastic coupling techniques for variance reduction. Here we demonstrate that these two methods can be combined and deployed together by means of a new sensitivity bound which incorporates the variance of the quantity of interest as well as the Fisher Information Matrix estimated from the first method. The first step of the proposed strategy labels sensitivities using the bound and screens out the insensitive parameters in a controlled manner. In the second step of the proposed strategy, a finite-difference method is applied only for the sensitivity estimation of the (potentially) sensitive parameters that have not been screened out in the first step. Results on an epidermal growth factor network with fifty parameters and on a protein homeostasis with eighty parameters demonstrate that the proposed strategy is able to quickly discover and discard the insensitive parameters and in the remaining potentially sensitive parameters it accurately estimates the sensitivities. The new sensitivity strategy can be several times faster than current state-of-the-art approaches that test all parameters, especially in "sloppy" systems. In particular, the computational acceleration is quantified by the ratio between the total number of parameters over the

  10. [Advances in biomolecular machine: methane monooxygenases].

    Science.gov (United States)

    Lu, Jixue; Wang, Shizhen; Fang, Baishan

    2015-07-01

    Methane monooxygenases (MMO), regarded as "an amazing biomolecular machine", catalyze the oxidation of methane to methanol under aerobic conditions. MMO catalyze the oxidation of methane elaborately, which is a novel way to catalyze methane to methanol. Furthermore, MMO can inspire the biomolecular machine design. In this review, we introduced MMO including structure, gene and catalytic mechanism. The history and the taxonomy of MMO were also introduced.

  11. Biomolecular transport and separation in nanotubular networks.

    Energy Technology Data Exchange (ETDEWEB)

    Stachowiak, Jeanne C.; Stevens, Mark Jackson (Sandia National Laboratories, Albuquerque, NM); Robinson, David B.; Branda, Steven S.; Zendejas, Frank; Meagher, Robert J.; Sasaki, Darryl Yoshio; Bachand, George David (Sandia National Laboratories, Albuquerque, NM); Hayden, Carl C.; Sinha, Anupama; Abate, Elisa; Wang, Julia; Carroll-Portillo, Amanda (Sandia National Laboratories, Albuquerque, NM); Liu, Haiqing (Sandia National Laboratories, Albuquerque, NM)

    2010-09-01

    Cell membranes are dynamic substrates that achieve a diverse array of functions through multi-scale reconfigurations. We explore the morphological changes that occur upon protein interaction to model membrane systems that induce deformation of their planar structure to yield nanotube assemblies. In the two examples shown in this report we will describe the use of membrane adhesion and particle trajectory to form lipid nanotubes via mechanical stretching, and protein adsorption onto domains and the induction of membrane curvature through steric pressure. Through this work the relationship between membrane bending rigidity, protein affinity, and line tension of phase separated structures were examined and their relationship in biological membranes explored.

  12. Defect reaction network in Si-doped InAs. Numerical predictions.

    Energy Technology Data Exchange (ETDEWEB)

    Schultz, Peter A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-05-01

    This Report characterizes the defects in the def ect reaction network in silicon - doped, n - type InAs predicted with first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si - doped InAs , until culminating in immobile reaction p roducts. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon - related defects in the reaction network. This Report serves to extend the results for the properties of intrinsic defects in bulk InAs as colla ted in SAND 2013 - 2477 : Simple intrinsic defects in InAs : Numerical predictions to include Si - containing simple defects likely to be present in a radiation - induced defect reaction sequence . This page intentionally left blank

  13. Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation.

    Science.gov (United States)

    Cardelli, Luca; Kwiatkowska, Marta; Laurenti, Luca

    2016-11-01

    Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analyzed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN.

  14. On the Mathematical Structure of Balanced Chemical Reaction Networks Governed by Mass Action Kinetics

    CERN Document Server

    van der Schaft, Arjan; Jayawardhana, Bayu

    2011-01-01

    Motivated by recent progress on the interplay between graph theory, dynamics, and systems theory, we revisit the analysis of chemical reaction networks described by mass action kinetics. For reaction networks possessing a thermodynamic equilibrium we derive a compact formulation exhibiting at the same time the structure of the complex graph and the stoichiometry of the network, and which admits a direct thermodynamical interpretation. This formulation allows us to easily characterize the set of equilibria and their stability properties. Furthermore, we develop a framework for interconnection of chemical reaction networks. Finally we discuss how the established framework leads to a new approach for model reduction.

  15. Global analysis of time-resolved fluorescence microspectroscopy and applications in biomolecular studies

    NARCIS (Netherlands)

    Laptenok, S.

    2009-01-01

    Understanding the properties of biomolecular networks is of central importance in life sciences. Optical microscopy has been very useful to determine the sub-cellular localisation of proteins but it cannot reveal whether proteins interact with one another. Micro-spectroscopic techniques (combining m

  16. Sensitivity of chemical reaction networks: a structural approach 3. Regular multimolecular systems

    CERN Document Server

    Brehm, Bernhard

    2016-01-01

    We present a systematic mathematical analysis of the qualitative steady-state response to rate perturbations in large classes of reaction networks. This includes multimolecular reactions and allows for catalysis, enzymatic reactions, multiple reaction products, nonmonotone rate functions, and non-closed autonomous systems. Our structural sensitivity analysis is based on the stoichiometry of the reaction network, only. It does not require numerical data on reaction rates. Instead, we impose mild and generic nondegeneracy conditions of algebraic type. From the structural data, only, we derive which steady-state concentrations are sensitive to, and hence influenced by, changes of any particular reaction rate - and which are not. We also establish transitivity properties for influences involving rate perturbations. This allows us to derive an influence graph which globally summarizes the influence pattern of the given network. The influence graph allows the computational, but meaningful, automatic identification ...

  17. Metabolome based reaction graphs of M. tuberculosis and M. leprae: a comparative network analysis.

    Directory of Open Access Journals (Sweden)

    Ketki D Verkhedkar

    Full Text Available BACKGROUND: Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. METHODOLOGY/PRINCIPAL FINDINGS: Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. CONCLUSIONS: We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a

  18. Sparse Regression Based Structure Learning of Stochastic Reaction Networks from Single Cell Snapshot Time Series

    Science.gov (United States)

    Ganscha, Stefan; Claassen, Manfred

    2016-01-01

    Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso. PMID:27923064

  19. To address surface reaction network complexity using scaling relations machine learning and DFT calculations

    Science.gov (United States)

    Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.

    2017-03-01

    Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.

  20. To address surface reaction network complexity using scaling relations machine learning and DFT calculations

    Science.gov (United States)

    Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.

    2017-01-01

    Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations. PMID:28262694

  1. Piecewise linear and Boolean models of chemical reaction networks.

    Science.gov (United States)

    Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir

    2014-12-01

    Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.

  2. Piecewise linear and Boolean models of chemical reaction networks

    Science.gov (United States)

    Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir

    2014-01-01

    Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions (xn/(Jn + xn)). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent n is large. However, while the case of small constant J appears in practice, it is not well understood. We provide a mathematical analysis of this limit, and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator. PMID:25412739

  3. Conducting polymer based biomolecular electronic devices

    Indian Academy of Sciences (India)

    B D Malhotra; Rahul Singhal

    2003-08-01

    Biomolecular electronics is rapidly evolving from physics, chemistry, biology, electronics and information technology. Organic materials such as proteins, pigments and conducting polymers have been considered as alternatives for carrying out the functions that are presently being performed by semiconductor silicon. Conducting polymers such as polypyrroles, polythiophenes and polyanilines have been projected for applications for a wide range of biomolecular electronic devices such as optical, electronic, drug-delivery, memory and biosensing devices. Our group has been actively working towards the application of conducting polymers to Schottky diodes, metal–insulator–semiconductor (MIS) devices and biosensors for the past 10 years. This paper is a review of some of the results obtained at our laboratory in the area of conducting polymer biomolecular electronics.

  4. Graphical reduction of reaction networks by linear elimination of species

    DEFF Research Database (Denmark)

    Saez Cornellana, Meritxell; Wiuf, Carsten Henrik; Feliu, Elisenda

    2016-01-01

    of the network and its kinetics. We conclude by comparing our approach to an older similar approach by Temkin and co-workers. Finally, we apply the procedure to biological examples such as substrate mechanisms, post-translational modification systems and networks with intermediates (transient) steps....

  5. Lattice based Kinetic Monte Carlo Simulations of a complex chemical reaction network

    Science.gov (United States)

    Danielson, Thomas; Savara, Aditya; Hin, Celine

    Lattice Kinetic Monte Carlo (KMC) simulations offer a powerful alternative to using ordinary differential equations for the simulation of complex chemical reaction networks. Lattice KMC provides the ability to account for local spatial configurations of species in the reaction network, resulting in a more detailed description of the reaction pathway. In KMC simulations with a large number of reactions, the range of transition probabilities can span many orders of magnitude, creating subsets of processes that occur more frequently or more rarely. Consequently, processes that have a high probability of occurring may be selected repeatedly without actually progressing the system (i.e. the forward and reverse process for the same reaction). In order to avoid the repeated occurrence of fast frivolous processes, it is necessary to throttle the transition probabilities in such a way that avoids altering the overall selectivity. Likewise, as the reaction progresses, new frequently occurring species and reactions may be introduced, making a dynamic throttling algorithm a necessity. We present a dynamic steady-state detection scheme with the goal of accurately throttling rate constants in order to optimize the KMC run time without compromising the selectivity of the reaction network. The algorithm has been applied to a large catalytic chemical reaction network, specifically that of methanol oxidative dehydrogenation, as well as additional pathways on CeO2(111) resulting in formaldehyde, CO, methanol, CO2, H2 and H2O as gas products.

  6. Synchronization criteria for generalized reaction-diffusion neural networks via periodically intermittent control.

    Science.gov (United States)

    Gan, Qintao; Lv, Tianshi; Fu, Zhenhua

    2016-04-01

    In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.

  7. Ion irradiation and biomolecular radiation damage II. Indirect effect

    CERN Document Server

    Wang, Wei; Su, Wenhui

    2010-01-01

    It has been reported that damage of genome in a living cell by ionizing radiation is about one-third direct and two-thirds indirect. The former which has been introduced in our last paper, concerns direct energy deposition and ionizing reactions in the biomolecules; the latter results from radiation induced reactive species (mainly radicals) in the medium (mainly water) surrounding the biomolecules. In this review, a short description of ion implantation induced radical formation in water is presented. Then we summarize the aqueous radical reaction chemistry of DNA, protein and their components, followed by a brief introduction of biomolecular damage induced by secondary particles (ions and electron). Some downstream biological effects are also discussed.

  8. Network structural analysis using directed graph for chemical reaction analysis in weakly-ionized plasmas

    Science.gov (United States)

    Nobuto, Kyosuke; Mizui, Yasutaka; Miyagi, Shigeyuki; Sakai, Osamu; Murakami, Tomoyuki

    2016-09-01

    We visualize complicated chemical reaction systems in weakly-ionized plasmas by analysing network structure for chemical processes, and calculate some indexes by assuming interspecies relationships to be a network to clarify them. With the current social evolution, the mean size of general data which we can use in computers grows huge, and significance of the data analysis increases. The methods of the network analysis which we focus on in this study do not depend on a specific analysis target, but the field where it has been already applied is still limited. In this study, we analyse chemical reaction systems in plasmas for configuring the network structure. We visualize them by expressing a reaction system in a specific plasma by a directed graph and examine the indexes and the relations with the characteristic of the species in the reaction system. For example, in the methane plasma network, the centrality index reveals importance of CH3 in an influential position of species in the reaction. In addition, silane and atmospheric pressure plasmas can be also visualized in reaction networks, suggesting other characteristics in the centrality indexes.

  9. Reaction Networks For Interstellar Chemical Modelling: Improvements and Challenges

    CERN Document Server

    Wakelam, V; Herbst, E; Troe, J; Geppert, W; Linnartz, H; Oberg, K; Roueff, E; Agundez, M; Pernot, P; Cuppen, H M; Loison, J C; Talbi, D

    2010-01-01

    We survey the current situation regarding chemical modelling of the synthesis of molecules in the interstellar medium. The present state of knowledge concerning the rate coefficients and their uncertainties for the major gas-phase processes -- ion-neutral reactions, neutral-neutral reactions, radiative association, and dissociative recombination -- is reviewed. Emphasis is placed on those reactions that have been identified, by sensitivity analyses, as 'crucial' in determining the predicted abundances of the species observed in the interstellar medium. These sensitivity analyses have been carried out for gas-phase models of three representative, molecule-rich, astronomical sources: the cold dense molecular clouds TMC-1 and L134N, and the expanding circumstellar envelope IRC +10216. Our review has led to the proposal of new values and uncertainties for the rate coefficients of many of the key reactions. The impact of these new data on the predicted abundances in TMC-1 and L134N is reported. Interstellar dust p...

  10. On the graph and systems analysis of reversible chemical reaction networks with mass action kinetics

    NARCIS (Netherlands)

    Rao, Shodhan; Jayawardhana, Bayu; Schaft, Arjan van der

    2012-01-01

    Motivated by the recent progresses on the interplay between the graph theory and systems theory, we revisit the analysis of reversible chemical reaction networks described by mass action kinetics by reformulating it using the graph knowledge of the underlying networks. Based on this formulation, we

  11. Origin of organic molecules and biomolecular homochirality.

    Science.gov (United States)

    Podlech, J

    2001-01-01

    Theories about the origin of biomolecular homochirality, which seems to be a prerequisite for the creation of life, are discussed. First, possible terrestrial and extraterrestrial sources of organic molecules are outlined. Then, mechanisms for the formation of enantiomerically enriched compounds and for the amplification of their chirality are described.

  12. Thermodynamic properties of water solvating biomolecular surfaces

    Science.gov (United States)

    Heyden, Matthias

    Changes in the potential energy and entropy of water molecules hydrating biomolecular interfaces play a significant role for biomolecular solubility and association. Free energy perturbation and thermodynamic integration methods allow calculations of free energy differences between two states from simulations. However, these methods are computationally demanding and do not provide insights into individual thermodynamic contributions, i.e. changes in the solvent energy or entropy. Here, we employ methods to spatially resolve distributions of hydration water thermodynamic properties in the vicinity of biomolecular surfaces. This allows direct insights into thermodynamic signatures of the hydration of hydrophobic and hydrophilic solvent accessible sites of proteins and small molecules and comparisons to ideal model surfaces. We correlate dynamic properties of hydration water molecules, i.e. translational and rotational mobility, to their thermodynamics. The latter can be used as a guide to extract thermodynamic information from experimental measurements of site-resolved water dynamics. Further, we study energy-entropy compensations of water at different hydration sites of biomolecular surfaces. This work is supported by the Cluster of Excellence RESOLV (EXC 1069) funded by the Deutsche Forschungsgemeinschaft.

  13. Self-organized criticality of a catalytic reaction network under flow.

    Science.gov (United States)

    Awazu, Akinori; Kaneko, Kunihiko

    2009-07-01

    Self-organized critical behavior in a catalytic reaction network system induced by smallness in the molecule number is reported. The system under a flow of chemicals is shown to undergo a transition from a stationary to an intermittent reaction phase when the flow rate is decreased. In the intermittent reaction phase, two temporal regimes with active and halted reactions alternate. The number frequency of reaction events at each active regime and its duration time are shown to obey a universal power law with the exponents 4/3 and 3/2, respectively, independently of the parameters and network structure. These power laws are explained by a one-dimensional random-walk representation of the number of catalytically active chemicals. Possible relevance of the result to reaction dynamics in artificial and biological cells is briefly discussed.

  14. Separation of time-scales and model reduction for stochastic reaction networks

    CERN Document Server

    Kang, Hye-Won

    2010-01-01

    A stochastic model for a chemical reaction network is embedded in a one-parameter family of models with species numbers and rate constants scaled by powers of the parameter. A systematic approach is developed for determining appropriate choices of the exponents that can be applied to large complex networks. When the scaling implies subnetworks have different time-scales, the subnetworks can be approximated separately providing insight into the behavior of the full network through the analysis of these lower dimensional approximations.

  15. Programming in Biomolecular Computation: Programs, Self-Interpretation and Visualisation

    Directory of Open Access Journals (Sweden)

    J.G. Simonsen

    2011-01-01

    Full Text Available Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We identify a number of common features in programming that seem conspicuously absent from the literature on biomolecular computing; to partially redress this absence, we introduce a model of computation that is evidently programmable, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only executable, but are also compilable and interpretable. It is universal: all computable functions can be computed (in natural ways and without arcane encodings of data and algorithm; it is also uniform: new ``hardware'' is not needed to solve new problems; and (last but not least it is Turing complete in a strong sense: a universal algorithm exists, that is able to execute any program, and is not asymptotically inefficient.

  16. Biochemical filter with sigmoidal response: increasing the complexity of biomolecular logic.

    Science.gov (United States)

    Privman, Vladimir; Halámek, Jan; Arugula, Mary A; Melnikov, Dmitriy; Bocharova, Vera; Katz, Evgeny

    2010-11-11

    The first realization of a designed, rather than natural, biochemical filter process is reported and analyzed as a promising network component for increasing the complexity of biomolecular logic systems. Key challenge in biochemical logic research has been achieving scalability for complex network designs. Various logic gates have been realized, but a "toolbox" of analog elements for interconnectivity and signal processing has remained elusive. Filters are important as network elements that allow control of noise in signal transmission and conversion. We report a versatile biochemical filtering mechanism designed to have sigmoidal response in combination with signal-conversion process. Horseradish peroxidase-catalyzed oxidation of chromogenic electron donor by H(2)O(2) was altered by adding ascorbate, allowing to selectively suppress the output signal, modifying the response from convex to sigmoidal. A kinetic model was developed for evaluation of the quality of filtering. The results offer improved capabilities for design of scalable biomolecular information processing systems.

  17. [The psychoimmunological network og panic disorders, agoraphobia and allergic reactions].

    Science.gov (United States)

    Schmidt-Traub, S

    1995-02-01

    While treating panic and agoraphobia patients with behaviour therapy, a high frequency of allergic reaction of the IgE-mediated type I was observed. Panic disorder, agoraphobia, allergic disorder, and vasomotor reactions are briefly discussed in the framework of psycho-endocrino-immunological research. A pilot study had shown a high correlation between panic disorder with and without agoraphobia and allergic reaction. A controlled study was then planned to test the hypothesized psychoimmunological relationship. 100 allergic patients, 79 panic/agoraphobic patients, and 66 controls underwent psychodiagnostic and allergic screening. 70% of the anxiety patients responded to test allergens with IgE-mediated type-I immediate reactions in comparison to 28% of the control persons. Another 15% of the panic patients reacted to nickle compound with type-IV delayed skin reactions (7% of the controls). Conversely, 10% of the allergic patients suffered from panic disorder (45% had experienced panic attacks) in contrast to 2% of the controls (24% of these reported panic attacks). The relative risk for allergic patients to develop panic disorder with and without agoraphobia is obviously five times as high as for controls. With this assumption of a psychoimmunological preparedness in mind, a behavioural medical diagnostic and therapeutic concept seems more adequate in coping both with panic/agoraphobia and allergic disorder.

  18. The Activity Reaction Core and Plasticity of Metabolic Networks.

    Directory of Open Access Journals (Sweden)

    2005-12-01

    Full Text Available Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.

  19. Discovering missing reactions of metabolic networks by using gene co-expression data

    Science.gov (United States)

    Hosseini, Zhaleh; Marashi, Sayed-Amir

    2017-02-01

    Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.

  20. Integration of metabolome data with metabolic networks reveals reporter reactions

    DEFF Research Database (Denmark)

    Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen;

    2006-01-01

    Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...

  1. Abundances in Astrophysical Environments: Reaction Network Simulations with Reaction Rates from Many-nucleon Modeling

    Science.gov (United States)

    Amason, Charlee; Dreyfuss, Alison; Launey, Kristina; Draayer, Jerry

    2017-01-01

    We use the ab initio (first-principle) symmetry-adapted no-core shell model (SA-NCSM) to calculate reaction rates of significance to type I X-ray burst nucleosynthesis. We consider the 18O(p,γ)19F reaction, which may influence the production of fluorine, as well as the 16O(α,γ)20Ne reaction, which is key to understanding the production of heavier elements in the universe. Results are compared to those obtained in the no-core sympletic shell model (NCSpM) with a schematic interaction. We discuss how these reaction rates affect the relevant elemental abundances. We thank the NSF for supporting this work through the REU Site in Physics & Astronomy (NSF grant #1560212) at Louisiana State University. This work was also supported by the U.S. NSF (OCI-0904874, ACI -1516338) and the U.S. DOE (DE-SC0005248).

  2. Chemical reaction networks as a model to describe UVC- and radiolytically-induced reactions of simple compounds.

    Science.gov (United States)

    Dondi, Daniele; Merli, Daniele; Albini, Angelo; Zeffiro, Alberto; Serpone, Nick

    2012-05-01

    When a chemical system is submitted to high energy sources (UV, ionizing radiation, plasma sparks, etc.), as is expected to be the case of prebiotic chemistry studies, a plethora of reactive intermediates could form. If oxygen is present in excess, carbon dioxide and water are the major products. More interesting is the case of reducing conditions where synthetic pathways are also possible. This article examines the theoretical modeling of such systems with random-generated chemical networks. Four types of random-generated chemical networks were considered that originated from a combination of two connection topologies (viz., Poisson and scale-free) with reversible and irreversible chemical reactions. The results were analyzed taking into account the number of the most abundant products required for reaching 50% of the total number of moles of compounds at equilibrium, as this may be related to an actual problem of complex mixture analysis. The model accounts for multi-component reaction systems with no a priori knowledge of reacting species and the intermediates involved if system components are sufficiently interconnected. The approach taken is relevant to an earlier study on reactions that may have occurred in prebiotic systems where only a few compounds were detected. A validation of the model was attained on the basis of results of UVC and radiolytic reactions of prebiotic mixtures of low molecular weight compounds likely present on the primeval Earth.

  3. Brownian dynamics simulations of an idealized chemical reaction network under spatial confinement and crowding conditions

    CERN Document Server

    Bellesia, Giovanni

    2015-01-01

    We investigate, via Brownian dynamics simulations, the reaction dynamics of a simple, non-linear chemical network (the Willamowski-Rossler network) under spatial confinement and crowding conditions. Our results show that the presence of inert crowders has a non-nontrivial effect on the dynamics of the network and, consequently, that effective modeling efforts aiming at a general understanding of the behavior of biochemical networks in vivo should be stochastic in nature and based on an explicit representation of both spatial confinement and macromolecular crowding.

  4. Material balance studies on animal cell metabolism using a stoichiometrically based reaction network.

    Science.gov (United States)

    Xie, L; Wang, D I

    1996-12-05

    A detailed reaction network of mammalian cell metabolism contains hundreds of enzymatic reactions. By grouping serial reactions into single overall reactions and separating overlapped pathways into independent reactions, the total number of reactions of the network is significantly reduced. This strategy of manipulating the reaction network avoids the manipulations of a large number of reactions otherwise needed to determine the reaction extents. A stoichiometric material balance model is developed based on the stoichiometry of the simplified reaction network. Closures of material balances on glucose and each of the 20 amino acids are achieved using experimental data from three controlled fed-batch and one-batch hybridoma cultures. Results show that the critical role of essential amino acids, except glutamine, is to provide precursors for protein synthesis. The catabolism of some of the essential amino acids, particularly isoleucine and leucine, is observed when an excess amount of these amino acids is available in the culture medium. It was found that the reduction of glutamine utilization (for reducing ammonia production) is accompanied by an increase in the uptake of nonessential amino acids (NAAs) from the culture medium. This suggests that NAAs are necessary even though they are not essential for cell growth. A glutamine balance shows that less than 20% of the glutamine nitrogen is utilized for essential roles, such as protein and nucleotide syntheses. A relatively constant percentage (about 45%) of the glutamine nitrogen is utilized for NAA biosynthesis, despite the fact that the absolute amount varies among the four experiments. As to the carbon skeleton of glutamine, a significant portion enters the tricarboxylic acid (TCA) cycle. A material balance on glucose shows that most of the glucose (81%) is converted into lactate when glucose is in excess. On the other hand, when glucose is limited, lactate production is considerably reduced, while a major portion

  5. Mathematics of small stochastic reaction networks: a boundary layer theory for eigenstate analysis.

    Science.gov (United States)

    Mjolsness, Eric; Prasad, Upendra

    2013-03-14

    We study and analyze the stochastic dynamics of a reversible bimolecular reaction A + B ↔ C called the "trivalent reaction." This reaction is of a fundamental nature and is part of many biochemical reaction networks. The stochastic dynamics is given by the stochastic master equation, which is difficult to solve except when the equilibrium state solution is desired. We present a novel way of finding the eigenstates of this system of difference-differential equations, using perturbation analysis of ordinary differential equations arising from approximation of the difference equations. The time evolution of the state probabilities can then be expressed in terms of the eigenvalues and the eigenvectors.

  6. Biomolecular decision-making process for self assembly.

    Energy Technology Data Exchange (ETDEWEB)

    Osbourn, Gordon Cecil

    2005-01-01

    The brain is often identified with decision-making processes in the biological world. In fact, single cells, single macromolecules (proteins) and populations of molecules also make simple decisions. These decision processes are essential to survival and to the biological self-assembly and self-repair processes that we seek to emulate. How do these tiny systems make effective decisions? How do they make decisions in concert with a cooperative network of other molecules or cells? How can we emulate the decision-making behaviors of small-scale biological systems to program and self-assemble microsystems? This LDRD supported research to answer these questions. Our work included modeling and simulation of protein populations to help us understand, mimic, and categorize molecular decision-making mechanisms that nonequilibrium systems can exhibit. This work is an early step towards mimicking such nanoscale and microscale biomolecular decision-making processes in inorganic systems.

  7. Quantum dynamics of bio-molecular systems in noisy environments

    CERN Document Server

    Plenio, M B

    2012-01-01

    We discuss three different aspects of the quantum dynamics of bio-molecular systems and more generally complex networks in the presence of strongly coupled environments. Firstly, we make a case for the systematic study of fundamental structural elements underlying the quantum dynamics of these systems, identify such elements and explore the resulting interplay of quantum dynamics and environmental decoherence. Secondly, we critically examine some existing approaches to the numerical description of system-environment interaction in the non-perturbative regime and present a promising new method that can overcome some limitations of existing methods. Thirdly, we present an approach towards deciding and quantifying the non-classicality of the action of the environment and the observed system-dynamics. We stress the relevance of these tools for strengthening the interplay between theoretical and experimental research in this field.

  8. The US nuclear reaction data network. Summary of the first meeting, March 13 & 14 1996

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-03-01

    The first meeting of the US Nuclear Reaction Data Network (USNRDN) was held at the Colorado School of Mines, March 13-14, 1996 chaired by F. Edward Cecil. The Agenda of the meeting is attached. The Network, its mission, products and services; related nuclear data and data networks, members, and organization are described in Attachment 1. The following progress reports from the members of the USNRDN were distributed prior to the meeting and are given as Attachment 2. (1) Measurements and Development of Analytic Techniques for Basic Nuclear Physics and Nuclear Applications; (2) Nuclear Reaction Data Activities at the National Nuclear Data Center; (3) Studies of nuclear reactions at very low energies; (4) Nuclear Reaction Data Activities, Nuclear Data Group; (5) Progress in Neutron Physics at Los Alamos - Experiments; (6) Nuclear Reaction Data Activities in Group T2; (7) Progress Report for the US Nuclear Reaction Data Network Meeting; (8) Nuclear Astrophysics Research Group (ORNL); (9) Progress Report from Ohio University; (10) Exciton Model Phenomenology; and (11) Progress Report for Coordination Meeting USNRDN.

  9. Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.

    Directory of Open Access Journals (Sweden)

    Andrea Ciliberto

    2007-03-01

    Full Text Available In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C is much less than the free substrate concentration (S0. However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1 it unveils the modular structure of the enzymatic reactions, (2 it suggests a simple algorithm to formulate correct kinetic equations, and (3 contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.

  10. Application of Nanodiamonds in Biomolecular Mass Spectrometry

    Directory of Open Access Journals (Sweden)

    Ping Cheng

    2010-03-01

    Full Text Available The combination of nanodiamond (ND with biomolecular mass spectrometry (MS makes rapid, sensitive detection of biopolymers from complex biosamples feasible. Due to its chemical inertness, optical transparency and biocompatibility, the advantage of NDs in MS study is unique. Furthermore, functionalization on the surfaces of NDs expands their application in the fields of proteomics and genomics for specific requirements greatly. This review presents methods of MS analysis based on solid phase extraction and elution on NDs and different application examples including peptide, protein, DNA, glycan and others. Owing to the quick development of nanotechnology, surface chemistry, new MS methods and the intense interest in proteomics and genomics, a huge increase of their applications in biomolecular MS analysis in the near future can be predicted.

  11. Improvements in continuum modeling for biomolecular systems

    CERN Document Server

    Qiao, Yu

    2015-01-01

    Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson-Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of biological systems. Considerable efforts have been made to improve the continuum model to account for discrete particle interactions and to make progress in numerical methods to provide accurate and efficient simulation. This review will summarize recent main improvements in continuum modeling for biomolecular systems, with focus on the size-modified models, the coupling of the classical density functional theory and PNP equations, the coupling of polar and nonpolar interactions, and numerical progress.

  12. Application of Nanodiamonds in Biomolecular Mass Spectrometry

    OpenAIRE

    Ping Cheng; Xianglei Kong

    2010-01-01

    The combination of nanodiamond (ND) with biomolecular mass spectrometry (MS) makes rapid, sensitive detection of biopolymers from complex biosamples feasible. Due to its chemical inertness, optical transparency and biocompatibility, the advantage of NDs in MS study is unique. Furthermore, functionalization on the surfaces of NDs expands their application in the fields of proteomics and genomics for specific requirements greatly. This review presents methods of MS analysis based on solid phase...

  13. A cascade reaction network mimicking the basic functional steps of adaptive immune response

    Science.gov (United States)

    Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong

    2015-10-01

    Biological systems use complex ‘information-processing cores’ composed of molecular networks to coordinate their external environment and internal states. An example of this is the acquired, or adaptive, immune system (AIS), which is composed of both humoral and cell-mediated components. Here we report the step-by-step construction of a prototype mimic of the AIS that we call an adaptive immune response simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system that responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner that is superficially similar to the most basic responses of the vertebrate AIS, including reaction sequences that mimic both humoral and cellular responses. As such, AIRS provides guidelines for the design and engineering of artificial reaction networks and molecular devices.

  14. Biomolecular electrostatics and solvation: a computational perspective.

    Science.gov (United States)

    Ren, Pengyu; Chun, Jaehun; Thomas, Dennis G; Schnieders, Michael J; Marucho, Marcelo; Zhang, Jiajing; Baker, Nathan A

    2012-11-01

    An understanding of molecular interactions is essential for insight into biological systems at the molecular scale. Among the various components of molecular interactions, electrostatics are of special importance because of their long-range nature and their influence on polar or charged molecules, including water, aqueous ions, proteins, nucleic acids, carbohydrates, and membrane lipids. In particular, robust models of electrostatic interactions are essential for understanding the solvation properties of biomolecules and the effects of solvation upon biomolecular folding, binding, enzyme catalysis, and dynamics. Electrostatics, therefore, are of central importance to understanding biomolecular structure and modeling interactions within and among biological molecules. This review discusses the solvation of biomolecules with a computational biophysics view toward describing the phenomenon. While our main focus lies on the computational aspect of the models, we provide an overview of the basic elements of biomolecular solvation (e.g. solvent structure, polarization, ion binding, and non-polar behavior) in order to provide a background to understand the different types of solvation models.

  15. The origin of large molecules in primordial autocatalytic reaction networks

    CERN Document Server

    Giri, Varun

    2011-01-01

    Large molecules such as proteins and nucleic acids are crucial for life, yet their primordial origin remains a major puzzle. The production of large molecules, as we know it today, requires good catalysts, and the only good catalysts we know that can accomplish this task consist of large molecules. Thus the origin of large molecules is a chicken and egg problem in chemistry. Here we present a mechanism, based on autocatalytic sets (ACSs), that is a possible solution to this problem. We discuss a mathematical model describing the population dynamics of molecules in a stylized but prebiotically plausible chemistry. Large molecules can be produced in this chemistry by the coalescing of smaller ones, with the smallest molecules, the `food set', being buffered. Some of the reactions can be catalyzed by molecules within the chemistry with varying catalytic strengths. Normally the concentrations of large molecules in such a scenario are very small, diminishing exponentially with their size. ACSs, if present in the c...

  16. Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks

    DEFF Research Database (Denmark)

    Anderson, David F; Craciun, Gheorghe; Gopalkrishnan, Manoj;

    2015-01-01

    We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potent......We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non......-equilibrium potential of the stationary distribution of stochastically modeled complex balanced systems. We extend this result to general birth-death models and demonstrate via example that similar scaling limits can yield Lyapunov functions even for models that are not complex or detailed balanced, and may even have...

  17. Optimization Models for Reaction Networks: Information Divergence, Quadratic Programming and Kirchhoff’s Laws

    Directory of Open Access Journals (Sweden)

    Julio Michael Stern

    2014-03-01

    Full Text Available This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

  18. A stronger necessary condition for the multistationarity of chemical reaction networks.

    Science.gov (United States)

    Soliman, Sylvain

    2013-11-01

    Biochemical reaction networks grow bigger and bigger, fed by the high-throughput data provided by biologists and bred in open repositories of models allowing merging and evolution. Nevertheless, since the available data is still very far from permitting the identification of the increasing number of kinetic parameters of such models, the necessity of structural analyses for describing the dynamics of chemical networks appears stronger every day. Using the structural information, notably from the stoichiometric matrix, of a biochemical reaction system, we state a more strict version of the famous Thomas' necessary condition for multistationarity. In particular, the obvious cases where Thomas' condition was trivially satisfied, mutual inhibition due to a multimolecular reaction and mutual activation due to a reversible reaction, can now easily be ruled out. This more strict condition shall not be seen as some version of Thomas' circuit functionality for the continuous case but rather as related and complementary to the whole domain of the structural analysis of (bio)chemical reaction systems, as pioneered by the chemical reaction network theory.

  19. Global sensitivity analysis in stochastic simulators of uncertain reaction networks

    KAUST Repository

    Navarro Jimenez, M.

    2016-12-26

    Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

  20. Global sensitivity analysis in stochastic simulators of uncertain reaction networks.

    Science.gov (United States)

    Navarro Jimenez, M; Le Maître, O P; Knio, O M

    2016-12-28

    Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

  1. Markov chain aggregation and its applications to combinatorial reaction networks.

    Science.gov (United States)

    Ganguly, Arnab; Petrov, Tatjana; Koeppl, Heinz

    2014-09-01

    We consider a continuous-time Markov chain (CTMC) whose state space is partitioned into aggregates, and each aggregate is assigned a probability measure. A sufficient condition for defining a CTMC over the aggregates is presented as a variant of weak lumpability, which also characterizes that the measure over the original process can be recovered from that of the aggregated one. We show how the applicability of de-aggregation depends on the initial distribution. The application section is devoted to illustrate how the developed theory aids in reducing CTMC models of biochemical systems particularly in connection to protein-protein interactions. We assume that the model is written by a biologist in form of site-graph-rewrite rules. Site-graph-rewrite rules compactly express that, often, only a local context of a protein (instead of a full molecular species) needs to be in a certain configuration in order to trigger a reaction event. This observation leads to suitable aggregate Markov chains with smaller state spaces, thereby providing sufficient reduction in computational complexity. This is further exemplified in two case studies: simple unbounded polymerization and early EGFR/insulin crosstalk.

  2. Global sensitivity analysis in stochastic simulators of uncertain reaction networks

    Science.gov (United States)

    Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.

    2016-12-01

    Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.

  3. A computational framework for the automated construction of glycosylation reaction networks.

    Directory of Open Access Journals (Sweden)

    Gang Liu

    Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme

  4. Self-Organized Stationary Patterns in Networks of Bistable Chemical Reactions.

    Science.gov (United States)

    Kouvaris, Nikos E; Sebek, Michael; Mikhailov, Alexander S; Kiss, István Z

    2016-10-10

    Experiments with networks of discrete reactive bistable electrochemical elements organized in regular and nonregular tree networks are presented to confirm an alternative to the Turing mechanism for the formation of self-organized stationary patterns. The results show that the pattern formation can be described by the identification of domains that can be activated individually or in combinations. The method also enabled the localization of chemical reactions to network substructures and the identification of critical sites whose activation results in complete activation of the system. Although the experiments were performed with a specific nickel electrodissolution system, they reproduced all the salient dynamic behavior of a general network model with a single nonlinearity parameter. Thus, the considered pattern-formation mechanism is very robust, and similar behavior can be expected in other natural or engineered networked systems that exhibit, at least locally, a treelike structure.

  5. BRUSLIB and NETGEN: the Brussels nuclear reaction rate library and nuclear network generator for astrophysics

    CERN Document Server

    Aikawa, M; Goriely, S; Jorissen, A; Takahashi, K

    2005-01-01

    Nuclear reaction rates are quantities of fundamental importance in astrophysics. Substantial efforts have been devoted in the last decades to measure or calculate them. The present paper presents for the first time a detailed description of the Brussels nuclear reaction rate library BRUSLIB and of the nuclear network generator NETGEN so as to make these nuclear data packages easily accessible to astrophysicists for a large variety of applications. BRUSLIB is made of two parts. The first one contains the 1999 NACRE compilation based on experimental data for 86 reactions with (mainly) stable targets up to Si. The second part of BRUSLIB concerns nuclear reaction rate predictions calculated within a statistical Hauser-Feshbach approximation, which limits the reliability of the rates to reactions producing compound nuclei with a high enough level density. These calculations make use of global and coherent microscopic nuclear models for the quantities entering the rate calculations. The use of such models is utterl...

  6. Variable elimination in chemical reaction networks with mass-action kinetics

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Wiuf, C.

    2012-01-01

    We consider chemical reaction networks taken with mass-action kinetics. The steady states of such a system are solutions to a system of polynomial equations. Even for small systems the task of finding the solutions is daunting. We develop an algebraic framework and procedure for linear elimination...

  7. Biomolecular Assembly of Gold Nanocrystals

    Energy Technology Data Exchange (ETDEWEB)

    Micheel, Christine Marya [Univ. of California, Berkeley, CA (United States)

    2005-05-20

    Over the past ten years, methods have been developed to construct discrete nanostructures using nanocrystals and biomolecules. While these frequently consist of gold nanocrystals and DNA, semiconductor nanocrystals as well as antibodies and enzymes have also been used. One example of discrete nanostructures is dimers of gold nanocrystals linked together with complementary DNA. This type of nanostructure is also known as a nanocrystal molecule. Discrete nanostructures of this kind have a number of potential applications, from highly parallel self-assembly of electronics components and rapid read-out of DNA computations to biological imaging and a variety of bioassays. My research focused in three main areas. The first area, the refinement of electrophoresis as a purification and characterization method, included application of agarose gel electrophoresis to the purification of discrete gold nanocrystal/DNA conjugates and nanocrystal molecules, as well as development of a more detailed understanding of the hydrodynamic behavior of these materials in gels. The second area, the development of methods for quantitative analysis of transmission electron microscope data, used computer programs written to find pair correlations as well as higher order correlations. With these programs, it is possible to reliably locate and measure nanocrystal molecules in TEM images. The final area of research explored the use of DNA ligase in the formation of nanocrystal molecules. Synthesis of dimers of gold particles linked with a single strand of DNA possible through the use of DNA ligase opens the possibility for amplification of nanostructures in a manner similar to polymerase chain reaction. These three areas are discussed in the context of the work in the Alivisatos group, as well as the field as a whole.

  8. Pinning Control Strategies for Synchronization of Linearly Coupled Neural Networks With Reaction-Diffusion Terms.

    Science.gov (United States)

    Wang, Jin-Liang; Wu, Huai-Ning; Huang, Tingwen; Ren, Shun-Yan

    2016-04-01

    Two types of coupled neural networks with reaction-diffusion terms are considered in this paper. In the first one, the nodes are coupled through their states. In the second one, the nodes are coupled through the spatial diffusion terms. For the former, utilizing Lyapunov functional method and pinning control technique, we obtain some sufficient conditions to guarantee that network can realize synchronization. In addition, considering that the theoretical coupling strength required for synchronization may be much larger than the needed value, we propose an adaptive strategy to adjust the coupling strength for achieving a suitable value. For the latter, we establish a criterion for synchronization using the designed pinning controllers. It is found that the coupled reaction-diffusion neural networks with state coupling under the given linear feedback pinning controllers can realize synchronization when the coupling strength is very large, which is contrary to the coupled reaction-diffusion neural networks with spatial diffusion coupling. Moreover, a general criterion for ensuring network synchronization is derived by pinning a small fraction of nodes with adaptive feedback controllers. Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the theoretical results.

  9. A novel Chemical Reaction Optimization based Higher order Neural Network (CRO-HONN for nonlinear classification

    Directory of Open Access Journals (Sweden)

    Janmenjoy Nayak

    2015-09-01

    Full Text Available In this paper, a Chemical Reaction Optimization (CRO based higher order neural network with a single hidden layer called Pi–Sigma Neural Network (PSNN has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others.

  10. Azurin for Biomolecular Electronics: a Reliability Study

    Science.gov (United States)

    Bramanti, Alessandro; Pompa, Pier Paolo; Maruccio, Giuseppe; Calabi, Franco; Arima, Valentina; Cingolani, Roberto; Corni, Stefano; Di Felice, Rosa; De Rienzo, Francesca; Rinaldi, Ross

    2005-09-01

    The metalloprotein azurin, used in biomolecular electronics, is investigated with respect to its resilience to high electric fields and ambient conditions, which are crucial reliability issues. Concerning the effect of electric fields, two models of different complexity agree indicating an unexpectedly high robustness. Experiments in device-like conditions confirm that no structural modifications occur, according to fluorescence spectra, even after a 40-min exposure to tens of MV/m. Ageing is then investigated experimentally, at ambient conditions and without field, over several days. Only a small conformational rearrangement is observed in the first tens of hours, followed by an equilibrium state.

  11. Nanotube-Based Chemical and Biomolecular Sensors

    Institute of Scientific and Technical Information of China (English)

    J.Koh; B.Kim; S.Hong; H.Lim; H.C.Choi

    2008-01-01

    We present a brief review about recent results regarding carbon nanotube (CNT)-based chemical and biomolecular sensors. For the fabrication of CNT-based sensors, devices containing CNT channels between two metal electrodes are first fabricated usually via chemical vapor deposition (CVD) process or "surface programmed assembly" method. Then, the CNT surfaces are often functionalized to enhance the selectivity of the sensors. Using this process, highly-sensitive CNT-based sensors can be fabricated for the selective detection of various chemical and biological molecules such as hydrogen, ammonia, carbon monoxide, chlorine gas, DNA, glucose, alcohol, and proteins.

  12. Scalable Molecular Dynamics for Large Biomolecular Systems

    Directory of Open Access Journals (Sweden)

    Robert K. Brunner

    2000-01-01

    Full Text Available We present an optimized parallelization scheme for molecular dynamics simulations of large biomolecular systems, implemented in the production-quality molecular dynamics program NAMD. With an object-based hybrid force and spatial decomposition scheme, and an aggressive measurement-based predictive load balancing framework, we have attained speeds and speedups that are much higher than any reported in literature so far. The paper first summarizes the broad methodology we are pursuing, and the basic parallelization scheme we used. It then describes the optimizations that were instrumental in increasing performance, and presents performance results on benchmark simulations.

  13. Micro and Nanotechnologies Enhanced Biomolecular Sensing

    Directory of Open Access Journals (Sweden)

    Tza-Huei Wang

    2013-07-01

    Full Text Available This editorial summarizes some of the recent advances of micro and nanotechnology-based tools and devices for biomolecular detection. These include the incorporation of nanomaterials into a sensor surface or directly interfacing with molecular probes to enhance target detection via more rapid and sensitive responses, and the use of self-assembled organic/inorganic nanocomposites that inhibit exceptional spectroscopic properties to enable facile homogenous assays with efficient binding kinetics. Discussions also include some insight into microfluidic principles behind the development of an integrated sample preparation and biosensor platform toward a miniaturized and fully functional system for point of care applications.

  14. Fundamentos biomoleculares de la diabetes mellitus

    OpenAIRE

    2013-01-01

    La diabetes mellitus es una enfermedad endocrina con importantes implicaciones a nivel sistémico, como: angiopatía, neuropatía, retinopatía y nefropatía, entre otras. Estas  complicaciones tienen su origen en eventos biomoleculares desencadenados por la hiperglicemia.  La presente revisión de tema trata sobre la estructura y síntesis de la insulina en las células β del páncreas; los eventos moleculares y bioquímicos que activan su secreción como respuesta a una alta concentración de glucosa e...

  15. Nanoarchitectonics of biomolecular assemblies for functional applications

    Science.gov (United States)

    Avinash, M. B.; Govindaraju, T.

    2014-10-01

    The stringent processes of natural selection and evolution have enabled extraordinary structure-function properties of biomolecules. Specifically, the archetypal designs of biomolecules, such as amino acids, nucleobases, carbohydrates and lipids amongst others, encode unparalleled information, selectivity and specificity. The integration of biomolecules either with functional molecules or with an embodied functionality ensures an eclectic approach for novel and advanced nanotechnological applications ranging from electronics to biomedicine, besides bright prospects in systems chemistry and synthetic biology. Given this intriguing scenario, our feature article intends to shed light on the emerging field of functional biomolecular engineering.

  16. Parametric sensitivity analysis for biochemical reaction networks based on pathwise information theory

    Science.gov (United States)

    2013-01-01

    Background Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. Results We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as “pathwise”. The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. Conclusions As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the

  17. HRSSA – Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)

    2016-07-15

    This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

  18. HRSSA - Efficient hybrid stochastic simulation for spatially homogeneous biochemical reaction networks

    Science.gov (United States)

    Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong

    2016-07-01

    This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.

  19. Improvements in continuum modeling for biomolecular systems

    Science.gov (United States)

    Yu, Qiao; Ben-Zhuo, Lu

    2016-01-01

    Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson- Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of the biological systems. Considerable efforts have been made to improve the continuum model to account for discrete particle interactions and to make progress in numerical methods to provide accurate and efficient simulations. This review will summarize recent main improvements in continuum modeling for biomolecular systems, with focus on the size-modified models, the coupling of the classical density functional theory and the PNP equations, the coupling of polar and nonpolar interactions, and numerical progress. Project supported by the National Natural Science Foundation of China (Grant No. 91230106) and the Chinese Academy of Sciences Program for Cross & Cooperative Team of the Science & Technology Innovation.

  20. Smartphones for cell and biomolecular detection.

    Science.gov (United States)

    Liu, Xiyuan; Lin, Tung-Yi; Lillehoj, Peter B

    2014-11-01

    Recent advances in biomedical science and technology have played a significant role in the development of new sensors and assays for cell and biomolecular detection. Generally, these efforts are aimed at reducing the complexity and costs associated with diagnostic testing so that it can be performed outside of a laboratory or hospital setting, requiring minimal equipment and user involvement. In particular, point-of-care (POC) testing offers immense potential for many important applications including medical diagnosis, environmental monitoring, food safety, and biosecurity. When coupled with smartphones, POC systems can offer portability, ease of use and enhanced functionality while maintaining performance. This review article focuses on recent advancements and developments in smartphone-based POC systems within the last 6 years with an emphasis on cell and biomolecular detection. These devices typically comprise multiple components, such as detectors, sample processors, disposable chips, batteries, and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. Researchers have demonstrated several promising approaches employing various detection schemes and device configurations, and it is expected that further developments in biosensors, battery technology and miniaturized electronics will enable smartphone-based POC technologies to become more mainstream tools in the scientific and biomedical communities.

  1. Interindividual reaction time variability is related to resting-state network topology: an electroencephalogram study.

    Science.gov (United States)

    Zhou, G; Liu, P; He, J; Dong, M; Yang, X; Hou, B; Von Deneen, K M; Qin, W; Tian, J

    2012-01-27

    Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.

  2. Accurate High-Temperature Reaction Networks for Alternative Fuels: Butanol Isomers

    Energy Technology Data Exchange (ETDEWEB)

    Van Geem, K. M.; Pyl, S. P.; Marin, G. B.; Harper, M. R.; Green, W. H.

    2010-11-03

    Oxygenated hydrocarbons, particularly alcohol compounds, are being studied extensively as alternatives and additives to conventional fuels due to their propensity of decreasing soot formation and improving the octane number of gasoline. However, oxygenated fuels also increase the production of toxic byproducts, such as formaldehyde. To gain a better understanding of the oxygenated functional group’s influence on combustion properties—e.g., ignition delay at temperatures above the negative temperature coefficient regime, and the rate of benzene production, which is the common precursor to soot formation—a detailed pressure-dependent reaction network for n-butanol, sec-butanol, and tert-butanol consisting of 281 species and 3608 reactions is presented. The reaction network is validated against shock tube ignition delays and doped methane flame concentration profiles reported previously in the literature, in addition to newly acquired pyrolysis data. Good agreement between simulated and experimental data is achieved in all cases. Flux and sensitivity analyses for each set of experiments have been performed, and high-pressure-limit reaction rate coefficients for important pathways, e.g., the dehydration reactions of the butanol isomers, have been computed using statistical mechanics and quantum chemistry. The different alcohol decomposition pathways, i.e., the pathways from primary, secondary, and tertiary alcohols, are discussed. Furthermore, comparisons between ethanol and n-butanol, two primary alcohols, are presented, as they relate to ignition delay.

  3. Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks.

    Science.gov (United States)

    Anderson, David F; Craciun, Gheorghe; Gopalkrishnan, Manoj; Wiuf, Carsten

    2015-09-01

    We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potential of the stationary distribution of stochastically modeled complex balanced systems. We extend this result to general birth-death models and demonstrate via example that similar scaling limits can yield Lyapunov functions even for models that are not complex or detailed balanced, and may even have multiple equilibria.

  4. Coupling sample paths to the partial thermodynamic limit in stochastic chemical reaction networks

    CERN Document Server

    Levien, Ethan

    2016-01-01

    We present a new technique for reducing the variance in Monte Carlo estimators of stochastic chemical reaction networks. Our method makes use of the fact that many stochastic reaction networks converge to piecewise deterministic Markov processes in the large system-size limit. The statistics of the piecewise deterministic process can be obtained much more efficiently than those of the exact process. By coupling sample paths of the exact model to the piecewise deterministic process we are able to reduce the variance, and hence the computational complexity of the Monte Carlo estimator. In addition to rigorous results concerning the asymptotic behavior of our method, numerical simulations are performed on some simple biological models suggesting that significant computational gains are made for even moderate system-sizes.

  5. Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction

    Directory of Open Access Journals (Sweden)

    Lin JianJhong

    2009-01-01

    Full Text Available Szekeley observed that the dynamic pattern of the locomotion of salamanders can be explained by periodic vector sequences generated by logical neural networks. Such sequences can mathematically be described by "doubly periodic traveling waves" and therefore it is of interest to propose dynamic models that may produce such waves. One such dynamic network model is built here based on reaction-diffusion principles and a complete discussion is given for the existence of doubly periodic waves as outputs. Since there are 2 parameters in our model and 4 a priori unknown parameters involved in our search of solutions, our results are nontrivial. The reaction term in our model is a linear function and hence our results can also be interpreted as existence criteria for solutions of a nontrivial linear problem depending on 6 parameters.

  6. INFLUENCE OF NOISE AND DELAY ON REACTION-DIFFUSION RECURRENT NEURAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    Li Wu

    2006-01-01

    In this paper, the influence of the noise and delay upon the stability property of reaction-diffusion recurrent neural networks (RNNs) with the time-varying delay is discussed. The new and easily verifiable conditions to guarantee the mean value exponential stability of an equilibrium solution are derived. The rate of exponential convergence can be estimated by means of a simple computation based on these criteria.

  7. Stability of Stochastic Reaction-Diffusion Recurrent Neural Networks with Unbounded Distributed Delays

    Directory of Open Access Journals (Sweden)

    Chuangxia Huang

    2011-01-01

    Full Text Available Stability of reaction-diffusion recurrent neural networks (RNNs with continuously distributed delays and stochastic influence are considered. Some new sufficient conditions to guarantee the almost sure exponential stability and mean square exponential stability of an equilibrium solution are obtained, respectively. Lyapunov's functional method, M-matrix properties, some inequality technique, and nonnegative semimartingale convergence theorem are used in our approach. The obtained conclusions improve some published results.

  8. Dynamics of small autocatalytic reaction network; 2, replication, mutation and catalysis

    CERN Document Server

    Stadler, P F; Först, C J; Schuster, P; Stadler, Peter F; Schnabl, Wolfgang; Forst, Christian V; Schuster, Peter; Biotechnology, Molecuar

    1994-01-01

    Mutation is introduced into autocatalytic reaction networks. Examples of low dimensional dynamical systems --- n = 2, 3 and 4 --- are discussed and complete qualitative analysis is presented. Error thresholds known from simple replication-mutation kinetics with frequency independent replication rates occur here as well. Instead of cooperative transitions or higher order phase transistions the thresholds appear here as supercritical or subcritical bifurcations being analogous to first order phase transitions.

  9. Variable elimination in post-translational modification reaction networks with mass-action kinetics

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Wiuf, Carsten

    2013-01-01

    We define a subclass of chemical reaction networks called post-translational modification systems. Important biological examples of such systems include MAPK cascades and two-component systems which are well-studied experimentally as well as theoretically. The steady states of such a system are s...... of the species graph and provide conservation laws. A criterion for when a (maximal) set of independent conservation laws can be derived from cuts is given....

  10. A reaction-diffusion model of ROS-induced ROS release in a mitochondrial network.

    Directory of Open Access Journals (Sweden)

    Lufang Zhou

    2010-01-01

    Full Text Available Loss of mitochondrial function is a fundamental determinant of cell injury and death. In heart cells under metabolic stress, we have previously described how the abrupt collapse or oscillation of the mitochondrial energy state is synchronized across the mitochondrial network by local interactions dependent upon reactive oxygen species (ROS. Here, we develop a mathematical model of ROS-induced ROS release (RIRR based on reaction-diffusion (RD-RIRR in one- and two-dimensional mitochondrial networks. The nodes of the RD-RIRR network are comprised of models of individual mitochondria that include a mechanism of ROS-dependent oscillation based on the interplay between ROS production, transport, and scavenging; and incorporating the tricarboxylic acid (TCA cycle, oxidative phosphorylation, and Ca(2+ handling. Local mitochondrial interaction is mediated by superoxide (O2.- diffusion and the O2.(--dependent activation of an inner membrane anion channel (IMAC. In a 2D network composed of 500 mitochondria, model simulations reveal DeltaPsi(m depolarization waves similar to those observed when isolated guinea pig cardiomyocytes are subjected to a localized laser-flash or antioxidant depletion. The sensitivity of the propagation rate of the depolarization wave to O(2.- diffusion, production, and scavenging in the reaction-diffusion model is similar to that observed experimentally. In addition, we present novel experimental evidence, obtained in permeabilized cardiomyocytes, confirming that DeltaPsi(m depolarization is mediated specifically by O2.-. The present work demonstrates that the observed emergent macroscopic properties of the mitochondrial network can be reproduced in a reaction-diffusion model of RIRR. Moreover, the findings have uncovered a novel aspect of the synchronization mechanism, which is that clusters of mitochondria that are oscillating can entrain mitochondria that would otherwise display stable dynamics. The work identifies the

  11. On the rejection-based algorithm for simulation and analysis of large-scale reaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento, Trento (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento, Trento (Italy)

    2015-06-28

    Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.

  12. Programmable chemical reaction networks: emulating regulatory functions in living cells using a bottom-up approach.

    Science.gov (United States)

    van Roekel, Hendrik W H; Rosier, Bas J H M; Meijer, Lenny H H; Hilbers, Peter A J; Markvoort, Albert J; Huck, Wilhelm T S; de Greef, Tom F A

    2015-11-07

    Living cells are able to produce a wide variety of biological responses when subjected to biochemical stimuli. It has become apparent that these biological responses are regulated by complex chemical reaction networks (CRNs). Unravelling the function of these circuits is a key topic of both systems biology and synthetic biology. Recent progress at the interface of chemistry and biology together with the realisation that current experimental tools are insufficient to quantitatively understand the molecular logic of pathways inside living cells has triggered renewed interest in the bottom-up development of CRNs. This builds upon earlier work of physical chemists who extensively studied inorganic CRNs and showed how a system of chemical reactions can give rise to complex spatiotemporal responses such as oscillations and pattern formation. Using purified biochemical components, in vitro synthetic biologists have started to engineer simplified model systems with the goal of mimicking biological responses of intracellular circuits. Emulation and reconstruction of system-level properties of intracellular networks using simplified circuits are able to reveal key design principles and molecular programs that underlie the biological function of interest. In this Tutorial Review, we present an accessible overview of this emerging field starting with key studies on inorganic CRNs followed by a discussion of recent work involving purified biochemical components. Finally, we review recent work showing the versatility of programmable biochemical reaction networks (BRNs) in analytical and diagnostic applications.

  13. Coupling reaction on gold nanoparticle to yield polythiophene/gold nanoparticle alternate network film.

    Science.gov (United States)

    Tanaka, Manabu; Fujita, Remi; Nishide, Hiroyuki

    2009-01-01

    The novel gold nanoparticle, which was stabilized with pi-conjugated molecules bearing functional groups at the terminals, was prepared via conventional procedure by using 5-bromo-2,2'-bithiophene-5'-thiol as a stabilizer. The gold nanoparticle (ca. 3 nm-diameter) showed good dispersion stability in various organic solvents, and its electrochemical and spectroscopic study revealed peculiar properties originated in the pi-conjugated molecular stabilizer, bithiophene derivative. The Pd-catalyzed coupling reaction on the gold nanoparticle was first achieved by using the gold nanoparticle bearing bromo groups at the particle surface and the model boronic acid molecule, 5-formyl-2-thiopheneboronic acid, to yield the terthiophene derivatives on the gold nanoparticle. The 1H-NMR, UV, and TGA analysis supported the progress of the coupling reaction on the gold nanoparticle. This Pd-catalyzed coupling reaction was applied with the borate-terminated polythiophene to form polythiophene/gold nanoparticle alternate network film. The electron microscopic images supported the formation of the network structure. The high electric conductivity on the network film suggested that the conductive characteristic of the film originated from that of the pi-conjugated polythiophene backbone connected with the gold nanoparticle.

  14. A multi-time-scale analysis of chemical reaction networks: II. Stochastic systems.

    Science.gov (United States)

    Kan, Xingye; Lee, Chang Hyeong; Othmer, Hans G

    2016-11-01

    We consider stochastic descriptions of chemical reaction networks in which there are both fast and slow reactions, and for which the time scales are widely separated. We develop a computational algorithm that produces the generator of the full chemical master equation for arbitrary systems, and show how to obtain a reduced equation that governs the evolution on the slow time scale. This is done by applying a state space decomposition to the full equation that leads to the reduced dynamics in terms of certain projections and the invariant distributions of the fast system. The rates or propensities of the reduced system are shown to be the rates of the slow reactions conditioned on the expectations of fast steps. We also show that the generator of the reduced system is a Markov generator, and we present an efficient stochastic simulation algorithm for the slow time scale dynamics. We illustrate the numerical accuracy of the approximation by simulating several examples. Graph-theoretic techniques are used throughout to describe the structure of the reaction network and the state-space transitions accessible under the dynamics.

  15. Spreading of infection in a two species reaction-diffusion process in networks.

    Science.gov (United States)

    Korosoglou, Paschalis; Kittas, Aristotelis; Argyrakis, Panos

    2010-12-01

    We study the dynamics of the infection of a two mobile species reaction from a single infected agent in a population of healthy agents. Historically, the main focus for infection propagation has been through spreading phenomena, where a random location of the system is initially infected and then propagates by successfully infecting its neighbor sites. Here both the infected and healthy agents are mobile, performing classical random walks. This may be a more realistic picture to such epidemiological models, such as the spread of a virus in communication networks of routers, where data travel in packets, the communication time of stations in ad hoc mobile networks, information spreading (such as rumor spreading) in social networks, etc. We monitor the density of healthy particles ρ(t), which we find in all cases to be an exponential function in the long-time limit in two-dimensional and three-dimensional lattices and Erdős-Rényi (ER) and scale-free (SF) networks. We also investigate the scaling of the crossover time t(c) from short- to long-time exponential behavior, which we find to be a power law in lattices and ER networks. This crossover is shown to be absent in SF networks, where we reveal the role of the connectivity of the network in the infection process. We compare this behavior to ER networks and lattices and highlight the significance of various connectivity patterns, as well as the important differences of this process in the various underlying geometries, revealing a more complex behavior of ρ(t).

  16. Biomolecular Markers in Cancer of the Tongue

    Directory of Open Access Journals (Sweden)

    Daris Ferrari

    2009-01-01

    Full Text Available The incidence of tongue cancer is increasing worldwide, and its aggressiveness remains high regardless of treatment. Genetic changes and the expression of abnormal proteins have been frequently reported in the case of head and neck cancers, but the little information that has been published concerning tongue tumours is often contradictory. This review will concentrate on the immunohistochemical expression of biomolecular markers and their relationships with clinical behaviour and prognosis. Most of these proteins are associated with nodal stage, tumour progression and metastases, but there is still controversy concerning their impact on disease-free and overall survival, and treatment response. More extensive clinical studies are needed to identify the patterns of molecular alterations and the most reliable predictors in order to develop tailored anti-tumour strategies based on the targeting of hypoxia markers, vascular and lymphangiogenic factors, epidermal growth factor receptors, intracytoplasmatic signalling and apoptosis.

  17. Biomolecular computing systems: principles, progress and potential.

    Science.gov (United States)

    Benenson, Yaakov

    2012-06-12

    The task of information processing, or computation, can be performed by natural and man-made 'devices'. Man-made computers are made from silicon chips, whereas natural 'computers', such as the brain, use cells and molecules. Computation also occurs on a much smaller scale in regulatory and signalling pathways in individual cells and even within single biomolecules. Indeed, much of what we recognize as life results from the remarkable capacity of biological building blocks to compute in highly sophisticated ways. Rational design and engineering of biological computing systems can greatly enhance our ability to study and to control biological systems. Potential applications include tissue engineering and regeneration and medical treatments. This Review introduces key concepts and discusses recent progress that has been made in biomolecular computing.

  18. Biomolecular rods and tubes in nanotechnology

    Science.gov (United States)

    Bittner, Alexander M.

    2005-02-01

    Biomolecules are vitally important elements in nanoscale science and also in future nanotechnology. Their shape and their chemical and physical functionality can give them a big advantage over inorganic and organic substances. While this becomes most obvious in proteins and peptides, with their complicated, but easily controlled chemistry, other biomolecular substances such as DNA, lipids and carbohydrates can also be important. In this review, the emphasis is on one-dimensional molecules and on molecules that self-assemble into linear structures, and on their potential applications. An important aspect is that biomolecules can act as templates, i.e. their shape and chemical properties can be employed to arrange inorganic substances such as metals or metal compounds on the nanometre scale. In particular, rod- and tube-like nanostructures can show physical properties that are different from those of the bulk material, and thus these structures are likely to be a basis for new technology.

  19. Fundamentos biomoleculares de la diabetes mellitus

    Directory of Open Access Journals (Sweden)

    Katiana Mendoza

    2013-12-01

    Full Text Available La diabetes mellitus es una enfermedad endocrina con importantes implicaciones a nivel sistémico, como: angiopatía, neuropatía, retinopatía y nefropatía, entre otras. Estas  complicaciones tienen su origen en eventos biomoleculares desencadenados por la hiperglicemia.  La presente revisión de tema trata sobre la estructura y síntesis de la insulina en las células β del páncreas; los eventos moleculares y bioquímicos que activan su secreción como respuesta a una alta concentración de glucosa en sangre; la cascada de señalización generada por la unión de la insulina a su receptor sobre células diana; y las alteraciones metabólicas que los diferentes tipos de diabetes mellitus producen.

  20. BRUSLIB and NETGEN: the Brussels nuclear reaction rate library and nuclear network generator for astrophysics

    Science.gov (United States)

    Aikawa, M.; Arnould, M.; Goriely, S.; Jorissen, A.; Takahashi, K.

    2005-10-01

    Nuclear reaction rates are quantities of fundamental importance in astrophysics. Substantial efforts have been devoted in the last decades to measuring or calculating them. This paper presents a detailed description of the Brussels nuclear reaction rate library BRUSLIB and of the nuclear network generator NETGEN. BRUSLIB is made of two parts. The first one contains the 1999 NACRE compilation based on experimental data for 86 reactions with (mainly) stable targets up to Si. BRUSLIB provides an electronic link to the published, as well as to a large body of unpublished, NACRE data containing adopted rates, as well as lower and upper limits. The second part of BRUSLIB concerns nuclear reaction rate predictions to complement the experimentally-based rates. An electronic access is provided to tables of rates calculated within a statistical Hauser-Feshbach approximation, which limits the reliability of the rates to reactions producing compound nuclei with a high enough level density. These calculations make use of global and coherent microscopic nuclear models for the quantities entering the rate calculations. The use of such models makes the BRUSLIB rate library unique. A description of the Nuclear Network Generator NETGEN that complements the BRUSLIB package is also presented. NETGEN is a tool to generate nuclear reaction rates for temperature grids specified by the user. The information it provides can be used for a large variety of applications, including Big Bang nucleosynthesis, the energy generation and nucleosynthesis associated with the non-explosive and explosive hydrogen to silicon burning stages, or the synthesis of the heavy nuclides through the s-, α- and r-, rp- or p-processes.

  1. Global Langevin model of multidimensional biomolecular dynamics

    Science.gov (United States)

    Schaudinnus, Norbert; Lickert, Benjamin; Biswas, Mithun; Stock, Gerhard

    2016-11-01

    Molecular dynamics simulations of biomolecular processes are often discussed in terms of diffusive motion on a low-dimensional free energy landscape F ( 𝒙 ) . To provide a theoretical basis for this interpretation, one may invoke the system-bath ansatz á la Zwanzig. That is, by assuming a time scale separation between the slow motion along the system coordinate x and the fast fluctuations of the bath, a memory-free Langevin equation can be derived that describes the system's motion on the free energy landscape F ( 𝒙 ) , which is damped by a friction field and driven by a stochastic force that is related to the friction via the fluctuation-dissipation theorem. While the theoretical formulation of Zwanzig typically assumes a highly idealized form of the bath Hamiltonian and the system-bath coupling, one would like to extend the approach to realistic data-based biomolecular systems. Here a practical method is proposed to construct an analytically defined global model of structural dynamics. Given a molecular dynamics simulation and adequate collective coordinates, the approach employs an "empirical valence bond"-type model which is suitable to represent multidimensional free energy landscapes as well as an approximate description of the friction field. Adopting alanine dipeptide and a three-dimensional model of heptaalanine as simple examples, the resulting Langevin model is shown to reproduce the results of the underlying all-atom simulations. Because the Langevin equation can also be shown to satisfy the underlying assumptions of the theory (such as a delta-correlated Gaussian-distributed noise), the global model provides a correct, albeit empirical, realization of Zwanzig's formulation. As an application, the model can be used to investigate the dependence of the system on parameter changes and to predict the effect of site-selective mutations on the dynamics.

  2. FERN – a Java framework for stochastic simulation and evaluation of reaction networks

    Directory of Open Access Journals (Sweden)

    Zimmer Ralf

    2008-08-01

    Full Text Available Abstract Background Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a do not provide the most efficient simulation algorithms and are difficult to extend, b cannot be easily integrated into other applications or c do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. Results In this article, we present FERN (Framework for Evaluation of Reaction Networks, a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. Conclusion FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward

  3. Looking for chemical reaction networks exhibiting a drift along a manifold of marginally stable states.

    Science.gov (United States)

    Brogioli, Doriano

    2013-02-07

    I recently reported some examples of mass-action equations that have a continuous manifold of marginally stable stationary states [Brogioli, D., 2010. Marginally stable chemical systems as precursors of life. Phys. Rev. Lett. 105, 058102; Brogioli, D., 2011. Marginal stability in chemical systems and its relevance in the origin of life. Phys. Rev. E 84, 031931]. The corresponding chemical reaction networks show nonclassical effects, i.e. a violation of the mass-action equations, under the effect of the concentration fluctuations: the chemical system drifts along the marginally stable states. I proposed that this effect is potentially involved in abiogenesis. In the present paper, I analyze the mathematical properties of mass-action equations of marginally stable chemical reaction networks. The marginal stability implies that the mass-action equations obey some conservation law; I show that the mathematical properties of the conserved quantity characterize the motion along the marginally stable stationary state manifold, i.e. they allow to predict if the fluctuations give rise to a random walk or a drift under the effect of concentration fluctuations. Moreover, I show that the presence of the drift along the manifold of marginally stable stationary-states is a critical property, i.e. at least one of the reaction constants must be fine tuned in order to obtain the drift.

  4. Simulation and fitting of complex reaction network TPR: The key is the objective function

    Science.gov (United States)

    Savara, Aditya

    2016-11-01

    A method has been developed for finding improved fits during simulation and fitting of data from complex reaction network temperature programmed reactions (CRN-TPR). It was found that simulation and fitting of CRN-TPR presents additional challenges relative to simulation and fitting of simpler TPR systems. The method used here can enable checking the plausibility of proposed chemical mechanisms and kinetic models. The most important finding was that when choosing an objective function, use of an objective function that is based on integrated production provides more utility in finding improved fits when compared to an objective function based on the rate of production. The response surface produced by using the integrated production is monotonic, suppresses effects from experimental noise, requires fewer points to capture the response behavior, and can be simulated numerically with smaller errors. For CRN-TPR, there is increased importance (relative to simple reaction network TPR) in resolving of peaks prior to fitting, as well as from weighting of experimental data points. Using an implicit ordinary differential equation solver was found to be inadequate for simulating CRN-TPR. The method employed here was capable of attaining improved fits in simulation and fitting of CRN-TPR when starting with a postulated mechanism and physically realistic initial guesses for the kinetic parameters.

  5. An Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning

    Science.gov (United States)

    Parete-Koon, Suzanne; Hix, W.; Thielemann, F.

    2008-03-01

    The nuclei of the "iron peak" are formed in massive stars shortly before core collapse and during their supernova outbursts as well as during thermonuclear supernovae. Complete and incomplete silicon burning during these events are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. Because the size and membership of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. Toward this end, we are implementing a scheme beginning with a single QSE (NSE) group at appropriately high temperature, then progressing through 2, 3 and 4 group stages (with successively more independent variables) as temperature declines. This combination allows accurate prediction of the nuclear abundance evolution, deleptonization and energy generation at a further reduced computational cost when compared to a conventional nuclear reaction network or our previous 3 fixed group QSE-reduced network. During silicon burning, the resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications. This work has been supported by the National Science Foundation, by the Department of Energy's Scientic Discovery through Advanced Computing

  6. The thermodynamic properties of 2-aminobiphenyl (an intermediate in the carbazole/hydrogen reaction network)

    Energy Technology Data Exchange (ETDEWEB)

    Steele, W.V.; Chirico, R.D.; Knipmeyer, S.E.; Nguyen, A.

    1990-12-01

    Catalytic hydrodenitrogenation (HDN) is a key step in upgrading processes for conversion of heavy petroleum, shale oil, tar sands, and the products of the liquefaction of coal to economically viable products. This research program provides accurate experimental thermochemical and thermophysical properties for key organic nitrogen-containing compounds present in the range of alternative feedstocks, and applies the experimental information to thermodynamic analyses of key HDN reaction networks. This report is the first in a series that will lead to an analysis of a three-ring HDN system; the carbazole/hydrogen reaction network. 2-Aminobiphenyl is the initial intermediate in the HDN pathway for carbazole, which consumes the least hydrogen possible. Measurements leading to the calculation of the ideal-gas thermodynamic properties for 2-aminobiphenyl are reported. Experimental methods included combustion calorimetry, adiabatic heat-capacity calorimetry, comparative ebulliometry, inclined-piston gauge manometry, and differential-scanning calorimetry (d.s.c). Entropies, enthalpies, and Gibbs energies of formation were derived for the ideal gas for selected temperatures between 298.15 K and 820 K. The critical temperature and critical density were determined for 2-aminobiphenyl with the d.s.c., and the critical pressure was derived. The Gibbs energies of formation are used in thermodynamic calculations to compare the feasibility of the initial hydrogenolysis step in the carbazole/H{sub 2} network with that of its hydrocarbon and oxygen-containing analogous; i.e., fluorene/H{sub 2} and dibenzofuran/H{sub 2}. Results of the thermodynamic calculations are compared with those of batch-reaction studies reported in the literature. 57 refs., 8 figs., 18 tabs.

  7. Dynamical Behavior of Delayed Reaction-Diffusion Hopfield Neural Networks Driven by Infinite Dimensional Wiener Processes.

    Science.gov (United States)

    Liang, Xiao; Wang, Linshan; Wang, Yangfan; Wang, Ruili

    2016-09-01

    In this paper, we focus on the long time behavior of the mild solution to delayed reaction-diffusion Hopfield neural networks (DRDHNNs) driven by infinite dimensional Wiener processes. We analyze the existence, uniqueness, and stability of this system under the local Lipschitz function by constructing an appropriate Lyapunov-Krasovskii function and utilizing the semigroup theory. Some easy-to-test criteria affecting the well-posedness and stability of the networks, such as infinite dimensional noise and diffusion effect, are obtained. The criteria can be used as theoretic guidance to stabilize DRDHNNs in practical applications when infinite dimensional noise is taken into consideration. Meanwhile, considering the fact that the standard Brownian motion is a special case of infinite dimensional Wiener process, we undertake an analysis of the local Lipschitz condition, which has a wider range than the global Lipschitz condition. Two samples are given to examine the availability of the results in this paper. Simulations are also given using the MATLAB.

  8. Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality.

    Science.gov (United States)

    Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

    2016-06-01

    Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

  9. Neural Network Control of CSTR for Reversible Reaction Using Reverence Model Approach

    Directory of Open Access Journals (Sweden)

    Duncan ALOKO

    2007-01-01

    Full Text Available In this work, non-linear control of CSTR for reversible reaction is carried out using Neural Network as design tool. The Model Reverence approach in used to design ANN controller. The idea is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. We use PID control scheme as benchmark to study the performance of the controller. The comparison shows that ANN controller out perform PID in the extreme range of non-linearity.This paper represents a preliminary effort to design a simplified neutral network control scheme for a class of non-linear process. Future works will involve further investigation of the effectiveness of thin approach for the real industrial chemical process

  10. A reaction-diffusion-based coding rate control mechanism for camera sensor networks.

    Science.gov (United States)

    Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki

    2010-01-01

    A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.

  11. Reaction-diffusion-like formalism for plastic neural networks reveals dissipative solitons at criticality

    Science.gov (United States)

    Grytskyy, Dmytro; Diesmann, Markus; Helias, Moritz

    2016-06-01

    Self-organized structures in networks with spike-timing dependent synaptic plasticity (STDP) are likely to play a central role for information processing in the brain. In the present study we derive a reaction-diffusion-like formalism for plastic feed-forward networks of nonlinear rate-based model neurons with a correlation sensitive learning rule inspired by and being qualitatively similar to STDP. After obtaining equations that describe the change of the spatial shape of the signal from layer to layer, we derive a criterion for the nonlinearity necessary to obtain stable dynamics for arbitrary input. We classify the possible scenarios of signal evolution and find that close to the transition to the unstable regime metastable solutions appear. The form of these dissipative solitons is determined analytically and the evolution and interaction of several such coexistent objects is investigated.

  12. Simulation of Neurocomputing Based on Photophobic Reactions of Euglena: Toward Microbe-Based Neural Network Computing

    Science.gov (United States)

    Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko

    In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.

  13. A finite difference method for estimating second order parameter sensitivities of discrete stochastic chemical reaction networks.

    Science.gov (United States)

    Wolf, Elizabeth Skubak; Anderson, David F

    2012-12-14

    We present an efficient finite difference method for the approximation of second derivatives, with respect to system parameters, of expectations for a class of discrete stochastic chemical reaction networks. The method uses a coupling of the perturbed processes that yields a much lower variance than existing methods, thereby drastically lowering the computational complexity required to solve a given problem. Further, the method is simple to implement and will also prove useful in any setting in which continuous time Markov chains are used to model dynamics, such as population processes. We expect the new method to be useful in the context of optimization algorithms that require knowledge of the Hessian.

  14. Formulation of a Network and the Study of Reaction Paths for the Sustainable Reduction of CO2 Emissions

    DEFF Research Database (Denmark)

    Frauzem, Rebecca; Kongpanna, Pichayapan; Roh, Kosan

    . With the information of sources and reactions, a tree of reaction paths is formed and investigated. This forms a superstructure of CO2 utilization to a variety of products. Each of the paths in the network involves CO2 and a co-reactant, such as hydrogen, which may also be captured from process purge streams...

  15. Surface reaction network of CO oxidation on CeO2/Au(110) inverse model catalysts.

    Science.gov (United States)

    Ding, Liangbing; Xiong, Feng; Jin, Yuekang; Wang, Zhengming; Sun, Guanghui; Huang, Weixin

    2016-11-30

    CeO2/Au(110) inverse model catalysts were prepared and their activity toward the adsorption and co-adsorption of O2, CO, CO2 and water was studied by means of X-ray photoelectron spectroscopy, low energy electron diffraction, thermal desorption spectra and temperature-programmed reaction spectra. The Au surface of CeO2/Au(110) inverse model catalysts molecularly adsorbs CO, CO2 and water, and the polycrystalline CeO2 surface of CeO2/Au(110) inverse model catalysts molecularly adsorbs O2, and molecularly and reactively adsorbs CO, CO2 and water. By controllably preparing co-adsorbed surface species on CeO2/Au(110) inverse model catalysts, we successfully identified various surface reaction pathways of CO oxidation to produce CO2 with different barriers both on the CeO2 surface and at the Au-CeO2 interface, including CO oxidation by various oxygen species, and water/hydroxyl group-involved CO oxidation. These results establish a surface reaction network of CO oxidation catalyzed by Au/CeO2 catalysts, greatly advancing the fundamental understandings of catalytic CO oxidation reactions.

  16. Mechanisms of stochastic focusing and defocusing in biological reaction networks: insight from accurate chemical master equation (ACME) solutions.

    Science.gov (United States)

    Giirsoy, Gamze; Terebus, Anna; Cao, Youfang; Liang, Jie; Gursoy, Gamze; Terebus, Anna; Youfang Cao; Jie Liang; Gursoy, Gamze; Cao, Youfang; Terebus, Anna; Liang, Jie

    2016-08-01

    Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small. Studies based on Stochastic Simulation Algorithm (SSA) has shown that a basic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the signal noise. Although SSA has been widely used to study stochastic networks, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method to solve the exact solution of the discrete Chemical Master Equations and to study a network where SF was reported. We showed that the level of SF depends on the degree of the fluctuations of signal molecule. We discovered that signaling noise under certain conditions in the same reaction network can lead to a decrease in the system sensitivities, thus the network can experience stochastic defocusing. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system.

  17. On the Ionisation Fraction in Protoplanetary Disks I: Comparing Different Reaction Networks

    CERN Document Server

    Ilgner, M; Ilgner, Martin; Richard P. Nelson

    2005-01-01

    We calculate the ionisation fraction in protostellar disk models using a number of different chemical reaction networks, including gas-phase and gas-grain reaction schemes. The disk models we consider are conventional alpha-disks, which include viscous heating and radiative cooling. The primary source of ionisation is assumed to be X-ray irradiation from the central star. We consider a number of gas-phase chemical networks. In general we find that the simple models predict higher fractional ionisation levels and more extensive active zones than the more complex models. When heavy metal atoms are included the simple models predict that the disk is magnetically active throughout. The complex models predict that extensive regions of the disk remain magnetically uncoupled even with a fractional abundance of magnesium of 10(-8). The addition of submicron sized grains with a concentration of 10(-12) causes the size of the dead zone to increase dramatically for all kinetic models considered. We find that the simple ...

  18. Simulation and Statistical Inference of Stochastic Reaction Networks with Applications to Epidemic Models

    KAUST Repository

    Moraes, Alvaro

    2015-01-01

    Epidemics have shaped, sometimes more than wars and natural disasters, demo- graphic aspects of human populations around the world, their health habits and their economies. Ebola and the Middle East Respiratory Syndrome (MERS) are clear and current examples of potential hazards at planetary scale. During the spread of an epidemic disease, there are phenomena, like the sudden extinction of the epidemic, that can not be captured by deterministic models. As a consequence, stochastic models have been proposed during the last decades. A typical forward problem in the stochastic setting could be the approximation of the expected number of infected individuals found in one month from now. On the other hand, a typical inverse problem could be, given a discretely observed set of epidemiological data, infer the transmission rate of the epidemic or its basic reproduction number. Markovian epidemic models are stochastic models belonging to a wide class of pure jump processes known as Stochastic Reaction Networks (SRNs), that are intended to describe the time evolution of interacting particle systems where one particle interacts with the others through a finite set of reaction channels. SRNs have been mainly developed to model biochemical reactions but they also have applications in neural networks, virus kinetics, and dynamics of social networks, among others. 4 This PhD thesis is focused on novel fast simulation algorithms and statistical inference methods for SRNs. Our novel Multi-level Monte Carlo (MLMC) hybrid simulation algorithms provide accurate estimates of expected values of a given observable of SRNs at a prescribed final time. They are designed to control the global approximation error up to a user-selected accuracy and up to a certain confidence level, and with near optimal computational work. We also present novel dual-weighted residual expansions for fast estimation of weak and strong errors arising from the MLMC methodology. Regarding the statistical inference

  19. Biochemical Filter with Sigmoidal Response: Increasing the Complexity of Biomolecular Logic

    CERN Document Server

    Privman, Vladimir; Arugula, Mary A; Melnikov, Dmitriy; Bocharova, Vera; Katz, Evgeny

    2010-01-01

    The first realization of a designed, rather than natural, biochemical filter process is reported and analyzed as a promising network component for increasing the complexity of biomolecular logic systems. Key challenge in biochemical logic research has been achieving scalability for complex network designs. Various logic gates have been realized, but a "toolbox" of analog elements for interconnectivity and signal processing has remained elusive. Filters are important as network elements that allow control of noise in signal transmission and conversion. We report a versatile biochemical filtering mechanism designed to have sigmoidal response in combination with signal-conversion process. Horseradish peroxidase-catalyzed oxidation of chromogenic electron donor by hydrogen peroxide, was altered by adding ascorbate, allowing to selectively suppress the output signal, modifying the response from convex to sigmoidal. A kinetic model was developed for evaluation of the quality of filtering. The results offer improved...

  20. Propensity approach to nonequilibrium thermodynamics of a chemical reaction network: controlling single E-coli β-galactosidase enzyme catalysis through the elementary reaction steps.

    Science.gov (United States)

    Das, Biswajit; Banerjee, Kinshuk; Gangopadhyay, Gautam

    2013-12-28

    In this work, we develop an approach to nonequilibrium thermodynamics of an open chemical reaction network in terms of the elementary reaction propensities. The method is akin to the microscopic formulation of the dissipation function in terms of the Kullback-Leibler distance of phase space trajectories in Hamiltonian system. The formalism is applied to a single oligomeric enzyme kinetics at chemiostatic condition that leads the reaction system to a nonequilibrium steady state, characterized by a positive total entropy production rate. Analytical expressions are derived, relating the individual reaction contributions towards the total entropy production rate with experimentally measurable reaction velocity. Taking a real case of Escherichia coli β-galactosidase enzyme obeying Michaelis-Menten kinetics, we thoroughly analyze the temporal as well as the steady state behavior of various thermodynamic quantities for each elementary reaction. This gives a useful insight in the relative magnitudes of various energy terms and the dissipated heat to sustain a steady state of the reaction system operating far-from-equilibrium. It is also observed that, the reaction is entropy-driven at low substrate concentration and becomes energy-driven as the substrate concentration rises.

  1. Effects of network dissolution changes on pore-to-core upscaled reaction rates for kaolinite and anorthite reactions under acidic conditions

    KAUST Repository

    Kim, Daesang

    2013-11-01

    We have extended reactive flow simulation in pore-network models to include geometric changes in the medium from dissolution effects. These effects include changes in pore volume and reactive surface area, as well as topological changes that open new connections. The computed changes were based upon a mineral map from an X-ray computed tomography image of a sandstone core. We studied the effect of these changes on upscaled (pore-scale to core-scale) reaction rates and compared against the predictions of a continuum model. Specifically, we modeled anorthite and kaolinite reactions under acidic flow conditions during which the anorthite reactions remain far from equilibrium (dissolution only), while the kaolinite reactions can be near-equilibrium. Under dissolution changes, core-scale reaction rates continuously and nonlinearly evolved in time. At higher injection rates, agreement with predictions of the continuum model degraded significantly. For the far-from-equilibrium reaction, our results indicate that the ability to correctly capture the heterogeneity in dissolution changes in the reactive mineral surface area is critical to accurately predict upscaled reaction rates. For the near-equilibrium reaction, the ability to correctly capture the heterogeneity in the saturation state remains critical. Inclusion of a Nernst-Planck term to ensure neutral ionic currents under differential diffusion resulted in at most a 9% correction in upscaled rates.

  2. An efficient forward–reverse expectation-maximization algorithm for statistical inference in stochastic reaction networks

    KAUST Repository

    Bayer, Christian

    2016-02-20

    © 2016 Taylor & Francis Group, LLC. ABSTRACT: In this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the expectation-maximization algorithm to the phase I output. By selecting a set of overdispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.

  3. Biomolecular Modification of Inorganic Crystal Growth

    Energy Technology Data Exchange (ETDEWEB)

    De Yoreo, J J

    2007-04-27

    The fascinating shapes and hierarchical designs of biomineralized structures are an inspiration to materials scientists because of the potential they suggest for biomolecular control over materials synthesis. Conversely, the failure to prevent or limit tissue mineralization in the vascular, skeletal, and urinary systems is a common source of disease. Understanding the mechanisms by which organisms direct or limit crystallization has long been a central challenge to the biomineralization community. One prevailing view is that mineral-associated macromolecules are responsible for either inhibiting crystallization or initiating and stabilizing non-equilibrium crystal polymorphs and morphologies through interactions between anionic moieties and cations in solution or at mineralizing surfaces. In particular, biomolecules that present carboxyl groups to the growing crystal have been implicated as primary modulators of growth. Here we review the results from a combination of in situ atomic force microscopy (AFM) and molecular modeling (MM) studies to investigate the effect of specific interactions between carboxylate-rich biomolecules and atomic steps on crystal surfaces during the growth of carbonates, oxalates and phosphates of calcium. Specifically, we how the growth kinetics and morphology depend on the concentration of additives that include citrate, simple amino acids, synthetic Asp-rich polypeptides, and naturally occurring Asp-rich proteins found in both functional and pathological mineral tissues. The results reveal a consistent picture of shape modification in which stereochemical matching of modifiers to specific atomic steps drives shape modification. Inhibition and other changes in growth kinetics are shown to be due to a range of mechanisms that depend on chemistry and molecular size. Some effects are well described by classic crystal growth theories, but others, such as step acceleration due to peptide charge and hydrophylicity, were previously unrealized

  4. Bridging Nano- and Microtribology in Mechanical and Biomolecular Layers

    Science.gov (United States)

    Tomala, Agnieszka; Göçerler, Hakan; Gebeshuber, Ille C.

    The physical and chemical composition of surfaces determine various important properties of solids such as corrosion rates, adhesive properties, frictional properties, catalytic activity, wettability, contact potential and - finally and most importantly - failure mechanisms. Very thin, weak layers (of man-made and biological origin) on much harder substrates that reduce friction are the focus of the micro- and nanotribological investigations presented in this chapter.Biomolecular layers fulfil various functions in organs of the human body. Examples comprise the skin that provides a protective physical barrier between the body and the environment, preventing unwanted inward and outward passage of water and electrolytes, reducing penetration by destructive chemicals, arresting the penetration of microorganisms and external antigens and absorbing radiation from the sun, or the epithelium of the cornea that blocks the passage of foreign material, such as dust, water and bacteria, into the eye and that contributes to the lubrication layer that ensures smooth movement of the eyelids over the eyeballs.Monomolecular thin films, additive-derived reaction layers and hard coatings are widely used to tailor tribological properties of surfaces. Nanotribological investigations on these substrates can reveal novel properties regarding the orientation of chemisorbed additive layers, development of rubbing films with time and the relation of frictional properties to surface characteristics in diamond coatings.Depending on the questions to be answered with the tribological research, various micro- and nanotribological measurement methods are applied, including scanning probe microscopy (AFM, FFM), scanning electron microscopy, microtribometer investigations, angle-resolved photoelectron spectroscopy and optical microscopy. Thoughts on the feasibility of a unified approach to energy-dissipating systems and how it might be reached (touching upon new ways of scientific publishing

  5. Identification of alterations in the Jacobian of biochemical reaction networks from steady state covariance data at two conditions.

    Science.gov (United States)

    Kügler, Philipp; Yang, Wei

    2014-06-01

    Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.

  6. A mechanical Turing machine: blueprint for a biomolecular computer.

    Science.gov (United States)

    Shapiro, Ehud

    2012-08-06

    We describe a working mechanical device that embodies the theoretical computing machine of Alan Turing, and as such is a universal programmable computer. The device operates on three-dimensional building blocks by applying mechanical analogues of polymer elongation, cleavage and ligation, movement along a polymer, and control by molecular recognition unleashing allosteric conformational changes. Logically, the device is not more complicated than biomolecular machines of the living cell, and all its operations are part of the standard repertoire of these machines; hence, a biomolecular embodiment of the device is not infeasible. If implemented, such a biomolecular device may operate in vivo, interacting with its biochemical environment in a program-controlled manner. In particular, it may 'compute' synthetic biopolymers and release them into its environment in response to input from the environment, a capability that may have broad pharmaceutical and biological applications.

  7. Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks.

    Science.gov (United States)

    Hordijk, Wim

    2016-06-01

    Several computational models of chemical reaction networks have been presented in the literature in the past, showing the appearance and (potential) evolution of autocatalytic sets. However, the notion of autocatalytic sets has been defined differently in different modeling contexts, each one having some shortcoming or limitation. Here, we review four such models and definitions, and then formally describe and analyze them in the context of a mathematical framework for studying autocatalytic sets known as RAF theory. The main results are that: (1) RAF theory can capture the various previous definitions of autocatalytic sets and is therefore more complete and general, (2) the formal framework can be used to efficiently detect and analyze autocatalytic sets in all of these different computational models, (3) autocatalytic (RAF) sets are indeed likely to appear and evolve in such models, and (4) this could have important implications for a possible metabolism-first scenario for the origin of life.

  8. Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks

    Science.gov (United States)

    Hordijk, Wim

    2016-06-01

    Several computational models of chemical reaction networks have been presented in the literature in the past, showing the appearance and (potential) evolution of autocatalytic sets. However, the notion of autocatalytic sets has been defined differently in different modeling contexts, each one having some shortcoming or limitation. Here, we review four such models and definitions, and then formally describe and analyze them in the context of a mathematical framework for studying autocatalytic sets known as RAF theory. The main results are that: (1) RAF theory can capture the various previous definitions of autocatalytic sets and is therefore more complete and general, (2) the formal framework can be used to efficiently detect and analyze autocatalytic sets in all of these different computational models, (3) autocatalytic (RAF) sets are indeed likely to appear and evolve in such models, and (4) this could have important implications for a possible metabolism-first scenario for the origin of life.

  9. A moment-convergence method for stochastic analysis of biochemical reaction networks

    Science.gov (United States)

    Zhang, Jiajun; Nie, Qing; Zhou, Tianshou

    2016-05-01

    Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.

  10. A moment-convergence method for stochastic analysis of biochemical reaction networks.

    Science.gov (United States)

    Zhang, Jiajun; Nie, Qing; Zhou, Tianshou

    2016-05-21

    Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.

  11. Feature Selection in Detection of Adverse Drug Reactions from the Health Improvement Network (THIN Database

    Directory of Open Access Journals (Sweden)

    Yihui Liu

    2015-02-01

    Full Text Available Adverse drug reaction (ADR is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.

  12. Analytical solution of steady-state equations for chemical reaction networks with bilinear rate laws.

    Science.gov (United States)

    Halász, Adám M; Lai, Hong-Jian; McCabe Pryor, Meghan; Radhakrishnan, Krishnan; Edwards, Jeremy S

    2013-01-01

    True steady states are a rare occurrence in living organisms, yet their knowledge is essential for quasi-steady-state approximations, multistability analysis, and other important tools in the investigation of chemical reaction networks (CRN) used to describe molecular processes on the cellular level. Here, we present an approach that can provide closed form steady-state solutions to complex systems, resulting from CRN with binary reactions and mass-action rate laws. We map the nonlinear algebraic problem of finding steady states onto a linear problem in a higher-dimensional space. We show that the linearized version of the steady-state equations obeys the linear conservation laws of the original CRN. We identify two classes of problems for which complete, minimally parameterized solutions may be obtained using only the machinery of linear systems and a judicious choice of the variables used as free parameters. We exemplify our method, providing explicit formulae, on CRN describing signal initiation of two important types of RTK receptor-ligand systems, VEGF and EGF-ErbB1.

  13. Network output controllability-based method for drug target identification.

    Science.gov (United States)

    Wu, Lin; Shen, Yichao; Li, Min; Wu, Fang-Xiang

    2015-03-01

    Biomolecules do not perform their functions alone, but interactively with one another to form so called biomolecular networks. It is well known that a complex disease stems from the malfunctions of corresponding biomolecular networks. Therefore, one of important tasks is to identify drug targets from biomolecular networks. In this study, the drug target identification is formulated as a problem of finding steering nodes in biomolecular networks while the concept of network output controllability is applied to the problem of drug target identification. By applying control signals to these steering nodes, the biomolecular networks are expected to be transited from one state to another. A graph-theoretic algorithm has been proposed to find a minimum set of steering nodes in biomolecular networks which can be a potential set of drug targets. Application results of the method to real biomolecular networks show that identified potential drug targets are in agreement with existing research results. This indicates that the method can generate testable predictions and provide insights into experimental design of drug discovery.

  14. Computer-assisted design for scaling up systems based on DNA reaction networks

    Science.gov (United States)

    Aubert, Nathanaël; Mosca, Clément; Fujii, Teruo; Hagiya, Masami; Rondelez, Yannick

    2014-01-01

    In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the

  15. Computer-assisted design for scaling up systems based on DNA reaction networks.

    Science.gov (United States)

    Aubert, Nathanaël; Mosca, Clément; Fujii, Teruo; Hagiya, Masami; Rondelez, Yannick

    2014-04-06

    In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the

  16. Engineering intracellular active transport systems as in vivo biomolecular tools.

    Energy Technology Data Exchange (ETDEWEB)

    Bachand, George David; Carroll-Portillo, Amanda

    2006-11-01

    Active transport systems provide essential functions in terms of cell physiology and metastasis. These systems, however, are also co-opted by invading viruses, enabling directed transport of the virus to and from the cell's nucleus (i.e., the site of virus replication). Based on this concept, fundamentally new approaches for interrogating and manipulating the inner workings of living cells may be achievable by co-opting Nature's active transport systems as an in vivo biomolecular tool. The overall goal of this project was to investigate the ability to engineer kinesin-based transport systems for in vivo applications, specifically the collection of effector proteins (e.g., transcriptional regulators) within single cells. In the first part of this project, a chimeric fusion protein consisting of kinesin and a single chain variable fragment (scFv) of an antibody was successfully produced through a recombinant expression system. The kinesin-scFv retained both catalytic and antigenic functionality, enabling selective capture and transport of target antigens. The incorporation of a rabbit IgG-specific scFv into the kinesin established a generalized system for functionalizing kinesin with a wide range of target-selective antibodies raised in rabbits. The second objective was to develop methods of isolating the intact microtubule network from live cells as a platform for evaluating kinesin-based transport within the cytoskeletal architecture of a cell. Successful isolation of intact microtubule networks from two distinct cell types was demonstrated using glutaraldehyde and methanol fixation methods. This work provides a platform for inferring the ability of kinesin-scFv to function in vivo, and may also serve as a three-dimensional scaffold for evaluating and exploiting kinesin-based transport for nanotechnological applications. Overall, the technology developed in this project represents a first-step in engineering active transport system for in vivo

  17. The HADDOCK web server for data-driven biomolecular docking

    NARCIS (Netherlands)

    de Vries, S.J.; van Dijk, M.; Bonvin, A.M.J.J.

    2010-01-01

    Computational docking is the prediction or modeling of the three-dimensional structure of a biomolecular complex, starting from the structures of the individual molecules in their free, unbound form. HADDOC K is a popular docking program that takes a datadriven approach to docking, with support for

  18. Transient response characteristics in a biomolecular integral controller.

    Science.gov (United States)

    Sen, Shaunak

    2016-04-01

    The cellular behaviour of perfect adaptation is achieved through the use of an integral control element in the underlying biomolecular circuit. It is generally unclear how integral action affects the important aspect of transient response in these biomolecular systems, especially in light of the fact that it typically deteriorates the transient response in engineering contexts. To address this issue, the authors investigated the transient response in a computational model of a simple biomolecular integral control system involved in bacterial signalling. They find that the transient response can actually speed up as the integral gain parameter increases. On further analysis, they find that the underlying dynamics are composed of slow and fast modes and the speed-up of the transient response is because of the speed-up of the slow-mode dynamics. Finally, they note how an increase in the integral gain parameter also leads to a decrease in the amplitude of the transient response, consistent with the overall improvement in the transient response. These results should be useful in understanding the overall effect of integral action on system dynamics, particularly for biomolecular systems.

  19. Exposing biomolecular properties one molecule at a time

    NARCIS (Netherlands)

    Elmalk, Abdalmohsen

    2012-01-01

    The work described in this thesis was aimed at the study of the functional properties of (isolated and purified) biomolecular systems at the single-molecule level. Two prerequisites are essential for successfully achieving this goal. First of all, single biomolecules should be observable, which mean

  20. From dynamics to structure and function of model biomolecular systems

    NARCIS (Netherlands)

    Fontaine-Vive-Curtaz, F.

    2007-01-01

    The purpose of this thesis was to extend recent works on structure and dynamics of hydrogen bonded crystals to model biomolecular systems and biological processes. The tools that we have used are neutron scattering (NS) and density functional theory (DFT) and force field (FF) based simulation method

  1. Biomolecular recognition mechanisms studied by NMR spectroscopy and MD simulations

    NARCIS (Netherlands)

    Hsu, Shang-Te Danny

    2004-01-01

    This thesis describes the use of solution Nuclear Magnetic Resonance (NMR) spectroscopy and Molecular Dynamics (MD) simulations to study the mechanism of biomolecular recognition with two model systems: i) lipid II-binding lantibiotics (lanthionine-containing antibiotics) and ii) the human immunodef

  2. A DNA Network as an Information Processing System

    Directory of Open Access Journals (Sweden)

    Andy M. Tyrrell

    2012-04-01

    Full Text Available Biomolecular systems that can process information are sought for computational applications, because of their potential for parallelism and miniaturization and because their biocompatibility also makes them suitable for future biomedical applications. DNA has been used to design machines, motors, finite automata, logic gates, reaction networks and logic programs, amongst many other structures and dynamic behaviours. Here we design and program a synthetic DNA network to implement computational paradigms abstracted from cellular regulatory networks. These show information processing properties that are desirable in artificial, engineered molecular systems, including robustness of the output in relation to different sources of variation. We show the results of numerical simulations of the dynamic behaviour of the network and preliminary experimental analysis of its main components.

  3. fireball/amber: An Efficient Local-Orbital DFT QM/MM Method for Biomolecular Systems.

    Science.gov (United States)

    Mendieta-Moreno, Jesús I; Walker, Ross C; Lewis, James P; Gómez-Puertas, Paulino; Mendieta, Jesús; Ortega, José

    2014-05-13

    In recent years, quantum mechanics/molecular mechanics (QM/MM) methods have become an important computational tool for the study of chemical reactions and other processes in biomolecular systems. In the QM/MM technique, the active region is described by means of QM calculations, while the remainder of the system is described using a MM approach. Because of the complexity of biomolecules and the desire to achieve converged sampling, it is important that the QM method presents a good balance between accuracy and computational efficiency. Here, we report on the implementation of a QM/MM technique that combines a DFT approach specially designed for the study of complex systems using first-principles molecular dynamics simulations (fireball) with the amber force fields and simulation programs. We also present examples of the application of this QM/MM approach to three representative biomolecular systems: the analysis of the effect of electrostatic embedding in the behavior of a salt bridge between an aspartic acid and a lysine residue, a study of the intermediate states for the triosephosphate isomerase catalyzed conversion of dihydroxyacetone phosphate into glyceraldehyde 3-phosphate, and the detailed description, using DFT QM/MM molecular dynamics, of the cleavage of a phosphodiester bond in RNA catalyzed by the enzyme RNase A.

  4. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model.

    Science.gov (United States)

    Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

  5. Integer programming-based method for designing synthetic metabolic networks by Minimum Reaction Insertion in a Boolean model.

    Directory of Open Access Journals (Sweden)

    Wei Lu

    Full Text Available In this paper, we consider the Minimum Reaction Insertion (MRI problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."

  6. Integer Programming-Based Method for Designing Synthetic Metabolic Networks by Minimum Reaction Insertion in a Boolean Model

    Science.gov (United States)

    Song, Jiangning; Akutsu, Tatsuya

    2014-01-01

    In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at “http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html.” PMID:24651476

  7. Stability Analysis of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Time-Varying Delays

    Directory of Open Access Journals (Sweden)

    Chuangxia Huang

    2009-01-01

    Full Text Available This paper is concerned with pth moment exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method, stochastic analysis, and inequality techniques, a set of new suffcient conditions on pth moment exponential stability for the considered system is presented. The proposed results generalized and improved some earlier publications.

  8. Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement

    Science.gov (United States)

    Srinivas, Niranjan

    Over the last century, the silicon revolution has enabled us to build faster, smaller and more sophisticated computers. Today, these computers control phones, cars, satellites, assembly lines, and other electromechanical devices. Just as electrical wiring controls electromechanical devices, living organisms employ "chemical wiring" to make decisions about their environment and control physical processes. Currently, the big difference between these two substrates is that while we have the abstractions, design principles, verification and fabrication techniques in place for programming with silicon, we have no comparable understanding or expertise for programming chemistry. In this thesis we take a small step towards the goal of learning how to systematically engineer prescribed non-equilibrium dynamical behaviors in chemical systems. We use the formalism of chemical reaction networks (CRNs), combined with mass-action kinetics, as our programming language for specifying dynamical behaviors. Leveraging the tools of nucleic acid nanotechnology (introduced in Chapter 1), we employ synthetic DNA molecules as our molecular architecture and toehold-mediated DNA strand displacement as our reaction primitive. Abstraction, modular design and systematic fabrication can work only with well-understood and quantitatively characterized tools. Therefore, we embark on a detailed study of the "device physics" of DNA strand displacement (Chapter 2). We present a unified view of strand displacement biophysics and kinetics by studying the process at multiple levels of detail, using an intuitive model of a random walk on a 1-dimensional energy landscape, a secondary structure kinetics model with single base-pair steps, and a coarse-grained molecular model that incorporates three-dimensional geometric and steric effects. Further, we experimentally investigate the thermodynamics of three-way branch migration. Our findings are consistent with previously measured or inferred rates for

  9. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space.

    Science.gov (United States)

    Ruess, Jakob

    2015-12-28

    Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.

  10. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space

    Science.gov (United States)

    Ruess, Jakob

    2015-12-01

    Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.

  11. Population dynamics, information transfer, and spatial organization in a chemical reaction network under spatial confinement and crowding conditions.

    Science.gov (United States)

    Bellesia, Giovanni; Bales, Benjamin B

    2016-10-01

    We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.

  12. Population dynamics, information transfer, and spatial organization in a chemical reaction network under spatial confinement and crowding conditions

    Science.gov (United States)

    Bellesia, Giovanni; Bales, Benjamin B.

    2016-10-01

    We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.

  13. MONITORING POTENTIAL DRUG INTERACTIONS AND REACTIONS VIA NETWORK ANALYSIS OF INSTAGRAM USER TIMELINES.

    Science.gov (United States)

    Correia, Rion Brattig; Li, Lang; Rocha, Luis M

    2016-01-01

    Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that have not been hitherto possible. Given the large number of users, analysis of social media data may be useful to identify under-reported, population-level pathology associated with DDI, thus further contributing to improvements in population health. Moreover, tapping into this data allows us to infer drug interactions with natural products-including cannabis-which constitute an array of DDI very poorly explored by biomedical research thus far. Our goal is to determine the potential of Instagram for public health monitoring and surveillance for DDI, ADR, and behavioral pathology at large. Most social media analysis focuses on Twitter and Facebook, but Instagram is an increasingly important platform, especially among teens, with unrestricted access of public posts, high availability of posts with geolocation coordinates, and images to supplement textual analysis. Using drug, symptom, and natural product dictionaries for identification of the various types of DDI and ADR evidence, we have collected close to 7000 user timelines spanning from October 2010 to June 2015.We report on 1) the development of a monitoring tool to easily observe user-level timelines associated with drug and symptom terms of interest, and 2) population-level behavior via the analysis of co-occurrence networks computed from user timelines at three different scales: monthly, weekly, and daily occurrences. Analysis of these networks further reveals 3) drug and symptom direct and indirect associations with greater support in user timelines, as well as 4) clusters of symptoms and drugs revealed by the collective behavior of the observed population. This demonstrates that Instagram

  14. Multiscale simulations of anisotropic particles combining Brownian Dynamics and Green's Function Reaction Dynamics

    CERN Document Server

    Vijaykumar, Adithya; Wolde, Pieter Rein ten; Bolhuis, Peter G

    2016-01-01

    The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic Molecular Dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P.G. Bolhuis and P.R. ten Wolde, J. Chem. Phys. {\\bf 43}, 21: 214102 (2015)]. Here we extend this multiscale BD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm we discuss its performance. The rotational BD-GFRD multiscale method will open up the possibility for large scale simulations of e.g. protein signalling networks.

  15. Cholesterol photo-oxidation: A chemical reaction network for kinetic modeling.

    Science.gov (United States)

    Barnaba, Carlo; Rodríguez-Estrada, Maria Teresa; Lercker, Giovanni; García, Hugo Sergio; Medina-Meza, Ilce Gabriela

    2016-12-01

    In this work we studied the effect of polyunsaturated fatty acids (PUFAs) methyl esters on cholesterol photo-induced oxidation. The oxidative routes were modeled with a chemical reaction network (CRN), which represents the first application of CRN to the oxidative degradation of a food-related lipid matrix. Docosahexaenoic acid (DHA, T-I), eicosapentaenoic acid (EPA, T-II) and a mixture of both (T-III) were added to cholesterol using hematoporphyrin as sensitizer, and were exposed to a fluorescent lamp for 48h. High amounts of Type I cholesterol oxidation products (COPs) were recovered (epimers 7α- and 7β-OH, 7-keto and 25-OH), as well as 5β,6β-epoxy. Fitting the experimental data with the CRN allowed characterizing the associated kinetics. DHA and EPA exerted different effects on the oxidative process. DHA showed a protective effect to 7-hydroxy derivatives, whereas EPA enhanced side-chain oxidation and 7β-OH kinetic rates. The mixture of PUFAs increased the kinetic rates several fold, particularly for 25-OH. With respect to the control, the formation of β-epoxy was reduced, suggesting potential inhibition in the presence of PUFAs.

  16. Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks

    Science.gov (United States)

    Maginnis, P. A.; West, M.; Dullerud, G. E.

    2016-10-01

    We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a "black-box", i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.

  17. Advances in modeling of biomolecular interactions

    Institute of Scientific and Technical Information of China (English)

    Cong-zhongCAI; Ze-rongLI; Wan-luWANG; Yu-zongCHEN

    2004-01-01

    Modeling of molecular interactions is increasingly used in life science research and biotechnology development.Examples are computer aided drug design, prediction of protein interactions with other molecules, and simulation of networks of biomolecules in a particular process in human body. This article reviews recent progress in the related fields and provides a brief overview on the methods used in molecular modeling of biological systems.

  18. Founding an adverse drug reaction (ADR) network: a method for improving doctors spontaneous ADR reporting in a general hospital.

    Science.gov (United States)

    Goldstein, Lee Hilary; Berlin, Maya; Saliba, Walid; Elias, Mazen; Berkovitch, Matitiyahu

    2013-11-01

    Adverse drug reactions (ADR) are underreported by doctors despite numerous efforts. We aimed to determine if establishing an "ADR reporting doctor's network" within a hospital would increase the quantity of ADRs reported by hospital doctors. One hundred hospital doctors joined the network. Email reminders were sent to network members during the 1 year study period, conveying information about ADRs reported, amusingly and pleasantly reminding them to report ADRs in minimal detail, by phone, email, text message or mail to the Clinical Pharmacology Unit, who would further complete the report. A total of 114 ADRs were reported during the study period in comparison to 48, 26, and 17 in the previous 3 years (2008, 2009, 2010, respectively). In the 3 years prior, doctors reported 41.7% of the reported ADRs whereas in the study period, doctors reported 74.3% of ADRs (P reports. Ninety seven percent of doctors' reports were of ADR network members. Thirty-four (34%) network members reported an ADR during the study period and 31 of the 34 reporters had never reported ADRs before becoming network members. Establishing an ADR network of doctors substantially increases ADR reporting amongst its members.

  19. Drift-Implicit Multi-Level Monte Carlo Tau-Leap Methods for Stochastic Reaction Networks

    KAUST Repository

    Ben Hammouda, Chiheb

    2015-05-12

    In biochemical systems, stochastic e↵ects can be caused by the presence of small numbers of certain reactant molecules. In this setting, discrete state-space and stochastic simulation approaches were proved to be more relevant than continuous state-space and deterministic ones. These stochastic models constitute the theory of stochastic reaction networks (SRNs). Furthermore, in some cases, the dynamics of fast and slow time scales can be well separated and this is characterized by what is called sti↵ness. For such problems, the existing discrete space-state stochastic path simulation methods, such as the stochastic simulation algorithm (SSA) and the explicit tau-leap method, can be very slow. Therefore, implicit tau-leap approxima- tions were developed to improve the numerical stability and provide more e cient simulation algorithms for these systems. One of the interesting tasks for SRNs is to approximate the expected values of some observables of the process at a certain fixed time T. This is can be achieved using Monte Carlo (MC) techniques. However, in a recent work, Anderson and Higham in 2013, proposed a more computationally e cient method which combines multi-level Monte Carlo (MLMC) technique with explicit tau-leap schemes. In this MSc thesis, we propose new fast stochastic algorithm, particularly designed 5 to address sti↵ systems, for approximating the expected values of some observables of SRNs. In fact, we take advantage of the idea of MLMC techniques and drift-implicit tau-leap approximation to construct a drift-implicit MLMC tau-leap estimator. In addition to accurately estimating the expected values of a given observable of SRNs at a final time T , our proposed estimator ensures the numerical stability with a lower cost than the MLMC explicit tau-leap algorithm, for systems including simultane- ously fast and slow species. The key contribution of our work is the coupling of two drift-implicit tau-leap paths, which is the basic brick for

  20. Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

    Directory of Open Access Journals (Sweden)

    Qiankun Song

    2007-06-01

    Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

  1. Exponential Stability for Impulsive BAM Neural Networks with Time-Varying Delays and Reaction-Diffusion Terms

    Directory of Open Access Journals (Sweden)

    Cao Jinde

    2007-01-01

    Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.

  2. Finite-Time Stability Analysis of Reaction-Diffusion Genetic Regulatory Networks with Time-Varying Delays.

    Science.gov (United States)

    Fan, Xiaofei; Zhang, Xian; Wu, Ligang; Shi, Michael

    2016-04-11

    This paper is concerned with the finite-time stability problem of the delayed genetic regulatory networks (GRNs) with reaction-diffusion terms under Dirichlet boundary conditions. By constructing a Lyapunov-Krasovskii functional including quad- slope integrations, we establish delay-dependent finite-time stabil- ity criteria by employing the Wirtinger-type integral inequality, Gronwall inequality, convex technique, and reciprocally convex technique. In addition, the obtained criteria are also reaction- diffusion-dependent. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.

  3. LiHe$^+$ in the early Universe: a full assessment of its reaction network and final abundances

    CERN Document Server

    Bovino, Stefano; Galli, Daniele; Tacconi, Mario; Gianturco, Francesco A

    2012-01-01

    We present the results of quantum calculations based on entirely ab initio methods for a variety of molecular processes and chemical reactions involving the LiHe$^+$ ionic polar molecule. With the aid of these calculations we derive accurate reaction rates and fitting expressions valid over a range of gas temperatures representative of the typical conditions of the pregalactic gas. With the help of a full chemical network, we then compute the evolution of the abundance of LiHe$^+$ as function of redshift in the early Universe. Finally, we compare the relative abundance of LiHe$^+$ with that of other polar cations formed in the same redshift interval.

  4. Physics at the biomolecular interface fundamentals for molecular targeted therapy

    CERN Document Server

    Fernández, Ariel

    2016-01-01

    This book focuses primarily on the role of interfacial forces in understanding biological phenomena at the molecular scale. By providing a suitable statistical mechanical apparatus to handle the biomolecular interface, the book becomes uniquely positioned to address core problems in molecular biophysics. It highlights the importance of interfacial tension in delineating a solution to the protein folding problem, in unravelling the physico-chemical basis of enzyme catalysis and protein associations, and in rationally designing molecular targeted therapies. Thus grounded in fundamental science, the book develops a powerful technological platform for drug discovery, while it is set to inspire scientists at any level in their careers determined to address the major challenges in molecular biophysics. The acknowledgment of how exquisitely the structure and dynamics of proteins and their aqueous environment are related attests to the overdue recognition that biomolecular phenomena cannot be effectively understood w...

  5. Biomolecular recognition and detection using gold-based nanoprobes

    Science.gov (United States)

    Crew, Elizabeth

    The ability to control the biomolecular interactions is important for developing bioanalytical probes used in biomolecule and biomarker detections. This work aims at a fundamental understanding of the interactions and reactivities involving DNA, miRNA, and amino acids using gold-based nanoparticles as nanoprobes, which has implications for developing new strategies for the early detection of diseases, such as cancer, and controlled delivery of drugs. Surface modifications of the nanoprobes with DNA, miRNA, and amino acids and the nanoprobe directed biomolecular reactivities, such as complementary-strand binding, enzymatic cutting and amino acid interactions, have been investigated. Among various analytical techniques employed for the analysis of the biomolecule-nanoprobe interactions, surface enhanced Raman scattering spectroscopy (SERS) has been demonstrated to provide a powerful tool for real time monitoring of the DNA assembly and enzymatic cutting processes in solutions. This demonstration harnesses the "hot-spot" characteristic tuned by the interparticle biomolecular-regulated interactions and distances. The assembly of gold nanoparticles has also been exploited as sensing thin films on chemiresistor arrays for the detection of volatile organic compounds, including biomarker molecules associated with diabetes. Important findings of the nanoprobes in delivering miRNA to cells, detecting DNA hybridization kinetics, discerning chiral recognition with enantiomeric cysteines, and sensing biomarker molecules with the nanostructured thin films will be discussed, along with their implications to enhancing sensitivity, selectivity and limits of detection.

  6. Solution influence on biomolecular equilibria - Nucleic acid base associations

    Science.gov (United States)

    Pohorille, A.; Pratt, L. R.; Burt, S. K.; Macelroy, R. D.

    1984-01-01

    Various attempts to construct an understanding of the influence of solution environment on biomolecular equilibria at the molecular level using computer simulation are discussed. First, the application of the formal statistical thermodynamic program for investigating biomolecular equilibria in solution is presented, addressing modeling and conceptual simplications such as perturbative methods, long-range interaction approximations, surface thermodynamics, and hydration shell. Then, Monte Carlo calculations on the associations of nucleic acid bases in both polar and nonpolar solvents such as water and carbon tetrachloride are carried out. The solvent contribution to the enthalpy of base association is positive (destabilizing) in both polar and nonpolar solvents while negative enthalpies for stacked complexes are obtained only when the solute-solute in vacuo energy is added to the total energy. The release upon association of solvent molecules from the first hydration layer around a solute to the bulk is accompanied by an increase in solute-solvent energy and decrease in solvent-solvent energy. The techniques presented are expectd to displace less molecular and more heuristic modeling of biomolecular equilibria in solution.

  7. Programming biomolecular self-assembly pathways.

    Science.gov (United States)

    Yin, Peng; Choi, Harry M T; Calvert, Colby R; Pierce, Niles A

    2008-01-17

    In nature, self-assembling and disassembling complexes of proteins and nucleic acids bound to a variety of ligands perform intricate and diverse dynamic functions. In contrast, attempts to rationally encode structure and function into synthetic amino acid and nucleic acid sequences have largely focused on engineering molecules that self-assemble into prescribed target structures, rather than on engineering transient system dynamics. To design systems that perform dynamic functions without human intervention, it is necessary to encode within the biopolymer sequences the reaction pathways by which self-assembly occurs. Nucleic acids show promise as a design medium for engineering dynamic functions, including catalytic hybridization, triggered self-assembly and molecular computation. Here, we program diverse molecular self-assembly and disassembly pathways using a 'reaction graph' abstraction to specify complementarity relationships between modular domains in a versatile DNA hairpin motif. Molecular programs are executed for a variety of dynamic functions: catalytic formation of branched junctions, autocatalytic duplex formation by a cross-catalytic circuit, nucleated dendritic growth of a binary molecular 'tree', and autonomous locomotion of a bipedal walker.

  8. Molecular Dynamics Simulations of Polymer Networks Undergoing Sequential Cross-Linking and Scission Reactions

    DEFF Research Database (Denmark)

    Rottach, Dana R.; Curro, John G.; Budzien, Joanne;

    2007-01-01

    The effects of sequential cross-linking and scission of polymer networks formed in two states of strain are investigated using molecular dynamics simulations. Two-stage networks are studied in which a network formed in the unstrained state (stage 1) undergoes additional cross-linking in a uniaxia......The effects of sequential cross-linking and scission of polymer networks formed in two states of strain are investigated using molecular dynamics simulations. Two-stage networks are studied in which a network formed in the unstrained state (stage 1) undergoes additional cross...... good agreement with the predictions of Flory and Fricker. It was found that the fractional stress reduction upon removal of the first-stage cross-links could be accurately calculated from the slip tube model of Rubinstein and Panyukov modified to use the theoretical transfer functions of Fricker.  ...

  9. Constructing and visualizing chemical reaction networks from pi-calculus models

    OpenAIRE

    M. John; H.-J. Schulz; H. Schumann; A. M. Uhrmacher; Andrea Unger

    2013-01-01

    International audience; The pi-calculus, in particular its stochastic version the stochastic pi-calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic pi-calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequence...

  10. Resonance Reaction in Diffusion-Influenced Bimolecular Reactions

    CERN Document Server

    Kolb, Jakob J; Dzubiella, Joachim

    2016-01-01

    We investigate the influence of a stochastically fluctuating step-barrier potential on bimolecular reaction rates by exact analytical theory and stochastic simulations. We demonstrate that the system exhibits a new resonant reaction behavior with rate enhancement if an appropriately defined fluctuation decay length is of the order of the system size. Importantly, we find that in the proximity of resonance the standard reciprocal additivity law for diffusion and surface reaction rates is violated due to the dynamical coupling of multiple kinetic processes. Together, these findings may have important repercussions on the correct interpretation of various kinetic reaction problems in complex systems, as, e.g., in biomolecular association or catalysis.

  11. Characterizing and prototyping genetic networks with cell-free transcription-translation reactions.

    Science.gov (United States)

    Takahashi, Melissa K; Hayes, Clarmyra A; Chappell, James; Sun, Zachary Z; Murray, Richard M; Noireaux, Vincent; Lucks, Julius B

    2015-09-15

    A central goal of synthetic biology is to engineer cellular behavior by engineering synthetic gene networks for a variety of biotechnology and medical applications. The process of engineering gene networks often involves an iterative 'design-build-test' cycle, whereby the parts and connections that make up the network are built, characterized and varied until the desired network function is reached. Many advances have been made in the design and build portions of this cycle. However, the slow process of in vivo characterization of network function often limits the timescale of the testing step. Cell-free transcription-translation (TX-TL) systems offer a simple and fast alternative to performing these characterizations in cells. Here we provide an overview of a cell-free TX-TL system that utilizes the native Escherichia coli TX-TL machinery, thereby allowing a large repertoire of parts and networks to be characterized. As a way to demonstrate the utility of cell-free TX-TL, we illustrate the characterization of two genetic networks: an RNA transcriptional cascade and a protein regulated incoherent feed-forward loop. We also provide guidelines for designing TX-TL experiments to characterize new genetic networks. We end with a discussion of current and emerging applications of cell free systems.

  12. Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes

    Institute of Scientific and Technical Information of China (English)

    Xiao-gang JIN; Yong MIN

    2014-01-01

    The frequent outbreak of severe foodborne diseases (e.g., haemolytic uraemic syndrome and Listeriosis) in 2011 warns of a potential threat that world trade could spread fatal pathogens (e.g., enterohemorrhagic Escherichia coli). The epidemic potential from trade involves both intra-proliferation and inter-diffusion. Here, we present a worldwide vegetable trade network and a stochastic computational model to simulate global trade-mediated epidemics by considering the weighted nodes and edges of the network and the dual-scale dynamics of epidemics. We address two basic issues of network structural impact in global epi-demic patterns:(1) in contrast to the prediction of heterogeneous network models, the broad variability of node degree and edge weights of the vegetable trade network do not determine the threshold of global epidemics;(2) a‘penetration effect’, by which community structures do not restrict propagation at the global scale, quickly facilitates bridging the edges between communities, and leads to synchronized diffusion throughout the entire network. We have also defined an appropriate metric that combines dual-scale behavior and enables quantification of the critical role of bridging edges in disease diffusion from widespread trading. The unusual structure mechanisms of the trade network model may be useful in producing strategies for adaptive immunity and reducing international trade frictions.

  13. Thermal coupling at aqueous and biomolecular interfaces

    Science.gov (United States)

    Shenogina, Natalia B.

    Heat flow in the materials with nanoscopic features is dominated by thermal properties of the interfaces. While thermal properties of the solid-solid and solid-liquid interfaces are well studied, research of the thermal transport properties across soft (liquid-liquid) interfaces is very limited. Such interfaces are, however, plentiful in biological systems. In such systems the temperature control is of a great importance, because biochemical reactions, conformation of biomolecules as well as processes in biological cells and membranes have strong temperature sensitivity. The critical ingredient to temperature control in biological systems is the understanding of heat flow and thermal coupling across soft interfaces. To investigate heat transfer across biological and aqueous interfaces we chose to study a number of soft interfacial systems by means of molecular dynamic simulations. One of the interfaces under our investigation is the interface between protein (specifically green fluorescent protein) and water. Using this model we concentrated on the importance of vibrational frequency on coupling between water and proteins, and on significant differences between the roles of low and high frequency vibrations. Our thermal interfacial analysis allowed us to shed new light on to the issue of protein to water slaving, i.e., the concept of water controlling protein dynamics. Considering that the surface of the protein is composed of a complicated mixture of the hydrophobic and hydrophilic domains, to systematically explore the role of interfacial interactions we studied less complicated models with homogenous interfaces whith interfacial chemistry that could be changed in a controlled manner. We demonstrated that thermal transport measurements can be used to probe interfacial environments and to quantify interfacial bonding strength. Such ability provides a unique opportunity to characterize a variety of interfaces, which can be difficult to achieve with more direct

  14. Scanning probe and optical tweezer investigations of biomolecular interactions

    Energy Technology Data Exchange (ETDEWEB)

    Rigby-Singleton, Shellie

    2002-07-01

    A complex array of intermolecular forces controls the interactions between and within biological molecules. The desire to empirically explore the fundamental forces has led to the development of several biophysical techniques. Of these, the atomic force microscope (AFM) and the optical tweezers have been employed throughout this thesis to monitor the intermolecular forces involved in biomolecular interactions. The AFM is a well-established force sensing technique capable of measuring biomolecular interactions at a single molecule level. However, its versatility has not been extrapolated to the investigation of a drug-enzyme complex. The energy landscape for the force induced dissociation of the DHFR-methotrexate complex was studied. Revealing an energy barrier to dissociation located {approx}0.3 nm from the bound state. Unfortunately, the AFM has a limited range of accessible loading rates and in order to profile the complete energy landscape alternative force sensing instrumentation should be considered, for example the BFP and optical tweezers. Thus, this thesis outlines the development and construction an optical trap capable of measuring intermolecular forces between biomolecules at the single molecule level. To demonstrate the force sensing abilities of the optical set up, proof of principle measurements were performed which investigate the interactions between proteins and polymer surfaces subjected to varying degrees of argon plasma treatment. Complementary data was gained from measurements performed independently by the AFM. Changes in polymer resistance to proteins as a response to changes in polymer surface chemistry were detected utilising both AFM and optical tweezers measurements. Finally, the AFM and optical tweezers were employed as ultrasensitive biosensors. Single molecule investigations of the antibody-antigen interaction between the cardiac troponin I marker and its complementary antibody, reveals the impact therapeutic concentrations of heparin

  15. Averaging methods for stochastic dynamics of complex reaction networks: description of multi-scale couplings

    CERN Document Server

    Plyasunov, S

    2005-01-01

    This paper is concerned with classes of models of stochastic reaction dynamics with time-scales separation. We demonstrate that the existence of the time-scale separation naturally leads to the application of the averaging principle and elimination of degrees of freedom via the renormalization of transition rates of slow reactions. The method suggested in this work is more general than other approaches presented previously: it is not limited to a particular type of stochastic processes and can be applied to different types of processes describing fast dynamics, and also provides crossover to the case when separation of time scales is not well pronounced. We derive a family of exact fluctuation-dissipation relations which establish the connection between effective rates and the statistics of the reaction events in fast reaction channels. An illustration of the technique is provided. Examples show that renormalized transition rates exhibit in general non-exponential relaxation behavior with a broad range of pos...

  16. Pattern formation in a two-component reaction-diffusion system with delayed processes on a network

    Science.gov (United States)

    Petit, Julien; Asllani, Malbor; Fanelli, Duccio; Lauwens, Ben; Carletti, Timoteo

    2016-11-01

    Reaction-diffusion systems with time-delay defined on complex networks have been studied in the framework of the emergence of Turing instabilities. The use of the Lambert W-function allowed us to get explicit analytic conditions for the onset of patterns as a function of the main involved parameters, the time-delay, the network topology and the diffusion coefficients. Depending on these parameters, the analysis predicts whether the system will evolve towards a stationary Turing pattern or rather to a wave pattern associated to a Hopf bifurcation. The possible outcomes of the linear analysis overcome the respective limitations of the single-species case with delay, and that of the classical activator-inhibitor variant without delay. Numerical results gained from the Mimura-Murray model support the theoretical approach.

  17. Sign conditions for injectivity of generalized polynomial maps with applications to chemical reaction networks and real algebraic geometry

    DEFF Research Database (Denmark)

    Müller, Stefan; Feliu, Elisenda; Regensburger, Georg;

    2016-01-01

    We give necessary and sufficient conditions in terms of sign vectors for the injectivity of families of polynomials maps with arbitrary real exponents defined on the positive orthant. Our work relates and extends existing injectivity conditions expressed in terms of Jacobian matrices and determin...... and determinants. In the context of chemical reaction networks with power-law kinetics, our results can be used to preclude as well as to guarantee multiple positive steady states. In the context of real algebraic geometry, our results reveal the first ...

  18. A ladder network modelling the electrochemical impedance of the diffusion and reaction processes in semi-infinite space.

    Science.gov (United States)

    Moya, A A

    2016-02-01

    The Gerischer impedance, i.e., the diffusion-reaction impedance of an ionic species in semi-infinite space, has been modelled by means of a novel simple equivalent ladder electric circuit constituted by a finite number of resistors and capacitors, which corresponds to the Cauer structure obtained from development into continued fractions. The Nyquist plots of the impedance of the ladder network or Cauer circuit and the deviation with respect to the Gerischer impedance have been originally analysed as a function of the number of circuit elements. From the Cauer equivalent circuit, a new and simple expression modelling the Gerischer impedance at the limit of the lowest frequencies has been derived.

  19. MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations.

    Science.gov (United States)

    Vergara-Perez, Sandra; Marucho, Marcelo

    2016-01-01

    One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post- analysis of structural and electrical properties of biomolecules.

  20. MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations

    Science.gov (United States)

    Vergara-Perez, Sandra; Marucho, Marcelo

    2016-01-01

    One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson-Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post-analysis of structural and electrical properties of biomolecules.

  1. Knowledge based cluster ensemble for cancer discovery from biomolecular data.

    Science.gov (United States)

    Yu, Zhiwen; Wongb, Hau-San; You, Jane; Yang, Qinmin; Liao, Hongying

    2011-06-01

    The adoption of microarray techniques in biological and medical research provides a new way for cancer diagnosis and treatment. In order to perform successful diagnosis and treatment of cancer, discovering and classifying cancer types correctly is essential. Class discovery is one of the most important tasks in cancer classification using biomolecular data. Most of the existing works adopt single clustering algorithms to perform class discovery from biomolecular data. However, single clustering algorithms have limitations, which include a lack of robustness, stability, and accuracy. In this paper, we propose a new cluster ensemble approach called knowledge based cluster ensemble (KCE) which incorporates the prior knowledge of the data sets into the cluster ensemble framework. Specifically, KCE represents the prior knowledge of a data set in the form of pairwise constraints. Then, the spectral clustering algorithm (SC) is adopted to generate a set of clustering solutions. Next, KCE transforms pairwise constraints into confidence factors for these clustering solutions. After that, a consensus matrix is constructed by considering all the clustering solutions and their corresponding confidence factors. The final clustering result is obtained by partitioning the consensus matrix. Comparison with single clustering algorithms and conventional cluster ensemble approaches, knowledge based cluster ensemble approaches are more robust, stable and accurate. The experiments on cancer data sets show that: 1) KCE works well on these data sets; 2) KCE not only outperforms most of the state-of-the-art single clustering algorithms, but also outperforms most of the state-of-the-art cluster ensemble approaches.

  2. Photochirogenesis: Photochemical Models on the Origin of Biomolecular Homochirality

    Directory of Open Access Journals (Sweden)

    Cornelia Meinert

    2010-05-01

    Full Text Available Current research focuses on a better understanding of the origin of biomolecular asymmetry by the identification and detection of the possibly first chiral molecules that were involved in the appearance and evolution of life on Earth. We have reasons to assume that these molecules were specific chiral amino acids. Chiral amino acids have been identified in both chondritic meteorites and simulated interstellar ices. Present research reasons that circularly polarized electromagnetic radiation was identified in interstellar environments and an asymmetric interstellar photon-molecule interaction might have triggered biomolecular symmetry breaking. We review on the possible prebiotic interaction of ‘chiral photons’ in the form of circularly polarized light, with early chiral organic molecules. We will highlight recent studies on enantioselective photolysis of racemic amino acids by circularly polarized light and experiments on the asymmetric photochemical synthesis of amino acids from only one C and one N containing molecules by simulating interstellar environments. Both approaches are based on circular dichroic transitions of amino acids that will be presented as well.

  3. A palladium-doped ceria@carbon core-sheath nanowire network: a promising catalyst support for alcohol electrooxidation reactions

    Science.gov (United States)

    Tan, Qiang; Du, Chunyu; Sun, Yongrong; Du, Lei; Yin, Geping; Gao, Yunzhi

    2015-08-01

    A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique interconnected one-dimensional core-sheath structure is revealed to facilitate immobilization of the metal catalysts, leading to the improved durability. This core-sheath nanowire network opens up a new strategy for catalyst performance optimization for next-generation fuel cells.A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique

  4. Preclusion of switch behavior in reaction networks with mass-action kinetics

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Wiuf, C.

    2012-01-01

    consists of components of the species formation rate function and a maximal set of independent conservation laws. The determinant of the function is a polynomial in the species concentrations and the rate constants (linear in the latter) and its coefficients are fully determined. The criterion also......We study networks taken with mass-action kinetics and provide a Jacobian criterion that applies to an arbitrary network to preclude the existence of multiple positive steady states within any stoichiometric class for any choice of rate constants. We are concerned with the characterization...

  5. Full-dimensional and reduced-dimensional calculations of initial state-selected reaction probabilities studying the H + CH4 → H2 + CH3 reaction on a neural network PES

    Science.gov (United States)

    Welsch, Ralph; Manthe, Uwe

    2015-02-01

    Initial state-selected reaction probabilities of the H + CH4 → H2 + CH3 reaction are calculated in full and reduced dimensionality on a recent neural network potential [X. Xu, J. Chen, and D. H. Zhang, Chin. J. Chem. Phys. 27, 373 (2014)]. The quantum dynamics calculation employs the quantum transition state concept and the multi-layer multi-configurational time-dependent Hartree approach and rigorously studies the reaction for vanishing total angular momentum (J = 0). The calculations investigate the accuracy of the neutral network potential and study the effect resulting from a reduced-dimensional treatment. Very good agreement is found between the present results obtained on the neural network potential and previous results obtained on a Shepard interpolated potential energy surface. The reduced-dimensional calculations only consider motion in eight degrees of freedom and retain the C3v symmetry of the methyl fragment. Considering reaction starting from the vibrational ground state of methane, the reaction probabilities calculated in reduced dimensionality are moderately shifted in energy compared to the full-dimensional ones but otherwise agree rather well. Similar agreement is also found if reaction probabilities averaged over similar types of vibrational excitation of the methane reactant are considered. In contrast, significant differences between reduced and full-dimensional results are found for reaction probabilities starting specifically from symmetric stretching, asymmetric (f2-symmetric) stretching, or e-symmetric bending excited states of methane.

  6. Confinement and diffusion modulate bistability and stochastic switching in a reaction network with positive feedback

    Science.gov (United States)

    Mlynarczyk, Paul J.; Pullen, Robert H.; Abel, Steven M.

    2016-01-01

    Positive feedback is a common feature in signal transduction networks and can lead to phenomena such as bistability and signal propagation by domain growth. Physical features of the cellular environment, such as spatial confinement and the mobility of proteins, play important but inadequately understood roles in shaping the behavior of signaling networks. Here, we use stochastic, spatially resolved kinetic Monte Carlo simulations to explore a positive feedback network as a function of system size, system shape, and mobility of molecules. We show that these physical properties can markedly alter characteristics of bistability and stochastic switching when compared with well-mixed simulations. Notably, systems of equal volume but different shapes can exhibit qualitatively different behaviors under otherwise identical conditions. We show that stochastic switching to a state maintained by positive feedback occurs by cluster formation and growth. Additionally, the frequency at which switching occurs depends nontrivially on the diffusion coefficient, which can promote or suppress switching relative to the well-mixed limit. Taken together, the results provide a framework for understanding how confinement and protein mobility influence emergent features of the positive feedback network by modulating molecular concentrations, diffusion-influenced rate parameters, and spatiotemporal correlations between molecules.

  7. Combined Geometric and Neural Network Approach to Generic Fault Diagnosis in Satellite Reaction Wheels

    DEFF Research Database (Denmark)

    Baldi, P.; Blanke, Mogens; Castaldi, P.

    2015-01-01

    This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank...

  8. Pushing back the frontiers of mercury speciation using a combination of biomolecular and isotopic signatures: challenge and perspectives.

    Science.gov (United States)

    Pedrero, Zoyne; Donard, Olivier F X; Amouroux, David

    2016-04-01

    Mercury (Hg) pollution is considered a major environmental problem due to the extreme toxicity of Hg. However, Hg metabolic pathways in biota remain elusive. An understanding of these pathways is crucial to elucidating the (eco)toxic effects of Hg and its biogeochemical cycle. The development of a new analytical methodology based on both speciation and natural isotopic fractionation represents a promising approach for metabolic studies of Hg and other metal(loid)s. Speciation provides valuable information about the reactivity and potential toxicity of metabolites, while the use of natural isotopic signature analysis adds a complementary dynamic dimension that allows the life history of the target element to be probed, the source of the target element (i.e., the source of pollution) to be identified, and reactions to be tracked. The resulting combined (bio)molecular and isotopic signature affords precious insight into the behavior of Hg in biota and Hg detoxification mechanisms. In the long term, this highly innovative methodology could be used in life and environmental science studies of metal(loid)s to push back the frontiers of our knowledge in this field. This paper summarizes the current status of the application of Hg speciation and the isotopic signature of Hg at the biomolecular level in living organisms, and discusses potential future uses of this combination of techniques.

  9. Photoisomerization Reaction Mechanisms of o-Nitrophenol Revealed by Analyzing Intersystem Crossing Network at the MRCI Level.

    Science.gov (United States)

    Xu, Chao; Yu, Le; Zhu, Chaoyuan; Yu, Jianguo

    2015-10-22

    6SA-CASSCF(10, 10) /6-31G (d, p) and MRCI/cc-pVDZ methods were performed to probe photoisomerization reaction mechanisms of o-nitrophenol. Two low-lying singlet electronic states (S0 and S1) and two low-lying triplet electronic states (T1 and T2) were found to weave an intersystem crossing network in which a dominant stepwise photoisomerization provides a very efficient reaction pathway; the reaction takes place in the wide region of crossing seam-surface woven by S1 and T1 states first, followed by T1 and S0 states. Both intersystem crossing regions show strong spin-orbital coupling in the order of 40 wavenumbers. All nitro and aci-nitro isomers and transition states on four electronic potential energy surfaces are calculated along with analysis of both dominant and subdominant relaxation pathways, especially weak spin-orbital coupling (∼10 wavenumbers) between T2 and S1 states and effective conical intersection between T2 and T1 states opening a new relaxation pathway S1 → T2→ T1.

  10. Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.

    Science.gov (United States)

    Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek

    2015-07-06

    Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.

  11. Gait Phases Recognition from Accelerations and Ground Reaction Forces: Application of Neural Networks

    Directory of Open Access Journals (Sweden)

    S. Rafajlović

    2009-06-01

    Full Text Available The goal of this study was to test the applicability of accelerometer as the sensor for assessment of the walking. We present here the comparison of gait phases detected from the data recorded by force sensing resistors mounted in the shoe insoles, non-processed acceleration and processed acceleration perpendicular to the direction of the foot. The gait phases in all three cases were detected by means of a neural network. The output from the neural network was the gait phase, while the inputs were data from the sensors. The results show that the errors were in the ranges: 30 ms (2.7% – force sensors; 150 ms (13.6% – nonprocessed acceleration, and 120 ms (11% – processed acceleration data. This result suggests that it is possible to use the accelerometer as the gait phase detector, however, with the knowledge that the gait phases are time shifted for about 100 ms with respect the neural network predicted times.

  12. Systematic evaluation of bundled SPC water for biomolecular simulations.

    Science.gov (United States)

    Gopal, Srinivasa M; Kuhn, Alexander B; Schäfer, Lars V

    2015-04-07

    In bundled SPC water models, the relative motion of groups of four water molecules is restrained by distance-dependent potentials. Bundled SPC models have been used in hybrid all-atom/coarse-grained (AA/CG) multiscale simulations, since they enable to couple atomistic SPC water with supra-molecular CG water models that effectively represent more than a single water molecule. In the present work, we systematically validated and critically tested bundled SPC water models as solvent for biomolecular simulations. To that aim, we investigated both thermodynamic and structural properties of various biomolecular systems through molecular dynamics (MD) simulations. Potentials of mean force of dimerization of pairs of amino acid side chains as well as hydration free energies of single side chains obtained with bundled SPC and standard (unrestrained) SPC water agree closely with each other and with experimental data. Decomposition of the hydration free energies into enthalpic and entropic contributions reveals that in bundled SPC, this favorable agreement of the free energies is due to a larger degree of error compensation between hydration enthalpy and entropy. The Ramachandran maps of Ala3, Ala5, and Ala7 peptides are similar in bundled and unrestrained SPC, whereas for the (GS)2 peptide, bundled water leads to a slight overpopulation of extended conformations. Analysis of the end-to-end distance autocorrelation times of the Ala5 and (GS)2 peptides shows that sampling in more viscous bundled SPC water is about two times slower. Pronounced differences between the water models were found for the structure of a coiled-coil dimer, which is instable in bundled SPC but not in standard SPC. In addition, the hydration of the active site of the serine protease α-chymotrypsin depends on the water model. Bundled SPC leads to an increased hydration of the active site region, more hydrogen bonds between water and catalytic triad residues, and a significantly slower exchange of water

  13. EVA reactive blending with Si-H terminated polysiloxane by carbonyl hydrosilylation reaction: From compatibilised blends to crosslinking networks

    Energy Technology Data Exchange (ETDEWEB)

    Bonnet, J.; Bounor-Legare, V.; Alcouffe, P. [Universite de Lyon, 69003 Lyon (France); Universite de Lyon 1, CNRS UMR5223, Ingenierie des Materiaux Polymeres, 15 Boulevard Latarjet, F-69622 Villeurbanne (France); Cassagnau, P., E-mail: philippe.cassagnau@univ-lyon1.fr [Universite de Lyon, 69003 Lyon (France); Universite de Lyon 1, CNRS UMR5223, Ingenierie des Materiaux Polymeres, 15 Boulevard Latarjet, F-69622 Villeurbanne (France)

    2012-10-15

    A new and original method based on carbonyl hydrosilylation was developed to prepare ethylene-vinyl acetate (EVA)/polysiloxane polymer blends. This focused on the addition of hydrogenosilane groups (SiH) from polysiloxane to the carbonyl groups of EVA. The influence of the nature of the polysiloxane on blend properties was investigated by rheology and scanning electron microscopy. Mixing of a low viscosity polysiloxane with a high viscosity EVA matrix produced a two-phase morphology. The occurrence of the hydrosilylation reaction at the EVA/polysiloxane interface promoted a homogenisation of the blend depending on the molar ratio SiH/vinyl acetate groups, [SiH]/[VA], and the viscosity ratio of the blend. Two distinct behaviours were observed. The formation of a crosslinked network under shear was obtained for a low viscosity ratio between polysiloxane and EVA ({lambda}{sub polysiloxane/EVA} = 4.0 Multiplication-Sign 10{sup -6}) with a high concentration of SiH groups ([SiH]/[VA] = 0.5), while the formation of a compatibilised blend was observed for high molar mass polysiloxanes (Mn > 15,000 g mol{sup -1}) with a low concentration of SiH ([SiH]/[VA] < 4.0 Multiplication-Sign 10{sup -3}). -- Highlights: Black-Right-Pointing-Pointer Carbonyl hydrosilylation reaction was found to enhance EVA/polysiloxane immiscible blends. Black-Right-Pointing-Pointer EVA crosslinking was obtained with a low molar mass polysiloxane. Black-Right-Pointing-Pointer EVA compatibilisation was obtained with a high molar mass polysiloxane. Black-Right-Pointing-Pointer Shear rate was found to improve the hydrosilylation reaction at the interface. Black-Right-Pointing-Pointer A two-phase morphology of the blends was observed after reaction with fine polysiloxane nodules.

  14. Application of an Artificial Neural Network to the Prediction of OH Radical Reaction Rate Constants for Evaluating Global Warming Potential.

    Science.gov (United States)

    Allison, Thomas C

    2016-03-03

    Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult and time-consuming to produce. High-level quantum chemistry predictions of the rate constant can suffer from the same issues. Therefore, it is valuable to devise estimation schemes that can give reasonable results on a variety of chemical compounds. In this article, the construction and training of an artificial neural network (ANN) for the prediction of rate constants at 298 K for reactions of hydroxyl radical with a diverse set of molecules is described. Input to the ANN consists of counts of the chemical bonds and bends present in the target molecule. The ANN is trained using 792 (•)OH reaction rate constants taken from the NIST Chemical Kinetics Database. The mean unsigned percent error (MUPE) for the training set is 12%, and the MUPE of the testing set is 51%. It is shown that the present methodology yields rate constants of reasonable accuracy for a diverse set of inputs. The results are compared to high-quality literature values and to another estimation scheme. This ANN methodology is expected to be of use in a wide range of applications for which (•)OH reaction rate constants are required. The model uses only information that can be gathered from a 2D representation of the molecule, making the present approach particularly appealing, especially for screening applications.

  15. Insights into cancer severity from biomolecular interaction mechanisms

    Science.gov (United States)

    Raimondi, Francesco; Singh, Gurdeep; Betts, Matthew J.; Apic, Gordana; Vukotic, Ranka; Andreone, Pietro; Stein, Lincoln; Russell, Robert B.

    2016-01-01

    To attain a deeper understanding of diseases like cancer, it is critical to couple genetics with biomolecular mechanisms. High-throughput sequencing has identified thousands of somatic mutations across dozens of cancers, and there is a pressing need to identify the few that are pathologically relevant. Here we use protein structure and interaction data to interrogate nonsynonymous somatic cancer mutations, identifying a set of 213 molecular interfaces (protein-protein, -small molecule or –nucleic acid) most often perturbed in cancer, highlighting several potentially novel cancer genes. Over half of these interfaces involve protein-small-molecule interactions highlighting their overall importance in cancer. We found distinct differences in the predominance of perturbed interfaces between cancers and histological subtypes and presence or absence of certain interfaces appears to correlate with cancer severity. PMID:27698488

  16. Simulation of Parallel Logical Operations with Biomolecular Computing

    Directory of Open Access Journals (Sweden)

    Mahnaz Kadkhoda

    2008-01-01

    Full Text Available Biomolecular computing is the computational method that uses the potential of DNA as a parallel computing device. DNA computing can be used to solve NP-complete problems. An appropriate application of DNA computation is large-scale evaluation of parallel computation models such as Boolean Circuits. In this study, we present a molecular-based algorithm for evaluation of Nand-based Boolean Circuits. The contribution of this paper is that the proposed algorithm has been implemented using only three molecular operations and the number of passes in each level is decreased to less than half of previously addressed in the literature. Thus, the proposed algorithm is much easier to implement in the laboratory.

  17. Structure and Interactions of Isolated Biomolecular Building Blocks.

    Science.gov (United States)

    de Vries, Mattanjah

    2006-03-01

    We investigate biomolecular building blocks and their clusters with each other and with water on a single molecular level. The motivation is the need to distinguish between intrinsic molecular properties and those that result from the biological environment. This is achieved by a combination of laser desorption and jet cooling, applied to aromatic amino acids, small peptides containing those, nucleobases and nucleosides. This approach is coupled with a number of laser spectroscopic techniques, including resonant multi-photon ionization, spectral hole burning and infra-red ion-dip spectroscopy. We will discuss examples illustrating how information can be obtained on spatial structure of individual biomolecules, including peptide conformations and details of DNA base-pairing.

  18. Orientation of biomolecular assemblies in a microfluidic jet

    Energy Technology Data Exchange (ETDEWEB)

    Priebe, M; Kalbfleisch, S; Tolkiehn, M; Salditt, T [Institut fuer Roentgenphysik, Universitaet Goettingen, Goettingen (Germany); Koester, S [Courant Research Centre Nano-Spectroscopy and X-Ray Imaging, Universitaet Goettingen, Goettingen (Germany); Abel, B [Institut fuer Physikalische Chemie, Universitaet Goettingen, Goettingen (Germany); Davies, R J, E-mail: tsalditt@gwdg.d [ID13, ESRF, Grenoble (France)

    2010-04-15

    We have investigated multilamellar lipid assemblies in a microfluidic jet, operating at high shear rates of the order of 10{sup 7} s{sup -1}. Compared to classical Couette cells or rheometers, the shear rate was increased by at least 2-3 orders of magnitude, and the sample volume was scaled down correspondingly. At the same time, the jet is characterized by high extensional stress due to elongational flow. A focused synchrotron x-ray beam was used to measure the structure and orientation of the lipid assemblies in the jet. The diffraction patterns indicate conventional multilamellar phases, aligned with the membrane normals oriented along the velocity gradient of the jet. The results indicate that the setup may be well suited for coherent diffractive imaging of oriented biomolecular assemblies and macromolecules at the future x-ray free electron laser (XFEL) sources.

  19. The biomolecular corona of nanoparticles in circulating biological media

    Science.gov (United States)

    Pozzi, D.; Caracciolo, G.; Digiacomo, L.; Colapicchioni, V.; Palchetti, S.; Capriotti, A. L.; Cavaliere, C.; Zenezini Chiozzi, R.; Puglisi, A.; Laganà, A.

    2015-08-01

    When nanoparticles come into contact with biological media, they are covered by a biomolecular `corona', which confers a new identity to the particles. In all the studies reported so far nanoparticles are incubated with isolated plasma or serum that are used as a model for protein adsorption. Anyway, bodily fluids are dynamic in nature so the question arises on whether the incubation protocol, i.e. dynamic vs. static incubation, could affect the composition and structure of the biomolecular corona. Here we let multicomponent liposomes interact with fetal bovine serum (FBS) both statically and dynamically, i.e. in contact with circulating FBS (~40 cm s-1). The structure and composition of the liposome-protein corona, as determined by dynamic light scattering, electrophoretic light scattering and liquid chromatography tandem mass spectrometry, were found to be dependent on the incubation protocol. Specifically, following dynamic exposure to FBS, multicomponent liposomes were less enriched in complement proteins and appreciably more enriched in apolipoproteins and acute phase proteins (e.g. alpha-1-antitrypsin and inter-alpha-trypsin inhibitor heavy chain H3) that are involved in relevant interactions between nanoparticles and living systems. Supported by our results, we speculate that efficient predictive modeling of nanoparticle behavior in vivo will require accurate knowledge of nanoparticle-specific protein fingerprints in circulating biological media.When nanoparticles come into contact with biological media, they are covered by a biomolecular `corona', which confers a new identity to the particles. In all the studies reported so far nanoparticles are incubated with isolated plasma or serum that are used as a model for protein adsorption. Anyway, bodily fluids are dynamic in nature so the question arises on whether the incubation protocol, i.e. dynamic vs. static incubation, could affect the composition and structure of the biomolecular corona. Here we let

  20. Computational and theoretical aspects of biomolecular structure and dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, A.E.; Berendzen, J.; Catasti, P., Chen, X. [and others

    1996-09-01

    This is the final report for a project that sought to evaluate and develop theoretical, and computational bases for designing, performing, and analyzing experimental studies in structural biology. Simulations of large biomolecular systems in solution, hydrophobic interactions, and quantum chemical calculations for large systems have been performed. We have developed a code that implements the Fast Multipole Algorithm (FMA) that scales linearly in the number of particles simulated in a large system. New methods have been developed for the analysis of multidimensional NMR data in order to obtain high resolution atomic structures. These methods have been applied to the study of DNA sequences in the human centromere, sequences linked to genetic diseases, and the dynamics and structure of myoglobin.

  1. Design and implementation of a biomolecular concentration tracker.

    Science.gov (United States)

    Hsiao, Victoria; de los Santos, Emmanuel L C; Whitaker, Weston R; Dueber, John E; Murray, Richard M

    2015-02-20

    As a field, synthetic biology strives to engineer increasingly complex artificial systems in living cells. Active feedback in closed loop systems offers a dynamic and adaptive way to ensure constant relative activity independent of intrinsic and extrinsic noise. In this work, we use synthetic protein scaffolds as a modular and tunable mechanism for concentration tracking through negative feedback. Input to the circuit initiates scaffold production, leading to colocalization of a two-component system and resulting in the production of an inhibitory antiscaffold protein. Using a combination of modeling and experimental work, we show that the biomolecular concentration tracker circuit achieves dynamic protein concentration tracking in Escherichia coli and that steady state outputs can be tuned.

  2. Hybrid organic semiconductor lasers for bio-molecular sensing.

    Science.gov (United States)

    Haughey, Anne-Marie; Foucher, Caroline; Guilhabert, Benoit; Kanibolotsky, Alexander L; Skabara, Peter J; Burley, Glenn; Dawson, Martin D; Laurand, Nicolas

    2014-01-01

    Bio-functionalised luminescent organic semiconductors are attractive for biophotonics because they can act as efficient laser materials while simultaneously interacting with molecules. In this paper, we present and discuss a laser biosensor platform that utilises a gain layer made of such an organic semiconductor material. The simple structure of the sensor and its operation principle are described. Nanolayer detection is shown experimentally and analysed theoretically in order to assess the potential and the limits of the biosensor. The advantage conferred by the organic semiconductor is explained, and comparisons to laser sensors using alternative dye-doped materials are made. Specific biomolecular sensing is demonstrated, and routes to functionalisation with nucleic acid probes, and future developments opened up by this achievement, are highlighted. Finally, attractive formats for sensing applications are mentioned, as well as colloidal quantum dots, which in the future could be used in conjunction with organic semiconductors.

  3. RCytoscape: tools for exploratory network analysis

    NARCIS (Netherlands)

    Shannon, P.T.; Grimes, M.; Kutlu, B.; Bot, J.J.; Galas, D.J.

    2013-01-01

    Background: Biomolecular pathways and networks are dynamic and complex, and the perturbations to them which cause disease are often multiple, heterogeneous and contingent. Pathway and network visualizations, rendered on a computer or published on paper, however, tend to be static, lacking in detail,

  4. Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase

    Science.gov (United States)

    Szaleniec, Maciej; Witko, Małgorzata; Tadeusiewicz, Ryszard; Goclon, Jakub

    2006-03-01

    Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure-activity relationship (QSAR) approaches.

  5. PARENT: A Parallel Software Suite for the Calculation of Configurational Entropy in Biomolecular Systems.

    Science.gov (United States)

    Fleck, Markus; Polyansky, Anton A; Zagrovic, Bojan

    2016-04-12

    Accurate estimation of configurational entropy from the in silico-generated biomolecular ensembles, e.g., from molecular dynamics (MD) trajectories, is dependent strongly on exhaustive sampling for physical reasons. This, however, creates a major computational problem for the subsequent estimation of configurational entropy using the Maximum Information Spanning Tree (MIST) or Mutual Information Expansion (MIE) approaches for internal molecular coordinates. In particular, the available software for such estimation exhibits serious limitations when it comes to molecules with hundreds or thousands of atoms, because of its reliance on a serial program architecture. To overcome this problem, we have developed a parallel, hybrid MPI/openMP C++ implementation of MIST and MIE, called PARENT, which is particularly optimized for high-performance computing and provides efficient estimation of configurational entropy in different biological processes (e.g., protein-protein interactions). In addition, PARENT also allows for a detailed mapping of intramolecular allosteric networks. Here, we benchmark the program on a set of 1-μs-long MD trajectories of 10 different protein complexes and their components, demonstrating robustness and good scalability. A direct comparison between MIST and MIE on the same dataset demonstrates a superior convergence behavior for the former approach, when it comes to total simulation length and configurational-space binning.

  6. Artificial Hormone Reaction Networks: Towards Higher Evolvability in Evolutionary Multi-Modular Robotics

    CERN Document Server

    Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl

    2010-01-01

    The semi-automatic or automatic synthesis of robot controller software is both desirable and challenging. Synthesis of rather simple behaviors such as collision avoidance by applying artificial evolution has been shown multiple times. However, the difficulty of this synthesis increases heavily with increasing complexity of the task that should be performed by the robot. We try to tackle this problem of complexity with Artificial Homeostatic Hormone Systems (AHHS), which provide both intrinsic, homeostatic processes and (transient) intrinsic, variant behavior. By using AHHS the need for pre-defined controller topologies or information about the field of application is minimized. We investigate how the principle design of the controller and the hormone network size affects the overall performance of the artificial evolution (i.e., evolvability). This is done by comparing two variants of AHHS that show different effects when mutated. We evolve a controller for a robot built from five autonomous, cooperating modu...

  7. Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.

    Science.gov (United States)

    Sun, Xiaodian; Medvedovic, Mario

    2016-02-01

    Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.

  8. Modeling Structural Dynamics of Biomolecular Complexes by Coarse-Grained Molecular Simulations.

    Science.gov (United States)

    Takada, Shoji; Kanada, Ryo; Tan, Cheng; Terakawa, Tsuyoshi; Li, Wenfei; Kenzaki, Hiroo

    2015-12-15

    Due to hierarchic nature of biomolecular systems, their computational modeling calls for multiscale approaches, in which coarse-grained (CG) simulations are used to address long-time dynamics of large systems. Here, we review recent developments and applications of CG modeling methods, focusing on our methods primarily for proteins, DNA, and their complexes. These methods have been implemented in the CG biomolecular simulator, CafeMol. Our CG model has resolution such that ∼10 non-hydrogen atoms are grouped into one CG particle on average. For proteins, each amino acid is represented by one CG particle. For DNA, one nucleotide is simplified by three CG particles, representing sugar, phosphate, and base. The protein modeling is based on the idea that proteins have a globally funnel-like energy landscape, which is encoded in the structure-based potential energy function. We first describe two representative minimal models of proteins, called the elastic network model and the classic Go̅ model. We then present a more elaborate protein model, which extends the minimal model to incorporate sequence and context dependent local flexibility and nonlocal contacts. For DNA, we describe a model developed by de Pablo's group that was tuned to well reproduce sequence-dependent structural and thermodynamic experimental data for single- and double-stranded DNAs. Protein-DNA interactions are modeled either by the structure-based term for specific cases or by electrostatic and excluded volume terms for nonspecific cases. We also discuss the time scale mapping in CG molecular dynamics simulations. While the apparent single time step of our CGMD is about 10 times larger than that in the fully atomistic molecular dynamics for small-scale dynamics, large-scale motions can be further accelerated by two-orders of magnitude with the use of CG model and a low friction constant in Langevin dynamics. Next, we present four examples of applications. First, the classic Go̅ model was used to

  9. A coarse-grained model for the simulations of biomolecular interactions in cellular environments

    Energy Technology Data Exchange (ETDEWEB)

    Xie, Zhong-Ru; Chen, Jiawen; Wu, Yinghao, E-mail: yinghao.wu@einstein.yu.edu [Department of Systems and Computational Biology, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, New York 10461 (United States)

    2014-02-07

    The interactions of bio-molecules constitute the key steps of cellular functions. However, in vivo binding properties differ significantly from their in vitro measurements due to the heterogeneity of cellular environments. Here we introduce a coarse-grained model based on rigid-body representation to study how factors such as cellular crowding and membrane confinement affect molecular binding. The macroscopic parameters such as the equilibrium constant and the kinetic rate constant are calibrated by adjusting the microscopic coefficients used in the numerical simulations. By changing these model parameters that are experimentally approachable, we are able to study the kinetic and thermodynamic properties of molecular binding, as well as the effects caused by specific cellular environments. We investigate the volumetric effects of crowded intracellular space on bio-molecular diffusion and diffusion-limited reactions. Furthermore, the binding constants of membrane proteins are currently difficult to measure. We provide quantitative estimations about how the binding of membrane proteins deviates from soluble proteins under different degrees of membrane confinements. The simulation results provide biological insights to the functions of membrane receptors on cell surfaces. Overall, our studies establish a connection between the details of molecular interactions and the heterogeneity of cellular environments.

  10. Computational Recipe for Efficient Description of Large-Scale Conformational Changes in Biomolecular Systems.

    Science.gov (United States)

    Moradi, Mahmoud; Tajkhorshid, Emad

    2014-07-01

    Characterizing large-scale structural transitions in biomolecular systems poses major technical challenges to both experimental and computational approaches. On the computational side, efficient sampling of the configuration space along the transition pathway remains the most daunting challenge. Recognizing this issue, we introduce a knowledge-based computational approach toward describing large-scale conformational transitions using (i) nonequilibrium, driven simulations combined with work measurements and (ii) free energy calculations using empirically optimized biasing protocols. The first part is based on designing mechanistically relevant, system-specific reaction coordinates whose usefulness and applicability in inducing the transition of interest are examined using knowledge-based, qualitative assessments along with nonequilirbrium work measurements which provide an empirical framework for optimizing the biasing protocol. The second part employs the optimized biasing protocol resulting from the first part to initiate free energy calculations and characterize the transition quantitatively. Using a biasing protocol fine-tuned to a particular transition not only improves the accuracy of the resulting free energies but also speeds up the convergence. The efficiency of the sampling will be assessed by employing dimensionality reduction techniques to help detect possible flaws and provide potential improvements in the design of the biasing protocol. Structural transition of a membrane transporter will be used as an example to illustrate the workings of the proposed approach.

  11. Pore-scale network modeling of microbially induced calcium carbonate precipitation: Insight into scale dependence of biogeochemical reaction rates

    Science.gov (United States)

    Qin, Chao-Zhong; Hassanizadeh, S. Majid; Ebigbo, Anozie

    2016-11-01

    The engineering of microbially induced calcium carbonate precipitation (MICP) has attracted much attention in a number of applications, such as sealing of CO2 leakage pathways, soil stabilization, and subsurface remediation of radionuclides and toxic metals. The goal of this work is to gain insight into pore-scale processes of MICP and scale dependence of biogeochemical reaction rates. This will help us develop efficient field-scale MICP models. In this work, we have developed a comprehensive pore-network model for MICP, with geochemical speciation calculated by the open-source PHREEQC module. A numerical pseudo-3-D micromodel as the computational domain was generated by a novel pore-network generation method. We modeled a three-stage process in the engineering of MICP including the growth of biofilm, the injection of calcium-rich medium, and the precipitation of calcium carbonate. A number of test cases were conducted to illustrate how calcite precipitation was influenced by different operating conditions. In addition, we studied the possibility of reducing the computational effort by simplifying geochemical calculations. Finally, the effect of mass transfer limitation of possible carbonate ions in a pore element on calcite precipitation was explored.

  12. ReacKnock: identifying reaction deletion strategies for microbial strain optimization based on genome-scale metabolic network.

    Directory of Open Access Journals (Sweden)

    Zixiang Xu

    Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.

  13. Computational methods to study the structure and dynamics of biomolecules and biomolecular processes from bioinformatics to molecular quantum mechanics

    CERN Document Server

    2014-01-01

    Since the second half of the 20th century machine computations have played a critical role in science and engineering. Computer-based techniques have become especially important in molecular biology, since they often represent the only viable way to gain insights into the behavior of a biological system as a whole. The complexity of biological systems, which usually needs to be analyzed on different time- and size-scales and with different levels of accuracy, requires the application of different approaches, ranging from comparative analysis of sequences and structural databases, to the analysis of networks of interdependence between cell components and processes, through coarse-grained modeling to atomically detailed simulations, and finally to molecular quantum mechanics. This book provides a comprehensive overview of modern computer-based techniques for computing the structure, properties and dynamics of biomolecules and biomolecular processes. The twenty-two chapters, written by scientists from all over t...

  14. Differential geometry-based solvation and electrolyte transport models for biomolecular modeling: a review

    OpenAIRE

    Wei, Guo Wei; Baker, Nathan A.

    2014-01-01

    This chapter reviews the differential geometry-based solvation and electrolyte transport for biomolecular solvation that have been developed over the past decade. A key component of these methods is the differential geometry of surfaces theory, as applied to the solvent-solute boundary. In these approaches, the solvent-solute boundary is determined by a variational principle that determines the major physical observables of interest, for example, biomolecular surface area, enclosed volume, el...

  15. Electron spin and the origin of Bio-homochirality II. Prebiotic inorganic-organic reaction model

    CERN Document Server

    Wang, Wei

    2014-01-01

    The emergence of biomolecular homochirality is a critically important question about life phenomenon and the origins of life. In a previous paper (arXiv:1309.1229), I tentatively put forward a new hypothesis that the emergence of a single chiral form of biomolecules in living organisms is specifically determined by the electron spin state during their enzyme-catalyzed synthesis processes. However, how a homochirality world of biomolecules could have formed in the absence of enzymatic networks before the origins of life remains unanswered. Here I discussed the electron spin properties in Fe3S4, ZnS, and transition metal doped dilute magnetic ZnS, and their possible roles in the prebiotic synthesis of chiral molecules. Since the existence of these minerals in hydrothermal vent systems is matter of fact, the suggested prebiotic inorganic-organic reaction model, if can be experimentally demonstrated, may help explain where and how life originated on early Earth.

  16. Hybrid Quantum Mechanics/Molecular Mechanics/Coarse Grained Modeling: A Triple-Resolution Approach for Biomolecular Systems.

    Science.gov (United States)

    Sokkar, Pandian; Boulanger, Eliot; Thiel, Walter; Sanchez-Garcia, Elsa

    2015-04-14

    We present a hybrid quantum mechanics/molecular mechanics/coarse-grained (QM/MM/CG) multiresolution approach for solvated biomolecular systems. The chemically important active-site region is treated at the QM level. The biomolecular environment is described by an atomistic MM force field, and the solvent is modeled with the CG Martini force field using standard or polarizable (pol-CG) water. Interactions within the QM, MM, and CG regions, and between the QM and MM regions, are treated in the usual manner, whereas the CG-MM and CG-QM interactions are evaluated using the virtual sites approach. The accuracy and efficiency of our implementation is tested for two enzymes, chorismate mutase (CM) and p-hydroxybenzoate hydroxylase (PHBH). In CM, the QM/MM/CG potential energy scans along the reaction coordinate yield reaction energies that are too large, both for the standard and polarizable Martini CG water models, which can be attributed to adverse effects of using large CG water beads. The inclusion of an atomistic MM water layer (10 Å for uncharged CG water and 5 Å for polarizable CG water) around the QM region improves the energy profiles compared to the reference QM/MM calculations. In analogous QM/MM/CG calculations on PHBH, the use of the pol-CG description for the outer water does not affect the stabilization of the highly charged FADHOOH-pOHB transition state compared to the fully atomistic QM/MM calculations. Detailed performance analysis in a glycine-water model system indicates that computation times for QM energy and gradient evaluations at the density functional level are typically reduced by 40-70% for QM/MM/CG relative to fully atomistic QM/MM calculations.

  17. Constrained approximation of effective generators for multiscale stochastic reaction networks and application to conditioned path sampling

    Science.gov (United States)

    Cotter, Simon L.

    2016-10-01

    Efficient analysis and simulation of multiscale stochastic systems of chemical kinetics is an ongoing area for research, and is the source of many theoretical and computational challenges. In this paper, we present a significant improvement to the constrained approach, which is a method for computing effective dynamics of slowly changing quantities in these systems, but which does not rely on the quasi-steady-state assumption (QSSA). The QSSA can cause errors in the estimation of effective dynamics for systems where the difference in timescales between the "fast" and "slow" variables is not so pronounced. This new application of the constrained approach allows us to compute the effective generator of the slow variables, without the need for expensive stochastic simulations. This is achieved by finding the null space of the generator of the constrained system. For complex systems where this is not possible, or where the constrained subsystem is itself multiscale, the constrained approach can then be applied iteratively. This results in breaking the problem down into finding the solutions to many small eigenvalue problems, which can be efficiently solved using standard methods. Since this methodology does not rely on the quasi steady-state assumption, the effective dynamics that are approximated are highly accurate, and in the case of systems with only monomolecular reactions, are exact. We will demonstrate this with some numerics, and also use the effective generators to sample paths of the slow variables which are conditioned on their endpoints, a task which would be computationally intractable for the generator of the full system.

  18. Bioelectronic Interface Connecting Reversible Logic Gates Based on Enzyme and DNA Reactions.

    Science.gov (United States)

    Guz, Nataliia; Fedotova, Tatiana A; Fratto, Brian E; Schlesinger, Orr; Alfonta, Lital; Kolpashchikov, Dmitry M; Katz, Evgeny

    2016-07-18

    It is believed that connecting biomolecular computation elements in complex networks of communicating molecules may eventually lead to a biocomputer that can be used for diagnostics and/or the cure of physiological and genetic disorders. Here, a bioelectronic interface based on biomolecule-modified electrodes has been designed to bridge reversible enzymatic logic gates with reversible DNA-based logic gates. The enzyme-based Fredkin gate with three input and three output signals was connected to the DNA-based Feynman gate with two input and two output signals-both representing logically reversible computing elements. In the reversible Fredkin gate, the routing of two data signals between two output channels was controlled by the control signal (third channel). The two data output signals generated by the Fredkin gate were directed toward two electrochemical flow cells, responding to the output signals by releasing DNA molecules that serve as the input signals for the next Feynman logic gate based on the DNA reacting cascade, producing, in turn, two final output signals. The Feynman gate operated as the controlled NOT gate (CNOT), where one of the input channels controlled a NOT operation on another channel. Both logic gates represented a highly sophisticated combination of input-controlled signal-routing logic operations, resulting in redirecting chemical signals in different channels and performing orchestrated computing processes. The biomolecular reaction cascade responsible for the signal processing was realized by moving the solution from one reacting cell to another, including the reacting flow cells and electrochemical flow cells, which were organized in a specific network mimicking electronic computing circuitries. The designed system represents the first example of high complexity biocomputing processes integrating enzyme and DNA reactions and performing logically reversible signal processing.

  19. Wind-driven gas networks and star formation in galaxies: reaction-advection hydrodynamic simulations

    Science.gov (United States)

    Chappell, David; Scalo, John

    2001-07-01

    The effects of wind-driven star formation feedback on the spatio-temporal organization of stars and gas in galaxies is studied using two-dimensional intermediate-representational quasi-hydrodynamical simulations. The model retains only a reduced subset of the physics, including mass and momentum conservation, fully non-linear fluid advection, inelastic macroscopic interactions, threshold star formation, and momentum forcing by winds from young star clusters on the surrounding gas. Expanding shells of swept-up gas evolve through the action of fluid advection to form a `turbulent' network of interacting shell fragments which have the overall appearance of a web of filaments (in two dimensions). A new star cluster is formed whenever the column density through a filament exceeds a critical threshold based on the gravitational instability criterion for an expanding shell, which then generates a new expanding shell after some time delay. A filament-finding algorithm is developed to locate the potential sites of new star formation. The major result is the dominance of multiple interactions between advectively distorted shells in controlling the gas and star morphology, gas velocity distribution and mass spectrum of high mass density peaks, and the global star formation history. The gas morphology strongly resembles the model envisioned by Norman & Silk, and observations of gas in the Large Magellanic Cloud (LMC)Q1 and local molecular clouds. The dependence of the frequency distribution of present-to-past average global star formation rate on a number of parameters is investigated. Bursts of star formation only occur when the time-averaged star formation rate per unit area is low, or the system is small. Percolation does not play a role. The broad distribution observed in late-type galaxies can be understood as a result of either small size or small metallicity, resulting in larger shell column densities required for gravitational instability. The star formation rate

  20. [Biobanking and Biomolecular Resources Research Infrastructure (BBMRI). Implications for pathology].

    Science.gov (United States)

    Viertler, C; Zatloukal, K

    2008-11-01

    High quality human biological samples (e.g. blood, tissue or DNA) with associated, well documented clinical and research data are key resources for advancement of life sciences, biotechnology, clinical medicine, drug development and also molecular pathology. Millions of samples of diseased tissues have been collected in the context of routine histopathological diagnosis and are stored in the archives of hospitals and institutes of pathology. A concerted effort is necessary to overcome the current fragmentation of the European biobanking community in order to tap the full research potential of existing biobanks. A pan-European research infrastructure for biobanking and biomolecular resources (BBMRI) is currently in its planning phase. The mission is to link and provide access to local biobanks of different formats, including tissue collections, harmonize standards, establish operational procedures which properly consider ethical, legal, societal aspects, and to secure sustainable funding. Pathology plays a key role in development and administration of tissue banks and is, thus, a major partner for collaboration, expertise and construction of this pan-European research infrastructure.

  1. Biomolecular Evidence of Silk from 8,500 Years Ago.

    Science.gov (United States)

    Gong, Yuxuan; Li, Li; Gong, Decai; Yin, Hao; Zhang, Juzhong

    2016-01-01

    Pottery, bone implements, and stone tools are routinely found at Neolithic sites. However, the integrity of textiles or silk is susceptible to degradation, and it is therefore very difficult for such materials to be preserved for 8,000 years. Although previous studies have provided important evidence of the emergence of weaving skills and tools, such as figuline spinning wheels and osseous lamellas with traces of filament winding, there is a lack of direct evidence proving the existence of silk. In this paper, we explored evidence of prehistoric silk fibroin through the analysis of soil samples collected from three tombs at the Neolithic site of Jiahu. Mass spectrometry was employed and integrated with proteomics to characterize the key peptides of silk fibroin. The direct biomolecular evidence reported here showed the existence of prehistoric silk fibroin, which was found in 8,500-year-old tombs. Rough weaving tools and bone needles were also excavated, indicating the possibility that the Jiahu residents may possess the basic weaving and sewing skills in making textile. This finding may advance the study of the history of silk, and the civilization of the Neolithic Age.

  2. Label-free screening of bio-molecular interactions.

    Science.gov (United States)

    Cooper, Matthew A

    2003-11-01

    The majority of techniques currently employed to interrogate a biomolecular interaction require some type of radio- or enzymatic- or fluorescent-labelling to report the binding event. However, there is an increasing awareness of novel techniques that do not require labelling of the ligand or the receptor, and that allow virtually any complex to be screened with minimal assay development. This review focuses on three major label-free screening platforms: surface plasmon resonance biosensors, acoustic biosensors, and calorimetric biosensors. Scientists in both academia and industry are using biosensors in areas that encompass almost all areas drug discovery, diagnostics, and the life sciences. The capabilities and advantages of each technique are compared and key applications involving small molecules, proteins, oligonucleotides, bacteriophage, viruses, bacteria, and cells are reviewed. The role of the interface between the biosensor surface (in the case of SPR and acoustic biosensors) and the chemical or biological systems to be studied is also covered with attention to the covalent and non-covalent coupling chemistries commonly employed.

  3. Microscale thermophoresis quantifies biomolecular interactions under previously challenging conditions.

    Science.gov (United States)

    Seidel, Susanne A I; Dijkman, Patricia M; Lea, Wendy A; van den Bogaart, Geert; Jerabek-Willemsen, Moran; Lazic, Ana; Joseph, Jeremiah S; Srinivasan, Prakash; Baaske, Philipp; Simeonov, Anton; Katritch, Ilia; Melo, Fernando A; Ladbury, John E; Schreiber, Gideon; Watts, Anthony; Braun, Dieter; Duhr, Stefan

    2013-03-01

    Microscale thermophoresis (MST) allows for quantitative analysis of protein interactions in free solution and with low sample consumption. The technique is based on thermophoresis, the directed motion of molecules in temperature gradients. Thermophoresis is highly sensitive to all types of binding-induced changes of molecular properties, be it in size, charge, hydration shell or conformation. In an all-optical approach, an infrared laser is used for local heating, and molecule mobility in the temperature gradient is analyzed via fluorescence. In standard MST one binding partner is fluorescently labeled. However, MST can also be performed label-free by exploiting intrinsic protein UV-fluorescence. Despite the high molecular weight ratio, the interaction of small molecules and peptides with proteins is readily accessible by MST. Furthermore, MST assays are highly adaptable to fit to the diverse requirements of different biomolecules, such as membrane proteins to be stabilized in solution. The type of buffer and additives can be chosen freely. Measuring is even possible in complex bioliquids like cell lysate allowing close to in vivo conditions without sample purification. Binding modes that are quantifiable via MST include dimerization, cooperativity and competition. Thus, its flexibility in assay design qualifies MST for analysis of biomolecular interactions in complex experimental settings, which we herein demonstrate by addressing typically challenging types of binding events from various fields of life science.

  4. A fast mollified impulse method for biomolecular atomistic simulations

    Science.gov (United States)

    Fath, L.; Hochbruck, M.; Singh, C. V.

    2017-03-01

    Classical integration methods for molecular dynamics are inherently limited due to resonance phenomena occurring at certain time-step sizes. The mollified impulse method can partially avoid this problem by using appropriate filters based on averaging or projection techniques. However, existing filters are computationally expensive and tedious in implementation since they require either analytical Hessians or they need to solve nonlinear systems from constraints. In this work we follow a different approach based on corotation for the construction of a new filter for (flexible) biomolecular simulations. The main advantages of the proposed filter are its excellent stability properties and ease of implementation in standard softwares without Hessians or solving constraint systems. By simulating multiple realistic examples such as peptide, protein, ice equilibrium and ice-ice friction, the new filter is shown to speed up the computations of long-range interactions by approximately 20%. The proposed filtered integrators allow step sizes as large as 10 fs while keeping the energy drift less than 1% on a 50 ps simulation.

  5. MOVING OBJECTS TRAJECTOTY PREDICTION BASED ON ARTIFICIAL NEURAL NETWORK APPROXIMATOR BY CONSIDERING INSTANTANEOUS REACTION TIME, CASE STUDY: CAR FOLLOWING

    Directory of Open Access Journals (Sweden)

    M. Poor Arab Moghadam

    2015-12-01

    Full Text Available Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher - and its computational costs were lower - than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving

  6. Moving Objects Trajectoty Prediction Based on Artificial Neural Network Approximator by Considering Instantaneous Reaction Time, Case Study: CAR Following

    Science.gov (United States)

    Poor Arab Moghadam, M.; Pahlavani, P.

    2015-12-01

    Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher - and its computational costs were lower - than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving objects being

  7. Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors.

    Science.gov (United States)

    Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto

    2014-01-27

    The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W

  8. Noise-induced modulation of the relaxation kinetics around a non-equilibrium steady state of non-linear chemical reaction networks.

    Science.gov (United States)

    Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido

    2011-01-28

    Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confinement increases the lifetimes of all species that are involved in any non-linear reaction as a reactant. Burst monotonically increases or decreases lifetimes. Competition between burst-induced and confinement-induced modulation may hence lead to a non-monotonic modulation. We quantify lifetime as the integral of the time autocorrelation function (ACF) of concentration fluctuations around a non-equilibrium steady state of the reaction network. Furthermore, we look at the first and second derivatives of the ACF, each of which is affected in opposite ways by burst and confinement. This allows discriminating between these two noise sources. We analytically derive the ACF from the linear Fokker-Planck approximation of the chemical master equation in order to establish a baseline for the burst-induced modulation at low confinement. Effects of higher confinement are then studied using a partial-propensity stochastic simulation algorithm. The results presented here may help understand the mechanisms that deviate stochastic kinetics from its deterministic counterpart. In addition, they may be instrumental when using fluorescence-lifetime imaging microscopy (FLIM) or fluorescence-correlation spectroscopy (FCS) to measure confinement and burst in systems with known reaction rates, or, alternatively, to correct for the effects of confinement and burst when experimentally measuring reaction rates.

  9. Biomolecular detection using a metal semiconductor field effect transistor

    Science.gov (United States)

    Estephan, Elias; Saab, Marie-Belle; Buzatu, Petre; Aulombard, Roger; Cuisinier, Frédéric J. G.; Gergely, Csilla; Cloitre, Thierry

    2010-04-01

    In this work, our attention was drawn towards developing affinity-based electrical biosensors, using a MESFET (Metal Semiconductor Field Effect Transistor). Semiconductor (SC) surfaces must be prepared before the incubations with biomolecules. The peptides route was adapted to exceed and bypass the limits revealed by other types of surface modification due to the unwanted unspecific interactions. As these peptides reveal specific recognition of materials, then controlled functionalization can be achieved. Peptides were produced by phage display technology using a library of M13 bacteriophage. After several rounds of bio-panning, the phages presenting affinities for GaAs SC were isolated; the DNA of these specific phages were sequenced, and the peptide with the highest affinity was synthesized and biotinylated. To explore the possibility of electrical detection, the MESFET fabricated with the GaAs SC were used to detect the streptavidin via the biotinylated peptide in the presence of the bovine Serum Albumin. After each surface modification step, the IDS (current between the drain and the source) of the transistor was measured and a decrease in the intensity was detected. Furthermore, fluorescent microscopy was used in order to prove the specificity of this peptide and the specific localisation of biomolecules. In conclusion, the feasibility of producing an electrical biosensor using a MESFET has been demonstrated. Controlled placement, specific localization and detection of biomolecules on a MESFET transistor were achieved without covering the drain and the source. This method of functionalization and detection can be of great utility for biosensing application opening a new way for developing bioFETs (Biomolecular Field-Effect Transistor).

  10. Bases biomoleculares do fotoenvelhecimento Molecular basis of photoaging

    Directory of Open Access Journals (Sweden)

    Suelen Montagner

    2009-07-01

    Full Text Available Com o aumento da expectativa de vida, o estudo do processo de envelhecimento orgânico tem sido estimulado. O envelhecimento da pele, órgão que espelha os sinais do tempo, é processo de deterioração progressiva, tempo-dependente, e pode ser intensificado pela exposição solar, então designado fotoenvelhecimento. O dano das radiações sobre diversas estruturas celulares e cutâneas leva a alterações morfológicas nesses componentes, fruto de modificações biomoleculares. Muitas pesquisas são desenvolvidas com o intuito de combater ou minimizar os efeitos do fotoenvelhecimento, porém a principal estratégia nesse sentido continua sendo a prevenção, só conseguida pelo progressivo desvendar dos mecanismos fisiopatogênicos envolvidos nesse processo.As a result of the increase in life expectancy, the study of the organic process of aging has been stimulated. Skin ageing, which reflects the signs of time, is a time-dependent process of progressive deterioration that can be intensified by sun exposure, which is known as photoaging. The damage of radiation on various cell structures and on the skin results in molecular and morphological changes to these components. Many research studies are performed to try to minimize the effects of photoaging; however, the main strategy to manage it is still prevention, which will only be achieved once we learn about the mechanisms involved in the process.

  11. Recent applications of AC electrokinetics in biomolecular analysis on microfluidic devices.

    Science.gov (United States)

    Sasaki, Naoki

    2012-01-01

    AC electrokinetics is a generic term that refers to an induced motion of particles and fluids under nonuniform AC electric fields. The AC electric fields are formed by application of AC voltages to microelectrodes, which can be easily integrated into microfluidic devices by standard microfabrication techniques. Moreover, the magnitude of the motion is large enough to control the mass transfer on the devices. These advantages are attractive for biomolecular analysis on the microfluidic devices, in which the characteristics of small space and microfluidics have been mainly employed. In this review, I describe recent applications of AC electrokinetics in biomolecular analysis on microfluidic devices. The applications include fluid pumping and mixing by AC electrokinetic flow, and manipulation of biomolecules such as DNA and proteins by various AC electrokinetic techniques. Future prospects for highly functional biomolecular analysis on microfluidic devices with the aid of AC electrokinetics are also discussed.

  12. Inorganic nanoparticles for the spatial and temporal control of organic reactions: Applications to radical degradation of biopolymer networks

    Science.gov (United States)

    Walker, Joan Marie

    Nanoparticles of gold and iron oxide not only possess remarkable optical and magnetic properties, respectively, but are also capable of influencing their local environment with an astounding degree of precision. Using nanoparticles to direct the reactivity of organic molecules near their surface provides a unique method of spatial and temporal control. Enediynes represent an exceptional class of compounds that are thermally reactive to produce a diradical intermediate via Bergman cycloaromatization. While natural product enediynes are famously cytotoxic, a rich chemistry of synthetic enediynes has developed utilizing creative means to control this reactivity through structure, electronics, metal chelation, and external triggering mechanisms. In a heretofore unexplored arena for Bergman cyclization, we have investigated the reactivity of enediynes in connection with inorganic nanoparticles in which the physical properties of the nanomaterial are directly excited to thermally promote aromatization. As the first example of this methodology, gold nanoparticles conjugated with (Z)-octa-4-en-2,6-diyne-1,8-dithiol were excited with 514 nm laser irradiation. The formation of aromatic and polymeric products was confirmed through Raman spectroscopy and electron microscopy. Water soluble analogues Au-PEG-EDDA and Fe3O4-PEG-EDDA (EDDA = (Z)-octa-4-en-2,6-diyne-1,8-diamine) show similar reactivity under laser irradiation or alternating magnetic field excitation, respectively. Furthermore, we have used these functionalized nanoparticles to attack proteinaceous substrates including fibrin and extracellular matrix proteins, capitalizing on the ability of diradicals to disrupt peptidic bonds. By delivering a locally high payload of reactive molecules and thermal energy to the large biopolymer, network restructuring and collapse is achieved. As a synthetic extension towards multifunctional nanoparticles, noble metal seed-decorated iron oxides have also been prepared and assessed for

  13. Output-input coupling in thermally fluctuating biomolecular machines

    CERN Document Server

    Kurzynski, Michal; Chelminiak, Przemyslaw

    2011-01-01

    Biological molecular machines are proteins that operate under isothermal conditions hence are referred to as free energy transducers. They can be formally considered as enzymes that simultaneously catalyze two chemical reactions: the free energy-donating reaction and the free energy-accepting one. Most if not all biologically active proteins display a slow stochastic dynamics of transitions between a variety of conformational substates composing their native state. In the steady state, this dynamics is characterized by mean first-passage times between transition substates of the catalyzed reactions. On taking advantage of the assumption that each reaction proceeds through a single pair (the gate) of conformational transition substates of the enzyme-substrates complex, analytical formulas were derived for the flux-force dependence of the both reactions, the respective stalling forces and the degree of coupling between the free energy-accepting (output) reaction flux and the free energy-donating (input) one. Th...

  14. iBIOMES: managing and sharing biomolecular simulation data in a distributed environment.

    Science.gov (United States)

    Thibault, Julien C; Facelli, Julio C; Cheatham, Thomas E

    2013-03-25

    Biomolecular simulations, which were once batch queue or compute limited, have now become data analysis and management limited. In this paper we introduce a new management system for large biomolecular simulation and computational chemistry data sets. The system can be easily deployed on distributed servers to create a mini-grid at the researcher's site. The system not only offers a simple data deposition mechanism but also a way to register data into the system without moving the data from their original location. Any registered data set can be searched and downloaded using a set of defined metadata for molecular dynamics and quantum mechanics and visualized through a dynamic Web interface.

  15. Self-Assembly of Single-Layer CoAl-Layered Double Hydroxide Nanosheets on 3D Graphene Network Used as Highly Efficient Electrocatalyst for Oxygen Evolution Reaction.

    Science.gov (United States)

    Ping, Jianfeng; Wang, Yixian; Lu, Qipeng; Chen, Bo; Chen, Junze; Huang, Ying; Ma, Qinglang; Tan, Chaoliang; Yang, Jian; Cao, Xiehong; Wang, Zhijuan; Wu, Jian; Ying, Yibin; Zhang, Hua

    2016-09-01

    A non-noble metal based 3D porous electrocatalyst is prepared by self-assembly of the liquid-exfoliated single-layer CoAl-layered double hydroxide nanosheets (CoAl-NSs) onto 3D graphene network, which exhibits higher catalytic activity and better stability for electrochemical oxygen evolution reaction compared to the commercial IrO2 nanoparticle-based 3D porous electrocatalyst.

  16. Steric, quantum, and electrostatic effects on SN2 reaction barriers in gas phase

    OpenAIRE

    Liu, Shubin; Hu, Hao; Pedersen, Lee G.

    2010-01-01

    Biomolecular nucleophilic substitution reactions, SN2, are fundamental and commonplace in chemistry. It is the well-documented experimental finding in the literature that vicinal substitution with bulkier groups near the reaction center significantly slows the reaction due to steric hindrance, but theoretical understanding in the quantitative manner about factors dictating the SN2 reaction barrier height is still controversial. In this work, employing the new quantification approach that we r...

  17. Adaptively biased sequential importance sampling for rare events in reaction networks with comparison to exact solutions from finite buffer dCME method.

    Science.gov (United States)

    Cao, Youfang; Liang, Jie

    2013-07-14

    Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively

  18. Computer Programming and Biomolecular Structure Studies: A Step beyond Internet Bioinformatics

    Science.gov (United States)

    Likic, Vladimir A.

    2006-01-01

    This article describes the experience of teaching structural bioinformatics to third year undergraduate students in a subject titled "Biomolecular Structure and Bioinformatics." Students were introduced to computer programming and used this knowledge in a practical application as an alternative to the well established Internet bioinformatics…

  19. A random walk solution for modeling solute transport with network reactions and multi-rate mass transfer in heterogeneous systems: Impact of biofilms

    Science.gov (United States)

    Henri, Christopher V.; Fernàndez-Garcia, Daniel

    2015-12-01

    The interplay between the spatial variability of the aquifer hydraulic properties, mass transfer due to sub-grid heterogeneity and chemical reactions often complicates reactive transport simulations. It is well documented that hydro-biochemical properties are ubiquitously heterogeneous and that diffusion and slow advection at the sub-grid scale typically leads to the conceptualization of an aquifer as a multi-porosity system. Within this context, chemical reactions taking place in mobile/immobile water regions can be substantially different between each other. This paper presents a particle-based method that can efficiently simulate heterogeneity, network reactions and multi-rate mass transfer. The approach is based on the development of transition probabilities that describe the likelihood that particles belonging to a given species and mobile/immobile domain at a given time will be transformed into another species and mobile/immobile domain afterwards. The joint effect of mass transfer and sequential degradation is shown to be non-trivial. A characteristic rebound of degradation products can be observed. This late rebound of concentrations is not driven by any change in the flow regime (e.g., pumping ceases in the pump-and-treat remediation strategy) but due to the natural interplay between mass transfer and chemical reactions. To illustrate that the method can simultaneously represent mass transfer, spatially varying properties and network reactions without numerical problems, we have simulated the degradation of tetrachloroethylene (PCE) in a three-dimensional fully heterogeneous aquifer subjected to rate-limited mass transfer. Two types of degradation modes were considered to compare the effect of an active biofilm with that of clay pods present in the aquifer. Results of the two scenarios display significantly differences. Biofilms that promote the degradation of compounds in an immobile region are shown to significantly enhance degradation, rapidly producing

  20. Acorn: A grid computing system for constraint based modeling and visualization of the genome scale metabolic reaction networks via a web interface

    Directory of Open Access Journals (Sweden)

    Bushell Michael E

    2011-05-01

    Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a

  1. New product development with the innovative biomolecular sublingual immunotherapy formulations for the management of allergic rhinitis

    Directory of Open Access Journals (Sweden)

    Frati F

    2014-09-01

    Full Text Available Franco Frati,1 Lorenzo Cecchi,2,3 Enrico Scala,4 Erminia Ridolo,5 Ilaria Dell'Albani,1 Eleni Makrì,6 Giovanni Pajno,7 Cristoforo Incorvaia6 1Medical and Scientific Department, Stallergenes, Milan, Italy; 2Interdepartmental Centre of Bioclimatology, University of Florence, Florence, Italy; 3Allergy and Clinical Immunology Section, Azienda Sanitaria di Prato, Prato, Italy; 4Experimental Allergy Unit, IDI-IRCCS, Rome, Italy; 5Department of Clinical and Experimental Medicine, University of Parma, Parma, Italy; 6Allergy/Pulmonary Rehabilitation, ICP Hospital, Milan, Italy; 7Department of Pediatrics, Allergy Unit, University of Messina, Messina, Italy Abstract: The molecular allergy technique, currently defined as component-resolved diagnosis, significantly improved the diagnosis of allergy, allowing for differentiation between molecules actually responsible for clinical symptoms (genuine sensitizers and those simply cross-reacting or shared by several sources (panallergens, thus influencing the appropriate management of a patient's allergy. This also concerns allergen immunotherapy (AIT, which may be prescribed more precisely based on the component-resolved diagnosis results. However, the advance in diagnosis needs to be mirrored in AIT. According to consensus documents and to expectations of specialists, therapy should be based on standardized extracts containing measured amounts of the clinically relevant molecules, ie, the major allergens. The new generation of extracts for sublingual immunotherapy fulfills these requirements and are thus defined as biomolecular (BM. BM refers to natural extracts with a defined content of major allergens in micrograms. All Staloral BM products are indicated for the treatment of allergic rhinitis with or without asthma. The effectiveness of AIT is related to its ability to modify the immunological response of allergic subjects. The 5-grass and house dust mite extracts were evaluated addressing the T helper 1, T

  2. In situ (α-Al2O3+ZrB2)/Al composites with network distribution fabricated by reaction hot pressing

    Institute of Scientific and Technical Information of China (English)

    El Oualid Mokhnache; Gui-song Wang; Lin Geng; Kaveendran Balasubramaniam; Abdelkhalek Henniche; Noureddine Ramdani

    2015-01-01

    In situ (α-Al2O3+ZrB2)/Al composites with network distribution were fabricated using low-energy ball milling and reaction hot pressing. Differential thermal analysis (DTA) was used to study the reaction mechanisms in the Al–ZrO2–B system. X-ray diffraction (XRD) and scanning electron microscopy (SEM) in conjunction with energy-dispersive X-ray spectroscopy (EDX) were used to investigate the composite phases, morphology, and microstructure of the composites. The effect of matrix network size on the microstructure and mechani-cal properties was investigated.The results show that the optimum sintering parameters to complete reactions in the Al–ZrO2–B system are 850℃ and 60 min.In situ-synthesizedα-Al2O3 and ZrB2 particles are dispersed uniformly around Al particles, forming a network micro-structure; the diameters of theα-Al2O3 and ZrB2 particles are approximately 1–3μm. When the size of Al powder increases from 60–110μm to 150–300μm, the overall surface contact between Al powders and reactants decreases, thereby increasing the local volume fraction of re-inforcements from 12% to 21%. This increase of the local volume leads to a significant increase in microhardness of thein situ (α-Al2O3–ZrB2)/Al composites from Hv 163 to Hv 251.

  3. Reactive monolayers for surface gradients and biomolecular patterned interfaces

    NARCIS (Netherlands)

    Nicosia, C.

    2013-01-01

    Self-assembled monolayers (SAMs) are an excellent platform to implement and develop interfacial reactions for the preparation of versatile materials of pivotal importance for the fabrication of, among others, biochips, sensors, catalysts, smart surfaces and electronic devices. The development of met

  4. Exponential synchronization of generalized neural networks with mixed time-varying delays and reaction-diffusion terms via aperiodically intermittent control

    Science.gov (United States)

    Gan, Qintao

    2017-01-01

    In this paper, the exponential synchronization problem of generalized reaction-diffusion neural networks with mixed time-varying delays is investigated concerning Dirichlet boundary conditions in terms of p-norm. Under the framework of the Lyapunov stability method, stochastic theory, and mathematical analysis, some novel synchronization criteria are derived, and an aperiodically intermittent control strategy is proposed simultaneously. Moreover, the effects of diffusion coefficients, diffusion space, and stochastic perturbations on the synchronization process are explicitly expressed under the obtained conditions. Finally, some numerical simulations are performed to illustrate the feasibility of the proposed control strategy and show different synchronization dynamics under a periodically/aperiodically intermittent control.

  5. Pyrolysis reaction networks for lignin model compounds: unraveling thermal deconstruction of β-O-4 and α-O-4 compounds

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Yong S.; Singh, Rahul; Zhang, Jing; Balasubramanian, Ganesh; Sturgeon, Matthew R.; Katahira, Rui; Chupka, Gina; Beckham, Gregg T.; Shanks, Brent H.

    2016-01-01

    Although lignin is one of the main components of biomass, its pyrolysis chemistry is not well understood due to complex heterogeneity. To gain insights into this chemistry, the pyrolysis of seven lignin model compounds (five ..beta..-O-4 and two ..alpha..-O-4 linked molecules) was investigated in a micropyrolyzer connected to GC-MS/FID. According to quantitative product mole balance for the reaction networks, concerted retro-ene fragmentation and homolytic dissociation were strongly suggested as the initial reaction step for ..beta..-O-4 compounds and ..alpha..-O-4 compounds, respectively. The difference in reaction pathway between compounds with different linkages was believed to result from thermodynamics of the radical initiation. The rate constants for the different reaction pathways were predicted from ab initio density functional theory calculations and pre-exponential literature values. The computational findings were consistent with the experiment results, further supporting the different pyrolysis mechanisms for the ..beta..-ether linked and ..alpha..-ether linked compounds. A combination of the two pathways from the dimeric model compounds was able to describe qualitatively the pyrolysis of a trimeric lignin model compound containing both ..beta..-O-4 and ..alpha..-O-4 linkages.

  6. Imaging and chemical surface analysis of biomolecular functionalization of monolithically integrated on silicon Mach-Zehnder interferometric immunosensors

    Science.gov (United States)

    Gajos, Katarzyna; Angelopoulou, Michailia; Petrou, Panagiota; Awsiuk, Kamil; Kakabakos, Sotirios; Haasnoot, Willem; Bernasik, Andrzej; Rysz, Jakub; Marzec, Mateusz M.; Misiakos, Konstantinos; Raptis, Ioannis; Budkowski, Andrzej

    2016-11-01

    Time-of-flight secondary ion mass spectrometry (imaging, micro-analysis) has been employed to evaluate biofunctionalization of the sensing arm areas of Mach-Zehnder interferometers monolithically integrated on silicon chips for the immunochemical (competitive) detection of bovine κ-casein in goat milk. Biosensor surfaces are examined after: modification with (3-aminopropyl)triethoxysilane, application of multiple overlapping spots of κ-casein solutions, blocking with 100-times diluted goat milk, and reaction with monoclonal mouse anti-κ-casein antibodies in blocking solution. The areas spotted with κ-casein solutions of different concentrations are examined and optimum concentration providing homogeneous coverage is determined. Coverage of biosensor surfaces with biomolecules after each of the sequential steps employed in immunodetection is also evaluated with TOF-SIMS, supplemented by Atomic force microscopy and X-ray photoelectron spectroscopy. Uniform molecular distributions are observed on the sensing arm areas after spotting with optimum κ-casein concentration, blocking and immunoreaction. The corresponding biomolecular compositions are determined with a Principal Component Analysis that distinguished between protein amino acids and milk glycerides, as well as between amino acids characteristic for Mabs and κ-casein, respectively. Use of the optimum conditions (κ-casein concentration) for functionalization of chips with arrays of ten Mach-Zehnder interferometers provided on-chips assays with dramatically improved both intra-chip response repeatability and assay detection sensitivity.

  7. Illuminating the Reaction Pathways of Viromimetic Assembly

    Science.gov (United States)

    2017-01-01

    The coassembly of well-defined biological nanostructures relies on a delicate balance between attractive and repulsive interactions between biomolecular building blocks. Viral capsids are a prototypical example, where coat proteins exhibit not only self-interactions but also interact with the cargo they encapsulate. In nature, the balance between antagonistic and synergistic interactions has evolved to avoid kinetic trapping and polymorphism. To date, it has remained a major challenge to experimentally disentangle the complex kinetic reaction pathways that underlie successful coassembly of biomolecular building blocks in a noninvasive approach with high temporal resolution. Here we show how macromolecular force sensors, acting as a genome proxy, allow us to probe the pathways through which a viromimetic protein forms capsids. We uncover the complex multistage process of capsid assembly, which involves recruitment and complexation, followed by allosteric growth of the proteinaceous coat. Under certain conditions, the single-genome particles condense into capsids containing multiple copies of the template. Finally, we derive a theoretical model that quantitatively describes the kinetics of recruitment and growth. These results shed new light on the origins of the pathway complexity in biomolecular coassembly.

  8. Stress reduction in phase-separated, cross-linked networks: influence of phase structure and kinetics of reaction.

    Science.gov (United States)

    Szczepanski, Caroline R; Stansbury, Jeffrey W

    2014-10-05

    A mechanism for polymerization shrinkage and stress reduction was developed for heterogeneous networks formed via ambient, photo-initiated polymerization-induced phase separation (PIPS). The material system used consists of a bulk homopolymer matrix of triethylene glycol dimethacrylate (TEGDMA) modified with one of three non-reactive, linear prepolymers (poly-methyl, ethyl and butyl methacrylate). At higher prepolymer loading levels (10-20 wt%) an enhanced reduction in both shrinkage and polymerization stress is observed. The onset of gelation in these materials is delayed to a higher degree of methacrylate conversion (~15-25%), providing more time for phase structure evolution by thermodynamically driven monomer diffusion between immiscible phases prior to network macro-gelation. The resulting phase structure was probed by introducing a fluorescently tagged prepolymer into the matrix. The phase structure evolves from a dispersion of prepolymer at low loading levels to a fully co-continuous heterogeneous network at higher loadings. The bulk modulus in phase separated networks is equivalent or greater than that of poly(TEGDMA), despite a reduced polymerization rate and cross-link density in the prepolymer-rich domains.

  9. Use of pruned computational neural networks for processing the response of oscillating chemical reactions with a view to analyzing nonlinear multicomponent mixtures.

    Science.gov (United States)

    Hervás, C; Toledo, R; Silva, M

    2001-01-01

    The suitability of pruned computational neural networks (CNNs) for resolving nonlinear multicomponent systems involving synergistic effects by use of oscillating chemical reaction-based methods implemented using the analyte pulse perturbation technique is demonstrated. The CNN input data used for this purpose are estimates provided by the Levenberg-Marquardt method in the form of a three-parameter Gaussian curve associated with the singular profile obtained when the oscillating system is perturbed by an analyte mixture. The performance of the proposed method was assessed by applying it to the resolution of mixtures of pyrogallol and gallic acid based on their perturbating effect on a classical oscillating chemical system, viz. the Belousov-Zhabotinskyi reaction. A straightforward network topology (3:3:2, with 18 connections after pruning) allowed the resolution of mixtures of the two analytes in concentration ratios from 1:7 to 6:2 with a standard error of prediction for the testing set of 4.01 and 8.98% for pyrogallol and gallic acid, respectively. The reduced dimensions of the selected CNN architecture allowed a mathematical transformation of the input vector into the output one that can be easily implemented via software. Finally, the suitability of response surface analysis as an alternative to CNNs was also tested. The results were poor (relative errors were high), which confirms that properly selected pruned CNNs are effective tools for solving the analytical problem addressed in this work.

  10. Estimation of MHD boundary layer slip flow over a permeable stretching cylinder in the presence of chemical reaction through numerical and artificial neural network modeling

    Directory of Open Access Journals (Sweden)

    P. Bala Anki Reddy

    2016-09-01

    Full Text Available In this paper, the prediction of the magnetohydrodynamic boundary layer slip flow over a permeable stretched cylinder with chemical reaction is investigated by using some mathematical techniques, namely Runge–Kutta fourth order method along with shooting technique and artificial neural network (ANN. A numerical method is implemented to approximate the flow of heat and mass transfer characteristics as a function of some input parameters, explicitly the curvature parameter, magnetic parameter, permeability parameter, velocity slip, Grashof number, solutal Grashof number, Prandtl number, temperature exponent, Schmidt number, concentration exponent and chemical reaction parameter. The non-linear partial differential equations of the governing flow are converted into a system of highly non-linear ordinary differential equations by using the suitable similarity transformations, which are then solved numerically by a Runge–Kutta fourth order along with shooting technique and then ANN is applied to them. The Back Propagation Neural Network is applied for forecasting the desired outputs. The reported numerical values and the ANN values are in good agreement than those published works on various special cases. According to the findings of this study, the ANN approach is reliable, effective and easily applicable for simulating heat and mass transfer flow over a stretched cylinder.

  11. Quantitative characterization of biomolecular assemblies and interactions using atomic force microscopy.

    Science.gov (United States)

    Yang, Yong; Wang, Hong; Erie, Dorothy A

    2003-02-01

    Atomic force microscopy (AFM) has been applied in many biological investigations in the past 15 years. This review focuses on the application of AFM for quantitatively characterizing the structural and thermodynamic properties of protein-protein and protein-nucleic acid complexes. AFM can be used to determine the stoichiometries and association constants of multiprotein assemblies and to quantify changes in conformations of proteins and protein-nucleic acid complexes. In addition, AFM in solution permits the observation of the dynamic properties of biomolecular complexes and the measurement of intermolecular forces between biomolecules. Recent advances in cryogenic AFM, AFM on two-dimensional crystals, carbon nanotube probes, solution imaging, high-speed AFM, and manipulation capabilities enhance these applications by improving AFM resolution and the dynamic and operative capabilities of the AFM. These developments make AFM a powerful tool for investigating the biomolecular assemblies and interactions that govern gene regulation.

  12. PREFACE: India-Japan Workshop on Biomolecular Electronics & Organic Nanotechnology for Environment Preservation

    Science.gov (United States)

    Onoda, Mitsuyoshi; Malhotra, Bansi D.

    2012-04-01

    The 'India-Japan Workshop on Biomolecular Electronics & Organic Nanotechnology for Environment Preservation' (IJWBME 2011) will be held on 7-10 December 2011 at EGRET Himeji, Himeji, Hyogo, Japan. This workshop was held for the first time on 17-19 December 2009 at NPL, New Delhi. Keeping in mind the importance of organic nanotechnology and biomolecular electronics for environmental preservation and their anticipated impact on the economics of both the developing and the developed world, IJWBME 2009 was jointly organized by the Department of Biological Functions, Graduate School of Life Sciences and Systems Engineering, the Kyushu Institute of Technology (KIT), Kitakyushu, Japan, and the Department of Science & Technology Centre on Biomolecular Electronics (DSTCBE), National Physical Laboratory (NPL). Much progress in the field of biomolecular electronics and organic nanotechnology for environmental preservation is expected for the 21st Century. Organic optoelectronic devices, such as organic electroluminescent devices, organic thin-film transistors, organic sensors, biological systems and so on have especially attracted much attention. The main purpose of this workshop is to provide an opportunity for researchers interested in biomolecular electronics and organic nanotechnology for environmental preservation, to come together in an informal and friendly atmosphere and exchange technical knowledge and experience. We are sure that this workshop will be very useful and fruitful for all participants in summarizing the recent progress in biomolecular electronics and organic nanotechnology for environmental preservation and preparing new ground for the next generation. Many papers have been submitted from India and Japan and more than 30 papers have been accepted for presentation. The main topics of interest are as follows: Bioelectronics Biomolecular Electronics Fabrication Techniques Self-assembled Monolayers Nano-sensors Environmental Monitoring Organic Devices

  13. Specificity quantification of biomolecular recognition and its implication for drug discovery

    Science.gov (United States)

    Yan, Zhiqiang; Wang, Jin

    2012-03-01

    Highly efficient and specific biomolecular recognition requires both affinity and specificity. Previous quantitative descriptions of biomolecular recognition were mostly driven by improving the affinity prediction, but lack of quantification of specificity. We developed a novel method SPA (SPecificity and Affinity) based on our funneled energy landscape theory. The strategy is to simultaneously optimize the quantified specificity of the ``native'' protein-ligand complex discriminating against ``non-native'' binding modes and the affinity prediction. The benchmark testing of SPA shows the best performance against 16 other popular scoring functions in industry and academia on both prediction of binding affinity and ``native'' binding pose. For the target COX-2 of nonsteroidal anti-inflammatory drugs, SPA successfully discriminates the drugs from the diversity set, and the selective drugs from non-selective drugs. The remarkable performance demonstrates that SPA has significant potential applications in identifying lead compounds for drug discovery.

  14. Constructing Bio-molecular Databases on a DNA-based Computer

    CERN Document Server

    Chang, Weng-Long; Ho,; Guo, Minyi

    2007-01-01

    Codd [Codd 1970] wrote the first paper in which the model of a relational database was proposed. Adleman [Adleman 1994] wrote the first paper in which DNA strands in a test tube were used to solve an instance of the Hamiltonian path problem. From [Adleman 1994], it is obviously indicated that for storing information in molecules of DNA allows for an information density of approximately 1 bit per cubic nm (nanometer) and a dramatic improvement over existing storage media such as video tape which store information at a density of approximately 1 bit per 1012 cubic nanometers. This paper demonstrates that biological operations can be applied to construct bio-molecular databases where data records in relational tables are encoded as DNA strands. In order to achieve the goal, DNA algorithms are proposed to perform eight operations of relational algebra (calculus) on bio-molecular relational databases, which include Cartesian product, union, set difference, selection, projection, intersection, join and division. Fu...

  15. Out-of-equilibrium biomolecular interactions monitored by picosecond fluorescence in microfluidic droplets.

    Science.gov (United States)

    Maillot, Sacha; Carvalho, Alain; Vola, Jean-Pierre; Boudier, Christian; Mély, Yves; Haacke, Stefan; Léonard, Jérémie

    2014-05-21

    We developed a new experimental approach combining Time-Resolved Fluorescence (TRF) spectroscopy and Droplet Microfluidics (DμF) to investigate the relaxation dynamics of structurally heterogeneous biomolecular systems. Here DμF was used to produce with minimal material consumption an out-of-equilibrium, fluorescently labeled biomolecular complex by rapid mixing within the droplets. TRF detection was implemented with a streak camera to monitor the time evolution of the structural heterogeneity of the complex along its relaxation towards equilibrium while it propagates inside the microfluidic channel. The approach was validated by investigating the fluorescence decay kinetics of a model interacting system of bovine serum albumin and Patent Blue V. Fluorescence decay kinetics are acquired with very good signal-to-noise ratio and allow for global, multicomponent fluorescence decay analysis, evidencing heterogeneous structural relaxation over several 100 ms.

  16. Charge transport through biomolecular wires in a solvent: bridging molecular dynamics and model Hamiltonian approaches.

    Science.gov (United States)

    Gutiérrez, R; Caetano, R A; Woiczikowski, B P; Kubar, T; Elstner, M; Cuniberti, G

    2009-05-22

    We present a hybrid method based on a combination of classical molecular dynamics simulations, quantum-chemical calculations, and a model Hamiltonian approach to describe charge transport through biomolecular wires with variable lengths in presence of a solvent. The core of our approach consists in a mapping of the biomolecular electronic structure, as obtained from density-functional based tight-binding calculations of molecular structures along molecular dynamics trajectories, onto a low-dimensional model Hamiltonian including the coupling to a dissipative bosonic environment. The latter encodes fluctuation effects arising from the solvent and from the molecular conformational dynamics. We apply this approach to the case of pG-pC and pA-pT DNA oligomers as paradigmatic cases and show that the DNA conformational fluctuations are essential in determining and supporting charge transport.

  17. Conformation of bovine submaxillary mucin layers on hydrophobic surface as studied by biomolecular probes

    DEFF Research Database (Denmark)

    Pakkanen, Kirsi I.; Madsen, Jan Busk; Lee, Seunghwan

    2015-01-01

    In the present study, the conformational changes of bovine submaxillary mucin (BSM) adsorbed on a hydrophobic surface (polystyrene (PS)) as a function of concentration in bulk solution (up to 2mg/mL) have been investigated with biomolecular probe-based approaches, including bicinchoninic acid (BCA...... solution. Adsorbed masses of BSM onto hydrophobic surface, as probe by BCA, showed a continuously increasing trend up to 2mg/mL. But, the signals from EIA and ELLA, which probe the concentration of available unglycosylatedC-terminals and the central glycosylated regions, respectively, showed complicated...... non-linear responses with increasing surface concentration. The results from this study support the conventional amphiphilic, triblock model of BSM in the adsorption onto hydrophobic surface from aqueous solution.The biomolecular probe-based approaches employed in this study, however, provided further...

  18. Classification of Modern and Old Río Tinto Sedimentary Deposits Through the Biomolecular Record Using a Life Marker Biochip: Implications for Detecting Life on Mars

    Science.gov (United States)

    Parro, Victor; Fernández-Remolar, David; Rodríguez-Manfredi, José A.; Cruz-Gil, Patricia; Rivas, Luis A.; Ruiz-Bermejo, Marta; Moreno-Paz, Mercedes; García-Villadangos, Miriam; Gómez-Ortiz, David; Blanco-López, Yolanda; Menor-Salván, César; Prieto-Ballesteros, Olga; Gómez-Elvira, Javier

    2011-01-01

    The particular mineralogy formed in the acidic conditions of the Río Tinto has proven to be a first-order analogue for the acid-sulfate aqueous environments of Mars. Therefore, studies about the formation and preservation of biosignatures in the Río Tinto will provide insights into equivalent processes on Mars. We characterized the biomolecular patterns recorded in samples of modern and old fluvial sediments along a segment of the river by means of an antibody microarray containing more than 200 antibodies (LDCHIP200, for Life Detector Chip) against whole microorganisms, universal biomolecules, or environmental extracts. Samples containing 0.3-0.5g of solid material were automatically analyzed in situ by the Signs Of LIfe Detector instrument (SOLID2), and the results were corroborated by extensive analysis in the laboratory. Positive antigen-antibody reactions indicated the presence of microbial strains or high-molecular-weight biopolymers that originated from them. The LDCHIP200 results were quantified and subjected to a multivariate analysis for immunoprofiling. We associated similar immunopatterns, and biomolecular markers, to samples with similar sedimentary age. Phyllosilicate-rich samples from modern fluvial sediments gave strong positive reactions with antibodies against bacteria of the genus Acidithiobacillus and against biochemical extracts from Río Tinto sediments and biofilms. These samples contained high amounts of sugars (mostly polysaccharides) with monosaccharides like glucose, rhamnose, fucose, and so on. By contrast, the older deposits, which are a mix of clastic sands and evaporites, showed only a few positives with LDCHIP200, consistent with lower protein and sugar content. We conclude that LDCHIP200 results can establish a correlation between microenvironments, diagenetic stages, and age with the biomarker profile associated with a sample. Our results would help in the search for putative martian biomarkers in acidic deposits with similar

  19. Parity Violation in Chiral Molecules: From Theory towards Spectroscopic Experiment and the Evolution of Biomolecular Homochirality

    CERN Document Server

    CERN. Geneva

    2016-01-01

    The observation of biomolecular homochirality can be considered as a quasi-fossil of the evolution of life [1], the interpretation of which has been an open question for more than a century, with numerous related hypotheses, but no definitive answers. We shall briefly discuss the current status and the relation to the other two questions. The discovery of parity violation led to important developm...

  20. Colloid-in-Liquid Crystal Gels that Respond to Biomolecular Interactions

    OpenAIRE

    Agarwal, Ankit; Sidiq, Sumyra; Setia, Shilpa; Bukusoglu, Emre; de Pablo, Juan J.; Pal, Santanu Kumar; Abbott, Nicholas L.

    2013-01-01

    This paper advances the design of stimuli-responsive materials based on colloidal particles dispersed in liquid crystals (LCs). Specifically, we report that thin films of colloid-in-liquid crystal (CLC) gels can undergo easily visualized ordering transitions in response to reversible and irreversible (enzymatic) biomolecular interactions occurring at aqueous interfaces of the gels. In particular, we demonstrate that LC ordering transitions can propagate across the entire thickness of the gels...

  1. Assembly of single wall carbon nanotube-metal nanohybrids using biomolecular components

    Science.gov (United States)

    Kim, Sang Nyon; Slocik, Joseph M.; Naik, Rajesh R.

    2010-08-01

    Biomaterials such as nucleic acids and proteins can be exploited to create higher order structures. The biomolecular components such as DNA and peptides have been used to assemble nanoparticles with high fidelity. Here, we use DNA and peptides, and their preferential interaction with inorganic and carbon nanomaterials to form homogeneous hybrids. The enhanced binding of Pt ions to both DNA and peptide functionalized nanoparticles mediates the assembly of carbon nanotubes functionalized with DNA with peptide coated gold nanoparticles.

  2. Rational Design of Biomolecular Templates for Synthesizing Multifunctional Noble Metal Nanoclusters toward Personalized Theranostic Applications.

    Science.gov (United States)

    Yu, Yong; Mok, Beverly Y L; Loh, Xian Jun; Tan, Yen Nee

    2016-08-01

    Biomolecule-templated or biotemplated metal nanoclusters (NCs) are ultrasmall (<2 nm) metal (Au, Ag) particles stabilized by a certain type of biomolecular template (e.g., peptides, proteins, and DNA). Due to their unique physiochemical properties, biotemplated metal NCs have been widely used in sensing, imaging, delivery and therapy. The overwhelming applications in these individual areas imply the great promise of harnessing biotemplated metal NCs in more advanced biomedical aspects such as theranostics. Although applications of biotemplated metal NCs as theranostic agents are trending, the rational design of biomolecular templates suitable for the synthesis of multifunctional metal NCs for theranostics is comparatively underexplored. This progress report first identifies the essential attributes of biotemplated metal NCs for theranostics by reviewing the state-of-art applications in each of the four modalities of theranostics, namely sensing, imaging, delivery and therapy. To achieve high efficacy in these modalities, we elucidate the design principles underlying the use of biomolecules (proteins, peptides and nucleic acids) to control the NC size, emission color and surface chemistries for post-functionalization of therapeutic moieties. We then propose a unified strategy to engineer biomolecular templates that combine all these modalities to produce multifunctional biotemplated metal NCs that can serve as the next-generation personalized theranostic agents.

  3. Changes in biomolecular profile in a single nucleolus during cell fixation.

    Science.gov (United States)

    Kuzmin, Andrey N; Pliss, Artem; Prasad, Paras N

    2014-11-01

    Fixation of biological sample is an essential technique applied in order to "freeze" in time the intracellular molecular content. However, fixation induces changes of the cellular molecular structure, which mask physiological distribution of biomolecules and bias interpretation of results. Accurate, sensitive, and comprehensive characterization of changes in biomolecular composition, occurring during fixation, is crucial for proper analysis of experimental data. Here we apply biomolecular component analysis for Raman spectra measured in the same nucleoli of HeLa cells before and after fixation by either formaldehyde solution or by chilled ethanol. It is found that fixation in formaldehyde does not strongly affect the Raman spectra of nucleolar biomolecular components, but may significantly decrease the nucleolar RNA concentration. At the same time, ethanol fixation leads to a proportional increase (up to 40%) in concentrations of nucleolar proteins and RNA, most likely due to cell shrinkage occurring in the presence of coagulant fixative. Ethanol fixation also triggers changes in composition of nucleolar proteome, as indicated by an overall reduction of the α-helical structure of proteins and increase in the concentration of proteins containing the β-sheet conformation. We conclude that cross-linking fixation is a more appropriate protocol for mapping of proteins in situ. At the same time, ethanol fixation is preferential for studies of RNA-containing macromolecules. We supplemented our quantitative Raman spectroscopic measurements with mapping of the protein and lipid macromolecular groups in live and fixed cells using coherent anti-Stokes Raman scattering nonlinear optical imaging.

  4. Application of isothermal titration calorimetry and column chromatography for identification of biomolecular targets.

    Science.gov (United States)

    Zhou, Xingding; Kini, R Manjunatha; Sivaraman, J

    2011-02-01

    This protocol describes a method for identifying unknown target proteins from a mixture of biomolecules for a given drug or a lead compound. This method is based on a combination of chromatography and isothermal titration calorimetry (ITC) where ITC is used as a tracking tool. The first step involves the use of ITC to confirm the binding of ligand to a component in the biomolecular mixture. Subsequently, the biomolecular mixture is fractionated by chromatography, and the binding of the ligand with individual fractions (or subfractions) is verified by ITC. The iteration of chromatographic purification on the fractions combined with ITC results in identifying the target protein. This method is useful when the target protein or ligand is unknown and/or not amenable to labeling, chemical modification or immobilization. This protocol has been successfully used by our team and by others to identify both low-abundance and highly abundant target proteins present in biomolecular mixtures. With this protocol, it takes approximately 3-5 d to identify the target protein from a mixture.

  5. Nanogap biosensors for electrical and label-free detection of biomolecular interactions

    Energy Technology Data Exchange (ETDEWEB)

    Kyu Kim, Sang; Cho, Hyunmin; Park, Hye-Jung; Kwon, Dohyoung; Min Lee, Jeong; Hyun Chung, Bong, E-mail: chungbh@kribb.re.k [BioNanotechnology Research Center, Korea Research Institute of Bioscience and Biotechnology, PO Box 115, Yuseong, Daejeon 305-600 (Korea, Republic of)

    2009-11-11

    We demonstrate nanogap biosensors for electrical and label-free detection of biomolecular interactions. Parallel fabrication of nanometer distance gaps has been achieved using a silicon anisotropic wet etching technique on a silicon-on-insulator (SOI) wafer with a finely controllable silicon device layer. Since silicon anisotropic wet etching resulted in a trapezoid-shaped structure whose end became narrower during the etching, the nanogap structure was simply fabricated on the device layer of a SOI wafer. The nanogap devices were individually addressable and a gap size of less than 60 nm was obtained. We demonstrate that the nanogap biosensors can electrically detect biomolecular interactions such as biotin/streptavidin and antigen/antibody pairs. The nanogap devices show a current increase when the proteins are bound to the surface. The current increases proportionally depending upon the concentrations of the molecules in the range of 100 fg ml{sup -1}-100 ng ml{sup -1} at 1 V bias. It is expected that the nanogap developed here could be a highly sensitive biosensor platform for label-free detection of biomolecular interactions.

  6. An improved simple polarisable water model for use in biomolecular simulation

    Energy Technology Data Exchange (ETDEWEB)

    Bachmann, Stephan J.; Gunsteren, Wilfred F. van, E-mail: wfvgn@igc.phys.chem.ethz.ch [Laboratory of Physical Chemistry, ETH Zürich, CH-8093 Zürich (Switzerland)

    2014-12-14

    The accuracy of biomolecular simulations depends to some degree on the accuracy of the water model used to solvate the biomolecules. Because many biomolecules such as proteins are electrostatically rather inhomogeneous, containing apolar, polar, and charged moieties or side chains, a water model should be able to represent the polarisation response to a local electrostatic field, while being compatible with the force field used to model the biomolecules or protein. The two polarisable water models, COS/G2 and COS/D, that are compatible with the GROMOS biomolecular force fields leave room for improvement. The COS/G2 model has a slightly too large dielectric permittivity and the COS/D model displays a much too slow dynamics. The proposed COS/D2 model has four interaction sites: only one Lennard-Jones interaction site, the oxygen atom, and three permanent charge sites, the two hydrogens, and one massless off-atom site that also serves as charge-on-spring (COS) polarisable site with a damped or sub-linear dependence of the induced dipole on the electric field strength for large values of the latter. These properties make it a cheap and yet realistic water model for biomolecular solvation.

  7. Multiresolution persistent homology for excessively large biomolecular datasets

    Energy Technology Data Exchange (ETDEWEB)

    Xia, Kelin; Zhao, Zhixiong [Department of Mathematics, Michigan State University, East Lansing, Michigan 48824 (United States); Wei, Guo-Wei, E-mail: wei@math.msu.edu [Department of Mathematics, Michigan State University, East Lansing, Michigan 48824 (United States); Department of Electrical and Computer Engineering, Michigan State University, East Lansing, Michigan 48824 (United States); Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, Michigan 48824 (United States)

    2015-10-07

    Although persistent homology has emerged as a promising tool for the topological simplification of complex data, it is computationally intractable for large datasets. We introduce multiresolution persistent homology to handle excessively large datasets. We match the resolution with the scale of interest so as to represent large scale datasets with appropriate resolution. We utilize flexibility-rigidity index to access the topological connectivity of the data set and define a rigidity density for the filtration analysis. By appropriately tuning the resolution of the rigidity density, we are able to focus the topological lens on the scale of interest. The proposed multiresolution topological analysis is validated by a hexagonal fractal image which has three distinct scales. We further demonstrate the proposed method for extracting topological fingerprints from DNA molecules. In particular, the topological persistence of a virus capsid with 273 780 atoms is successfully analyzed which would otherwise be inaccessible to the normal point cloud method and unreliable by using coarse-grained multiscale persistent homology. The proposed method has also been successfully applied to the protein domain classification, which is the first time that persistent homology is used for practical protein domain analysis, to our knowledge. The proposed multiresolution topological method has potential applications in arbitrary data sets, such as social networks, biological networks, and graphs.

  8. Analogies between the measurement of acoustic impedance via the reaction on the source method and the automatic microwave vector network analyzer technique

    Science.gov (United States)

    McLean, James; Sutton, Robert; Post, John

    2003-10-01

    One useful method of acoustic impedance measurement involves the measurement of the electrical impedance ``looking into'' the electrical port of a reciprocal electroacoustic transducer. This reaction on the source method greatly facilitates the measurement of acoustic impedance by borrowing highly refined techniques to measure electrical impedance. It is also well suited for in situ acoustic impedance measurements. In order to accurately determine acoustic impedance from the measured electrical impedance, the characteristics of the transducer must be accurately known, i.e., the characteristics of the transducer must be ``removed'' completely from the data. The measurement of acoustic impedance via the measurement of the reaction on the source is analogous to modern microwave measurements made with an automatic vector network analyzer. The action of the analyzer is described as de-embedding the desired data (such as acoustic impedance) from the raw data. Such measurements are fundamentally substitution measurements in that the transducer's characteristics are determined by measuring a set of reference standards. The reaction on the source method is extended to take advantage of improvements in microwave measurement techniques which allow calibration via imperfect standard loads. This removes one of the principal weaknesses of the method in that the requirement of high-quality reference standards is relaxed.

  9. Towards a calculus of biomolecular complexes at equilibrium.

    Science.gov (United States)

    Mjolsness, Eric

    2007-07-01

    An overview is presented of the construction and use of algebraic partition functions to represent the equilibrium statistical mechanics of multimolecular complexes and their action within a larger regulatory network. Unlike many applications of equilibrium statistical mechanics, multimolecular complexes may operate with various subsets of their components present and connected to the others, the rest remaining in solution. Thus they are variable-structure systems. This aspect of their behavior may be accounted for by the use of 'fugacity' variables as a representation within the partition functions. Four principles are proposed by which the combinatorics of molecular complex construction can be reflected in the construction of their partition functions. The corresponding algebraic operations on partition functions are multiplication, addition, function composition and a less commonly used operation called contraction. Each has a natural interpretation in terms of probability distributions on multimolecular structures. Possible generalizations to nonequilibrium statistical mechanics are briefly discussed.

  10. Effectiveness and reaction networks of H2O2 vapor with NH3 gas for decontamination of the toxic warfare nerve agent, VX on a solid surface.

    Science.gov (United States)

    Gon Ryu, Sam; Wan Lee, Hae

    2015-01-01

    The nerve agent, O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate (VX) must be promptly eliminated following its release into the environment because it is extremely toxic, can cause death within a few minutes after exposure, acts through direct skin contact as well as inhalation, and persists in the environment for several weeks after release. A mixture of hydrogen peroxide vapor and ammonia gas was examined as a decontaminant for the removal of VX on solid surfaces at ambient temperature, and the reaction products were analyzed by gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectrometry (NMR). All the VX on glass wool filter disks was found to be eliminated after 2 h of exposure to the decontaminant mixtures, and the primary decomposition product was determined to be non-toxic ethyl methylphosphonic acid (EMPA); no toxic S-[2-(diisopropylamino)ethyl] methylphosphonothioic acid (EA-2192), which is usually produced in traditional basic hydrolysis systems, was found to be formed. However, other by-products, such as toxic O-ethyl S-vinyl methylphosphonothioate and (2-diisopropylaminoethyl) vinyl disulfide, were detected up to 150 min of exposure to the decontaminant mixture; these by-products disappeared after 3 h. The two detected vinyl byproducts were identified first in this study with the decontamination system of liquid VX on solid surfaces using a mixture of hydrogen peroxide vapor and ammonia gas. The detailed decontamination reaction networks of VX on solid surfaces produced by the mixture of hydrogen peroxide vapor and ammonia gas were suggested based on the reaction products. These findings suggest that the mixture of hydrogen peroxide vapor and ammonia gas investigated in this study is an efficient decontaminant mixture for the removal of VX on solid surfaces at ambient temperature despite the formation of a toxic by-product in the reaction process.

  11. Redox control and hydrogen bonding networks: proton-coupled electron transfer reactions and tyrosine Z in the photosynthetic oxygen-evolving complex.

    Science.gov (United States)

    Keough, James M; Zuniga, Ashley N; Jenson, David L; Barry, Bridgette A

    2013-02-07

    In photosynthetic oxygen evolution, redox active tyrosine Z (YZ) plays an essential role in proton-coupled electron transfer (PCET) reactions. Four sequential photooxidation reactions are necessary to produce oxygen at a Mn(4)CaO(5) cluster. The sequentially oxidized states of this oxygen-evolving cluster (OEC) are called the S(n) states, where n refers to the number of oxidizing equivalents stored. The neutral radical, YZ•, is generated and then acts as an electron transfer intermediate during each S state transition. In the X-ray structure, YZ, Tyr161 of the D1 subunit, is involved in an extensive hydrogen bonding network, which includes calcium-bound water. In electron paramagnetic resonance experiments, we measured the YZ• recombination rate, in the presence of an intact Mn(4)CaO(5) cluster. We compared the S(0) and S(2) states, which differ in Mn oxidation state, and found a significant difference in the YZ• decay rate (t(1/2) = 3.3 ± 0.3 s in S(0); t(1/2) = 2.1 ± 0.3 s in S(2)) and in the solvent isotope effect (SIE) on the reaction (1.3 ± 0.3 in S(0); 2.1 ± 0.3 in S(2)). Although the YZ site is known to be solvent accessible, the recombination rate and SIE were pH independent in both S states. To define the origin of these effects, we measured the YZ• recombination rate in the presence of ammonia, which inhibits oxygen evolution and disrupts the hydrogen bond network. We report that ammonia dramatically slowed the YZ• recombination rate in the S(2) state but had a smaller effect in the S(0) state. In contrast, ammonia had no significant effect on YD•, the stable tyrosyl radical. Therefore, the alterations in YZ• decay, observed with S state advancement, are attributed to alterations in OEC hydrogen bonding and consequent differences in the YZ midpoint potential/pK(a). These changes may be caused by activation of metal-bound water molecules, which hydrogen bond to YZ. These observations document the importance of redox control in proton

  12. Optical sensing systems based on biomolecular recognition of recombinant proteins

    Science.gov (United States)

    Salins, Lyndon L.; Schauer-Vukasinovic, Vesna; Daunert, Sylvia

    1998-05-01

    SIte-directed mutagenesis and the associated site-specific fluorescent labeling of proteins can be used to rationally design reagentless fluorescent molecular senors. The phosphate binding protein (PBP) and calmodulin (CaM) bind to phosphate and calcium in a highly specific manner. These ions induce a hinge motion in the proteins, and the resultant conformational change constitutes the basis of the sensor development. By labeling each protein at a specific site with environment-sensitive fluorescent probes, these conformational changes can be monitored and related to the amount of analyte ion present. In this study, the polymerase chain reaction was used to construct mutants of PBP and CaM that have a single cysteine at positions 197 and 109, respectively. Each protein was site-specifically labeled through the sulfhydryl group of the introduced cysteine residue at a single location with an environment-sensitive fluorescent probe. Characterization of the steady-state fluorescence indicated an enhancement of signal intensity upon binding of the analyte ion. Highly sensitive and selective and selective sensing systems for phosphate and calcium were obtained by using this approach.

  13. Research on the Reaction Mechanism of Network Public Opinion Related to Procuratorate%涉检网络舆情反应机制研究

    Institute of Scientific and Technical Information of China (English)

    张亚力; 余芳

    2011-01-01

    新形势下的舆论环境要求检察机关提高处置网络舆情危机的能力。只有建立涉检网络舆情反应机制,敏锐地把握、驾驭应对舆论的能力,及时、坦诚地公开检务信息,才能培养人民群众对司法权威的认同感,提高检察工作的执法公信力,化解社会矛盾、维护社会和谐稳定。%Procuratorial organs are required to improve their abilities to deal with the crisis of network public opinion under the new situation. Only by establishing the reaction mechanism of network public opinion, mastering how to handle the public opinion keenly, opening the procuratorial information timely and frankly, people's sense of identity to judicial authority can be educated, the law enforcement credibility of procuratorial work can be improved, social contradictions can be defused, social harmony and stability can be maintained.

  14. A nitrogen-doped mesoporous carbon containing an embedded network of carbon nanotubes as a highly efficient catalyst for the oxygen reduction reaction.

    Science.gov (United States)

    Li, Jin-Cheng; Zhao, Shi-Yong; Hou, Peng-Xiang; Fang, Ruo-Pian; Liu, Chang; Liang, Ji; Luan, Jian; Shan, Xu-Yi; Cheng, Hui-Ming

    2015-12-01

    A nitrogen-doped mesoporous carbon containing a network of carbon nanotubes (CNTs) was produced for use as a catalyst for the oxygen reduction reaction (ORR). SiO2 nanoparticles were decorated with clusters of Fe atoms to act as catalyst seeds for CNT growth, after which the material was impregnated with aniline. After polymerization of the aniline, the material was pyrolysed and the SiO2 was removed by acid treatment. The resulting carbon-based hybrid also contained some Fe from the CNT growth catalyst and was doped with N from the aniline. The Fe-N species act as active catalytic sites and the CNT network enables efficient electron transport in the material. Mesopores left by the removal of the SiO2 template provide short transport pathways and easy access to ions. As a result, the catalyst showed not only excellent ORR activity, with 59 mV more positive onset potential and 30 mV more positive half-wave potential than a Pt/C catalyst, but also much longer durability and stronger tolerance to methanol crossover than a Pt/C catalyst.

  15. Databases and tools for nuclear astrophysics applications BRUSsels Nuclear LIBrary (BRUSLIB), Nuclear Astrophysics Compilation of REactions II (NACRE II) and Nuclear NETwork GENerator (NETGEN)

    CERN Document Server

    Xu, Yi; Jorissen, Alain; Chen, Guangling; Arnould, Marcel; 10.1051/0004-6361/201220537

    2012-01-01

    An update of a previous description of the BRUSLIB+NACRE package of nuclear data for astrophysics and of the web-based nuclear network generator NETGEN is presented. The new version of BRUSLIB contains the latest predictions of a wide variety of nuclear data based on the most recent version of the Brussels-Montreal Skyrme-HFB model. The nuclear masses, radii, spin/parities, deformations, single-particle schemes, matter densities, nuclear level densities, E1 strength functions, fission properties, and partition functions are provided for all nuclei lying between the proton and neutron drip lines over the 8<=Z<=110 range, whose evaluation is based on a unique microscopic model that ensures a good compromise between accuracy, reliability, and feasibility. In addition, these various ingredients are used to calculate about 100000 Hauser-Feshbach n-, p-, a-, and gamma-induced reaction rates based on the reaction code TALYS. NACRE is superseded by the NACRE II compilation for 15 charged-particle transfer react...

  16. Imaging and chemical surface analysis of biomolecular functionalization of monolithically integrated on silicon Mach-Zehnder interferometric immunosensors

    Energy Technology Data Exchange (ETDEWEB)

    Gajos, Katarzyna, E-mail: kasia.fornal@uj.edu.pl [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland); Angelopoulou, Michailia; Petrou, Panagiota [Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, NCSR Demokritos, P. Grigoriou & Neapoleos St, Aghia Paraksevi 15310, Athens (Greece); Awsiuk, Kamil [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland); Kakabakos, Sotirios [Institute of Nuclear & Radiological Sciences & Technology, Energy & Safety, NCSR Demokritos, P. Grigoriou & Neapoleos St, Aghia Paraksevi 15310, Athens (Greece); Haasnoot, Willem [RIKILT Wageningen UR, Akkermaalsbos 2, 6708 WB Wageningen (Netherlands); Bernasik, Andrzej [Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków (Poland); Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków (Poland); Rysz, Jakub [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland); Marzec, Mateusz M. [Academic Centre for Materials and Nanotechnology, AGH University of Science and Technology, Mickiewicza 30, 30-059 Kraków (Poland); Misiakos, Konstantinos; Raptis, Ioannis [Department of Microelectronics, Institute of Nanoscience and Nanotechnology, NCSR Demokritos, P. Grigoriou & Neapoleos St, Aghia Paraksevi 15310, Athens (Greece); Budkowski, Andrzej [M. Smoluchowski Institute of Physics, Jagiellonian University, Łojasiewicza 11, 30-348 Kraków (Poland)

    2016-11-01

    Highlights: • Optimization of probe immobilization with robotic spotter printing overlapping spots. • In-situ inspection of microstructured surfaces of biosensors integrated on silicon. • Imaging and chemical analysis of immobilization, surface blocking and immunoreaction. • Insight with molecular discrimination into step-by-step sensor surface modifications. • Optimized biofunctionalization improves sensor sensitivity and response repeatability. - Abstract: Time-of-flight secondary ion mass spectrometry (imaging, micro-analysis) has been employed to evaluate biofunctionalization of the sensing arm areas of Mach-Zehnder interferometers monolithically integrated on silicon chips for the immunochemical (competitive) detection of bovine κ-casein in goat milk. Biosensor surfaces are examined after: modification with (3-aminopropyl)triethoxysilane, application of multiple overlapping spots of κ-casein solutions, blocking with 100-times diluted goat milk, and reaction with monoclonal mouse anti-κ-casein antibodies in blocking solution. The areas spotted with κ-casein solutions of different concentrations are examined and optimum concentration providing homogeneous coverage is determined. Coverage of biosensor surfaces with biomolecules after each of the sequential steps employed in immunodetection is also evaluated with TOF-SIMS, supplemented by Atomic force microscopy and X-ray photoelectron spectroscopy. Uniform molecular distributions are observed on the sensing arm areas after spotting with optimum κ-casein concentration, blocking and immunoreaction. The corresponding biomolecular compositions are determined with a Principal Component Analysis that distinguished between protein amino acids and milk glycerides, as well as between amino acids characteristic for Mabs and κ-casein, respectively. Use of the optimum conditions (κ-casein concentration) for functionalization of chips with arrays of ten Mach-Zehnder interferometers provided on-chips assays

  17. The universal statistical distributions of the affinity, equilibrium constants, kinetics and specificity in biomolecular recognition.

    Directory of Open Access Journals (Sweden)

    Xiliang Zheng

    2015-04-01

    Full Text Available We uncovered the universal statistical laws for the biomolecular recognition/binding process. We quantified the statistical energy landscapes for binding, from which we can characterize the distributions of the binding free energy (affinity, the equilibrium constants, the kinetics and the specificity by exploring the different ligands binding with a particular receptor. The results of the analytical studies are confirmed by the microscopic flexible docking simulations. The distribution of binding affinity is Gaussian around the mean and becomes exponential near the tail. The equilibrium constants of the binding follow a log-normal distribution around the mean and a power law distribution in the tail. The intrinsic specificity for biomolecular recognition measures the degree of discrimination of native versus non-native binding and the optimization of which becomes the maximization of the ratio of the free energy gap between the native state and the average of non-native states versus the roughness measured by the variance of the free energy landscape around its mean. The intrinsic specificity obeys a Gaussian distribution near the mean and an exponential distribution near the tail. Furthermore, the kinetics of binding follows a log-normal distribution near the mean and a power law distribution at the tail. Our study provides new insights into the statistical nature of thermodynamics, kinetics and function from different ligands binding with a specific receptor or equivalently specific ligand binding with different receptors. The elucidation of distributions of the kinetics and free energy has guiding roles in studying biomolecular recognition and function through small-molecule evolution and chemical genetics.

  18. The Universal Statistical Distributions of the Affinity, Equilibrium Constants, Kinetics and Specificity in Biomolecular Recognition

    Science.gov (United States)

    Zheng, Xiliang; Wang, Jin

    2015-01-01

    We uncovered the universal statistical laws for the biomolecular recognition/binding process. We quantified the statistical energy landscapes for binding, from which we can characterize the distributions of the binding free energy (affinity), the equilibrium constants, the kinetics and the specificity by exploring the different ligands binding with a particular receptor. The results of the analytical studies are confirmed by the microscopic flexible docking simulations. The distribution of binding affinity is Gaussian around the mean and becomes exponential near the tail. The equilibrium constants of the binding follow a log-normal distribution around the mean and a power law distribution in the tail. The intrinsic specificity for biomolecular recognition measures the degree of discrimination of native versus non-native binding and the optimization of which becomes the maximization of the ratio of the free energy gap between the native state and the average of non-native states versus the roughness measured by the variance of the free energy landscape around its mean. The intrinsic specificity obeys a Gaussian distribution near the mean and an exponential distribution near the tail. Furthermore, the kinetics of binding follows a log-normal distribution near the mean and a power law distribution at the tail. Our study provides new insights into the statistical nature of thermodynamics, kinetics and function from different ligands binding with a specific receptor or equivalently specific ligand binding with different receptors. The elucidation of distributions of the kinetics and free energy has guiding roles in studying biomolecular recognition and function through small-molecule evolution and chemical genetics. PMID:25885453

  19. Raman spectroscopy detects biomolecular changes associated with nanoencapsulated hesperetin treatment in experimental oral carcinogenesis

    Science.gov (United States)

    Gurushankar, K.; Gohulkumar, M.; Kumar, Piyush; Krishna, C. Murali; Krishnakumar, N.

    2016-03-01

    Recently it has been shown that Raman spectroscopy possesses great potential in the investigation of biomolecular changes of tumor tissues with therapeutic drug response in a non-invasive and label-free manner. The present study is designed to investigate the antitumor effect of hespertin-loaded nanoparticles (HETNPs) relative to the efficacy of native hesperetin (HET) in modifying the biomolecular changes during 7,12-dimethyl benz(a)anthracene (DMBA)-induced oral carcinogenesis using a Raman spectroscopic technique. Significant differences in the intensity and shape of the Raman spectra between the control and the experimental tissues at 1800-500 cm-1 were observed. Tumor tissues are characterized by an increase in the relative amount of proteins, nucleic acids, tryptophan and phenylalanine and a decrease in the percentage of lipids when compared to the control tissues. Further, oral administration of HET and its nanoparticulates restored the status of the lipids and significantly decreased the levels of protein and nucleic acid content. Treatment with HETNPs showed a more potent antitumor effect than treatment with native HET, which resulted in an overall reduction in the intensity of several biochemical Raman bands in DMBA-induced oral carcinogenesis being observed. Principal component and linear discriminant analysis (PC-LDA), together with leave-one-out cross validation (LOOCV) on Raman spectra yielded diagnostic sensitivities of 100%, 80%, 91.6% and 65% and specificities of 100%, 65%, 60% and 55% for classification of control versus DMBA, DMBA versus DMBA  +  HET, DMBA versus DMBA  +  HETNPs and DMBA  +  HET versus DMBA  +  HETNPs treated tissue groups, respectively. These results further demonstrate that Raman spectroscopy associated with multivariate statistical algorithms could be a valuable tool for developing a comprehensive understanding of the process of biomolecular changes, and could reveal the signatures of the

  20. Interacting with the biomolecular solvent accessible surface via a haptic feedback device

    Directory of Open Access Journals (Sweden)

    Hayward Steven

    2009-10-01

    Full Text Available Abstract Background From the 1950s computer based renderings of molecules have been produced to aid researchers in their understanding of biomolecular structure and function. A major consideration for any molecular graphics software is the ability to visualise the three dimensional structure of the molecule. Traditionally, this was accomplished via stereoscopic pairs of images and later realised with three dimensional display technologies. Using a haptic feedback device in combination with molecular graphics has the potential to enhance three dimensional visualisation. Although haptic feedback devices have been used to feel the interaction forces during molecular docking they have not been used explicitly as an aid to visualisation. Results A haptic rendering application for biomolecular visualisation has been developed that allows the user to gain three-dimensional awareness of the shape of a biomolecule. By using a water molecule as the probe, modelled as an oxygen atom having hard-sphere interactions with the biomolecule, the process of exploration has the further benefit of being able to determine regions on the molecular surface that are accessible to the solvent. This gives insight into how awkward it is for a water molecule to gain access to or escape from channels and cavities, indicating possible entropic bottlenecks. In the case of liver alcohol dehydrogenase bound to the inhibitor SAD, it was found that there is a channel just wide enough for a single water molecule to pass through. Placing the probe coincident with crystallographic water molecules suggests that they are sometimes located within small pockets that provide a sterically stable environment irrespective of hydrogen bonding considerations. Conclusion By using the software, named HaptiMol ISAS (available from http://www.haptimol.co.uk, one can explore the accessible surface of biomolecules using a three-dimensional input device to gain insights into the shape and water

  1. Recycler Reaction for the Government Behavior in Closed-Loop Supply Chain Distribution Network: Based on the System Dynamics

    Directory of Open Access Journals (Sweden)

    Xi gang Yuan

    2015-01-01

    Full Text Available With system dynamics, we establish three-closed-loop supply chain distribution network system model which consists of supplier, manufacturer, two retailers, and products (parts recycler. We proposed that recycler make reflect for the government policy by adjusting the recycling ratio and recycling delay. We use vensim software to simulate this model and investigate how the products (parts recyclers behavior influences the loop supply chain distribution system. The result shows that (1 when recyclers respond positively to government policies, recycling will increase the proportion of recyclers. When recyclers respond negatively to government policy making, recycling will reduce the proportion of recyclers. (2 When the recovery percentage of recyclers improves, manufacturers, Retailer 1, and Retailer 2 quantity fluctuations will reduce and the bullwhip effect will diminish. (3 When the proportion of recycled parts recyclers is lowered, manufacturers, Retailer 1, and Retailer 2 inventory fluctuation will increase and the bullwhip effect will be enhanced. (4 When recyclers recycling product delays increased, volatility manufacturers order quantity will rise, but there is little change in the amount of fluctuation of orders of the two retailers. (5 When recycling parts recyclers delay increases, fluctuations in the supplier order quantity will rise, but there is little change in the amount of fluctuation of orders of the two retailers.

  2. Biomolecular interactions in HCV nucleocapsid-like particles as revealed by vibrational spectroscopy

    Science.gov (United States)

    Rodríguez-Casado, Arantxa; Molina, Marina; Carmona, Pedro

    2007-05-01

    Hepatitis C virus (HCV) occurs in the form of 55-65 nm spherical particles, but the structure of the virion remains to be clarified. Structural studies of HCV have been hampered by the lack of an appropriate cell culture system. However, structural analyses of HCV components can provide an essential framework for understanding of the molecular mechanism of virion assembly. This article reviews the potential of vibrational spectroscopy aimed at the knowledge of HCV structural biology, particularly regarding biomolecular interactions in nucleocapsid-like particles obtained in vitro.

  3. In situ monitoring of biomolecular processes in living systems using surface-enhanced Raman scattering

    Science.gov (United States)

    Altunbek, Mine; Kelestemur, Seda; Culha, Mustafa

    2015-12-01

    Surface-enhanced Raman scattering (SERS) continues to strive to gather molecular level information from dynamic biological systems. It is our ongoing effort to utilize the technique for understanding of the biomolecular processes in living systems such as eukaryotic and prokaryotic cells. In this study, the technique is investigated to identify cell death mechanisms in 2D and 3D in vitro cell culture models, which is a very important process in tissue engineering and pharmaceutical applications. Second, in situ biofilm formation monitoring is investigated to understand how microorganisms respond to the environmental stimuli, which inferred information can be used to interfere with biofilm formation and fight against their pathogenic activity.

  4. Force sensors based on piezoresistive and MOSFET cantilevers for biomolecular sensing

    OpenAIRE

    Tosolini, Giordano

    2013-01-01

    Los procesos de reconocimiento biomolecular entre receptores y ligandos son muy importantes en biología. Estas biomoléculas pueden desarrollar complejos muy específicos y tener una variedad de funciones como replicación y transcripción genómica, actividad enzimática, respuesta inmune, señalamiento celular, etc. La complementariedad inequívoca mostrada por estos componentes biológicos es ampliamente utilizada para desarrollar biosensores. Dependiendo de la naturaleza de las señales que se conv...

  5. A benchmark for reaction coordinates in the transition path ensemble.

    Science.gov (United States)

    Li, Wenjin; Ma, Ao

    2016-04-01

    The molecular mechanism of a reaction is embedded in its transition path ensemble, the complete collection of reactive trajectories. Utilizing the information in the transition path ensemble alone, we developed a novel metric, which we termed the emergent potential energy, for distinguishing reaction coordinates from the bath modes. The emergent potential energy can be understood as the average energy cost for making a displacement of a coordinate in the transition path ensemble. Where displacing a bath mode invokes essentially no cost, it costs significantly to move the reaction coordinate. Based on some general assumptions of the behaviors of reaction and bath coordinates in the transition path ensemble, we proved theoretically with statistical mechanics that the emergent potential energy could serve as a benchmark of reaction coordinates and demonstrated its effectiveness by applying it to a prototypical system of biomolecular dynamics. Using the emergent potential energy as guidance, we developed a committor-free and intuition-independent method for identifying reaction coordinates in complex systems. We expect this method to be applicable to a wide range of reaction processes in complex biomolecular systems.

  6. A hydrogen-bonding network is important for oxidation and isomerization in the reaction catalyzed by cholesterol oxidase

    Energy Technology Data Exchange (ETDEWEB)

    Lyubimov, Artem Y. [Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Sinsheimer Laboratories, 1156 High Street, Santa Cruz, CA 95064 (United States); Chen, Lin; Sampson, Nicole S. [Department of Chemistry, Stony Brook University, Stony Brook, NY 11794-3400 (United States); Vrielink, Alice, E-mail: alice.vrielink@uwa.edu.au [School of Biomedical, Biomolecular and Chemical Sciences, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (Australia); Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Sinsheimer Laboratories, 1156 High Street, Santa Cruz, CA 95064 (United States)

    2009-11-01

    The importance of active-site electrostatics for oxidative and reductive half-reactions in a redox flavoenzyme (cholesterol oxidase) have been investigated by a combination of biochemistry and atomic resolution crystallography. A detailed examination of active-site dynamics demonstrates that the oxidation of substrate and the re-oxidation of the flavin cofactor by molecular oxygen are linked by a single active-site asparagine. Cholesterol oxidase is a flavoenzyme that catalyzes the oxidation and isomerization of 3β-hydroxysteroids. Structural and mutagenesis studies have shown that Asn485 plays a key role in substrate oxidation. The side chain makes an NH⋯π interaction with the reduced form of the flavin cofactor. A N485D mutant was constructed to further test the role of the amide group in catalysis. The mutation resulted in a 1800-fold drop in the overall k{sub cat}. Atomic resolution structures were determined for both the N485L and N485D mutants. The structure of the N485D mutant enzyme (at 1.0 Å resolution) reveals significant perturbations in the active site. As predicted, Asp485 is oriented away from the flavin moiety, such that any stabilizing interaction with the reduced flavin is abolished. Met122 and Glu361 form unusual hydrogen bonds to the functional group of Asp485 and are displaced from the positions they occupy in the wild-type active site. The overall effect is to disrupt the stabilization of the reduced FAD cofactor during catalysis. Furthermore, a narrow transient channel that is shown to form when the wild-type Asn485 forms the NH⋯π interaction with FAD and that has been proposed to function as an access route of molecular oxygen, is not observed in either of the mutant structures, suggesting that the dynamics of the active site are altered.

  7. Effects of Clear Kefir on Biomolecular Aspects of Glycemic Status of Type 2 Diabetes Mellitus (T2DM Patients in Bandung, West Java [Study on Human Blood Glucose, c Peptide and Insulin

    Directory of Open Access Journals (Sweden)

    Judiono J

    2014-08-01

    Full Text Available Background: Diabetes Mellitus (DM triggers an excessive reaction of free-radicals. It increases reactive oxygen species and reduces antioxidants status as well as the β cell damage. Clear kefir was used for DM therapies, however it limited biomolecular exploration of its bioactive roles. Research aimed to investigate the effects of clear kefir on the biomolecular nature of the glycemic status of T2DM in Bandung. Methods: The randomized pretest-posttest control group was conducted by 106 T2DM patients. Research was done in several hospitals in Bandung and Cimahi, West Java from 2012–2013. Samples were divided randomly into three groups: (1 T2DM with HbA1c 7 fed standard diet and supplemented 200 ml/day by clear kefir, (3 T2DM with HbA1c was fed a standard diet as a control group. Dose response was obtained from a preeliminary vivo study, and then converted to human dosage by year 2011. Intervention was effectively done for 30 days. HbA1c was measured by HPLC. Fasting blood glucose (FBG and Postprandial blood glucose levels (PBG were measured by enzymes levels. C Peptide and insulin were measured by Elisa. Data was analyzed by a statictics programme by significance p<0,05. Study was approved by ethic committee. Results : HbA1c was significantly reduced in delta level (p<0.01 and FBG (p<0.015 among kefir groups. PBG was not significantly reduced among groups. C-Peptide was significantly increased in delta level, except in control group (p<0.014. Insulin was reduced significantly, except in control group (p<0.003. Conclusions : Supplementation of clear kefir reduced blood glucose levels (HbA1c, FBG, PBG and increased c-peptide. Clear kefir’s biomolecular mechanisms and chemistry characterization is a challenge for future studies.

  8. Photochemical functionalization of gallium nitride thin films with molecular and biomolecular layers.

    Science.gov (United States)

    Kim, Heesuk; Colavita, Paula E; Metz, Kevin M; Nichols, Beth M; Sun, Bin; Uhlrich, John; Wang, Xiaoyu; Kuech, Thomas F; Hamers, Robert J

    2006-09-12

    We demonstrate that photochemical functionalization can be used to functionalize and photopattern the surface of gallium nitride crystalline thin films with well-defined molecular and biomolecular layers. GaN(0001) surfaces exposed to a hydrogen plasma will react with organic molecules bearing an alkene (C=C) group when illuminated with 254 nm light. Using a bifunctional molecule with an alkene group at one end and a protected amine group at the other, this process can be used to link the alkene group to the surface, leaving the protected amine exposed. Using a simple contact mask, we demonstrate the ability to directly pattern the spatial distribution of these protected amine groups on the surface with a lateral resolution of <12 mum. After deprotection of the amines, single-stranded DNA oligonucleotides were linked to the surface using a bifunctional cross-linker. Measurements using fluorescently labeled complementary and noncomplementary sequences show that the DNA-modified GaN surfaces exhibit excellent selectivity, while repeated cycles of hybridization and denaturation in urea show good stability. These results demonstrate that photochemical functionalization can be used as an attractive starting point for interfacing molecular and biomolecular systems with GaN and other compound semiconductors.

  9. A new approach to implement absorbing boundary condition in biomolecular electrostatics.

    Science.gov (United States)

    Goni, Md Osman

    2013-01-01

    This paper discusses a novel approach to employ the absorbing boundary condition in conjunction with the finite-element method (FEM) in biomolecular electrostatics. The introduction of Bayliss-Turkel absorbing boundary operators in electromagnetic scattering problem has been incorporated by few researchers. However, in the area of biomolecular electrostatics, this boundary condition has not been investigated yet. The objective of this paper is twofold. First, to solve nonlinear Poisson-Boltzmann equation using Newton's method and second, to find an efficient and acceptable solution with minimum number of unknowns. In this work, a Galerkin finite-element formulation is used along with a Bayliss-Turkel absorbing boundary operator that explicitly accounts for the open field problem by mapping the Sommerfeld radiation condition from the far field to near field. While the Bayliss-Turkel condition works well when the artificial boundary is far from the scatterer, an acceptable tolerance of error can be achieved with the second order operator. Numerical results on test case with simple sphere show that the treatment is able to reach the same level of accuracy achieved by the analytical method while using a lower grid density. Bayliss-Turkel absorbing boundary condition (BTABC) combined with the FEM converges to the exact solution of scattering problems to within discretization error.

  10. Biomolecular detection at ssDNA-conjugated nanoparticles by nano-impact electrochemistry.

    Science.gov (United States)

    Karimi, Anahita; Hayat, Akhtar; Andreescu, Silvana

    2017-01-15

    We describe the use of ssDNA functionalized silver nanoparticle (AgNP) probes for quantitative investigation of biorecognition and real time detection of biomolecular targets using nano-impact electrochemistry. The method is based on measurements of the individual collision events between ssDNA aptamer-functionalized AgNPs and a carbon fiber miroelectrode (CFME). Specific binding events of target analyte induced collision frequency changes enabling ultrasensitive detection of the aptamer target in a single step. These changes are assigned to the surface coverage of the NP by the ssDNA aptamers and subsequent conformational changes of the aptamer probe which affect the electron transfer between the NP and the electrode surface. The method enables sensitive and selective detection of ochratoxin A (OTA), chosen here as a model target, with a limit of detection of 0.05nM and a relative standard deviation of 4.9%. The study provides a means of characterizing bioconjugation of AgNPs with aptamers and assessing biomolecular recognition events with high sensitivity and without the use of exogenous reagents or enzyme amplification steps. This methodology can be broadly applicable to other bioconjugated systems, biosensing and related bioanalytical applications.

  11. DockScreen: A Database of In Silico Biomolecular Interactions to Support Computational Toxicology

    Directory of Open Access Journals (Sweden)

    Michael-Rock Goldsmith

    2014-01-01

    Full Text Available We have developed DockScreen, a database of in silico biomolecular interactions designed to enable rational molecular toxicological insight within a computational toxicology framework. This database is composed of chemical/target (receptor and enzyme binding scores calculated by molecular docking of more than 1000 chemicals into 150 protein targets and contains nearly 135 thousand unique ligand/target binding scores. Obtaining this dataset was achieved using eHiTS (Simbiosys Inc., a fragment-based molecular docking approach with an exhaustive search algorithm, on a heterogeneous distributed high-performance computing framework. The chemical landscape covered in DockScreen comprises selected environmental and therapeutic chemicals. The target landscape covered in DockScreen was selected based on the availability of high-quality crystal structures that covered the assay space of phase I ToxCast in vitro assays. This in silico data provides continuous information that establishes a means for quantitatively comparing, on a structural biophysical basis, a chemical’s profile of biomolecular interactions. The combined minimum-score chemical/target matrix is provided.

  12. Characterization of a nanoscale S-layer protein based template for biomolecular patterning.

    Science.gov (United States)

    Wong, Wing Sze; Yung, Pun To

    2014-01-01

    Well organized template for biomolecular conjugation is the foundation for biosensing. Most of the current devices are fabricated using lithographic patterning processes and self-assembly monolayer (SAM) methods. However, the research toward developing a sub-10 nm patterned, self-regenerated template on various types of substrates is limited, mainly due to the limited functional groups of the building material. Bacterial surface layer proteins (S-layer proteins) can self-assemble into ordered lattice with regular pore sizes of 2-8 nm on different material supports and interfaces. The ordered structure can regenerate after extreme variations of solvent conditions. In this work, we developed a nanoscale biomolecular template based on S-layer proteins on gold surface for fabrication of sensing layer in biosensors. S-layer proteins were isolated from Bacillus cereus, Lysinibacillus sphaericus and Geobacillus stearothermophilus. Protein concentrations were measured by Bradford assay. The protein purities were verified by SDS-PAGE, showing molecular weights ranging from 97-135 kDa. The hydrophilicity of the substrate surface was measured after surface treatments of protein recrystallization. Atomic force microscopic (AFM) measurement was performed on substrate surface, indicating a successful immobilization of a monolayer of S-layer protein with 8-9 nm height on gold surface. The template can be applied on various material supports and acts as a self-regenerated sensing layer of biosensors in the future.

  13. Optimizing water hyperpolarization and dissolution for sensitivity-enhanced 2D biomolecular NMR

    Science.gov (United States)

    Olsen, Greg; Markhasin, Evgeny; Szekely, Or; Bretschneider, Christian; Frydman, Lucio

    2016-03-01

    A recent study explored the use of hyperpolarized water, to enhance the sensitivity of nuclei in biomolecules thanks to rapid proton exchanges with labile amide backbone and sidechain groups. Further optimizations of this approach have now allowed us to achieve proton polarizations approaching 25% in the water transferred into the NMR spectrometer, effective water T1 times approaching 40 s, and a reduction in the dilution demanded for the cryogenic dissolution process. Further hardware developments have allowed us to perform these experiments, repeatedly and reliably, in 5 mm NMR tubes. All these ingredients - particularly the ⩾3000× 1H polarization enhancements over 11.7 T thermal counterparts, long T1 times and a compatibility with high-resolution biomolecular NMR setups - augur well for hyperpolarized 2D NMR studies of peptides, unfolded proteins and intrinsically disordered systems undergoing fast exchanges of their protons with the solvent. This hypothesis is here explored by detailing the provisions that lead to these significant improvements over previous reports, and demonstrating 1D coherence transfer experiments and 2D biomolecular HMQC acquisitions delivering NMR spectral enhancements of 100-500× over their optimized, thermally-polarized, counterparts.

  14. Time-resolved methods in biophysics. 9. Laser temperature-jump methods for investigating biomolecular dynamics.

    Science.gov (United States)

    Kubelka, Jan

    2009-04-01

    Many important biochemical processes occur on the time-scales of nanoseconds and microseconds. The introduction of the laser temperature-jump (T-jump) to biophysics more than a decade ago opened these previously inaccessible time regimes up to direct experimental observation. Since then, laser T-jump methodology has evolved into one of the most versatile and generally applicable methods for studying fast biomolecular kinetics. This perspective is a review of the principles and applications of the laser T-jump technique in biophysics. A brief overview of the T-jump relaxation kinetics and the historical development of laser T-jump methodology is presented. The physical principles and practical experimental considerations that are important for the design of the laser T-jump experiments are summarized. These include the Raman conversion for generating heating pulses, considerations of size, duration and uniformity of the temperature jump, as well as potential adverse effects due to photo-acoustic waves, cavitation and thermal lensing, and their elimination. The laser T-jump apparatus developed at the NIH Laboratory of Chemical Physics is described in detail along with a brief survey of other laser T-jump designs in use today. Finally, applications of the laser T-jump in biophysics are reviewed, with an emphasis on the broad range of problems where the laser T-jump methodology has provided important new results and insights into the dynamics of the biomolecular processes.

  15. Biomolecular Systems of Disease Buried Across Multiple GWAS Unveiled by Information Theory and Ontology

    Science.gov (United States)

    Lee, Younghee; Li, Jianrong; Gamazon, Eric; Chen, James L.; Tikhomirov, Anna; Cox, Nancy J.; Lussier, Yves A.

    2010-01-01

    A key challenge for genome-wide association studies (GWAS) is to understand how single nucleotide polymorphisms (SNPs) mechanistically underpin complex diseases. While this challenge has been addressed partially by Gene Ontology (GO) enrichment of large list of host genes of SNPs prioritized in GWAS, these enrichment have not been formally evaluated. Here, we develop a novel computational approach anchored in information theoretic similarity, by systematically mining lists of host genes of SNPs prioritized in three adult-onset diabetes mellitus GWAS. The “gold-standard” is based on GO associated with 20 published diabetes SNPs’ host genes and on our own evaluation. We computationally identify 69 similarity-predicted GO independently validated in all three GWAS (FDR<5%), enriched with those of the gold-standard (odds ratio=5.89, P=4.81e-05), and these terms can be organized by similarity criteria into 11 groupings termed “biomolecular systems”. Six biomolecular systems were corroborated by the gold-standard and the remaining five were previously uncharacterized. http://lussierlab.org/publications/ITS-GWAS PMID:21347143

  16. The HADDOCK2.2 Web Server: User-Friendly Integrative Modeling of Biomolecular Complexes.

    Science.gov (United States)

    van Zundert, G C P; Rodrigues, J P G L M; Trellet, M; Schmitz, C; Kastritis, P L; Karaca, E; Melquiond, A S J; van Dijk, M; de Vries, S J; Bonvin, A M J J

    2016-02-22

    The prediction of the quaternary structure of biomolecular macromolecules is of paramount importance for fundamental understanding of cellular processes and drug design. In the era of integrative structural biology, one way of increasing the accuracy of modeling methods used to predict the structure of biomolecular complexes is to include as much experimental or predictive information as possible in the process. This has been at the core of our information-driven docking approach HADDOCK. We present here the updated version 2.2 of the HADDOCK portal, which offers new features such as support for mixed molecule types, additional experimental restraints and improved protocols, all of this in a user-friendly interface. With well over 6000 registered users and 108,000 jobs served, an increasing fraction of which on grid resources, we hope that this timely upgrade will help the community to solve important biological questions and further advance the field. The HADDOCK2.2 Web server is freely accessible to non-profit users at http://haddock.science.uu.nl/services/HADDOCK2.2.

  17. Application of Frontal Affinity Chromatography to Study the Biomolecular Interactions with Trypsin.

    Science.gov (United States)

    Hu, YuanYuan; Qian, Junqing; Guo, Hui; Jiang, ShengLan; Zhang, Zheng

    2015-07-01

    Trypsin is a serine protease that has been proposed as a potential therapeutic target for metabolic disorders and malignancy diseases, thus the identification of biomolecular interactions of compounds to trypsin could be of great therapeutic importance. In this study, trypsin was immobilized on a monolithic silica capillary column via sol-gel. The binding properties of four small molecules (daidzin, genistin, matrine and oxymatrine) to trypsin were examined using the trypsin affinity columns by frontal analysis. The results indicate that the matrine (dissociation constant, Kd = 7.904 μM) has stronger interaction with trypsin than the oxymatrine (Kd = 8.204 μM), whereas daidzin and genistin were nearly have no affinity with trypsin. The results demonstrated that the frontal affinity chromatography can be used for the direct determination of protein-protease inhibitor binding interactions and have several significant advantages, including easy fabricating, reproducible, minimal technological requirements and potential to become a reliable alternative for quantitative studies of biomolecular interactions.

  18. Submicrometer Hall sensors for detection of magnetic nanoparticles in biomolecular sensing

    Science.gov (United States)

    Mihajlovic, Goran; Xiong, P.; von Molnar, S.; Ohtani, K.; Ohno, H.; Field, M.; Sullivan, G. J.

    2006-03-01

    Significant progress has been made in the recent years in synthesis and biomolecular functionalization of magnetic nanoparticles. These magnetic bio-nanolabels can be utilized as protein or gene markers in biomolecular sensing assays, in contrast to the much larger micron sized magnetic beads that are usually limited to cell labeling. However, the low magnetic moments of individual nanoparticles (10^4-10^5 μB) render their sensitive detection still a challenging task. In order to address this issue we are developing miniaturized Hall sensors from InAs/AlSb quantum well semiconductor heterostructures with active Hall cross areas down to 300 nm x 300 nm. Our preliminary characterization measurements performed at room temperature show functional devices with magnetic field resolution < 100 μT/√Hz at frequencies above 100 Hz, yielding a moment sensitivity ˜ 10^5 μB. In addition to the progress in improving the moment sensitivity of the submicrometer Hall detectors, we will also present efforts in device integration with on-chip microcoils for the generation of local magnetic excitation fields. Results on nanoparticle detection will also be presented.

  19. The detection of specific biomolecular interactions with micro-Hall magnetic sensors

    Science.gov (United States)

    Manandhar, Pradeep; Chen, Kan-Sheng; Aledealat, Khaled; Mihajlović, Goran; Yun, C. Steven; Field, Mark; Sullivan, Gerard J.; Strouse, Geoffrey F.; Bryant Chase, P.; von Molnár, Stephan; Xiong, Peng

    2009-09-01

    The detection of reagent-free specific biomolecular interactions through sensing of nanoscopic magnetic labels provides one of the most promising routes to biosensing with solid-state devices. In particular, Hall sensors based on semiconductor heterostructures have shown exceptional magnetic moment sensitivity over a large dynamic field range suitable for magnetic biosensing using superparamagnetic labels. Here we demonstrate the capability of such micro-Hall sensors to detect specific molecular binding using biotin-streptavidin as a model system. We apply dip-pen nanolithography to selectively biotinylate the active areas of InAs micro-Hall devices with nanoscale precision. Specific binding of complementarily functionalized streptavidin-coated superparamagnetic beads to the Hall crosses occurs via molecular recognition, and magnetic detection of the assembled beads is achieved at room temperature using phase sensitive micro-Hall magnetometry. The experiment constitutes the first unambiguous demonstration of magnetic detection of specific biomolecular interactions with semiconductor micro-Hall sensors, and the selective molecular functionalization and resulting localized bead assembly demonstrate the possibility of multiplexed sensing of multiple target molecules using a single device with an array of micro-Hall sensors.

  20. Combination of artificial neural networks with statistics for prediction of yields in organic reactions%人工神经网络与统计方法集成预测有机反应产率

    Institute of Scientific and Technical Information of China (English)

    朱京科; 苏云

    2001-01-01

    在应用人工神经网络预测有机反应产率中,由于结合了统计方法,使人工神经网络易产生的随机性和过拟合作用造成的不利影响减小,从而提高了预测可靠性。%This work presents a backpropagation neural network trained toreproduce the reaction yield of aryl fluorides by the halex technique. The work shows that a ten-dimensional input space is able to reproduce reasonably the observed reaction yields by employing statistics in artificial neural system. By means of a number of multilayer feedforward (MLF) networks rather than one, the disadvantages caused by network randomness are limited greatly, and therefore the prediction quality is improved. The combined approach is suitable for relatively small training set, which often causes overfitting and leads to unreliable prediction results.

  1. Dynamics of simple gene-network motifs subject to extrinsic fluctuations

    Science.gov (United States)

    Roberts, Elijah; Be'er, Shay; Bohrer, Chris; Sharma, Rati; Assaf, Michael

    2015-12-01

    Cellular processes do not follow deterministic rules; even in identical environments genetically identical cells can make random choices leading to different phenotypes. This randomness originates from fluctuations present in the biomolecular interaction networks. Most previous work has been focused on the intrinsic noise (IN) of these networks. Yet, especially for high-copy-number biomolecules, extrinsic or environmental noise (EN) has been experimentally shown to dominate the variation. Here, we develop an analytical formalism that allows for calculation of the effect of EN on gene-expression motifs. We introduce a method for modeling bounded EN as an auxiliary species in the master equation. The method is fully generic and is not limited to systems with small EN magnitudes. We focus our study on motifs that can be viewed as the building blocks of genetic switches: a nonregulated gene, a self-inhibiting gene, and a self-promoting gene. The role of the EN properties (magnitude, correlation time, and distribution) on the statistics of interest are systematically investigated, and the effect of fluctuations in different reaction rates is compared. Due to its analytical nature, our formalism can be used to quantify the effect of EN on the dynamics of biochemical networks and can also be used to improve the interpretation of data from single-cell gene-expression experiments.

  2. A developmental systems perspective on epistasis: computational exploration of mutational interactions in model developmental regulatory networks.

    Directory of Open Access Journals (Sweden)

    Jayson Gutiérrez

    Full Text Available The way in which the information contained in genotypes is translated into complex phenotypic traits (i.e. embryonic expression patterns depends on its decoding by a multilayered hierarchy of biomolecular systems (regulatory networks. Each layer of this hierarchy displays its own regulatory schemes (i.e. operational rules such as +/- feedback and associated control parameters, resulting in characteristic variational constraints. This process can be conceptualized as a mapping issue, and in the context of highly-dimensional genotype-phenotype mappings (GPMs epistatic events have been shown to be ubiquitous, manifested in non-linear correspondences between changes in the genotype and their phenotypic effects. In this study I concentrate on epistatic phenomena pervading levels of biological organization above the genetic material, more specifically the realm of molecular networks. At this level, systems approaches to studying GPMs are specially suitable to shed light on the mechanistic basis of epistatic phenomena. To this aim, I constructed and analyzed ensembles of highly-modular (fully interconnected networks with distinctive topologies, each displaying dynamic behaviors that were categorized as either arbitrary or functional according to early patterning processes in the Drosophila embryo. Spatio-temporal expression trajectories in virtual syncytial embryos were simulated via reaction-diffusion models. My in silico mutational experiments show that: 1 the average fitness decay tendency to successively accumulated mutations in ensembles of functional networks indicates the prevalence of positive epistasis, whereas in ensembles of arbitrary networks negative epistasis is the dominant tendency; and 2 the evaluation of epistatic coefficients of diverse interaction orders indicates that, both positive and negative epistasis are more prevalent in functional networks than in arbitrary ones. Overall, I conclude that the phenotypic and fitness effects of

  3. Databases and tools for nuclear astrophysics applications. BRUSsels Nuclear LIBrary (BRUSLIB), Nuclear Astrophysics Compilation of REactions II (NACRE II) and Nuclear NETwork GENerator (NETGEN)

    Science.gov (United States)

    Xu, Y.; Goriely, S.; Jorissen, A.; Chen, G. L.; Arnould, M.

    2013-01-01

    An update of a previous description of the BRUSLIB + NACRE package of nuclear data for astrophysics and of the web-based nuclear network generator NETGEN is presented. The new version of BRUSLIB contains the latest predictions of a wide variety of nuclear data based on the most recent version of the Brussels-Montreal Skyrme-Hartree-Fock-Bogoliubov model. The nuclear masses, radii, spin/parities, deformations, single-particle schemes, matter densities, nuclear level densities, E1 strength functions, fission properties, and partition functions are provided for all nuclei lying between the proton and neutron drip lines over the 8 ≤ Z ≤ 110 range, whose evaluation is based on a unique microscopic model that ensures a good compromise between accuracy, reliability, and feasibility. In addition, these various ingredients are used to calculate about 100 000 Hauser-Feshbach neutron-, proton-, α-, and γ-induced reaction rates based on the reaction code TALYS. NACRE is superseded by the NACRE II compilation for 15 charged-particle transfer reactions and 19 charged-particle radiative captures on stable targets with mass numbers A < 16. NACRE II features the inclusion of experimental data made available after the publication of NACRE in 1999 and up to 2011. In addition, the extrapolation of the available data to the very low energies of astrophysical relevance is improved through the systematic use of phenomenological potential models. Uncertainties in the rates are also evaluated on this basis. Finally, the latest release v10.0 of the web-based tool NETGEN is presented. In addition to the data already used in the previous NETGEN package, it contains in a fully documented form the new BRUSLIB and NACRE II data, as well as new experiment-based radiative neutron capture cross sections. The full new versions of BRUSLIB, NACRE II, and NETGEN are available electronically from the nuclear database at http://www.astro.ulb.ac.be/NuclearData. The nuclear material is presented in

  4. Assessment of nitric oxide (NO) redox reactions contribution to nitrous oxide (N2 O) formation during nitrification using a multispecies metabolic network model.

    Science.gov (United States)

    Perez-Garcia, Octavio; Chandran, Kartik; Villas-Boas, Silas G; Singhal, Naresh

    2016-05-01

    Over the coming decades nitrous oxide (N2O) is expected to become a dominant greenhouse gas and atmospheric ozone depleting substance. In wastewater treatment systems, N2O is majorly produced by nitrifying microbes through biochemical reduction of nitrite (NO2(-)) and nitric oxide (NO). However it is unknown if the amount of N2O formed is affected by alternative NO redox reactions catalyzed by oxidative nitrite oxidoreductase (NirK), cytochromes (i.e., P460 [CytP460] and 554 [Cyt554 ]) and flavohemoglobins (Hmp) in ammonia- and nitrite-oxidizing bacteria (AOB and NOB, respectively). In this study, a mathematical model is developed to assess how N2O formation is affected by such alternative nitrogen redox transformations. The developed multispecies metabolic network model captures the nitrogen respiratory pathways inferred from genomes of eight AOB and NOB species. The performance of model variants, obtained as different combinations of active NO redox reactions, was assessed against nine experimental datasets for nitrifying cultures producing N2O at different concentration of electron donor and acceptor. Model predicted metabolic fluxes show that only variants that included NO oxidation to NO2(-) by CytP460 and Hmp in AOB gave statistically similar estimates to observed production rates of N2O, NO, NO2(-) and nitrate (NO3(-)), together with fractions of AOB and NOB species in biomass. Simulations showed that NO oxidation to NO2(-) decreased N2O formation by 60% without changing culture's NO2(-) production rate. Model variants including NO reduction to N2O by Cyt554 and cNor in NOB did not improve the accuracy of experimental datasets estimates, suggesting null N2O production by NOB during nitrification. Finally, the analysis shows that in nitrifying cultures transitioning from dissolved oxygen levels above 3.8 ± 0.38 to <1.5 ± 0.8 mg/L, NOB cells can oxidize the NO produced by AOB through reactions catalyzed by oxidative NirK.

  5. Chemistry and photochemistry of 2,6-bis(2-hydroxybenzilidene)cyclohexanone. An example of a compound following the anthocyanins network of chemical reactions.

    Science.gov (United States)

    Moro, Artur J; Pana, Ana-Maria; Cseh, Liliana; Costisor, Otilia; Parola, Jorge; Cunha-Silva, L; Puttreddy, Rakesh; Rissanen, Kari; Pina, Fernando

    2014-08-14

    The kinetics and thermodynamics of the 2,6-bis(2-hydroxybenzilidene)cyclohexanone chemical reactions network was studied at different pH values using NMR, UV-vis, continuous irradiation, and flash photolysis. The chemical behavior of the system partially resembles anthocyanins and their analogue compounds. 2,6-Bis(2-hydroxybenzilidene)cyclohexanone exhibits a slow color change from yellow to red styrylflavylium under extreme acidic conditions. The rate constant for this process (5 × 10(-5) s(-1)) is pH independent and controlled by the cis-trans isomerization barrier. However, the interesting feature is the appearance of the colorless compound, 7,8-dihydro-6H-chromeno[3,2-d]xanthene, isolated from solutions of acid to neutral range, characterized by (1)H NMR and single crystal X-ray diffraction. Light absorption by 2,6-bis(2-hydroxybenzilidene)cyclohexanone solutions immediately after preparation exclusively results in cis-isomer as photoproduct, which via hemiketal formation yields (i) red styrylflavylium by dehydration under extremely acidic solutions (pH < 1) and (ii) colorless 7,8-dihydro-6H-chromeno[3,2-d]xanthene by cyclization in solutions of acid to neutral range.

  6. Investigation of the Human Disease Osteogenesis Imperfecta: A Research-Based Introduction to Concepts and Skills in Biomolecular Analysis

    Science.gov (United States)

    Mate, Karen; Sim, Alistair; Weidenhofer, Judith; Milward, Liz; Scott, Judith

    2013-01-01

    A blended approach encompassing problem-based learning (PBL) and structured inquiry was used in this laboratory exercise based on the congenital disease Osteogenesis imperfecta (OI), to introduce commonly used techniques in biomolecular analysis within a clinical context. During a series of PBL sessions students were presented with several…

  7. Electrochemical sensor for multiplex screening of genetically modified DNA: identification of biotech crops by logic-based biomolecular analysis.

    Science.gov (United States)

    Liao, Wei-Ching; Chuang, Min-Chieh; Ho, Ja-An Annie

    2013-12-15

    Genetically modified (GM) technique, one of the modern biomolecular engineering technologies, has been deemed as profitable strategy to fight against global starvation. Yet rapid and reliable analytical method is deficient to evaluate the quality and potential risk of such resulting GM products. We herein present a biomolecular analytical system constructed with distinct biochemical activities to expedite the computational detection of genetically modified organisms (GMOs). The computational mechanism provides an alternative to the complex procedures commonly involved in the screening of GMOs. Given that the bioanalytical system is capable of processing promoter, coding and species genes, affirmative interpretations succeed to identify specified GM event in terms of both electrochemical and optical fashions. The biomolecular computational assay exhibits detection capability of genetically modified DNA below sub-nanomolar level and is found interference-free by abundant coexistence of non-GM DNA. This bioanalytical system, furthermore, sophisticates in array fashion operating multiplex screening against variable GM events. Such a biomolecular computational assay and biosensor holds great promise for rapid, cost-effective, and high-fidelity screening of GMO.

  8. Biochemical and biomolecular aspects of oxidative stress due to acute and severe hypoxia in human muscle tissue.

    Science.gov (United States)

    Corbucci, G G; Sessego, R; Velluti, C; Salvi, M

    1995-01-01

    Mitochondrial oxidative stress was investigated in severe and acute hypoxia and in reperfusion applied to human muscle tissues. The biochemical and biomolecular relationship between the response of the respiratory-chain enzymic complexes and the metabolism of specific hypoxia stress proteins (HSP) suggest an adaptive mechanism which antagonizes the oxidative damage due to acute and severe tissue hypoxia.

  9. Urban networks and Arctic outlands: Craft specialists and reindeer antler in Viking towns

    DEFF Research Database (Denmark)

    Ashby, Steven P.; Coutu, Ashley N.; Sindbæk, Søren Michael

    2015-01-01

    This paper presents the results of the use of a minimally destructive biomolecular technique to explore the resource networks behind one of the first specialized urban crafts in early mediaeval northern Europe: the manufacture of composite combs of deer antler. The research incorporates the large...

  10. Resolution-Adapted All-Atomic and Coarse-Grained Model for Biomolecular Simulations.

    Science.gov (United States)

    Shen, Lin; Hu, Hao

    2014-06-10

    We develop here an adaptive multiresolution method for the simulation of complex heterogeneous systems such as the protein molecules. The target molecular system is described with the atomistic structure while maintaining concurrently a mapping to the coarse-grained models. The theoretical model, or force field, used to describe the interactions between two sites is automatically adjusted in the simulation processes according to the interaction distance/strength. Therefore, all-atomic, coarse-grained, or mixed all-atomic and coarse-grained models would be used together to describe the interactions between a group of atoms and its surroundings. Because the choice of theory is made on the force field level while the sampling is always carried out in the atomic space, the new adaptive method preserves naturally the atomic structure and thermodynamic properties of the entire system throughout the simulation processes. The new method will be very useful in many biomolecular simulations where atomistic details are critically needed.

  11. Biomolecular papain thin films grown by matrix assisted and conventional pulsed laser deposition: A comparative study

    Science.gov (United States)

    György, E.; Pérez del Pino, A.; Sauthier, G.; Figueras, A.

    2009-12-01

    Biomolecular papain thin films were grown both by matrix assisted pulsed laser evaporation (MAPLE) and conventional pulsed laser deposition (PLD) techniques with the aid of an UV KrF∗ (λ =248 nm, τFWHM≅20 ns) excimer laser source. For the MAPLE experiments the targets submitted to laser radiation consisted on frozen composites obtained by dissolving the biomaterial powder in distilled water at 10 wt % concentration. Conventional pressed biomaterial powder targets were used in the PLD experiments. The surface morphology of the obtained thin films was studied by atomic force microscopy and their structure and composition were investigated by Fourier transform infrared spectroscopy. The possible physical mechanisms implied in the ablation processes of the two techniques, under comparable experimental conditions were identified. The results showed that the growth mode, surface morphology as well as structure of the deposited biomaterial thin films are determined both by the incident laser fluence value as well as target preparation procedure.

  12. Self-chemisorption of azurin on functionalized oxide surfaces for the implementation of biomolecular devices

    Energy Technology Data Exchange (ETDEWEB)

    Biasco, A.; Maruccio, G.; Visconti, P.; Bramanti, A.; Calogiuri, P.; Cingolani, R.; Rinaldi, R

    2004-06-01

    In this work, we investigate the formation of redox protein Azurin (Az) monolayers on functionalized oxygen exposing surfaces. These metallo-proteins mediate electron transfer in the denitrifying chain of Pseudomonas bacteria and exhibit self-assembly properties, therefore they are good candidates for bio-electronic applications. Azurin monolayers are self-assembled onto silane functionalized surfaces and characterized by atomic force microscopy (AFM). We show also that a biomolecular field effect transistor (FET) in the solid state can be implemented by interconnecting an Azurin monolayer immobilized on SiO{sub 2} with two gold nanoelectrodes. Transport experiments, carried out at room temperature and ambient pressure, show FET behavior with conduction modulated by the gate potential.

  13. In situ characterization of nanoparticle biomolecular interactions in complex biological media by flow cytometry

    Science.gov (United States)

    Lo Giudice, Maria Cristina; Herda, Luciana M.; Polo, Ester; Dawson, Kenneth A.

    2016-11-01

    Nanoparticles interacting with, or derived from, living organisms are almost invariably coated in a variety of biomolecules presented in complex biological milieu, which produce a bio-interface or `biomolecular corona' conferring a biological identity to the particle. Biomolecules at the surface of the nanoparticle-biomolecule complex present molecular fragments that may be recognized by receptors of cells or biological barriers, potentially engaging with different biological pathways. Here we demonstrate that using intense fluorescent reporter binders, in this case antibodies bound to quantum dots, we can map out the availability of such recognition fragments, allowing for a rapid and meaningful biological characterization. The application in microfluidic flow, in small detection volumes, with appropriate thresholding of the detection allows the study of even complex nanoparticles in realistic biological milieu, with the emerging prospect of making direct connection to conditions of cell level and in vivo experiments.

  14. Advances in biomolecular surface meshing and its applications to mathematical modeling

    Institute of Scientific and Technical Information of China (English)

    CHEN MinXin; LU BenZhuo

    2013-01-01

    In the field of molecular modeling and simulation,molecular surface meshes are necessary for many problems,such as molecular structure visualization and analysis,docking problem and implicit solvent modeling and simulation.Recently,with the developments of advanced mathematical modeling in the field of implicit solvent modeling and simulation,providing surface meshes with good qualities efficiently for large real biomolecular systems becomes an urgent issue beyond its traditional purposes for visualization and geometry analyses for molecular structure.In this review,we summarize recent works on this issue.First,various definitions of molecular surfaces and corresponding meshing methods are introduced.Second,our recent meshing tool,TMSmesh,and its performances are presented.Finally,we show the applications of the molecular surface mesh in implicit solvent modeling and simulations using boundary element method (BEM) and finite element method (FEM).

  15. Biomolecular Electrostatics Simulation by an FMM-based BEM on 512 GPUs

    CERN Document Server

    Yokota, Rio; Bardhan, Jaydeep P; Knepley, Matthew G; Barba, L A

    2010-01-01

    We present simulations of biomolecular electrostatics at a scale not reached before, thanks to both algorithmic and hardware acceleration. The algorithmic acceleration is achieved with the fast multipole method (FMM) in conjunction with a boundary element method (BEM) formulation of the continuum electrostatic model. The hardware acceleration is achieved through graphics processors, GPUs. We demonstrate the power of our algorithms and software for the calculation of the electrostatic interactions between biological molecules in solution. Computational experiments are presented simulating the electrostatics of protein--drug binding and several multi-million atom systems consisting of hundreds to thousands of copies of the problems, which models over 20 million atoms and has more than six billion unknowns, one iteration step requires only a few minutes on 512 GPU nodes. We achieved a sustained performance of 34.6TFlops for the entire BEM calculation. We are currently adapting our solver to model the linearized ...

  16. Towards local electromechanical probing of cellular and biomolecular systems in a liquid environment

    Energy Technology Data Exchange (ETDEWEB)

    Kalinin, Sergei V [Materials Sciences and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37931 (United States); Rodriguez, Brian J [Materials Sciences and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37931 (United States); Jesse, Stephen [Materials Sciences and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN 37931 (United States); Seal, Katyayani [Center for Nanophase Materials Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37931 (United States); Proksch, Roger [Asylum Research, Santa Barbara, CA 93117 (United States); Hohlbauch, Sophia [Asylum Research, Santa Barbara, CA 93117 (United States); Revenko, Irene [Asylum Research, Santa Barbara, CA 93117 (United States); Thompson, Gary Lee [Department of Bioengineering, Clemson University, Clemson, SC 29634 (United States); Vertegel, Alexey A [Department of Bioengineering, Clemson University, Clemson, SC 29634 (United States)

    2007-10-24

    Electromechanical coupling is ubiquitous in biological systems, with examples ranging from simple piezoelectricity in calcified and connective tissues to voltage-gated ion channels, energy storage in mitochondria, and electromechanical activity in cardiac myocytes and outer hair cell stereocilia. Piezoresponse force microscopy (PFM) originally emerged as a technique to study electromechanical phenomena in ferroelectric materials, and in recent years has been employed to study a broad range of non-ferroelectric polar materials, including piezoelectric biomaterials. At the same time, the technique has been extended from ambient to liquid imaging on model ferroelectric systems. Here, we present results on local electromechanical probing of several model cellular and biomolecular systems, including insulin and lysozyme amyloid fibrils, breast adenocarcinoma cells, and bacteriorhodopsin in a liquid environment. The specific features of PFM operation in liquid are delineated and bottlenecks on the route towards nanometre-resolution electromechanical imaging of biological systems are identified.

  17. Computer programming and biomolecular structure studies: A step beyond internet bioinformatics.

    Science.gov (United States)

    Likić, Vladimir A

    2006-01-01

    This article describes the experience of teaching structural bioinformatics to third year undergraduate students in a subject titled Biomolecular Structure and Bioinformatics. Students were introduced to computer programming and used this knowledge in a practical application as an alternative to the well established Internet bioinformatics approach that relies on access to the Internet and biological databases. This was an ambitious approach considering that the students mostly had a biological background. There were also time constraints of eight lectures in total and two accompanying practical sessions. The main challenge was that students had to be introduced to computer programming from a beginner level and in a short time provided with enough knowledge to independently solve a simple bioinformatics problem. This was accomplished with a problem directly relevant to the rest of the subject, concerned with the structure-function relationships and experimental techniques for the determination of macromolecular structure.

  18. A Review of Salam Phase Transition in Protein Amino Acids Implication for Biomolecular Homochirality

    CERN Document Server

    Bai, F; Bai, Fan; Wang, Wenqing

    2002-01-01

    The origin of chirality, closely related to the evolution of life on the earth, has long been debated. In 1991, Abdus Salam suggested a novel approach to achieve biomolecular homochirality by a phase transition. In his subsequent publication, he predicted that this phase transition could eventually change D-amino acids to L-amino acids as C -H bond would break and H atom became a superconductive atom. Since many experiments denied the configuration change in amino acids, Salam hypothesis aroused suspicion. This paper is aimed to provide direct experimental evidence of a phase transition in alanine, valine single crystals but deny the configuration change of D- to L- enantiomers. New views on Salam phase transition are presented to revalidate its great importance in the origin of homochirality.

  19. Effect of temperature and magnetic field on the photocurrent response of biomolecular bulk-hetero junction

    Science.gov (United States)

    Tajima, Hiroyuki; Sekiguchi, Yusuke; Matsuda, Masaki

    2012-02-01

    The photocurrent responses were investigated for the biomolecular bulk-hetero junction of chlorophyll α (Chl-α) and 1-(3-methoxycarbonyl)-propyl-1-phenyl-1-phenyl-(6,6)C61 (PCBM) in the temperature range between 300 K and 1.5 K under the magnetic field up to 8 T. The chopped-light photocurrent decreases on lowering the temperature. Below 10 K, photocurrent decrease was observed under the applied magnetic field. Decay of the photocurrent observed at 10 K was ascribed to the formation of the charged trap under light irradiation. The magnetic field effect (MFE) observed in this device was found to be very similar to that observed in P3HT:PCBM bulk-hetero junction at low temperatures.

  20. Gold nanoshells with gain-assisted silica core for ultra-sensitive bio-molecular sensors

    Science.gov (United States)

    Tao, Yifei; Guo, Zhongyi; Zhang, Anjun; Zhang, Jingran; Wang, Benyang; Qu, Shiliang

    2015-08-01

    A novel bio-molecular nanostructured sensor composed of Au spherical nanoshell and gain-assisted silica-core has been proposed and investigated theoretically, which shows a superior performance compared to the existing structured sensor. Using quasi-static approximation calculation, it is found that the scattering efficiency and the quality factor of SPR can be enhanced greatly by introducing proper amount of gain. The simulated results demonstrate that our designed Au spherical nanoshell and gain-assisted silica-core can obtain as high as 166.7 nm/RIU for the sensitivity of refractive index, and the sensors' figure of merit is enhanced 2000 times nearly compared to that of g=0, which indicates that the designed spherical core-shell sensors have the powerful ability to detect a subtle change in the concentration of its background medium.

  1. Versatile single-molecule multi-color excitation and detection fluorescence setup for studying biomolecular dynamics

    KAUST Repository

    Sobhy, M. A.

    2011-11-07

    Single-molecule fluorescence imaging is at the forefront of tools applied to study biomolecular dynamics both in vitro and in vivo. The ability of the single-molecule fluorescence microscope to conduct simultaneous multi-color excitation and detection is a key experimental feature that is under continuous development. In this paper, we describe in detail the design and the construction of a sophisticated and versatile multi-color excitation and emission fluorescence instrument for studying biomolecular dynamics at the single-molecule level. The setup is novel, economical and compact, where two inverted microscopes share a laser combiner module with six individual laser sources that extend from 400 to 640 nm. Nonetheless, each microscope can independently and in a flexible manner select the combinations, sequences, and intensities of the excitation wavelengths. This high flexibility is achieved by the replacement of conventional mechanical shutters with acousto-optic tunable filter (AOTF). The use of AOTF provides major advancement by controlling the intensities, duration, and selection of up to eight different wavelengths with microsecond alternation time in a transparent and easy manner for the end user. To our knowledge this is the first time AOTF is applied to wide-field total internal reflection fluorescence (TIRF) microscopy even though it has been commonly used in multi-wavelength confocal microscopy. The laser outputs from the combiner module are coupled to the microscopes by two sets of four single-mode optic fibers in order to allow for the optimization of the TIRF angle for each wavelength independently. The emission is split into two or four spectral channels to allow for the simultaneous detection of up to four different fluorophores of wide selection and using many possible excitation and photoactivation schemes. We demonstrate the performance of this new setup by conducting two-color alternating excitation single-molecule fluorescence resonance energy

  2. Differential geometry-based solvation and electrolyte transport models for biomolecular modeling: a review

    Energy Technology Data Exchange (ETDEWEB)

    Wei, Guowei; Baker, Nathan A.

    2016-11-11

    This chapter reviews the differential geometry-based solvation and electrolyte transport for biomolecular solvation that have been developed over the past decade. A key component of these methods is the differential geometry of surfaces theory, as applied to the solvent-solute boundary. In these approaches, the solvent-solute boundary is determined by a variational principle that determines the major physical observables of interest, for example, biomolecular surface area, enclosed volume, electrostatic potential, ion density, electron density, etc. Recently, differential geometry theory has been used to define the surfaces that separate the microscopic (solute) domains for biomolecules from the macroscopic (solvent) domains. In these approaches, the microscopic domains are modeled with atomistic or quantum mechanical descriptions, while continuum mechanics models (including fluid mechanics, elastic mechanics, and continuum electrostatics) are applied to the macroscopic domains. This multiphysics description is integrated through an energy functional formalism and the resulting Euler-Lagrange equation is employed to derive a variety of governing partial differential equations for different solvation and transport processes; e.g., the Laplace-Beltrami equation for the solvent-solute interface, Poisson or Poisson-Boltzmann equations for electrostatic potentials, the Nernst-Planck equation for ion densities, and the Kohn-Sham equation for solute electron density. Extensive validation of these models has been carried out over hundreds of molecules, including proteins and ion channels, and the experimental data have been compared in terms of solvation energies, voltage-current curves, and density distributions. We also propose a new quantum model for electrolyte transport.

  3. Generalized semi-analytical solutions to multispecies transport equation coupled with sequential first-order reaction network with spatially or temporally variable transport and decay coefficients

    Science.gov (United States)

    Suk, Heejun

    2016-08-01

    This paper presents a semi-analytical procedure for solving coupled the multispecies reactive solute transport equations, with a sequential first-order reaction network on spatially or temporally varying flow velocities and dispersion coefficients involving distinct retardation factors. This proposed approach was developed to overcome the limitation reported by Suk (2013) regarding the identical retardation values for all reactive species, while maintaining the extensive capability of the previous Suk method involving spatially variable or temporally variable coefficients of transport, general initial conditions, and arbitrary temporal variable inlet concentration. The proposed approach sequentially calculates the concentration distributions of each species by employing only the generalized integral transform technique (GITT). Because the proposed solutions for each species' concentration distributions have separable forms in space and time, the solution for subsequent species (daughter species) can be obtained using only the GITT without the decomposition by change-of-variables method imposing the limitation of identical retardation values for all the reactive species by directly substituting solutions for the preceding species (parent species) into the transport equation of subsequent species (daughter species). The proposed solutions were compared with previously published analytical solutions or numerical solutions of the numerical code of the Two-Dimensional Subsurface Flow, Fate and Transport of Microbes and Chemicals (2DFATMIC) in three verification examples. In these examples, the proposed solutions were well matched with previous analytical solutions and the numerical solutions obtained by 2DFATMIC model. A hypothetical single-well push-pull test example and a scale-dependent dispersion example were designed to demonstrate the practical application of the proposed solution to a real field problem.

  4. Investigating rare events with nonequilibrium work measurements. II. Transition and reaction rates.

    Science.gov (United States)

    Moradi, Mahmoud; Sagui, Celeste; Roland, Christopher

    2014-01-21

    We present a formalism for investigating transition pathways and transition probabilities for rare events in biomolecular systems. The formalism is based on combining Transition Path Theory with the results of nonequilibrium work relations, and shows that the equilibrium and nonequilibrium transition rates are in fact related. Aside from its fundamental importance, this allows for the calculation of relative equilibrium reaction rates with driven nonequilibrium simulations such as Steered Molecular Dynamics. The workings of the formalism are illustrated with a few typical numerical examples.

  5. A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process%A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process

    Institute of Scientific and Technical Information of China (English)

    朱群雄; 赵乃伟; 徐圆

    2012-01-01

    Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.

  6. Tibialis anterior muscle needle biopsy and sensitive biomolecular methods: a useful tool in myotonic dystrophy type 1

    Directory of Open Access Journals (Sweden)

    S. Iachettini

    2015-10-01

    Full Text Available Myotonic dystrophy type 1 (DM1 is a neuromuscular disorder caused by a CTG repeat expansion in 3’UTR of DMPK gene. This mutation causes accumulation of toxic RNA in nuclear foci leading to splicing misregulation of specific genes. In view of future clinical trials with antisense oligonucleotides in DM1 patients, it is important to set up sensitive and minimally-invasive tools to monitor the efficacy of treatments on skeletal muscle. A tibialis anterior (TA muscle sample of about 60 mg was obtained from 5 DM1 patients and 5 healthy subjects through a needle biopsy. A fragment of about 40 mg was used for histological examination and a fragment of about 20 mg was used for biomolecular analysis. The TA fragments obtained with the minimally-invasive needle biopsy technique is enough to perform all the histopathological and biomolecular evaluations useful to monitor a clinical trial on DM1 patients.

  7. Tibialis anterior muscle needle biopsy and sensitive biomolecular methods: a useful tool in myotonic dystrophy type 1.

    Science.gov (United States)

    Iachettini, S; Valaperta, R; Marchesi, A; Perfetti, A; Cuomo, G; Fossati, B; Vaienti, L; Costa, E; Meola, G; Cardani, R

    2015-10-26

    Myotonic dystrophy type 1 (DM1) is a neuromuscular disorder caused by a CTG repeat expansion in 3'UTR of DMPK gene. This mutation causes accumulation of toxic RNA in nuclear foci leading to splicing misregulation of specific genes. In view of future clinical trials with antisense oligonucleotides in DM1 patients, it is important to set up sensitive and minimally-invasive tools to monitor the efficacy of treatments on skeletal muscle. A tibialis anterior (TA) muscle sample of about 60 mg was obtained from 5 DM1 patients and 5 healthy subjects through a needle biopsy. A fragment of about 40 mg was used for histological examination and a fragment of about 20 mg was used for biomolecular analysis. The TA fragments obtained with the minimally-invasive needle biopsy technique is enough to perform all the histopathological and biomolecular evaluations useful to monitor a clinical trial on DM1 patients.

  8. A model of protocell based on the introduction of a semi-permeable membrane in a stochastic model of catalytic reaction networks

    Directory of Open Access Journals (Sweden)

    Marco Villani

    2013-09-01

    Full Text Available In this work we introduce some preliminary analyses on the role of a semi-permeable membrane in the dynamics of a stochastic model of catalytic reaction sets (CRSs of molecules. The results of the simulations performed on ensembles of randomly generated reaction schemes highlight remarkable differences between this very simple protocell description model and the classical case of the continuous stirred-tank reactor (CSTR. In particular, in the CSTR case, distinct simulations with the same reaction scheme reach the same dynamical equilibrium, whereas, in the protocell case, simulations with identical reaction schemes can reach very different dynamical states, despite starting from the same initial conditions.

  9. Phase sensitive spectral domain interferometry for label free biomolecular interaction analysis and biosensing applications

    Science.gov (United States)

    Chirvi, Sajal

    Biomolecular interaction analysis (BIA) plays vital role in wide variety of fields, which include biomedical research, pharmaceutical industry, medical diagnostics, and biotechnology industry. Study and quantification of interactions between natural biomolecules (proteins, enzymes, DNA) and artificially synthesized molecules (drugs) is routinely done using various labeled and label-free BIA techniques. Labeled BIA (Chemiluminescence, Fluorescence, Radioactive) techniques suffer from steric hindrance of labels on interaction site, difficulty of attaching labels to molecules, higher cost and time of assay development. Label free techniques with real time detection capabilities have demonstrated advantages over traditional labeled techniques. The gold standard for label free BIA is surface Plasmon resonance (SPR) that detects and quantifies the changes in refractive index of the ligand-analyte complex molecule with high sensitivity. Although SPR is a highly sensitive BIA technique, it requires custom-made sensor chips and is not well suited for highly multiplexed BIA required in high throughput applications. Moreover implementation of SPR on various biosensing platforms is limited. In this research work spectral domain phase sensitive interferometry (SD-PSI) has been developed for label-free BIA and biosensing applications to address limitations of SPR and other label free techniques. One distinct advantage of SD-PSI compared to other label-free techniques is that it does not require use of custom fabricated biosensor substrates. Laboratory grade, off-the-shelf glass or plastic substrates of suitable thickness with proper surface functionalization are used as biosensor chips. SD-PSI is tested on four separate BIA and biosensing platforms, which include multi-well plate, flow cell, fiber probe with integrated optics and fiber tip biosensor. Sensitivity of 33 ng/ml for anti-IgG is achieved using multi-well platform. Principle of coherence multiplexing for multi

  10. Single-Molecule Pull-down FRET (SiMPull-FRET) to dissect the mechanisms of biomolecular machines

    Science.gov (United States)

    Kahlscheuer, Matthew L.; Widom, Julia; Walter, Nils G.

    2016-01-01

    Spliceosomes are multi-megadalton RNA-protein complexes responsible for the faithful removal of non-coding segments (introns) from pre-messenger RNAs (pre-mRNAs), a process critical for the maturation of eukaryotic mRNAs for subsequent translation by the ribosome. Both the spliceosome and ribosome, as well as many other RNA and DNA processing machineries, contain central RNA components that endow biomolecular complexes with precise, sequence-specific nucleic acid recognition and versatile structural dynamics. Single molecule fluorescence (or Förster) resonance energy transfer (smFRET) microscopy is a powerful tool for the study of local and global conformational changes of both simple and complex biomolecular systems involving RNA. The integration of biochemical tools such as immunoprecipitation with advanced methods in smFRET microscopy and data analysis has opened up entirely new avenues towards studying the mechanisms of biomolecular machines isolated directly from complex biological specimens such as cell extracts. Here we detail the general steps for using prism-based total internal reflection fluorescence (TIRF) microscopy in exemplary single molecule pull-down FRET (SiMPull-FRET) studies of the yeast spliceosome and discuss the broad application potential of this technique. PMID:26068753

  11. Biomolecular interactions and tools for their recognition: focus on the quartz crystal microbalance and its diverse surface chemistries and applications.

    Science.gov (United States)

    Cheng, Cathy I; Chang, Yi-Pin; Chu, Yen-Ho

    2012-03-07

    Interactions between molecules are ubiquitous and occur in our bodies, the food we eat, the air we breathe, and myriad additional contexts. Although numerous tools are available for the recognition of biomolecular interactions, such tools are often limited in their sensitivity, expensive, and difficult to modify for various uses. In contrast, the quartz crystal microbalance (QCM) has sub-nanogram detection capabilities, is label-free, is inexpensive to create, and can be readily modified with a number of diverse surface chemistries to detect and characterize diverse interactions. To maximize the versatility of the QCM, scientists need to know available methods by which QCM surfaces can be modified. Therefore, in addition to summarizing the various tools currently used for biomolecular recognition, explicating the fundamental principles of the QCM as a tool for biomolecular recognition, and comparing the QCM with other acoustic sensors, we systematically review the numerous types of surface chemistries-including hydrophobic bonds, ionic bonds, hydrogen bonds, self-assembled monolayers, plasma-polymerized films, photochemistry, and sensing ionic liquids-used to functionalize QCMs for various purposes. We also review the QCM's diverse applications, which include the detection of gaseous species, detection of carbohydrates, detection of nucleic acids, detection of non-enzymatic proteins, characterization of enzymatic activity, detection of antigens and antibodies, detection of cells, and detection of drugs. Finally, we discuss the ultimate goals of and potential barriers to the development of future QCMs.

  12. Solving the 0/1 Knapsack Problem by a Biomolecular DNA Computer

    Directory of Open Access Journals (Sweden)

    Hassan Taghipour

    2013-01-01

    Full Text Available Solving some mathematical problems such as NP-complete problems by conventional silicon-based computers is problematic and takes so long time. DNA computing is an alternative method of computing which uses DNA molecules for computing purposes. DNA computers have massive degrees of parallel processing capability. The massive parallel processing characteristic of DNA computers is of particular interest in solving NP-complete and hard combinatorial problems. NP-complete problems such as knapsack problem and other hard combinatorial problems can be easily solved by DNA computers in a very short period of time comparing to conventional silicon-based computers. Sticker-based DNA computing is one of the methods of DNA computing. In this paper, the sticker based DNA computing was used for solving the 0/1 knapsack problem. At first, a biomolecular solution space was constructed by using appropriate DNA memory complexes. Then, by the application of a sticker-based parallel algorithm using biological operations, knapsack problem was resolved in polynomial time.

  13. Indirect readout in protein-peptide recognition: a different story from classical biomolecular recognition.

    Science.gov (United States)

    Yu, Hua; Zhou, Peng; Deng, Maolin; Shang, Zhicai

    2014-07-28

    Protein-peptide interactions are prevalent and play essential roles in many living activities. Peptides recognize their protein partners by direct nonbonded interactions and indirect adjustment of conformations. Although processes of protein-peptide recognition have been comprehensively studied in both sequences and structures recently, flexibility of peptides and the configuration entropy penalty in recognition did not get enough attention. In this study, 20 protein-peptide complexes and their corresponding unbound peptides were investigated by molecular dynamics simulations. Energy analysis revealed that configurational entropy penalty introduced by restriction of the degrees of freedom of peptides in indirect readout process of protein-peptide recognition is significant. Configurational entropy penalty has become the main content of the indirect readout energy in protein-peptide recognition instead of deformation energy which is the main source of the indirect readout energy in classical biomolecular recognition phenomena, such as protein-DNA binding. These results provide us a better understanding of protein-peptide recognition and give us some implications in peptide ligand design.

  14. A starting point for fluorescence-based single-molecule measurements in biomolecular research.

    Science.gov (United States)

    Gust, Alexander; Zander, Adrian; Gietl, Andreas; Holzmeister, Phil; Schulz, Sarah; Lalkens, Birka; Tinnefeld, Philip; Grohmann, Dina

    2014-09-30

    Single-molecule fluorescence techniques are ideally suited to provide information about the structure-function-dynamics relationship of a biomolecule as static and dynamic heterogeneity can be easily detected. However, what type of single-molecule fluorescence technique is suited for which kind of biological question and what are the obstacles on the way to a successful single-molecule microscopy experiment? In this review, we provide practical insights into fluorescence-based single-molecule experiments aiming for scientists who wish to take their experiments to the single-molecule level. We especially focus on fluorescence resonance energy transfer (FRET) experiments as these are a widely employed tool for the investigation of biomolecular mechanisms. We will guide the reader through the most critical steps that determine the success and quality of diffusion-based confocal and immobilization-based total internal reflection fluorescence microscopy. We discuss the specific chemical and photophysical requirements that make fluorescent dyes suitable for single-molecule fluorescence experiments. Most importantly, we review recently emerged photoprotection systems as well as passivation and immobilization strategies that enable the observation of fluorescently labeled molecules under biocompatible conditions. Moreover, we discuss how the optical single-molecule toolkit has been extended in recent years to capture the physiological complexity of a cell making it even more relevant for biological research.

  15. Toxicity evaluation of PEDOT/biomolecular composites intended for neural communication electrodes

    Energy Technology Data Exchange (ETDEWEB)

    Asplund, M; Thaning, E; Von Holst, H [Division of Neuronic Engineering, School of Technology and Health, Royal Institute of Technology, SE-14152 Huddinge (Sweden); Lundberg, J [Section for Neuroradiology, R2:02 NKK-lab, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, Solna, SE-171 76, Stockholm (Sweden); Sandberg-Nordqvist, A C [Section of Clinical CNS Research, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, Solna, SE-171 76, Stockholm (Sweden); Kostyszyn, B [Center for Hearing and Communication Research, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, M1:01, SE-171 76 Stockholm (Sweden); Inganaes, O, E-mail: maria.asplund@sth.kth.s [Biomolecular and Organic Electronics, IFM, Linkoeping University, SE-581 83 Linkoeping (Sweden)

    2009-08-15

    Electrodes coated with the conducting polymer poly(3,4-ethylene dioxythiophene) (PEDOT) possess attractive electrochemical properties for stimulation or recording in the nervous system. Biomolecules, added as counter ions in electropolymerization, could further improve the biomaterial properties, eliminating the need for surfactant counter ions in the process. Such PEDOT/biomolecular composites, using heparin or hyaluronic acid, have previously been investigated electrochemically. In the present study, their biocompatibility is evaluated. An agarose overlay assay using L929 fibroblasts, and elution and direct contact tests on human neuroblastoma SH-SY5Y cells are applied to investigate cytotoxicity in vitro. PEDOT:heparin was further evaluated in vivo through polymer-coated implants in rodent cortex. No cytotoxic response was seen to any of the PEDOT materials tested. The examination of cortical tissue exposed to polymer-coated implants showed extensive glial scarring irrespective of implant material (Pt:polymer or Pt). However, quantification of immunological response, through distance measurements from implant site to closest neuron and counting of ED1+ cell density around implant, was comparable to those of platinum controls. These results indicate that PEDOT:heparin surfaces were non-cytotoxic and show no marked difference in immunological response in cortical tissue compared to pure platinum controls.

  16. A Quick-responsive DNA Nanotechnology Device for Bio-molecular Homeostasis Regulation.

    Science.gov (United States)

    Wu, Songlin; Wang, Pei; Xiao, Chen; Li, Zheng; Yang, Bing; Fu, Jieyang; Chen, Jing; Wan, Neng; Ma, Cong; Li, Maoteng; Yang, Xiangliang; Zhan, Yi

    2016-08-10

    Physiological processes such as metabolism, cell apoptosis and immune responses, must be strictly regulated to maintain their homeostasis and achieve their normal physiological functions. The speed with which bio-molecular homeostatic regulation occurs directly determines the ability of an organism to adapt to conditional changes. To produce a quick-responsive regulatory system that can be easily utilized for various types of homeostasis, a device called nano-fingers that facilitates the regulation of physiological processes was constructed using DNA origami nanotechnology. This nano-fingers device functioned in linked open and closed phases using two types of DNA tweezers, which were covalently coupled with aptamers that captured specific molecules when the tweezer arms were sufficiently close. Via this specific interaction mechanism, certain physiological processes could be simultaneously regulated from two directions by capturing one biofactor and releasing the other to enhance the regulatory capacity of the device. To validate the universal application of this device, regulation of the homeostasis of the blood coagulant thrombin was attempted using the nano-fingers device. It was successfully demonstrated that this nano-fingers device achieved coagulation buffering upon the input of fuel DNA. This nano-device could also be utilized to regulate the homeostasis of other types of bio-molecules.

  17. Nanoscale Biomolecular Detection Limit for Gold Nanoparticles Based on Near-Infrared Response

    Directory of Open Access Journals (Sweden)

    Mario D’Acunto

    2012-01-01

    Full Text Available Gold nanoparticles have been widely used during the past few years in various technical and biomedical applications. In particular, the resonance optical properties of nanometer-sized particles have been employed to design biochips and biosensors used as analytical tools. The optical properties of nonfunctionalized gold nanoparticles and core-gold nanoshells play a crucial role for the design of biosensors where gold surface is used as a sensing component. Gold nanoparticles exhibit excellent optical tunability at visible and near-infrared frequencies leading to sharp peaks in their spectral extinction. In this paper, we study how the optical properties of gold nanoparticles and core-gold nanoshells are changed as a function of different sizes, shapes, composition, and biomolecular coating with characteristic shifts towards the near-infrared region. We show that the optical tenability can be carefully tailored for particle sizes falling in the range 100–150 nm. The results should improve the design of sensors working at the detection limit.

  18. Biomolecular characterization of the levansucrase of Erwinia amylovora, a promising biocatalyst for the synthesis of fructooligosaccharides.

    Science.gov (United States)

    Caputi, Lorenzo; Nepogodiev, Sergey A; Malnoy, Mickael; Rejzek, Martin; Field, Robert A; Benini, Stefano

    2013-12-18

    Erwinia amylovora is a plant pathogen that affects Rosaceae, such as apple and pear. In E. amylovora the fructans, produced by the action of a levansucrase (EaLsc), play a role in virulence and biofilm formation. Fructans are bioactive compounds, displaying health-promoting properties in their own right. Their use as food and feed supplements is increasing. In this study, we investigated the biomolecular properties of EaLsc using HPAEC-PAD, MALDI-TOF MS, and spectrophotometric assays. The enzyme, which was heterologously expressed in Escherichia coli in high yield, was shown to produce mainly fructooligosaccharides (FOSs) with a degree of polymerization between 3 and 6. The kinetic properties of EaLsc were similar to those of other phylogenetically related Gram-negative bacteria, but the good yield of FOSs, the product spectrum, and the straightforward production of the enzyme suggest that EaLsc is an interesting biocatalyst for future studies aimed at producing tailor-made fructans.

  19. Co-Immobilization of Proteins and DNA Origami Nanoplates to Produce High-Contrast Biomolecular Nanoarrays.

    Science.gov (United States)

    Hager, Roland; Burns, Jonathan R; Grydlik, Martyna J; Halilovic, Alma; Haselgrübler, Thomas; Schäffler, Friedrich; Howorka, Stefan

    2016-06-01

    The biofunctionalization of nanopatterned surfaces with DNA origami nanostructures is an important topic in nanobiotechnology. An unexplored challenge is, however, to co-immobilize proteins with DNA origami at pre-determined substrate sites in high contrast relative to the nontarget areas. The immobilization should, in addition, preferably be achieved on a transparent substrate to allow ultrasensitive optical detection. If successful, specific co-binding would be a step towards stoichiometrically defined arrays with few to individual protein molecules per site. Here, we successfully immobilize with high specificity positively charged avidin proteins and negatively charged DNA origami nanoplates on 100 nm-wide carbon nanoislands while suppressing undesired adsorption to surrounding nontarget areas. The arrays on glass slides achieve unprecedented selectivity factors of up to 4000 and allow ultrasensitive fluorescence read-out. The co-immobilization onto the nanoislands leads to layered biomolecular architectures, which are functional because bound DNA origami influences the number of capturing sites on the nanopatches for other proteins. The novel hybrid DNA origami-protein nanoarrays allow the fabrication of versatile research platforms for applications in biosensing, biophysics, and cell biology, and, in addition, represent an important step towards single-molecule protein arrays.

  20. Potential-of-mean-force description of ionic interactions and structural hydration in biomolecular systems

    Energy Technology Data Exchange (ETDEWEB)

    Hummer, G.; Garcia, A.E. [Los Alamos National Lab., NM (United States). Theoretical Biology and Biophysics Group; Soumpasis, D.M. [Max-Planck-Inst for Biophysical Chemistry, Goettingen (Germany). Biocomputation Group

    1994-10-01

    To understand the functioning of living organisms on a molecular level, it is crucial to dissect the intricate interplay of the immense number of biological molecules. Most of the biochemical processes in cells occur in a liquid environment formed mainly by water and ions. This solvent environment plays an important role in biological systems. The potential-of-mean-force (PMF) formalism attempts to describe quantitatively the interactions of the solvent with biological macromolecules on the basis of an approximate statistical-mechanical representation. At its current status of development, it deals with ionic effects on the biomolecular structure and with the structural hydration of biomolecules. The underlying idea of the PMF formalism is to identify the dominant sources of interactions and incorporate these interactions into the theoretical formalism using PMF`s (or particle correlation functions) extracted from bulk-liquid systems. In the following, the authors shall briefly outline the statistical-mechanical foundation of the PMF formalism and introduce the PMF expansion formalism, which is intimately linked to superposition approximations for higher-order particle correlation functions. The authors shall then sketch applications, which describe the effects of the ionic environment on nucleic-acid structure. Finally, the authors shall present the more recent extension of the PMF idea to describe quantitatively the structural hydration of biomolecules. Results for the interface of ice and water and for the hydration of deoxyribonucleic acid (DNA) will be discussed.

  1. Bridge- and Solvent-Mediated Intramolecular Electronic Communications in Ubiquinone-Based Biomolecular Wires

    Science.gov (United States)

    Liu, Xiao-Yuan; Ma, Wei; Zhou, Hao; Cao, Xiao-Ming; Long, Yi-Tao

    2015-05-01

    Intramolecular electronic communications of molecular wires play a crucial role for developing molecular devices. In the present work, we describe different degrees of intramolecular electronic communications in the redox processes of three ubiquinone-based biomolecular wires (Bis-CoQ0s) evaluated by electrochemistry and Density Functional Theory (DFT) methods in different solvents. We found that the bridges linkers have a significant effect on the electronic communications between the two peripheral ubiquinone moieties and solvents effects are limited and mostly depend on the nature of solvents. The DFT calculations for the first time indicate the intensity of the electronic communications during the redox processes rely on the molecular orbital elements VL for electron transfer (half of the energy splitting of the LUMO and LUMO+1), which is could be affected by the bridges linkers. The DFT calculations also demonstrates the effect of solvents on the latter two-electron transfer of Bis-CoQ0s is more significant than the former two electrons transfer as the observed electrochemical behaviors of three Bis-CoQ0s. In addition, the electrochemistry and theoretical calculations reveal the intramolecular electronic communications vary in the four-electron redox processes of three Bis-CoQ0s.

  2. Amplified Immunoassay of Human IgG Using Real-time Biomolecular Interaction Analysis (BIA) Technology

    Institute of Scientific and Technical Information of China (English)

    PEI,Ren-Jun(裴仁军); CUI,Xiao-Qiang(崔小强); YANG,Xiu-Rong(杨秀荣); WANG,Er-Kang(汪尔康)

    2002-01-01

    An automated biomolecular interaction analysis instrument (BIAcore) based on surface plasmon resonance (SPR) has been used to determine human immunoglobulin G (IgG) in real time. Polyclonal anti-human IgG antibody was covalently immobilized to a carboxymethyldextran-modified gold film surface. The samples of human IgG prepared in HBS buffer were poured over the immobilized surface. The signal amplification antibody was applied to amplify the response signal. After each measurement, the surface was regenerated with 0.1 mol/L H3PO4. The assay was rapid, requiring only 30 min for antibody immobilization and 20 min for each subsequent process of immune binding, antibody amplification and regeneration. The antibody immobilized surface had good response to human IgG in the range of 0.12-60 nmol/L with a detection limit of 60 pmoL/L. The same antibody immobilized surface could be used for more than 110 cycles of binding, amplificafion and regeneration. The results demonstrate that the sensitivity, specificity and reproducibility of amplified immunoassay using real-time BIA technology are satisfactory.

  3. A method for rapid quantitative assessment of biofilms with biomolecular staining and image analysis.

    Science.gov (United States)

    Larimer, Curtis; Winder, Eric; Jeters, Robert; Prowant, Matthew; Nettleship, Ian; Addleman, Raymond Shane; Bonheyo, George T

    2016-01-01

    The accumulation of bacteria in surface-attached biofilms can be detrimental to human health, dental hygiene, and many industrial processes. Natural biofilms are soft and often transparent, and they have heterogeneous biological composition and structure over micro- and macroscales. As a result, it is challenging to quantify the spatial distribution and overall intensity of biofilms. In this work, a new method was developed to enhance the visibility and quantification of bacterial biofilms. First, broad-spectrum biomolecular staining was used to enhance the visibility of the cells, nucleic acids, and proteins that make up biofilms. Then, an image analysis algorithm was developed to objectively and quantitatively measure biofilm accumulation from digital photographs and results were compared to independent measurements of cell density. This new method was used to quantify the growth intensity of Pseudomonas putida biofilms as they grew over time. This method is simple and fast, and can quantify biofilm growth over a large area with approximately the same precision as the more laborious cell counting method. Stained and processed images facilitate assessment of spatial heterogeneity of a biofilm across a surface. This new approach to biofilm analysis could be applied in studies of natural, industrial, and environmental biofilms.

  4. Review of Transducer Principles for Label-Free Biomolecular Interaction Analysis

    Directory of Open Access Journals (Sweden)

    Janos Vörös

    2011-07-01

    Full Text Available Label-free biomolecular interaction analysis is an important technique to study the chemical binding between e.g., protein and protein or protein and small molecule in real-time. The parameters obtained with this technique, such as the affinity, are important for drug development. While the surface plasmon resonance (SPR instruments are most widely used, new types of sensors are emerging. These developments are generally driven by the need for higher throughput, lower sample consumption or by the need of complimentary information to the SPR data. This review aims to give an overview about a wide range of sensor transducers, the working principles and the peculiarities of each technology, e.g., concerning the set-up, sensitivity, sensor size or required sample volume. Starting from optical technologies like the SPR and waveguide based sensors, acoustic sensors like the quartz crystal microbalance (QCM and the film bulk acoustic resonator (FBAR, calorimetric and electrochemical sensors are covered. Technologies long established in the market are presented together with those newly commercially available and with technologies in the early development stage. Finally, the commercially available instruments are summarized together with their sensitivity and the number of sensors usable in parallel and an outlook for potential future developments is given.

  5. Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components.

    Science.gov (United States)

    Pardee, Keith; Green, Alexander A; Takahashi, Melissa K; Braff, Dana; Lambert, Guillaume; Lee, Jeong Wook; Ferrante, Tom; Ma, Duo; Donghia, Nina; Fan, Melina; Daringer, Nichole M; Bosch, Irene; Dudley, Dawn M; O'Connor, David H; Gehrke, Lee; Collins, James J

    2016-05-19

    The recent Zika virus outbreak highlights the need for low-cost diagnostics that can be rapidly developed for distribution and use in pandemic regions. Here, we report a pipeline for the rapid design, assembly, and validation of cell-free, paper-based sensors for the detection of the Zika virus RNA genome. By linking isothermal RNA amplification to toehold switch RNA sensors, we detect clinically relevant concentrations of Zika virus sequences and demonstrate specificity against closely related Dengue virus sequences. When coupled with a novel CRISPR/Cas9-based module, our sensors can discriminate between viral strains with single-base resolution. We successfully demonstrate a simple, field-ready sample-processing workflow and detect Zika virus from the plasma of a viremic macaque. Our freeze-dried biomolecular platform resolves important practical limitations to the deployment of molecular diagnostics in the field and demonstrates how synthetic biology can be used to develop diagnostic tools for confronting global health crises. PAPERCLIP.

  6. Biomolecular Nano-Flow-Sensor to Measure Near-Surface Flow

    Directory of Open Access Journals (Sweden)

    Noji Hiroyuki

    2009-01-01

    Full Text Available Abstract We have proposed and experimentally demonstrated that the measurement of the near-surface flow at the interface between a liquid and solid using a 10 nm-sized biomolecular motor of F1-ATPase as a nano-flow-sensor. For this purpose, we developed a microfluidic test-bed chip to precisely control the liquid flow acting on the F1-ATPase. In order to visualize the rotation of F1-ATPase, several hundreds nanometer-sized particle was immobilized at the rotational axis of F1-ATPase to enhance the rotation to be detected by optical microscopy. The rotational motion of F1-ATPase, which was immobilized on an inner surface of the test-bed chip, was measured to obtain the correlation between the near-surface flow and the rotation speed of F1-ATPase. As a result, we obtained the relationship that the rotation speed of F1-ATPase was linearly decelerated with increasing flow velocity. The mechanism of the correlation between the rotation speed and the near-surface flow remains unclear, however the concept to use biomolecule as a nano-flow-sensor was proofed successfully. (See supplementary material 1 Electronic supplementary material The online version of this article (doi:10.1007/s11671-009-9479-3 contains supplementary material, which is available to authorized users. Click here for file

  7. Drug Transport Microdevice Mimicking an Idealized Nanoscale Bio-molecular Motor

    Institute of Scientific and Technical Information of China (English)

    Jae Hwan Lee; Ramana M. Pidaparti

    2011-01-01

    Molecular motors are nature's nano-devices and the essential agents of movement that are an integral part of many living organisms.The supramolecular motor,called Nuclear Pore Complex (NPC),controls the transport of all cellular material between the cytoplasm and the nucleus that occurs naturally in biological cells of many organisms.In order to understand the design characteristics of the NPC,we developed a microdevice for drug/fluidic transport mimicking the coarse-grained representation of the NPC geometry through computational fluid dynamic analysis and optimization.Specifically,the role of the central plug in active fluidic/particle transport and passive transport (without central plug) was investigated.Results of flow rate,pressure and velocity profiles obtained from the models indicate that the central plug plays a major role in transport through this biomolecular machine.The results of this investigation show that fluidic transport and flow passages are important factors in designing NPC based nano- and micro-devices for drug delivery.

  8. A Starting Point for Fluorescence-Based Single-Molecule Measurements in Biomolecular Research

    Directory of Open Access Journals (Sweden)

    Alexander Gust

    2014-09-01

    Full Text Available Single-molecule fluorescence techniques are ideally suited to provide information about the structure-function-dynamics relationship of a biomolecule as static and dynamic heterogeneity can be easily detected. However, what type of single-molecule fluorescence technique is suited for which kind of biological question and what are the obstacles on the way to a successful single-molecule microscopy experiment? In this review, we provide practical insights into fluorescence-based single-molecule experiments aiming for scientists who wish to take their experiments to the single-molecule level. We especially focus on fluorescence resonance energy transfer (FRET experiments as these are a widely employed tool for the investigation of biomolecular mechanisms. We will guide the reader through the most critical steps that determine the success and quality of diffusion-based confocal and immobilization-based total internal reflection fluorescence microscopy. We discuss the specific chemical and photophysical requirements that make fluorescent dyes suitable for single-molecule fluorescence experiments. Most importantly, we review recently emerged photoprotection systems as well as passivation and immobilization strategies that enable the observation of fluorescently labeled molecules under biocompatible conditions. Moreover, we discuss how the optical single-molecule toolkit has been extended in recent years to capture the physiological complexity of a cell making it even more relevant for biological research.

  9. Colloid-in-liquid crystal gels that respond to biomolecular interactions.

    Science.gov (United States)

    Agarwal, Ankit; Sidiq, Sumyra; Setia, Shilpa; Bukusoglu, Emre; de Pablo, Juan J; Pal, Santanu Kumar; Abbott, Nicholas L

    2013-08-26

    This paper advances the design of stimuli-responsive materials based on colloidal particles dispersed in liquid crystals (LCs). Specifically, thin films of colloid-in-liquid crystal (CLC) gels undergo easily visualized ordering transitions in response to reversible and irreversible (enzymatic) biomolecular interactions occurring at the aqueous interfaces of the gels. In particular, LC ordering transitions can propagate across the entire thickness of the gels. However, confinement of the LC to small domains with lateral sizes of ∼10 μm does change the nature of the anchoring transitions, as compared to films of pure LC, due to the effects of confinement on the elastic energy stored in the LC. The effects of confinement are also observed to cause the response of individual domains of the LC within the CLC gel to vary significantly from one to another, indicating that manipulation of LC domain size and shape can provide the basis of a general and facile method to tune the response of these LC-based physical gels to interfacial phenomena. Overall, the results presented in this paper establish that CLC gels offer a promising approach to the preparation of self-supporting, LC-based stimuli-responsive materials.

  10. AFMPB: An adaptive fast multipole Poisson-Boltzmann solver for calculating electrostatics in biomolecular systems

    Science.gov (United States)

    Lu, Benzhuo; Cheng, Xiaolin; Huang, Jingfang; McCammon, J. Andrew

    2013-11-01

    A Fortran program package is introduced for rapid evaluation of the electrostatic potentials and forces in biomolecular systems modeled by the linearized Poisson-Boltzmann equation. The numerical solver utilizes a well-conditioned boundary integral equation (BIE) formulation, a node-patch discretization scheme, a Krylov subspace iterative solver package with reverse communication protocols, and an adaptive new version of the fast multipole method in which the exponential expansions are used to diagonalize the multipole-to-local translations. The program and its full description, as well as several closely related libraries and utility tools are available at http://lsec.cc.ac.cn/~lubz/afmpb.html and a mirror site at http://mccammon.ucsd.edu/. This paper is a brief summary of the program: the algorithms, the implementation and the usage. Restrictions: Only three or six significant digits options are provided in this version. Unusual features: Most of the codes are in Fortran77 style. Memory allocation functions from Fortran90 and above are used in a few subroutines. Additional comments: The current version of the codes is designed and written for single core/processor desktop machines. Check http://lsec.cc.ac.cn/lubz/afmpb.html for updates and changes. Running time: The running time varies with the number of discretized elements (N) in the system and their distributions. In most cases, it scales linearly as a function of N.

  11. Affinity analysis for biomolecular interactions based on magneto-optical relaxation measurements

    Science.gov (United States)

    Aurich, Konstanze; Nagel, Stefan; Heister, Elena; Weitschies, Werner

    2008-12-01

    Magneto-optical relaxation measurements of magnetically labelled biomolecules are a promising tool for immunometric analyses. Carcinoembryonic antigen (CEA) and its polyclonal and monoclonal antibodies (anti-CEA) were utilized as a model system for affinity analysis of the interaction between antibody and antigen. For this purpose antibodies were coupled with magnetic nanoparticles (MNPs). Aggregation of these antibody sensors due to interactions with the CEA was observed subsequently by measuring the relaxation time of the birefringence of a transmitted laser beam that occurs in a pulsed magnetic field. A kinetic model of chain-like aggregation developed for these purposes enables the rapid and simple calculation of the kinetic parameters of the underlying protein interaction. From the known antigen concentration and the increase in particle size during the interaction we are able to estimate the unknown parameters with standard methods for the statistical description of stepwise polymerization. This novel affinity analysis was successfully applied for the antigen-antibody interaction described herein and can be applied to other biomolecular interactions. First efforts have been made to establish magneto-optical relaxation measurements in body fluids.

  12. Extension of the GLYCAM06 Biomolecular Force Field to Lipids, Lipid Bilayers and Glycolipids.

    Science.gov (United States)

    Tessier, Matthew B; Demarco, Mari L; Yongye, Austin B; Woods, Robert J

    2008-01-01

    GLYCAM06 is a generalisable biomolecular force field that is extendible to diverse molecular classes in the spirit of a small-molecule force field. Here we report parameters for lipids, lipid bilayers and glycolipids for use with GLYCAM06. Only three lipid-specific atom types have been introduced, in keeping with the general philosophy of transferable parameter development. Bond stretching, angle bending, and torsional force constants were derived by fitting to quantum mechanical data for a collection of minimal molecular fragments and related small molecules. Partial atomic charges were computed by fitting to ensemble-averaged quantum-computed molecular electrostatic potentials.In addition to reproducing quantum mechanical internal rotational energies and experimental valence geometries for an array of small molecules, condensed-phase simulations employing the new parameters are shown to reproduce the bulk physical properties of a DMPC lipid bilayer. The new parameters allow for molecular dynamics simulations of complex systems containing lipids, lipid bilayers, glycolipids, and carbohydrates, using an internally consistent force field. By combining the AMBER parameters for proteins with the GLYCAM06 parameters, it is also possible to simulate protein-lipid complexes and proteins in biologically relevant membrane-like environments.

  13. High-Throughput, Protein-Targeted Biomolecular Detection Using Frequency-Domain Faraday Rotation Spectroscopy.

    Science.gov (United States)

    Murdock, Richard J; Putnam, Shawn A; Das, Soumen; Gupta, Ankur; Chase, Elyse D Z; Seal, Sudipta

    2017-01-16

    A clinically relevant magneto-optical technique (fd-FRS, frequency-domain Faraday rotation spectroscopy) for characterizing proteins using antibody-functionalized magnetic nanoparticles (MNPs) is demonstrated. This technique distinguishes between the Faraday rotation of the solvent, iron oxide core, and functionalization layers of polyethylene glycol polymers (spacer) and model antibody-antigen complexes (anti-BSA/BSA, bovine serum albumin). A detection sensitivity of ≈10 pg mL(-1) and broad detection range of 10 pg mL(-1) ≲ cBSA ≲ 100 µg mL(-1) are observed. Combining this technique with predictive analyte binding models quantifies (within an order of magnitude) the number of active binding sites on functionalized MNPs. Comparative enzyme-linked immunosorbent assay (ELISA) studies are conducted, reproducing the manufacturer advertised BSA ELISA detection limits from 1 ng mL(-1) ≲ cBSA ≲ 500 ng mL(-1) . In addition to the increased sensitivity, broader detection range, and similar specificity, fd-FRS can be conducted in less than ≈30 min, compared to ≈4 h with ELISA. Thus, fd-FRS is shown to be a sensitive optical technique with potential to become an efficient diagnostic in the chemical and biomolecular sciences.

  14. Polymerase Chain Reaction on a Viral Nanoparticle.

    Science.gov (United States)

    Carr-Smith, James; Pacheco-Gómez, Raúl; Little, Haydn A; Hicks, Matthew R; Sandhu, Sandeep; Steinke, Nadja; Smith, David J; Rodger, Alison; Goodchild, Sarah A; Lukaszewski, Roman A; Tucker, James H R; Dafforn, Timothy R

    2015-12-18

    The field of synthetic biology includes studies that aim to develop new materials and devices from biomolecules. In recent years, much work has been carried out using a range of biomolecular chassis including α-helical coiled coils, β-sheet amyloids and even viral particles. In this work, we show how hybrid bionanoparticles can be produced from a viral M13 bacteriophage scaffold through conjugation with DNA primers that can template a polymerase chain reaction (PCR). This unprecedented example of a PCR on a virus particle has been studied by flow aligned linear dichroism spectroscopy, which gives information on the structure of the product as well as a new protototype methodology for DNA detection. We propose that this demonstration of PCR on the surface of a bionanoparticle is a useful addition to ways in which hybrid assemblies may be constructed using synthetic biology.

  15. Facile synthesis of platinum-gold alloyed string-bead nanochain networks with the assistance of allantoin and their enhanced electrocatalytic performance for oxygen reduction and methanol oxidation reactions

    Science.gov (United States)

    He, Li-Li; Zheng, Jie-Ning; Song, Pei; Zhong, Shu-Xian; Wang, Ai-Jun; Chen, Zhaojiang; Feng, Jiu-Ju

    2015-02-01

    In this work, a facile one-pot wet-chemical method is developed for preparation of bimetallic platinum-gold (Pt-Au) alloyed string-bead nanochain networks, using allantoin as a structure-directing agent, without any template, surfactant, or seed. The characterization experiments are mainly performed by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) spectroscopy. The as-prepared Pt-Au nanocrystals show enhanced electrocatalytic performance toward oxygen reduction reaction (ORR) mainly predominated by a four-electron pathway, and display improved catalytic activity and high stability for methanol oxidation reaction (MOR) over commercial Pt black and Pt-Ru black.

  16. Reaction mechanism and reaction coordinates from the viewpoint of energy flow.

    Science.gov (United States)

    Li, Wenjin; Ma, Ao

    2016-03-21

    Reaction coordinates are of central importance for correct understanding of reaction dynamics in complex systems, but their counter-intuitive nature made it a daunting challenge to identify them. Starting from an energetic view of a reaction process as stochastic energy flows biased towards preferred channels, which we deemed the reaction coordinates, we developed a rigorous scheme for decomposing energy changes of a system, both potential and kinetic, into pairwise components. The pairwise energy flows between different coordinates provide a concrete statistical mechanical language for depicting reaction mechanisms. Application of this scheme to the C7eq → C7ax transition of the alanine dipeptide in vacuum revealed novel and intriguing mechanisms that eluded previous investigations of this well studied prototype system for biomolecular conformational dynamics. Using a cost function developed from the energy decomposition components by proper averaging over the transition path ensemble, we were able to identify signatures of the reaction coordinates of this system without requiring any input from human intuition.

  17. Sop-GPU: accelerating biomolecular simulations in the centisecond timescale using graphics processors.

    Science.gov (United States)

    Zhmurov, A; Dima, R I; Kholodov, Y; Barsegov, V

    2010-11-01

    Theoretical exploration of fundamental biological processes involving the forced unraveling of multimeric proteins, the sliding motion in protein fibers and the mechanical deformation of biomolecular assemblies under physiological force loads is challenging even for distributed computing systems. Using a C(α)-based coarse-grained self organized polymer (SOP) model, we implemented the Langevin simulations of proteins on graphics processing units (SOP-GPU program). We assessed the computational performance of an end-to-end application of the program, where all the steps of the algorithm are running on a GPU, by profiling the simulation time and memory usage for a number of test systems. The ∼90-fold computational speedup on a GPU, compared with an optimized central processing unit program, enabled us to follow the dynamics in the centisecond timescale, and to obtain the force-extension profiles using experimental pulling speeds (v(f) = 1-10 μm/s) employed in atomic force microscopy and in optical tweezers-based dynamic force spectroscopy. We found that the mechanical molecular response critically depends on the conditions of force application and that the kinetics and pathways for unfolding change drastically even upon a modest 10-fold increase in v(f). This implies that, to resolve accurately the free energy landscape and to relate the results of single-molecule experiments in vitro and in silico, molecular simulations should be carried out under the experimentally relevant force loads. This can be accomplished in reasonable wall-clock time for biomolecules of size as large as 10(5) residues using the SOP-GPU package.

  18. An Analysis of Biomolecular Force Fields for Simulations of Polyglutamine in Solution

    Energy Technology Data Exchange (ETDEWEB)

    Fluitt, Aaron M. [Univ. of Chicago, IL (United States); de Pablo, Juan J. [Argonne National Lab. (ANL), Argonne, IL (United States)

    2015-09-01

    Polyglutamine (polyQ) peptides are a useful model system for biophysical studies of protein folding and aggregation, both for their intriguing aggregation properties and their own relevance to human disease. The genetic expansion of a polyQ tract triggers the formation of amyloid aggregates associated with nine neurodegenerative diseases. Several clearly identifiable and separable factors, notably the length of the polyQ tract, influence the mechanism of aggregation, its associated kinetics, and the ensemble of structures formed. Atomistic simulations are well positioned to answer open questions regarding the thermodynamics and kinetics of polyQ folding and aggregation. The additional, explicit representation of water permits deeper investigation of the role of solvent dynamics, and it permits a direct comparison of simulation results with infrared spectroscopy experiments. The generation of meaningful simulation results hinges on satisfying two essential criteria: achieving sufficient conformational sampling to draw statistically valid conclusions, and accurately reproducing the intermolecular forces that govern system structure and dynamics. In this work, we examine the ability of 12 biomolecular force fields to reproduce the properties of a simple, 30-residue polyQ peptide (Q30) in explicit water. In addition to secondary and tertiary structure, we consider generic structural properties of polymers that provide additional dimensions for analysis of the highly degenerate disordered states of the molecule. We find that the 12 force fields produce a wide range of predictions. We identify AMBER ff99SB, AMBER ff99SB*, and OPLS-AA/L to be most suitable for studies of polyQ folding and aggregation.

  19. g_contacts: Fast contact search in bio-molecular ensemble data

    Science.gov (United States)

    Blau, Christian; Grubmuller, Helmut

    2013-12-01

    Short-range interatomic interactions govern many bio-molecular processes. Therefore, identifying close interaction partners in ensemble data is an essential task in structural biology and computational biophysics. A contact search can be cast as a typical range search problem for which efficient algorithms have been developed. However, none of those has yet been adapted to the context of macromolecular ensembles, particularly in a molecular dynamics (MD) framework. Here a set-decomposition algorithm is implemented which detects all contacting atoms or residues in maximum O(Nlog(N)) run-time, in contrast to the O(N2) complexity of a brute-force approach. Catalogue identifier: AEQA_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEQA_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 8945 No. of bytes in distributed program, including test data, etc.: 981604 Distribution format: tar.gz Programming language: C99. Computer: PC. Operating system: Linux. RAM: ≈Size of input frame Classification: 3, 4.14. External routines: Gromacs 4.6[1] Nature of problem: Finding atoms or residues that are closer to one another than a given cut-off. Solution method: Excluding distant atoms from distance calculations by decomposing the given set of atoms into disjoint subsets. Running time:≤O(Nlog(N)) References: [1] S. Pronk, S. Pall, R. Schulz, P. Larsson, P. Bjelkmar, R. Apostolov, M. R. Shirts, J.C. Smith, P. M. Kasson, D. van der Spoel, B. Hess and Erik Lindahl, Gromacs 4.5: a high-throughput and highly parallel open source molecular simulation toolkit, Bioinformatics 29 (7) (2013).

  20. Biomolecularly capped uniformly sized nanocrystalline materials: glutathione-capped ZnS nanocrystals

    Science.gov (United States)

    Torres-Martínez, Claudia L.; Nguyen, Liem; Kho, Richard; Bae, Weon; Bozhilov, Krassimir; Klimov, Victor; Mehra, Rajesh K.

    1999-09-01

    Micro-organisms such as bacteria and yeasts form CdS to detoxify toxic cadmium ions. Frequently, CdS particles formed in yeasts and bacteria were found to be associated with specific biomolecules. It was later determined that these biomolecules were present at the surface of CdS. This coating caused a restriction in the growth of CdS particles and resulted in the formation of nanometre-sized semiconductors (NCs) that exhibited typical quantum confinement properties. Glutathione and related phytochelatin peptides were shown to be the biomolecules that capped CdS nanocrystallites synthesized by yeasts Candida glabrata and Schizosaccharomyces pombe. Although early studies showed the existence of specific biochemical pathways for the synthesis of biomolecularly capped CdS NCs, these NCs could be formed in vitro under appropriate conditions. We have recently shown that cysteine and cysteine-containing peptides such as glutathione and phytochelatins can be used in vitro to dictate the formation of discrete sizes of CdS and ZnS nanocrystals. We have evolved protocols for the synthesis of ZnS or CdS nanocrystals within a narrow size distribution range. These procedures involve three steps: (1) formation of metallo-complexes of cysteine or cysteine-containing peptides, (2) introduction of stoichiometric amounts of inorganic sulfide into the metallo-complexes to initiate the formation of nanocrystallites and finally (3) size-selective precipitation of NCs with ethanol in the presence of Na+. The resulting NCs were characterized by optical spectroscopy, high-resolution transmission electron microscopy (HRTEM), x-ray diffraction and electron diffraction. HRTEM showed that the diameter of the ZnS-glutathione nanocrystals was 3.45+/-0.5 nm. X-ray diffraction and electron diffraction analyses indicated ZnS-glutathione to be hexagonal. Photocatalytic studies suggest that glutathione-capped ZnS nanocrystals prepared by our procedure are highly efficient in degrading a test model

  1. Enthalpy-entropy compensation in biomolecular halogen bonds measured in DNA junctions.

    Science.gov (United States)

    Carter, Megan; Voth, Andrea Regier; Scholfield, Matthew R; Rummel, Brittany; Sowers, Lawrence C; Ho, P Shing

    2013-07-23

    Interest in noncovalent interactions involving halogens, particularly halogen bonds (X-bonds), has grown dramatically in the past decade, propelled by the use of X-bonding in molecular engineering and drug design. However, it is clear that a complete analysis of the structure-energy relationship must be established in biological systems to fully exploit X-bonds for biomolecular engineering. We present here the first comprehensive experimental study to correlate geometries with their stabilizing potentials for fluorine (F), chlorine (Cl), bromine (Br), or iodine (I) X-bonds in a biological context. For these studies, we determine the single-crystal structures of DNA Holliday junctions containing halogenated uracil bases that compete X-bonds against classic hydrogen bonds (H-bonds), estimate the enthalpic energies of the competing interactions in the crystal system through crystallographic titrations, and compare the enthalpic and entropic energies of bromine and iodine X-bonds in solution by differential scanning calorimetry. The culmination of these studies demonstrates that enthalpic stabilization of X-bonds increases with increasing polarizability from F to Cl to Br to I, which is consistent with the σ-hole theory of X-bonding. Furthermore, an increase in the X-bonding potential is seen to direct the interaction toward a more ideal geometry. However, the entropic contributions to the total free energies must also be considered to determine how each halogen potentially contributes to the overall stability of the interaction. We find that bromine has the optimal balance between enthalpic and entropic energy components, resulting in the lowest free energy for X-bonding in this DNA system. The X-bond formed by iodine is more enthalpically stable, but this comes with an entropic cost, which we attribute to crowding effects. Thus, the overall free energy of an X-bonding interaction balances the stabilizing electrostatic effects of the σ-hole against the competing

  2. Morbillivirus infection in cetaceans stranded along the Italian coastline: pathological, immunohistochemical and biomolecular findings.

    Science.gov (United States)

    Di Guardo, Giovanni; Di Francesco, Cristina Esmeralda; Eleni, Claudia; Cocumelli, Cristiano; Scholl, Francesco; Casalone, Cristina; Peletto, Simone; Mignone, Walter; Tittarelli, Cristiana; Di Nocera, Fabio; Leonardi, Leonardo; Fernández, Antonio; Marcer, Federica; Mazzariol, Sandro

    2013-02-01

    Morbilliviruses are recognized as biological agents highly impacting the health and conservation status of free-ranging cetaceans worldwide, as clearly exemplified by the two Dolphin Morbillivirus (DMV) epidemics of 1990-1992 and 2006-2008 among Mediterranean striped dolphins (Stenella coeruleoalba). After these two epidemics, morbilliviral infection (MI) cases with peculiar neurobiological features were reported in striped dolphins stranded along the Spanish coastline. Affected cetaceans showed a subacute-to-chronic, non-suppurative encephalitis, with brain lesions strongly resembling those found in human "subacute sclerosing panencephalitis" and "old dog encephalitis". Brain was the only tissue in which morbilliviral antigen and/or genome could be detected. Beside a case of morbilliviral encephalitis in a striped dolphin's calf stranded in 2009, we observed 5 additional MI cases in 2 striped dolphins, 1 bottlenose dolphin (Tursiops truncatus) and 2 fin whales (Balaenoptera physalus), all stranded in 2011 along the Italian coastline. Noteworthy, 3 of these animals (2 striped dolphins and 1 bottlenose dolphin) showed immunohistochemical (IHC) and/or biomolecular (PCR) evidence of morbilliviral antigen and/or genome exclusively in their brain, with 1 striped dolphin and 1 bottlenose dolphin also exhibiting a non-suppurative encephalitis. Furthermore, simultaneous IHC and PCR evidence of a Toxoplasma gondii coinfection was obtained in 1 fin whale. The above results are consistent with those reported in striped dolphins after the two MI epidemics of 1990-92 and 2006-2008, with evidence of morbilliviral antigen and/or genome being found exclusively in the brain tissue from affected animals.

  3. Immunohistochemical Study to Evaluate the Prognostic Significance of Four Biomolecular Markers in Radiotherapy of Nasopharyngeal Carcinoma

    Energy Technology Data Exchange (ETDEWEB)

    Kim, Yeon Joo; Lee, Seung Hee; Wu, Hong Gyun; Go, Heoun Jeong; Jeon, Yoon Kyung [Seoul National University College of Medicine, Seoul (Korea, Republic of)

    2010-11-15

    We performed an immunohistochemical study with pre-treatment biopsy specimens to evaluate the prognostic significance of four biomolecular markers which can be used as a predictive assay for radiotherapy (RT) treatment of nasopharyngeal carcinoma (NPC). From January 1998 through December 2006, 68 patients were histologically diagnosed as non-metastatic NPC and treated by RT. Only 38 patients had the paraffin block for the immunohistochemical study. Thirty-one patients had undifferentiated carcinoma and 7 patients had squamous cell carcinoma. Thirty two patients (84%) had advanced stage NPC (2002 AJCC Stage III{approx}IV). Immunohistochemical staining was performed for Met, COX-2, nm23-H1, and epidermal growth factor receptor (EGFR) expression using routine methods. The median follow-up time was 30 months (range, 11 to 83 months) for all patients, and 39 months (range, 19 to 83 months) for surviving patients. The 5-year overall survival (OS) rate of the patients with high Met extent ({>=}50%) was significantly lower than that of the patients with low Met extent (48% vs. 84%, p=0.02). In addition, Met extent was also a significant prognostic factor in multivariate analysis (p=0.01). No correlation was observed between Met extent and T stage, N stage, stage group, gender, age, and the response to chemotherapy or RT. Met extent showed moderate correlation with COX-2 expression (Pearson coefficient 0.496, p<0.01), but COX-2 expression did not affect OS. Neither nm23-H1 or EGFR expression was a prognostic factor for OS in this study. High Met extent ({>=}50%) might be an independent prognostic factor that predicts poor OS in NPC treated with RT.

  4. Evolução biomolecular homoquiral: a origem e a amplificação da quiralidade nas moléculas da vida Homochiral biomolecular evolution: the origin and the amplification of chirality in life molecules

    Directory of Open Access Journals (Sweden)

    José Augusto R. Rodrigues

    2010-01-01

    Full Text Available The fact that biologically relevant molecules exist only as one of the two enantiomers is a fascinating example of complete symmetry breaking of chirality and has long intrigued our curiosity. The origin of this selective chirality has remained a fundamental enigma with regard to the origin of life since the time of Pasteur, 160 years ago. The symmetry breaking processes, which include autocatalytic crystallization, asymmetric autocatalysis, spontaneous crystallization, adsorption and polymerization of amino acids on mineral surfaces, provide new insights into the origin of biomolecular homochirality.

  5. Heuristics-Guided Exploration of Reaction Mechanisms

    CERN Document Server

    Bergeler, Maike; Proppe, Jonny; Reiher, Markus

    2015-01-01

    For the investigation of chemical reaction networks, the efficient and accurate determination of all relevant intermediates and elementary reactions is inevitable. The complexity of such a network may grow rapidly, in particular if reactive species are involved that might cause a myriad of side reactions. Without automation, a complete investigation of complex reaction mechanisms is tedious and possibly unfeasible. Therefore, only the expected dominant reaction paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic cascade) are usually explored in practice. Here, we present a computational protocol that constructs such networks in a parallelized and automated manner. Molecular structures of reactive complexes are generated based on heuristic rules and subsequently optimized by electronic-structure methods. Pairs of reactive complexes related by an elementary reaction are then automatically detected and subjected to an automated search for the connecting transition state. The results are...

  6. Quantum algorithms and mathematical formulations of biomolecular solutions of the vertex cover problem in the finite-dimensional hilbert space.

    Science.gov (United States)

    Chang, Weng-Long; Ren, Ting-Ting; Feng, Mang

    2015-01-01

    In this paper, it is shown that the proposed quantum algorithm for implementing Boolean circuits generated from the DNA-based algorithm solving the vertex-cover problem of any graph G with m edges and n vertices is the optimal quantum algorithm. Next, it is also demonstrated that mathematical solutions of the same biomolecular solutions are represented in terms of a unit vector in the finite-dimensional Hilbert space. Furthermore, for testing our theory, a nuclear magnetic resonance (NMR) experiment of three quantum bits to solve the simplest vertex-cover problem is completed.

  7. Catalytic Oxidized Reaction of Paraffin Wax Based on BP Neural Network%基于BP神经网络的石蜡催化氧化反应的研究

    Institute of Scientific and Technical Information of China (English)

    黄玮; 丛玉凤; 郭大鹏

    2012-01-01

    The oxidized wax was prepared by catalytic oxidized reaction of paraffin wax which used BP neural network to build mathematical model of acid value and saponification value influenced by the amount of reactive catalyst and accessory ingredient, airflow rate, reaction temperature and time, and utilized the model of neutral network to calculate the technology condition of preparing oxidized wax through catalyzing and oxidizing paraffin wax. Consequently, optimum technology conditions were gained in order to achieve the objective of reducing experimental number of times.%在石蜡催化氧化反应制备氧化蜡的研究中,利用BP神经网络建立反应催化剂用量、助剂用量、空气流量、反应温度和反应时间对酸值和皂化值影响的数学模型,并利用该神经网络模型对石蜡催化氧化制备氧化蜡的工艺条件进行预测,从而获得最优工艺条件,达到缩短实验次数的目的.

  8. 随机时滞反应扩散广义细胞神经网络的均值指数稳定性%Exponential Stability of Stochastic Reaction-Diffusion General Cellular Neural Network with Time-Delays

    Institute of Scientific and Technical Information of China (English)

    周凤燕

    2012-01-01

    研究了一类反应扩散广义时滞细胞神经网络在噪声干扰下的指数稳定性.利用Ito公式,Holder不等式,M矩阵性质和微分不等式技巧,给出了系统均值指数稳定的充分条件,并且判断方法简单易操作.最后给出了主要定理的两个应用实例,表明结论的有效性.%The exponential stability of a class of reaction-diffusion general cellular neural network with time delay and noise perturbation is studied. Using the Ito formula, Holder inequality, M-matric properties and a skill of differential inequality, some sufficient conditions are given to guarantee the mean value exponential stability of the equilibrium for the stochastic reaction-diffusion general cellular neural network with time delay and the sufficient conditions are easier to operate. In the end, two examples are given to illustrate the main theoretical results.

  9. Synthetic tetracycline-inducible regulatory networks: computer-aided design of dynamic phenotypes

    Directory of Open Access Journals (Sweden)

    Kaznessis Yiannis N

    2007-01-01

    Full Text Available Abstract Background Tightly regulated gene networks, precisely controlling the expression of protein molecules, have received considerable interest by the biomedical community due to their promising applications. Among the most well studied inducible transcription systems are the tetracycline regulatory expression systems based on the tetracycline resistance operon of Escherichia coli, Tet-Off (tTA and Tet-On (rtTA. Despite their initial success and improved designs, limitations still persist, such as low inducer sensitivity. Instead of looking at these networks statically, and simply changing or mutating the promoter and operator regions with trial and error, a systematic investigation of the dynamic behavior of the network can result in rational design of regulatory gene expression systems. Sophisticated algorithms can accurately capture the dynamical behavior of gene networks. With computer aided design, we aim to improve the synthesis of regulatory networks and propose new designs that enable tighter control of expression. Results In this paper we engineer novel networks by recombining existing genes or part of genes. We synthesize four novel regulatory networks based on the Tet-Off and Tet-On systems. We model all the known individual biomolecular interactions involved in transcription, translation, regulation and induction. With multiple time-scale stochastic-discrete and stochastic-continuous models we accurately capture the transient and steady state dynamics of these networks. Important biomolecular interactions are identified and the strength of the interactions engineered to satisfy design criteria. A set of clear design rules is developed and appropriate mutants of regulatory proteins and operator sites are proposed. Conclusion The complexity of biomolecular interactions is accurately captured through computer simulations. Computer simulations allow us to look into the molecular level, portray the dynamic behavior of gene regulatory

  10. Heuristics-Guided Exploration of Reaction Mechanisms.

    Science.gov (United States)

    Bergeler, Maike; Simm, Gregor N; Proppe, Jonny; Reiher, Markus

    2015-12-08

    For the investigation of chemical reaction networks, the efficient and accurate determination of all relevant intermediates and elementary reactions is mandatory. The complexity of such a network may grow rapidly, in particular if reactive species are involved that might cause a myriad of side reactions. Without automation, a complete investigation of complex reaction mechanisms is tedious and possibly unfeasible. Therefore, only the expected dominant reaction paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic cascade) are usually explored in practice. Here, we present a computational protocol that constructs such networks in a parallelized and automated manner. Molecular structures of reactive complexes are generated based on heuristic rules derived from conceptual electronic-structure theory and subsequently optimized by quantum-chemical methods to produce stable intermediates of an emerging reaction network. Pairs of intermediates in this network that might be related by an elementary reaction according to some structural similarity measure are then automatically detected and subjected to an automated search for the connecting transition state. The results are visualized as an automatically generated network graph, from which a comprehensive picture of the mechanism of a complex chemical process can be obtained that greatly facilitates the analysis of the whole network. We apply our protocol to the Schrock dinitrogen-fixation catalyst to study alternative pathways of catalytic ammonia production.

  11. Reaction Graph

    Institute of Scientific and Technical Information of China (English)

    傅育熙

    1998-01-01

    The paper proposes reaction graphs as graphical representations of computational objects.A reaction graph is a directed graph with all its arrows and some of its nodes labeled.Computations are modled by graph rewriting of a simple nature.The basic rewriting rules embody the essence of both the communications among processes and cut-eliminations in proofs.Calculi of graphs are ideentified to give a formal and algebraic account of reaction graphs in the spirit of process algebra.With the help of the calculi,it is demonstrated that reaction graphs capture many interesting aspects of computations.

  12. Unraveling the biomolecular snapshots of mitosis in healthy and cancer cells using plasmonically-enhanced Raman spectroscopy.

    Science.gov (United States)

    Panikkanvalappil, Sajanlal R; Hira, Steven M; Mahmoud, Mahmoud A; El-Sayed, Mostafa A

    2014-11-12

    Owing to the dynamic and complex nature of mitosis, precise and timely executions of biomolecular events are critical for high fidelity cell division. In this context, visualization of such complex events at the molecular level can provide vital information on the biomolecular processes in abnormal cells. Here, we explored the plasmonically enhanced light scattering properties of functionalized gold nanocubes (AuNCs) together with surface-enhanced Raman spectroscopy (SERS) to unravel the complex and dynamic biological processes involved in mitosis of healthy and cancerous cells from its molecular perspectives. By monitoring various stages of mitosis using SERS, we noticed that relatively high rate of conversion of mitotic proteins from their α-helix structure to β-sheet conformation is likely in the cancer cells during meta-, ana-, and telophases. Unique biochemical modifications to the lipid and amino acid moieties, associated with the observed protein conformational modifications, were also identified. However, in healthy cells, the existence of proteins in their β conformation was momentary and was largely in the α-helix form. The role of abnormal conformational modifications of mitotic proteins on the development of anomalous mitotic activities was further confirmed by looking at plasmonic nanoparticle-induced cytokinesis failure in cancer cells. Our findings illustrate the vast possibilities of SERS in real-time tracking of complex, subtle, and momentary modifications of biomolecules in live cells, which could provide new insights to the role of protein conformation dynamics during mitosis on the development of cancer and many other diseases.

  13. Nanomechanical force transducers for biomolecular and intracellular measurements: is there room to shrink and why do it?

    Science.gov (United States)

    Sirbuly, Donald J; Friddle, Raymond W; Villanueva, Joshua; Huang, Qian

    2015-02-01

    Over the past couple of decades there has been a tremendous amount of progress on the development of ultrasensitive nanomechanical instruments, which has enabled scientists to peer for the first time into the mechanical world of biomolecular systems. Currently, work-horse instruments such as the atomic force microscope and optical/magnetic tweezers have provided the resolution necessary to extract quantitative force data from various molecular systems down to the femtonewton range, but it remains difficult to access the intracellular environment with these analytical tools as they have fairly large sizes and complicated feedback systems. This review is focused on highlighting some of the major milestones and discoveries in the field of biomolecular mechanics that have been made possible by the development of advanced atomic force microscope and tweezer techniques as well as on introducing emerging state-of-the-art nanomechanical force transducers that are addressing the size limitations presented by these standard tools. We will first briefly cover the basic setup and operation of these instruments, and then focus heavily on summarizing advances in in vitro force studies at both the molecular and cellular level. The last part of this review will include strategies for shrinking down the size of force transducers and provide insight into why this may be important for gaining a more complete understanding of cellular activity and function.

  14. Expanding the scope of CE reactor to ssDNA-binding protein-ssDNA complexes as exemplified for a tool for direct measurement of dissociation kinetics of biomolecular complexes.

    Science.gov (United States)

    Takahashi, Toru; Ohtsuka, Kei-Ichirou; Tomiya, Yoriyuki; Iki, Nobuhiko; Hoshino, Hitoshi

    2009-09-01

    CE reactor (CER), which was developed as a tool for direct measurement of the dissociation kinetics of metal complexes, was successfully applied to the complexes of Escherichia coli ssDNA-binding protein (SSB) with ssDNA. The basic concept of CER is the application of CE separation process as a dissociation kinetic reactor for the complex, and the observation of the on-capillary dissociation reaction profile of the complex as the decrease of the peak height of the complex with increase of the migration time. The peak height of [SSB-ssDNA] decreases as the migration time increases since the degree of the decrease of [SSB-ssDNA] through the on-capillary dissociation reaction is proportional to the degree of the decrease of the peak height of [SSB-ssDNA]. The dissociation degree-time profiles for the complexes are quantitatively described by analyzing a set of electropherograms with different migration times. Dissociation rate constants of [SSB-ssDNA] consisting of 20-mer, 25-mer and 31-mer ssDNA were directly determined to be 3.99x10(-4), 4.82x10(-4) and 1.50x10(-3)/s, respectively. CER is a concise and effective tool for dissociation kinetic analysis of biomolecular complexes.

  15. Facile synthesis of nitrogen-doped carbon nanotubes encapsulating nickel cobalt alloys 3D networks for oxygen evolution reaction in an alkaline solution

    Science.gov (United States)

    Yu, Jie; Zhong, Yijun; Zhou, Wei; Shao, Zongping

    2017-01-01

    Efficient oxygen evolution reaction (OER) catalysts are required to facilitate the large-scale exploitation of renewable energy resources and applications in electrochemical energy conversion technologies. Here, we show that metal alloy-based hybrids can provide higher electrocatalytic activity than their individual metal-based hybrids. In particular, NiCo alloys encapsulated within nitrogen-doped carbon nanotubes (NiCo@NCNTs) showed higher OER activities in an alkaline solution than the individual metal hybrids (Ni@NCNTs and Co@NCNTs), highlighting a synergy between the Ni and Co components. NiCo@NCNTs pyrolyzed at 800 °C displayed an overpotential of ∼41 mV at a current density of 10 mA cm-2 and were more stable than IrO2 during 1000-cycle accelerated durability testing at a scan rate of 100 mV s-1.

  16. Study of castor oil polyurethane - poly(methyl methacrylate semi-interpenetrating polymer network (SIPN reaction parameters using a 2³ factorial experimental design

    Directory of Open Access Journals (Sweden)

    Fernanda Oliveira Vieira da Cunha

    2004-12-01

    Full Text Available In this work was employed a 2³ factorial experiment design to evaluate the castor oil polyurethane-poly(methyl methacrylate semi-IPN synthesis. The reaction parameters used as independent variables were NCO/OH molar ratio, polyurethane polymerization time and methyl methacrylate (MMA content. The semi-IPNs were cured over 28 h using two thermal treatments. The polymers were characterized by infrared and Raman spectroscopy, thermal analysis and swelling profiles in n-hexane. The glass transition temperature (Tg and the swelling were more affect by the NCO/OH molar ratio variation. The semi-IPNs showed Tg from - 27 to - 6 °C and the swelling range was from 3 to 22%, according to the crosslink density. The IPN mechanical properties were dependent on the cure temperature and MMA content in it. Lower elastic modulus values were observed in IPNs cured at room temperature.

  17. Survey of Network-Based Approaches to Research of Cardiovascular Diseases

    Directory of Open Access Journals (Sweden)

    Anida Sarajlić

    2014-01-01

    Full Text Available Cardiovascular diseases (CVDs are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.

  18. Catalyst Initiation in the Oscillatory Carbonylation Reaction

    Directory of Open Access Journals (Sweden)

    Katarina Novakovic

    2011-01-01

    Full Text Available Palladium(II iodide is used as a catalyst in the phenylacetylene oxidative carbonylation reaction that has demonstrated oscillatory behaviour in both pH and heat of reaction. In an attempt to extract the reaction network responsible for the oscillatory nature of this reaction, the system was divided into smaller parts and they were studied. This paper focuses on understanding the reaction network responsible for the initial reactions of palladium(II iodide within this oscillatory reaction. The species researched include methanol, palladium(II iodide, potassium iodide, and carbon monoxide. Several chemical reactions were considered and applied in a modelling study. The study revealed the significant role played by traces of water contained in the standard HPLC grade methanol used.

  19. Diffusion Monte Carlo applied to weak interactions - hydrogen bonding and aromatic stacking in (bio-)molecular model systems

    Science.gov (United States)

    Fuchs, M.; Ireta, J.; Scheffler, M.; Filippi, C.

    2006-03-01

    Dispersion (Van der Waals) forces are important in many molecular phenomena such as self-assembly of molecular crystals or peptide folding. Calculating this nonlocal correlation effect requires accurate electronic structure methods. Usual density-functional theory with generalized gradient functionals (GGA-DFT) fails unless empirical corrections are added that still need extensive validation. Quantum chemical methods like MP2 and coupled cluster are more accurate, yet limited to rather small systems by their unfavorable computational scaling. Diffusion Monte Carlo (DMC) can provide accurate molecular total energies and remains feasible also for larger systems. Here we apply the fixed-node DMC method to (bio-)molecular model systems where dispersion forces are significant: (dimethyl-) formamide and benzene dimers, and adenine-thymine DNA base pairs. Our DMC binding energies agree well with data from coupled cluster (CCSD(T)), in particular for stacked geometries where GGA-DFT fails qualitatively and MP2 predicts too strong binding.

  20. PUPIL: A Software Integration System for Multi-Scale QM/MM-MD Simulations and Its Application to Biomolecular Systems.

    Science.gov (United States)

    Torras, Juan; Roberts, Benjamin P; Seabra, Gustavo M; Trickey, Samuel B

    2015-01-01

    PUPIL (Program for User Package Interfacing and Linking) implements a distinctive multi-scale approach to hybrid quantum mechanical/molecular mechanical molecular dynamics (QM/MM-MD) simulations. Originally developed to interface different external programs for multi-scale simulation with applications in the materials sciences, PUPIL is finding increasing use in the study of complex biological systems. Advanced MD techniques from the external packages can be applied readily to a hybrid QM/MM treatment in which the forces and energy for the QM region can be computed by any of the QM methods available in any of the other external packages. Here, we give a survey of PUPIL design philosophy, main features, and key implementation decisions, with an orientation to biomolecular simulation. We discuss recently implemented features which enable highly realistic simulations of complex biological systems which have more than one active site that must be treated concurrently. Examples are given.

  1. A Unified Monte Carlo Treatment of Gas-Grain Chemistry for Large Reaction Networks. II. A Multiphase Gas-Surface-Layered Bulk Model

    CERN Document Server

    Vasyunin, A I

    2012-01-01

    The observed gas-phase molecular inventory of hot cores is believed to be significantly impacted by the products of chemistry in interstellar ices. In this study, we report the construction of a full macroscopic Monte Carlo model of both the gas-phase chemistry and the chemistry occurring in the icy mantles of interstellar grains. Our model treats icy grain mantles in a layer-by-layer manner, which incorporates laboratory data on ice desorption correctly. The ice treatment includes a distinction between a reactive ice surface and an inert bulk. The treatment also distinguishes between zeroth and first order desorption, and includes the entrapment of volatile species in more refractory ice mantles. We apply the model to the investigation of the chemistry in hot cores, in which a thick ice mantle built up during the previous cold phase of protostellar evolution undergoes surface reactions and is eventually evaporated. For the first time, the impact of a detailed multilayer approach to grain mantle formation on ...

  2. Systems approach to excitation-energy and electron transfer reaction networks in photosystem II complex: model studies for chlorophyll a fluorescence induction kinetics.

    Science.gov (United States)

    Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi

    2015-09-07

    Photosystem II (PS II) is a protein complex which evolves oxygen and drives charge separation for photosynthesis employing electron and excitation-energy transfer processes over a wide timescale range from picoseconds to milliseconds. While the fluorescence emitted by the antenna pigments of this complex is known as an important indicator of the activity of photosynthesis, its interpretation was difficult because of the complexity of PS II. In this study, an extensive kinetic model which describes the complex and multi-timescale characteristics of PS II is analyzed through the use of the hierarchical coarse-graining method proposed in the authors׳ earlier work. In this coarse-grained analysis, the reaction center (RC) is described by two states, open and closed RCs, both of which consist of oxidized and neutral special pairs being in quasi-equilibrium states. Besides, the PS II model at millisecond scale with three-state RC, which was studied previously, could be derived by suitably adjusting the kinetic parameters of electron transfer between tyrosine and RC. Our novel coarse-grained model of PS II can appropriately explain the light-intensity dependent change of the characteristic patterns of fluorescence induction kinetics from O-J-I-P, which shows two inflection points, J and I, between initial point O and peak point P, to O-J-D-I-P, which shows a dip D between J and I inflection points.

  3. Capture reactions

    NARCIS (Netherlands)

    Endt, P.M.

    1956-01-01

    Capture reactions will be considered here from the viewpoint of the nuclear spectroscopist. Especially important to him are the capture of neutrons, protons, and alpha particles, which may proceed through narrow resonances, offering a well defined initial state for the subsequent deexcitation proces

  4. Boolean logic functions of a synthetic peptide network.

    Science.gov (United States)

    Ashkenasy, Gonen; Ghadiri, M Reza

    2004-09-15

    Living cells can process rapidly and simultaneously multiple extracellular input signals through the complex networks of evolutionary selected biomolecular interactions and chemical transformations. Recent approaches to molecular computation have increasingly sought to mimic or exploit various aspects of biology. A number of studies have adapted nucleic acids and proteins to the design of molecular logic gates and computational systems, while other works have affected computation in living cells via biochemical pathway engineering. Here we report that de novo designed synthetic peptide networks can also mimic some of the basic logic functions of the more complex biological networks. We show that segments of a small network whose graph structure is composed of five nodes and 15 directed edges can express OR, NOR, and NOTIF logic.

  5. "Clickable" Polymeric Nanofibers through Hydrophilic-Hydrophobic Balance: Fabrication of Robust Biomolecular Immobilization Platforms.

    Science.gov (United States)

    Kalaoglu-Altan, Ozlem I; Sanyal, Rana; Sanyal, Amitav

    2015-05-11

    Fabrication of hydrophilic polymeric nanofibers that undergo facile and selective functionalization through metal catalyst-free Diels-Alder "click" reaction in aqueous environment is outlined. Electrospinning of copolymers containing an electron-rich furan moiety, hydrophobic methyl methacrylate units and hydrophilic poly(ethylene glycol)s as side chains provide specifically functionalizable yet antibiofouling fibers that remain stable in aqueous media due to appropriate hydrophobic hydrophilic balance. Efficient functionalization of these nanofibers is accomplished through the Diels-Alder reaction by exposing them to maleimide-containing molecules and ligands. Diels-Alder conjugation based functionalization is demonstrated through attachment of fluorescein-maleimide and a maleimide tethered biotin ligand. Biotinylated nanofibers were utilized to mediate immobilization of the protein streptavidin, as well as streptavidin coated quantum dots. Facile fabrication from readily available polymers and their effective functionalization under mild and reagent-free conditions in aqueous media make these "clickable" nanofibers attractive candidates as functionalizable scaffolds for various biomedical applications.

  6. Modern Computational Physical Chemistry : An Introduction to Biomolecular Radiation Damage and Phototoxicity

    OpenAIRE

    2004-01-01

    The realm of molecular physical chemistry ranges from the structure of matter and the fundamental atomic and molecular interactions to the macroscopic properties and processes arising from the average microscopic behaviour. Herein, the conventional electrodic problem is recast into the simpler molecular problem of finding the electrochemical, real chemical, and chemical potentials of the species involved in redox half-reactions. This molecular approach is followed to define the three types of...

  7. GraphAlignment: Bayesian pairwise alignment of biological networks

    Directory of Open Access Journals (Sweden)

    Kolář Michal

    2012-11-01

    Full Text Available Abstract Background With increased experimental availability and accuracy of bio-molecular networks, tools for their comparative and evolutionary analysis are needed. A key component for such studies is the alignment of networks. Results We introduce the Bioconductor package GraphAlignment for pairwise alignment of bio-molecular networks. The alignment incorporates information both from network vertices and network edges and is based on an explicit evolutionary model, allowing inference of all scoring parameters directly from empirical data. We compare the performance of our algorithm to an alternative algorithm, Græmlin 2.0. On simulated data, GraphAlignment outperforms Græmlin 2.0 in several benchmarks except for computational complexity. When there is little or no noise in the data, GraphAlignment is slower than Græmlin 2.0. It is faster than Græmlin 2.0 when processing noisy data containing spurious vertex associations. Its typical case complexity grows approximately as O(N2.6. On empirical bacterial protein-protein interaction networks (PIN and gene co-expression networks, GraphAlignment outperforms Græmlin 2.0 with respect to coverage and specificity, albeit by a small margin. On large eukaryotic PIN, Græmlin 2.0 outperforms GraphAlignment. Conclusions The GraphAlignment algorithm is robust to spurious vertex associations, correctly resolves paralogs, and shows very good performance in identification of homologous vertices defined by high vertex and/or interaction similarity. The simplicity and generality of GraphAlignment edge scoring makes the algorithm an appropriate choice for global alignment of networks.

  8. Networks in Cognitive Science

    CERN Document Server

    Baronchelli, Andrea; Pastor-Satorras, Romualdo; Chater, Nick; Christiansen, Morten H

    2013-01-01

    Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the cognitive sciences.

  9. Application of isothermal titration calorimetry for characterizing thermodynamic parameters of biomolecular interactions: peptide self-assembly and protein adsorption case studies.

    Science.gov (United States)

    Kabiri, Maryam; Unsworth, Larry D

    2014-10-13

    The complex nature of macromolecular interactions usually makes it very hard to identify the molecular-level mechanisms that ultimately dictate the result of these interactions. This is especially evident in the case of biological systems, where the complex interaction of molecules in various situations may be responsible for driving biomolecular interactions themselves but also has a broader effect at the cell and/or tissue level. This review will endeavor to further the understanding of biomolecular interactions utilizing the isothermal titration calorimetry (ITC) technique for thermodynamic characterization of two extremely important biomaterial systems, viz., peptide self-assembly and nonfouling polymer-modified surfaces. The advantages and shortcomings of this technique will be presented along with a thorough review of the recent application of ITC to these two areas. Furthermore, the controversies associated with the enthalpy-entropy compensation effect as well as thermodynamic equilibrium state for such interactions will be discussed.

  10. PREFACE: 1st Nano-IBCT Conference 2011 - Radiation Damage of Biomolecular Systems: Nanoscale Insights into Ion Beam Cancer Therapy

    Science.gov (United States)

    Huber, Bernd A.; Malot, Christiane; Domaracka, Alicja; Solov'yov, Andrey V.

    2012-07-01

    The 1st Nano-IBCT Conference entitled 'Radiation Damage in Biomolecular Systems: Nanoscale Insights into Ion Beam Cancer Therapy' was held in Caen, France, in October 2011. The Meeting was organised in the framework of the COST Action MP1002 (Nano-IBCT) which was launched in December 2010 (http://fias.uni-frankfurt.de/nano-ibct). This action aims to promote the understanding of mechanisms and processes underlying the radiation damage of biomolecular systems at the molecular and nanoscopic level and to use the findings to improve the strategy of Ion Beam Cancer Therapy. In the hope of achieving this, participants from different disciplines were invited to represent the fields of physics, biology, medicine and chemistry, and also included those from industry and the operators of hadron therapy centres. Ion beam therapy offers the possibility of excellent dose localization for treatment of malignant tumours, minimizing radiation damage in normal healthy tissue, while maximizing cell killing within the tumour. Several ion beam cancer therapy clinical centres are now operating in Europe and elsewhere. However, the full potential of such therapy can only be exploited by better understanding the physical, chemical and biological mechanisms that lead to cell death under ion irradiation. Considering a range of spatio-temporal scales, the proposed action therefore aims to combine the unique experimental and theoretical expertise available within Europe to acquire greater insight at the nanoscopic and molecular level into radiation damage induced by ion impact. Success in this endeavour will be both an important scientific breakthrough and give great impetus to the practical improvement of this innovative therapeutic technique. Ion therapy potentially provides an important advance in cancer therapy and the COST action MP1002 will be very significant in ensuring Europe's leadership in this field, providing the scientific background, required data and mechanistic insight which

  11. Histopathological, immunohistochemical and biomolecular diagnosis of myocarditis due to Clostridium chauvoei in a bovine

    Directory of Open Access Journals (Sweden)

    Renata Assis Casagrande

    2015-08-01

    Full Text Available The aim of this study was to report a case of clostridial myocarditis in a bovine in Brazil with emphasis on the pathological findings, isolation and molecular identification associated with the in situ localization of C. chauvoei. The animal, a male Brangus bull with nine months of age, was found dead without prior clinical signs. Multifocal and coalescent areas of necrosis were observed in the myocardium. Rod cells in the cardiac muscle fibers were positive immunostaining for C. chauvoei, while this bacterium was also isolated and identified by polymerase chain reaction (PCR.

  12. Stable isotope applications in biomolecular structure and mechanisms. A meeting to bring together producers and users of stable-isotope-labeled compounds to assess current and future needs

    Energy Technology Data Exchange (ETDEWEB)

    Trewhella, J.; Cross, T.A.; Unkefer, C.J. [eds.

    1994-12-01

    Knowledge of biomolecular structure is a prerequisite for understanding biomolecular function, and stable isotopes play an increasingly important role in structure determination of biological molecules. The first Conference on Stable Isotope Applications in Biomolecular Structure and Mechanisms was held in Santa Fe, New Mexico, March 27--31, 1994. More than 120 participants from 8 countries and 44 institutions reviewed significant developments, discussed the most promising applications for stable isotopes, and addressed future needs and challenges. Participants focused on applications of stable isotopes for studies of the structure and function of proteins, peptides, RNA, and DNA. Recent advances in NMR techniques neutron scattering, EPR, and vibrational spectroscopy were highlighted in addition to the production and synthesis of labeled compounds. This volume includes invited speaker and poster presentations as well as a set of reports from discussion panels that focused on the needs of the scientific community and the potential roles of private industry, the National Stable Isotope Resource, and the National High Magnetic Field Laboratory in serving those needs. This is the leading abstract. Individual papers are processed separately for the database.

  13. Context-dependent metabolic networks

    CERN Document Server

    Beguerisse-Díaz, Mariano; Oyarzún, Diego; Picó, Jesús; Barahona, Mauricio

    2016-01-01

    Cells adapt their metabolism to survive changes in their environment. We present a framework for the construction and analysis of metabolic reaction networks that can be tailored to reflect different environmental conditions. Using context-dependent flux distributions from Flux Balance Analysis (FBA), we produce directed networks with weighted links representing the amount of metabolite flowing from a source reaction to a target reaction per unit time. Such networks are analyzed with tools from network theory to reveal salient features of metabolite flows in each biological context. We illustrate our approach with the directed network of the central carbon metabolism of Escherichia coli, and study its properties in four relevant biological scenarios. Our results show that both flow and network structure depend drastically on the environment: networks produced from the same metabolic model in different contexts have different edges, components, and flow communities, capturing the biological re-routing of metab...

  14. Environmental Light and Its Relationship with Electromagnetic Resonances of Biomolecular Interactions, as Predicted by the Resonant Recognition Model

    Directory of Open Access Journals (Sweden)

    Irena Cosic

    2016-06-01

    Full Text Available The meaning and influence of light to biomolecular interactions, and consequently to health, has been analyzed using the Resonant Recognition Model (RRM. The RRM proposes that biological processes/interactions are based on electromagnetic resonances between interacting biomolecules at specific electromagnetic frequencies within the infra-red, visible and ultra-violet frequency ranges, where each interaction can be identified by the certain frequency critical for resonant activation of specific biological activities of proteins and DNA. We found that: (1 the various biological interactions could be grouped according to their resonant frequency into super families of these functions, enabling simpler analyses of these interactions and consequently analyses of influence of electromagnetic frequencies to health; (2 the RRM spectrum of all analyzed biological functions/interactions is the same as the spectrum of the sun light on the Earth, which is in accordance with fact that life is sustained by the sun light; (3 the water is transparent to RRM frequencies, enabling proteins and DNA to interact without loss of energy; (4 the spectrum of some artificial sources of light, as opposed to the sun light, do not cover the whole RRM spectrum, causing concerns for disturbance to some biological functions and consequently we speculate that it can influence health.

  15. Sarcomatoid mesothelioma: future advances in diagnosis, biomolecular assessment, and therapeutic options in a poor-outcome disease.

    Science.gov (United States)

    Galetta, Domenico; Catino, Annamaria; Misino, Andrea; Logroscino, Antonio; Fico, Maria

    2016-01-01

    Malignant pleural mesothelioma (MPM) is the most frequent pleural neoplasm, with asbestos exposure as one of the recognized carcinogen agents, causative in 80% of cases. The prognosis is poor; median survival of untreated cases is 6-9 months, with fewer than 5% of patients surviving 5 years. Sarcomatoid mesothelioma (SM) represents the subtype with the worst outcome and median survival ranging from 3.5 to 8 months. In the last few years, an accurate differentiation between the subtypes of MPM has become a crucial issue, due to differences in chemosensitivity and clinical outcome, and several studies have evaluated different immunohistochemical markers to better define the diagnosis. The different and worse outcome of patients with SM and, in general, nonepithelioid subtypes makes it intriguing to select these cases to better study the biomolecular profile in order to find factors linked to prognosis and/or predictive of therapeutic response. Considering recent studies on miRNA and genetic mapping, further investigation of this rare subtype might represent a field for basic and clinical-translational research providing for more tailored therapies.

  16. Constructing Surrogate Models of Complex Systems with Enhanced Sparsity: Quantifying the influence of conformational uncertainty in biomolecular solvation

    Energy Technology Data Exchange (ETDEWEB)

    Lei, Huan; Yang, Xiu; Zheng, Bin; Baker, Nathan A.

    2015-11-05

    Biomolecules exhibit conformational fluctuations near equilibrium states, inducing uncertainty in various biological properties in a dynamic way. We have developed a general method to quantify the uncertainty of target properties induced by conformational fluctuations. Using a generalized polynomial chaos (gPC) expansion, we construct a surrogate model of the target property with respect to varying conformational states. We also propose a method to increase the sparsity of the gPC expansion by defining a set of conformational “active space” random variables. With the increased sparsity, we employ the compressive sensing method to accurately construct the surrogate model. We demonstrate the performance of the surrogate model by evaluating fluctuation-induced uncertainty in solvent-accessible surface area for the bovine trypsin inhibitor protein system and show that the new approach offers more accurate statistical information than standard Monte Carlo approaches. Further more, the constructed surrogate model also enables us to directly evaluate the target property under various conformational states, yielding a more accurate response surface than standard sparse grid collocation methods. In particular, the new method provides higher accuracy in high-dimensional systems, such as biomolecules, where sparse grid performance is limited by the accuracy of the computed quantity of interest. Our new framework is generalizable and can be used to investigate the uncertainty of a wide variety of target properties in biomolecular systems.

  17. Preparation of LiNbO{sub 3} nanoparticles using poly(L-lysine) as a biomolecular additive

    Energy Technology Data Exchange (ETDEWEB)

    Lim, Youngjoon; Lee, Sang-Yup, E-mail: leessy@yonsei.ac.kr

    2014-03-01

    The effects of poly(L-lysine) as a biomolecular additive on the synthesis of LiNbO{sub 3} were investigated. PLL is a widely-studied biomolecule containing amino groups that can interact with solid inorganic clusters. The addition of PLL to a LiNbO{sub 3} precursor solution enhanced the aggregation of the produced LiNbO{sub 3} nanoparticles. This aggregation was induced by the electrical attraction of PLL with LiNbO{sub 3} nanoparticles, and was enhanced with increasing PLL molecular weight. Furthermore, the association of PLL with LiNbO{sub 3} nanoparticles was increased by the addition of methanol, which enhanced the miscibility of PLL with the precursor solution working as a co-solvent. The LiNbO{sub 3} nanoparticles generated with PLL exhibited piezoelectric properties without post-thermal treatment, suggesting that PLL contributes to the piezoelectricity. The results of this study are intriguing in terms of the potential for diverse engineering nanomaterials synthesis through a biomolecule that can also improve the physicochemical properties. - Highlights: • Piezoelectric lithium niobate nanoparticles were synthesized with poly(L-lysine). • High molecular weight poly(L-lysine) and co-solvent promoted aggregation of nanoparticles. • Poly(L-lysine) enhanced piezoelectricity of lithium niobate nanoparticles.

  18. Tailored surface-enhanced Raman nanopillar arrays fabricated by laser-assisted replication for biomolecular detection using organic semiconductor lasers.

    Science.gov (United States)

    Liu, Xin; Lebedkin, Sergei; Besser, Heino; Pfleging, Wilhelm; Prinz, Stephan; Wissmann, Markus; Schwab, Patrick M; Nazarenko, Irina; Guttmann, Markus; Kappes, Manfred M; Lemmer, Uli

    2015-01-27

    Organic semiconductor distributed feedback (DFB) lasers are of interest as external or chip-integrated excitation sources in the visible spectral range for miniaturized Raman-on-chip biomolecular detection systems. However, the inherently limited excitation power of such lasers as well as oftentimes low analyte concentrations requires efficient Raman detection schemes. We present an approach using surface-enhanced Raman scattering (SERS) substrates, which has the potential to significantly improve the sensitivity of on-chip Raman detection systems. Instead of lithographically fabricated Au/Ag-coated periodic nanostructures on Si/SiO2 wafers, which can provide large SERS enhancements but are expensive and time-consuming to fabricate, we use low-cost and large-area SERS substrates made via laser-assisted nanoreplication. These substrates comprise gold-coated cyclic olefin copolymer (COC) nanopillar arrays, which show an estimated SERS enhancement factor of up to ∼ 10(7). The effect of the nanopillar diameter (60-260 nm) and interpillar spacing (10-190 nm) on the local electromagnetic field enhancement is studied by finite-difference-time-domain (FDTD) modeling. The favorable SERS detection capability of this setup is verified by using rhodamine 6G and adenosine as analytes and an organic semiconductor DFB laser with an emission wavelength of 631.4 nm as the external fiber-coupled excitation source.

  19. LARGE-SCALE TOPOLOGICAL PROPERTIES OF MOLECULAR NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Bio-molecular networks lack the top-down design. Instead, selective forces of biological evolution shape them from raw material provided by random events such as gene duplications and single gene mutations. As a result individual connections in these networks are characterized by a large degree of randomness. One may wonder which connectivity patterns are indeed random, while which arose due to the network growth, evolution, and/or its fundamental design principles and limitations? Here we introduce a general method allowing one to construct a random null-model version of a given network while preserving the desired set of its low-level topological features, such as, e.g., the number of neighbors of individual nodes, the average level of modularity, preferential connections between particular groups of nodes, etc. Such a null-model network can then be used to detect and quantify the non-random topological patterns present in large networks. In particular, we measured correlations between degrees of interacting nodes in protein interaction and regulatory networks in yeast. It was found that in both these networks, links between highly connected proteins are systematically suppressed. This effect decreases the likelihood of cross-talk between different functional modules of the cell, and increases the overall robustness of a network by localizing effects of deleterious perturbations. It also teaches us about the overall computational architecture of such networks and points at the origin of large differences in the number of neighbors of individual nodes.

  20. Hot biological catalysis: isothermal titration calorimetry to characterize enzymatic reactions.

    Science.gov (United States)

    Mazzei, Luca; Ciurli, Stefano; Zambelli, Barbara

    2014-04-04

    Isothermal titration calorimetry (ITC) is a well-described technique that measures the heat released or absorbed during a chemical reaction, using it as an intrinsic probe to characterize virtually every chemical process. Nowadays, this technique is extensively applied to determine thermodynamic parameters of biomolecular binding equilibria. In addition, ITC has been demonstrated to be able of directly measuring kinetics and thermodynamic parameters (kcat, KM, ΔH) of enzymatic reactions, even though this application is still underexploited. As heat changes spontaneously occur during enzymatic catalysis, ITC does not require any modification or labeling of the system under analysis and can be performed in solution. Moreover, the method needs little amount of material. These properties make ITC an invaluable, powerful and unique tool to study enzyme kinetics in several applications, such as, for example, drug discovery. In this work an experimental ITC-based method to quantify kinetics and thermodynamics of enzymatic reactions is thoroughly described. This method is applied to determine kcat and KM of the enzymatic hydrolysis of urea by Canavalia ensiformis (jack bean) urease. Calculation of intrinsic molar enthalpy (ΔHint) of the reaction is performed. The values thus obtained are consistent with previous data reported in literature, demonstrating the reliability of the methodology.

  1. Cyanine dyes in biophysical research: the photophysics of polymethine fluorescent dyes in biomolecular environments.

    Science.gov (United States)

    Levitus, Marcia; Ranjit, Suman

    2011-02-01

    The breakthroughs in single molecule spectroscopy of the last decade and the recent advances in super resolution microscopy have boosted the popularity of cyanine dyes in biophysical research. These applications have motivated the investigation of the reactions and relaxation processes that cyanines undergo in their electronically excited states. Studies show that the triplet state is a key intermediate in the photochemical reactions that limit the photostability of cyanine dyes. The removal of oxygen greatly reduces photobleaching, but induces rapid intensity fluctuations (blinking). The existence of non-fluorescent states lasting from milliseconds to seconds was early identified as a limitation in single-molecule spectroscopy and a potential source of artifacts. Recent studies demonstrate that a combination of oxidizing and reducing agents is the most efficient way of guaranteeing that the ground state is recovered rapidly and efficiently. Thiol-containing reducing agents have been identified as the source of long-lived dark states in some cyanines that can be photochemically switched back to the emissive state. The mechanism of this process is the reversible addition of the thiol-containing compound to a double bond in the polymethine chain resulting in a non-fluorescent molecule. This process can be reverted by irradiation at shorter wavelengths. Another mechanism that leads to non-fluorescent states in cyanine dyes is cis-trans isomerization from the singlet-excited state. This process, which competes with fluorescence, involves the rotation of one-half of the molecule with respect to the other with an efficiency that depends strongly on steric effects. The efficiency of fluorescence of most cyanine dyes has been shown to depend dramatically on their molecular environment within the biomolecule. For example, the fluorescence quantum yield of Cy3 linked covalently to DNA depends on the type of linkage used for attachment, DNA sequence and secondary structure

  2. Membraneless organelles can melt nucleic acid duplexes and act as biomolecular filters

    Science.gov (United States)

    Nott, Timothy J.; Craggs, Timothy D.; Baldwin, Andrew J.

    2016-06-01

    Membraneless organelles are cellular compartments made from drops of liquid protein inside a cell. These compartments assemble via the phase separation of disordered regions of proteins in response to changes in the cellular environment and the cell cycle. Here we demonstrate that the solvent environment within the interior of these cellular bodies behaves more like an organic solvent than like water. One of the most-stable biological structures known, the DNA double helix, can be melted once inside the liquid droplet, and simultaneously structures formed from regulatory single-stranded nucleic acids are stabilized. Moreover, proteins are shown to have a wide range of absorption or exclusion from these bodies, and can act as importers for otherwise-excluded nucleic acids, which suggests the existence of a protein-mediated trafficking system. A common strategy in organic chemistry is to utilize different solvents to influence the behaviour of molecules and reactions. These results reveal that cells have also evolved this capability by exploiting the interiors of membraneless organelles.

  3. Biomolecular identification of ancient Mycobacterium tuberculosis complex DNA in human remains from Britain and continental Europe.

    Science.gov (United States)

    Müller, Romy; Roberts, Charlotte A; Brown, Terence A

    2014-02-01

    Tuberculosis is known to have afflicted humans throughout history and re-emerged towards the end of the 20th century, to an extent that it was declared a global emergency in 1993. The aim of this study was to apply a rigorous analytical regime to the detection of Mycobacterium tuberculosis complex (MTBC) DNA in 77 bone and tooth samples from 70 individuals from Britain and continental Europe, spanning the 1st-19th centuries AD. We performed the work in dedicated ancient DNA facilities designed to prevent all types of modern contamination, we checked the authenticity of all products obtained by the polymerase chain reaction, and we based our conclusions on up to four replicate experiments for each sample, some carried out in an independent laboratory. We identified 12 samples that, according to our strict criteria, gave definite evidence for the presence of MTBC DNA, and another 22 that we classified as "probable" or "possible." None of the definite samples came from vertebrae displaying lesions associated with TB. Instead, eight were from ribs displaying visceral new bone formation, one was a tooth from a skeleton with rib lesions, one was taken from a skeleton with endocranial lesions, one from an individual with lesions to the sacrum and sacroiliac joint and the last was from an individual with no lesions indicative of TB or possible TB. Our results add to information on the past temporal and geographical distribution of TB and affirm the suitability of ribs for studying ancient TB.

  4. AnaBench: a Web/CORBA-based workbench for biomolecular sequence analysis

    Directory of Open Access Journals (Sweden)

    Burger Gertraud

    2003-12-01

    Full Text Available Abstract Background Sequence data analyses such as gene identification, structure modeling or phylogenetic tree inference involve a variety of bioinformatics software tools. Due to the heterogeneity of bioinformatics tools in usage and data requirements, scientists spend much effort on technical issues including data format, storage and management of input and output, and memorization of numerous parameters and multi-step analysis procedures. Results In this paper, we present the design and implementation of AnaBench, an interactive, Web-based bioinformatics Analysis workBench allowing streamlined data analysis. Our philosophy was to minimize the technical effort not only for the scientist who uses this environment to analyze data, but also for the administrator who manages and maintains the workbench. With new bioinformatics tools published daily, AnaBench permits easy incorporation of additional tools. This flexibility is achieved by employing a three-tier distributed architecture and recent technologies including CORBA middleware, Java, JDBC, and JSP. A CORBA server permits transparent access to a workbench management database, which stores information about the users, their data, as well as the description of all bioinformatics applications that can be launched from the workbench. Conclusion AnaBench is an efficient and intuitive interactive bioinformatics environment, which offers scientists application-driven, data-driven and protocol-driven analysis approaches. The prototype of AnaBench, managed by a team at the Université de Montréal, is accessible on-line at: http://malawimonas.bcm.umontreal.ca:8091/anabench. Please contact the authors for details about setting up a local-network AnaBench site elsewhere.

  5. Cellular and biomolecular responses of human ovarian cancer cells to cytostatic dinuclear platinum(II) complexes.

    Science.gov (United States)

    Lin, Miaoxin; Wang, Xiaoyong; Zhu, Jianhui; Fan, Damin; Zhang, Yangmiao; Zhang, Junfeng; Guo, Zijian

    2011-03-01

    Polynuclear platinum(II) complexes represent a class of potential anticancer agents that have shown promising pharmacological properties in preclinical studies. The nature of cellular responses induced by these complexes, however, is poorly understood. In this research, the cellular responses of human ovarian cancer COC1 cells to dinuclear platinum(II) complexes {[cis-Pt(NH₃)₂Cl]₂L¹}(NO₃)₂ (1) and {[cis-Pt(NH₃)₂Cl]₂L²}(NO₃)₂ (2) (L¹ = α,α'-diamino-p-xylene, L² = 4,4'-methylenedianiline) has been studied using cisplatin as a reference. The effect of platinum complexes on the proliferation, death mode, mitochondrial membrane potential, and cell cycle progression has been examined by MTT assay and flow cytometry. The activation of cell cycle checkpoint kinases (CHK1/2), extracellular signal-regulated kinases (ERK1/2), and p38 mitogen-activated protein kinase (p38 MAPK) of the cells by the complexes has also been analyzed using phospho-specific flow cytometry. Complex 1 is more cytotoxic than complex 2 and cisplatin at most concentrations; complex 2 and cisplatin are comparably cytotoxic. These complexes kill the cells through an apoptotic or apoptosis-like pathway characterized by exposure of phosphatidylserine and dissipation of mitochondrial membrane potential. Complex 1 shows the strongest inductive effect on the morphological changes of the cells, followed by cisplatin and complex 2. Complexes 1 and 2 arrest the cell cycle in G2 or M phase, while cisplatin arrests the cell cycle in S phase. The influence of these complexes on CHK1/2, ERK1/2, and p38 MAPK varies with the dose of the drugs or reaction time. Activation of phospho-ERK1/2 and phospho-p38 MAPK by these complexes is closely related to the cytostatic activity. The results demonstrate that dinuclear platinum(II) complexes can induce some cellular responses different from those caused by cisplatin.

  6. Determination of the Astrophysical S(E) Factors or Rates for Radiative Capture Reaction with One Nucleon Transfer Reaction%Determination of the Astrophysical S(E) Factors or Rates for Radiative Capture Reaction with One Nucleon Transfer Reaction

    Institute of Scientific and Technical Information of China (English)

    李志宏; 郭冰; 李云居; 苏俊; 李二涛; 白希祥; 王友宝; 曾晟; 王宝祥; 颜胜权; 李志常; 刘建成; 连钢; 金孙均; 刘鑫; 柳卫平

    2012-01-01

    The radiative capture reaction plays an important role in nuclear astrophysics. We have indirectly measured the astrophysical S(E) factors for some proton capture reactions and reaction rates for several neutron capture reactions with one nucleon transfer reactions at HI-13 tandem accelerator in recent years. Some of them are compiled into IAEA EXFOR database and JINA REACLIB project, and used in the network calculations of Big Bang nucleosynthesis and type-I X-ray bursts.

  7. Altered Micro-RNA Degradation Promotes Tumor Heterogeneity: A Result from Boolean Network Modeling.

    Science.gov (United States)

    Wu, Yunyi; Krueger, Gerhard R F; Wang, Guanyu

    2016-02-01

    Cancer heterogeneity may reflect differential dynamical outcomes of the regulatory network encompassing biomolecules at both transcriptional and post-transcriptional levels. In other words, differential gene-expression profiles may correspond to different stable steady states of a mathematical model for simulation of biomolecular networks. To test this hypothesis, we simplified a regulatory network that is important for soft-tissue sarcoma metastasis and heterogeneity, comprising of transcription factors, micro-RNAs, and signaling components of the NOTCH pathway. We then used a Boolean network model to simulate the dynamics of this network, and particularly investigated the consequences of differential miRNA degradation modes. We found that efficient miRNA degradation is crucial for sustaining a homogenous and healthy phenotype, while defective miRNA degradation may lead to multiple stable steady states and ultimately to carcinogenesis and heterogeneity.

  8. Macromolecular networks and intelligence in microorganisms

    Science.gov (United States)

    Westerhoff, Hans V.; Brooks, Aaron N.; Simeonidis, Evangelos; García-Contreras, Rodolfo; He, Fei; Boogerd, Fred C.; Jackson, Victoria J.; Goncharuk, Valeri; Kolodkin, Alexey

    2014-01-01

    Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call “intelligence.” Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as “human” and “brain” out of the defining features of “intelligence,” all forms of life – from microbes to humans – exhibit some or all characteristics consistent with “intelligence.” We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo. PMID:25101076

  9. Macromolecular networks and intelligence in microorganisms

    Directory of Open Access Journals (Sweden)

    Hans V Westerhoff

    2014-07-01

    Full Text Available Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of Information and Communication Technology (ICT: they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity – particularly activity of the human brain – with a phenomenon we call intelligence. Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as human and brain out of the defining features of intelligence, all forms of life – from microbes to humans – exhibit some or all characteristics consistent with intelligence. We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

  10. "Peak tracking chip" for label-free optical detection of bio-molecular interaction and bulk sensing.

    Science.gov (United States)

    Bougot-Robin, Kristelle; Li, Shunbo; Zhang, Yinghua; Hsing, I-Ming; Benisty, Henri; Wen, Weijia

    2012-10-21

    A novel imaging method for bulk refractive index sensing or label-free bio-molecular interaction sensing is presented. This method is based on specially designed "Peak tracking chip" (PTC) involving "tracks" of adjacent resonant waveguide gratings (RWG) "micropads" with slowly evolving resonance position. Using a simple camera the spatial information robustly retrieves the diffraction efficiency, which in turn transduces either the refractive index of the liquids on the tracks or the effective thickness of an immobilized biological layer. Our intrinsically multiplex chip combines tunability and versatility advantages of dielectric guided wave biochips without the need of costly hyperspectral instrumentation. The current success of surface plasmon imaging techniques suggests that our chip proposal could leverage an untapped potential to routinely extend such techniques in a convenient and sturdy optical configuration toward, for instance for large analytes detection. PTC design and fabrication are discussed with challenging process to control micropads properties by varying their period (step of 2 nm) or their duty cycle through the groove width (steps of 4 nm). Through monochromatic imaging of our PTC, we present experimental demonstration of bulk index sensing on the range [1.33-1.47] and of surface biomolecule detection of molecular weight 30 kDa in aqueous solution using different surface densities. A sensitivity of the order of 10(-5) RIU for bulk detection and a sensitivity of the order of ∼10 pg mm(-2) for label-free surface detection are expected, therefore opening a large range of application of our chip based imaging technique. Exploiting and chip design, we expect as well our chip to open new direction for multispectral studies through imaging.

  11. FTIR microscopy reveals distinct biomolecular profile of crustacean digestive glands upon subtoxic exposure to ZnO nanoparticles.

    Science.gov (United States)

    Romih, Tea; Jemec, Anita; Novak, Sara; Vaccari, Lisa; Ferraris, Paolo; Šimon, Martin; Kos, Monika; Susič, Robert; Kogej, Ksenija; Zupanc, Jernej; Drobne, Damjana

    2016-01-01

    Biomolecular profiling with Fourier-Transform InfraRed Microscopy was performed to distinguish the Zn(2+)-mediated effects on the crustacean (Porcellio scaber) digestive glands from the ones elicited by the ZnO nanoparticles (NPs). The exposure to ZnO NPs or ZnCl2 (1500 and 4000 µg Zn/g of dry food) activated different types of metabolic pathways: some were found in the case of both substances, some only in the case of ZnCl2, and some only upon exposure to ZnO NPs. Both the ZnO NPs and the ZnCl2 increased the protein (∼1312 cm(-1); 1720-1485 cm(-1)/3000-2830 cm(-1)) and RNA concentration (∼1115 cm(-1)). At the highest exposure concentration of ZnCl2, where the effects occurred also at the organismal level, some additional changes were found that were not detected upon the ZnO NP exposure. These included changed carbohydrate (most likely glycogen) concentrations (∼1043 cm(-1)) and the desaturation of cell membrane lipids (∼3014 cm(-1)). The activation of novel metabolic pathways, as evidenced by changed proteins' structure (at 1274 cm(-1)), was found only in the case of ZnO NPs. This proves that Zn(2+) are not the only inducers of the response to ZnO NPs. Low bioavailable fraction of Zn(2+) in the digestive glands exposed to ZnO NPs further supports the role of particles in the ZnO NP-generated effects. This study provides the evidence that ZnO NPs induce their own metabolic responses in the subtoxic range.

  12. The reaction index and positivity ratio revisited

    DEFF Research Database (Denmark)

    Andersen, Klaus Ejner; Andersen, Flemming

    2008-01-01

    BACKGROUND AND OBJECTIVES: Assessing the quality of patch test preparations continues to be a challenge. 2 parameters, the reaction index (RI) and positivity ratio (PR), have been proposed as quality indicators by the Information Network of Departments of Dermatology (IVDK). The value of these st......BACKGROUND AND OBJECTIVES: Assessing the quality of patch test preparations continues to be a challenge. 2 parameters, the reaction index (RI) and positivity ratio (PR), have been proposed as quality indicators by the Information Network of Departments of Dermatology (IVDK). The value...... of the IVDK and our department. Calculation of RI's and PR's for patch test allergens is of limited value as a measure of quality of the patch test materials, because it predominantly reflects differences in scoring and reading of patch test reactions. Further, questionable reactions (+?) may be clinically...... relevant and very important for the individual patient. Focus on standardization of patch test materials, patch test technique, and reading of patch test reactions is mandatory....

  13. Biomolecular Analysis Capability for Cellular and Omics Research on the International Space Station

    Science.gov (United States)

    Guinart-Ramirez, Y.; Cooley, V. M.; Love, J. E.

    2016-01-01

    International Space Station (ISS) assembly complete ushered a new era focused on utilization of this state-of-the-art orbiting laboratory to advance science and technology research in a wide array of disciplines, with benefits to Earth and space exploration. ISS enabling capability for research in cellular and molecular biology includes equipment for in situ, on-orbit analysis of biomolecules. Applications of this growing capability range from biomedicine and biotechnology to the emerging field of Omics. For example, Biomolecule Sequencer is a space-based miniature DNA sequencer that provides nucleotide sequence data for entire samples, which may be used for purposes such as microorganism identification and astrobiology. It complements the use of WetLab-2 SmartCycler"TradeMark", which extracts RNA and provides real-time quantitative gene expression data analysis from biospecimens sampled or cultured onboard the ISS, for downlink to ground investigators, with applications ranging from clinical tissue evaluation to multigenerational assessment of organismal alterations. And the Genes in Space-1 investigation, aimed at examining epigenetic changes, employs polymerase chain reaction to detect immune system alterations. In addition, an increasing assortment of tools to visualize the subcellular distribution of tagged macromolecules is becoming available onboard the ISS. For instance, the NASA LMM (Light Microscopy Module) is a flexible light microscopy imaging facility that enables imaging of physical and biological microscopic phenomena in microgravity. Another light microscopy system modified for use in space to image life sciences payloads is initially used by the Heart Cells investigation ("Effects of Microgravity on Stem Cell-Derived Cardiomyocytes for Human Cardiovascular Disease Modeling and Drug Discovery"). Also, the JAXA Microscope system can perform remotely controllable light, phase-contrast, and fluorescent observations. And upcoming confocal microscopy

  14. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter

    2011-01-01

    Sloep, P. B. (2011). Learning Networks, Networked Learning. Presentation at Annual Assembly of the European Society for the Systemic Innovation of Education - ESSIE. May, 27, 2011, Leuven, Belgium: Open University in the Netherlands.

  15. Learning Networks, Networked Learning

    NARCIS (Netherlands)

    Sloep, Peter; Berlanga, Adriana

    2010-01-01

    Sloep, P. B., & Berlanga, A. J. (2011). Learning Networks, Networked Learning [Redes de Aprendizaje, Aprendizaje en Red]. Comunicar, XIX(37), 55-63. Retrieved from http://dx.doi.org/10.3916/C37-2011-02-05

  16. Dynamic analysis of biochemical network using complex network method

    Directory of Open Access Journals (Sweden)

    Wang Shuqiang

    2015-01-01

    Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.

  17. Photoclick chemistry: a fluorogenic light-triggered in vivo ligation reaction.

    Science.gov (United States)

    Ramil, Carlo P; Lin, Qing

    2014-08-01

    The ability to use chemical reactivity to monitor and control biomolecular processes with a spatial and temporal precision motivated the development of light-triggered in vivo chemistries. To this end, the photoinduced tetrazole-alkene cycloaddition, also termed 'photoclick chemistry' offers a very rapid chemical ligation platform for the manipulation of biomolecules and matrices in vivo. Here we outline the recent developments in the optimization of this chemistry, ranging from the search for substrates that offer two-photon photoactivatability, superior reaction kinetics, and/or genetic encodability, to the study of the reaction mechanism. The applications of the photoclick chemistry in protein labeling in vitro and in vivo as well as in preparing 'smart' hydrogels for 3D cell culture are highlighted.

  18. A Practical Quantum Mechanics Molecular Mechanics Method for the Dynamical Study of Reactions in Biomolecules.

    Science.gov (United States)

    Mendieta-Moreno, Jesús I; Marcos-Alcalde, Iñigo; Trabada, Daniel G; Gómez-Puertas, Paulino; Ortega, José; Mendieta, Jesús

    2015-01-01

    Quantum mechanics/molecular mechanics (QM/MM) methods are excellent tools for the modeling of biomolecular reactions. Recently, we have implemented a new QM/MM method (Fireball/Amber), which combines an efficient density functional theory method (Fireball) and a well-recognized molecular dynamics package (Amber), offering an excellent balance between accuracy and sampling capabilities. Here, we present a detailed explanation of the Fireball method and Fireball/Amber implementation. We also discuss how this tool can be used to analyze reactions in biomolecules using steered molecular dynamics simulations. The potential of this approach is shown by the analysis of a reaction catalyzed by the enzyme triose-phosphate isomerase (TIM). The conformational space and energetic landscape for this reaction are analyzed without a priori assumptions about the protonation states of the different residues during the reaction. The results offer a detailed description of the reaction and reveal some new features of the catalytic mechanism. In particular, we find a new reaction mechanism that is characterized by the intramolecular proton transfer from O1 to O2 and the simultaneous proton transfer from Glu 165 to C2.

  19. H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations.

    Science.gov (United States)

    Anandakrishnan, Ramu; Aguilar, Boris; Onufriev, Alexey V

    2012-07-01

    The accuracy of atomistic biomolecular modeling and simulation studies depend on the accuracy of the input structures. Preparing these structures for an atomistic modeling task, such as molecular dynamics (MD) simulation, can involve the use of a variety of different tools for: correcting errors, adding missing atoms, filling valences with hydrogens, predicting pK values for titratable amino acids, assigning predefined partial charges and radii to all atoms, and generating force field parameter/topology files for MD. Identifying, installing and effectively using the appropriate tools for each of these tasks can be difficult for novice and time-consuming for experienced users. H++ (http://biophysics.cs.vt.edu/) is a free open-source web server that automates the above key steps in the preparation of biomolecular structures for molecular modeling and simulations. H++ also performs extensive error and consistency checking, providing error/warning messages together with the suggested corrections. In addition to numerous minor improvements, the latest version of H++ includes several new capabilities and options: fix erroneous (flipped) side chain conformations for HIS, GLN and ASN, include a ligand in the input structure, process nucleic acid structures and generate a solvent box with specified number of common ions for explicit solvent MD.

  20. Network modeling of membrane-based artificial cellular systems

    Science.gov (United States)

    Freeman, Eric C.; Philen, Michael K.; Leo, Donald J.

    2013-04-01

    Computational models are derived for predicting the behavior of artificial cellular networks for engineering applications. The systems simulated involve the use of a biomolecular unit cell, a multiphase material that incorporates a lipid bilayer between two hydrophilic compartments. These unit cells may be considered building blocks that enable the fabrication of complex electrochemical networks. These networks can incorporate a variety of stimuli-responsive biomolecules to enable a diverse range of multifunctional behavior. Through the collective properties of these biomolecules, the system demonstrates abilities that recreate natural cellular phenomena such as mechanotransduction, optoelectronic response, and response to chemical gradients. A crucial step to increase the utility of these biomolecular networks is to develop mathematical models of their stimuli-responsive behavior. While models have been constructed deriving from the classical Hodgkin-Huxley model focusing on describing the system as a combination of traditional electrical components (capacitors and resistors), these electrical elements do not sufficiently describe the phenomena seen in experiment as they are not linked to the molecular scale processes. From this realization an advanced model is proposed that links the traditional unit cell parameters such as conductance and capacitance to the molecular structure of the system. Rather than approaching the membrane as an isolated parallel plate capacitor, the model seeks to link the electrical properties to the underlying chemical characteristics. This model is then applied towards experimental cases in order that a more complete picture of the underlying phenomena responsible for the desired sensing mechanisms may be constructed. In this way the stimuli-responsive characteristics may be understood and optimized.