WorldWideScience

Sample records for biochemical reaction networks

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. Turing-Hopf instability in biochemical reaction networks arising from pairs of subnetworks.

    Science.gov (United States)

    Mincheva, Maya; Roussel, Marc R

    2012-11-01

    Network conditions for Turing instability in biochemical systems with two biochemical species are well known and involve autocatalysis or self-activation. On the other hand general network conditions for potential Turing instabilities in large biochemical reaction networks are not well developed. A biochemical reaction network with any number of species where only one species moves is represented by a simple digraph and is modeled by a reaction-diffusion system with non-mass action kinetics. A graph-theoretic condition for potential Turing-Hopf instability that arises when a spatially homogeneous equilibrium loses its stability via a single pair of complex eigenvalues is obtained. This novel graph-theoretic condition is closely related to the negative cycle condition for oscillations in ordinary differential equation models and its generalizations, and requires the existence of a pair of subnetworks, each containing an even number of positive cycles. The technique is illustrated with a double-cycle Goodwin type model. PMID:22698892

  6. 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.

  7. 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.

  8. The Chemical Master Equation Approach to Nonequilibrium Steady-State of Open Biochemical Systems: Linear Single-Molecule Enzyme Kinetics and Nonlinear Biochemical Reaction Networks

    Directory of Open Access Journals (Sweden)

    Lisa M. Bishop

    2010-09-01

    Full Text Available We develop the stochastic, chemical master equation as a unifying approach to the dynamics of biochemical reaction systems in a mesoscopic volume under a living environment. A living environment provides a continuous chemical energy input that sustains the reaction system in a nonequilibrium steady state with concentration fluctuations. We discuss the linear, unimolecular single-molecule enzyme kinetics, phosphorylation-dephosphorylation cycle (PdPC with bistability, and network exhibiting oscillations. Emphasis is paid to the comparison between the stochastic dynamics and the prediction based on the traditional approach based on the Law of Mass Action. We introduce the difference between nonlinear bistability and stochastic bistability, the latter has no deterministic counterpart. For systems with nonlinear bistability, there are three different time scales: (a individual biochemical reactions, (b nonlinear network dynamics approaching to attractors, and (c cellular evolution. For mesoscopic systems with size of a living cell, dynamics in (a and (c are stochastic while that with (b is dominantly deterministic. Both (b and (c are emergent properties of a dynamic biochemical network; We suggest that the (c is most relevant to major cellular biochemical processes such as epi-genetic regulation, apoptosis, and cancer immunoediting. The cellular evolution proceeds with transitions among the attractors of (b in a “punctuated equilibrium” manner.

  9. The chemical master equation approach to nonequilibrium steady-state of open biochemical systems: linear single-molecule enzyme kinetics and nonlinear biochemical reaction networks.

    Science.gov (United States)

    Qian, Hong; Bishop, Lisa M

    2010-01-01

    We develop the stochastic, chemical master equation as a unifying approach to the dynamics of biochemical reaction systems in a mesoscopic volume under a living environment. A living environment provides a continuous chemical energy input that sustains the reaction system in a nonequilibrium steady state with concentration fluctuations. We discuss the linear, unimolecular single-molecule enzyme kinetics, phosphorylation-dephosphorylation cycle (PdPC) with bistability, and network exhibiting oscillations. Emphasis is paid to the comparison between the stochastic dynamics and the prediction based on the traditional approach based on the Law of Mass Action. We introduce the difference between nonlinear bistability and stochastic bistability, the latter has no deterministic counterpart. For systems with nonlinear bistability, there are three different time scales: (a) individual biochemical reactions, (b) nonlinear network dynamics approaching to attractors, and (c) cellular evolution. For mesoscopic systems with size of a living cell, dynamics in (a) and (c) are stochastic while that with (b) is dominantly deterministic. Both (b) and (c) are emergent properties of a dynamic biochemical network; We suggest that the (c) is most relevant to major cellular biochemical processes such as epi-genetic regulation, apoptosis, and cancer immunoediting. The cellular evolution proceeds with transitions among the attractors of (b) in a "punctuated equilibrium" manner. PMID:20957107

  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. Stochastic focusing coupled with negative feedback enables robust regulation in biochemical reaction networks.

    Science.gov (United States)

    Milias-Argeitis, Andreas; Engblom, Stefan; Bauer, Pavol; Khammash, Mustafa

    2015-12-01

    Nature presents multiple intriguing examples of processes that proceed with high precision and regularity. This remarkable stability is frequently counter to modellers' experience with the inherent stochasticity of chemical reactions in the regime of low-copy numbers. Moreover, the effects of noise and nonlinearities can lead to 'counterintuitive' behaviour, as demonstrated for a basic enzymatic reaction scheme that can display stochastic focusing (SF). Under the assumption of rapid signal fluctuations, SF has been shown to convert a graded response into a threshold mechanism, thus attenuating the detrimental effects of signal noise. However, when the rapid fluctuation assumption is violated, this gain in sensitivity is generally obtained at the cost of very large product variance, and this unpredictable behaviour may be one possible explanation of why, more than a decade after its introduction, SF has still not been observed in real biochemical systems. In this work, we explore the noise properties of a simple enzymatic reaction mechanism with a small and fluctuating number of active enzymes that behaves as a high-gain, noisy amplifier due to SF caused by slow enzyme fluctuations. We then show that the inclusion of a plausible negative feedback mechanism turns the system from a noisy signal detector to a strong homeostatic mechanism by exchanging high gain with strong attenuation in output noise and robustness to parameter variations. Moreover, we observe that the discrepancy between deterministic and stochastic descriptions of stochastically focused systems in the evolution of the means almost completely disappears, despite very low molecule counts and the additional nonlinearity due to feedback. The reaction mechanism considered here can provide a possible resolution to the apparent conflict between intrinsic noise and high precision in critical intracellular processes. PMID:26609065

  12. 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.

  13. 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.

  14. Biochemical reaction engineering for redox reactions.

    Science.gov (United States)

    Wandrey, Christian

    2004-01-01

    Redox reactions are still a challenge for biochemical engineers. A personal view for the development of this field is given. Cofactor regeneration was an obstacle for quite some time. The first technical breakthrough was achieved with the system formate/formate dehydrogenase for the regeneration of NADH2. In cases where the same enzyme could be used for chiral reduction as well as for cofactor regeneration, isopropanol as a hydrogen source proved to be beneficial. The coproduct (acetone) can be removed by pervaporation. Whole-cell reductions (often yeast reductions) can also be used. By proper biochemical reaction engineering, it is possible to apply these systems in a continuous way. By cloning a formate dehydrogenase and an oxidoreductase "designer bug" can be obtained where formate is used instead of glucose as the hydrogen source. Complex sequences of redox reactions can be established by pathway engineering with a focus on gene overexpression or with a focus on establishing non-natural pathways. The success of pathway engineering can be controlled by measuring cytosolic metabolite concentrations. The optimal exploitation of such systems calls for the integrated cooperation of classical and molecular biochemical engineering.

  15. 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

    Full Text Available Abstract Background Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors. Methods In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology. Results Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4

  16. Vector Encoding in Biochemical Networks

    Science.gov (United States)

    Potter, Garrett; Sun, Bo

    Encoding of environmental cues via biochemical signaling pathways is of vital importance in the transmission of information for cells in a network. The current literature assumes a single cell state is used to encode information, however, recent research suggests the optimal strategy utilizes a vector of cell states sampled at various time points. To elucidate the optimal sampling strategy for vector encoding, we take an information theoretic approach and determine the mutual information of the calcium signaling dynamics obtained from fibroblast cells perturbed with different concentrations of ATP. Specifically, we analyze the sampling strategies under the cases of fixed and non-fixed vector dimension as well as the efficiency of these strategies. Our results show that sampling with greater frequency is optimal in the case of non-fixed vector dimension but that, in general, a lower sampling frequency is best from both a fixed vector dimension and efficiency standpoint. Further, we find the use of a simple modified Ornstein-Uhlenbeck process as a model qualitatively captures many of our experimental results suggesting that sampling in biochemical networks is based on a few basic components.

  17. Programmability of Chemical Reaction Networks

    Science.gov (United States)

    Cook, Matthew; Soloveichik, David; Winfree, Erik; Bruck, Jehoshua

    Motivated by the intriguing complexity of biochemical circuitry within individual cells we study Stochastic Chemical Reaction Networks (SCRNs), a formal model that considers a set of chemical reactions acting on a finite number of molecules in a well-stirred solution according to standard chemical kinetics equations. SCRNs have been widely used for describing naturally occurring (bio)chemical systems, and with the advent of synthetic biology they become a promising language for the design of artificial biochemical circuits. Our interest here is the computational power of SCRNs and how they relate to more conventional models of computation. We survey known connections and give new connections between SCRNs and Boolean Logic Circuits, Vector Addition Systems, Petri nets, Gate Implementability, Primitive Recursive Functions, Register Machines, Fractran, and Turing Machines. A theme to these investigations is the thin line between decidable and undecidable questions about SCRN behavior.

  18. Signal detection, modularity and the correlation between extrinsic and intrinsic noise in biochemical networks

    OpenAIRE

    Tanase-Nicola, Sorin; Warren, Patrick B.; Wolde, Pieter Rein ten

    2005-01-01

    Understanding cell function requires an accurate description of how noise is transmitted through biochemical networks. We present an analytical result for the power spectrum of the output signal of a biochemical network that takes into account the correlations between the noise in the input signal (the extrinsic noise) and the noise in the reactions that constitute the network (the intrinsic noise). These correlations arise from the fact that the reactions by which biochemical signals are det...

  19. 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.

  20. Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics

    Directory of Open Access Journals (Sweden)

    Lecca Paola

    2012-05-01

    Full Text Available Abstract Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identification of the microscopic pharmacokinetics and

  1. SIMULATING BIOCHEMICAL SIGNALING NETWORKS IN COMPLEX MOVING GEOMETRIES.

    Science.gov (United States)

    Strychalski, Wanda; Adalsteinsson, David; Elston, Timothy C

    2010-01-01

    Signaling networks regulate cellular responses to environmental stimuli through cascades of protein interactions. External signals can trigger cells to polarize and move in a specific direction. During migration, spatially localized activity of proteins is maintained. To investigate the effects of morphological changes on intracellular signaling, we developed a numerical scheme consisting of a cut cell finite volume spatial discretization coupled with level set methods to simulate the resulting advection-reaction-diffusion system. We then apply the method to several biochemical reaction networks in changing geometries. We found that a Turing instability can develop exclusively by cell deformations that maintain constant area. For a Turing system with a geometry-dependent single or double peak solution, simulations in a dynamically changing geometry suggest that a single peak solution is the only stable one, independent of the oscillation frequency. The method is also applied to a model of a signaling network in a migrating fibroblast. PMID:24086102

  2. Transient amplification limits noise suppression in biochemical networks

    Science.gov (United States)

    Dixon, John; Lindemann, Anika; McCoy, Jonathan H.

    2016-01-01

    Cell physiology is orchestrated, on a molecular level, through complex networks of biochemical reactions. The propagation of random fluctuations through these networks can significantly impact cell behavior, raising challenging questions about how network design shapes the cell's ability to suppress or exploit these fluctuations. Here, drawing on insights from statistical physics, fluid dynamics, and systems biology, we explore how transient amplification phenomena arising from network connectivity naturally limit a biochemical system's ability to suppress small fluctuations around steady-state behaviors. We find that even a simple system consisting of two variables linked by a single interaction is capable of amplifying small fluctuations orders of magnitude beyond the levels predicted by linear stability theory. We also find that adding additional interactions can promote further amplification, even when these interactions implement classic design strategies known to suppress fluctuations. These results establish that transient amplification is an essential factor determining baseline noise levels in stable intracellular networks. Significantly, our analysis is not bound to specific systems or interaction mechanisms: we find that noise amplification is an emergent phenomenon found near steady states in any network containing sufficiently strong interactions, regardless of its form or function.

  3. 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...

  4. BNDB – The Biochemical Network Database

    Directory of Open Access Journals (Sweden)

    Kaufmann Michael

    2007-10-01

    Full Text Available Abstract Background Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources. Description We present the Biochemical Network Database (BNDB, a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB. Conclusion BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.

  5. 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.

  6. SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks

    DEFF Research Database (Denmark)

    Draeger, Andreas; Zielinski, Daniel C.; Keller, Roland;

    2015-01-01

    Background: The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have...... during kinetic model construction would thus benefit from automated methods for rate law assignment. Results: We present a high-throughput algorithm to automatically suggest and create suitable rate laws based upon reaction type according to several criteria. The criteria for choices made...

  7. Markovian dynamics on complex reaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Goutsias, J., E-mail: goutsias@jhu.edu; Jenkinson, G., E-mail: jenkinson@jhu.edu

    2013-08-10

    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 underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, 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 the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.

  8. Plant Glutathione Biosynthesis: Diversity in Biochemical Regulation and Reaction Products

    Directory of Open Access Journals (Sweden)

    Ashley eGalant

    2011-09-01

    Full Text Available In plants, exposure to temperature extremes, heavy metal-contaminated soils, drought, air pollutants, and pathogens results in the generation of reactive oxygen species that alter the intracellular redox environment, which in turn influences signaling pathways and cell fate. As part of their response to these stresses, plants produce glutathione. Glutathione acts as an antioxidant by quenching reactive oxygen species, and is involved in the ascorbate-glutathione cycle that eliminates damaging peroxides. Plants also use glutathione for the detoxification of xenobiotics, herbicides, air pollutants (sulfur dioxide and ozone, and toxic heavy metals. Two enzymes catalyze glutathione synthesis: glutamate-cysteine ligase (GCL, and glutathione synthetase (GS. Glutathione is a ubiquitous protective compound in plants, but the structural and functional details of the proteins that synthesize it, as well as the potential biochemical mechanisms of their regulation, have only begun to be explored. As discussed here, the core reactions of glutathione synthesis are conserved across various organisms, but plants have diversified both the regulatory mechanisms that control its synthesis and the range of products derived from this pathway. Understanding the molecular basis of glutathione biosynthesis and its regulation will expand our knowledge of this component in the plant stress response network.

  9. Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations

    Energy Technology Data Exchange (ETDEWEB)

    Nemenman, Ilya [Los Alamos National Laboratory; Sinitsyn, Nikolai [Los Alamos National Laboratory; Hengartner, Nick [Los Alamos National Laboratory

    2008-01-01

    We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, follows from the stochastic path Integral representation of the cumulant generating function of reaction events. In applications with a small number of chemIcal reactions, It produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, Interpretable representation and can be used for coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. As an example, we derive the coarse-grained description for a chain of biochemical reactions, and show that the coarse-grained and the microscopic simulations are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.

  10. Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems

    Directory of Open Access Journals (Sweden)

    Goutsias John

    2010-11-01

    Full Text Available Abstract Background Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable. Results We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html. Conclusions Our approach provides an attractive statistical methodology for

  11. Ubiquitous ``glassy'' relaxation in catalytic reaction networks

    Science.gov (United States)

    Awazu, Akinori; Kaneko, Kunihiko

    2009-10-01

    Study of reversible catalytic reaction networks is important not only as an issue for chemical thermodynamics but also for protocells. From extensive numerical simulations and theoretical analysis, slow relaxation dynamics to sustain nonequlibrium states are commonly observed. These dynamics show two types of salient behaviors that are reminiscent of glassy behavior: slow relaxation along with the logarithmic time dependence of the correlation function and the emergence of plateaus in the relaxation-time course. The former behavior is explained by the eigenvalue distribution of a Jacobian matrix around the equilibrium state that depends on the distribution of kinetic coefficients of reactions. The latter behavior is associated with kinetic constraints rather than metastable states and is due to the absence of catalysts for chemicals in excess and the negative correlation between two chemical species. Examples are given and generality is discussed with relevance to bottleneck-type dynamics in biochemical reactions as well.

  12. Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm

    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); 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)

    2015-08-07

    We address the problem of simulating biochemical reaction networks with time-dependent rates and propose a new algorithm based on our rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)]. The computation for selecting next reaction firings by our time-dependent RSSA (tRSSA) is computationally efficient. Furthermore, the generated trajectory is exact by exploiting the rejection-based mechanism. We benchmark tRSSA on different biological systems with varying forms of reaction rates to demonstrate its applicability and efficiency. We reveal that for nontrivial cases, the selection of reaction firings in existing algorithms introduces approximations because the integration of reaction rates is very computationally demanding and simplifying assumptions are introduced. The selection of the next reaction firing by our approach is easier while preserving the exactness.

  13. An effective method for computing the noise in biochemical networks

    Science.gov (United States)

    Zhang, Jiajun; Nie, Qing; He, Miao; Zhou, Tianshou

    2013-02-01

    We present a simple yet effective method, which is based on power series expansion, for computing exact binomial moments that can be in turn used to compute steady-state probability distributions as well as the noise in linear or nonlinear biochemical reaction networks. When the method is applied to representative reaction networks such as the ON-OFF models of gene expression, gene models of promoter progression, gene auto-regulatory models, and common signaling motifs, the exact formulae for computing the intensities of noise in the species of interest or steady-state distributions are analytically given. Interestingly, we find that positive (negative) feedback does not enlarge (reduce) noise as claimed in previous works but has a counter-intuitive effect and that the multi-OFF (or ON) mechanism always attenuates the noise in contrast to the common ON-OFF mechanism and can modulate the noise to the lowest level independently of the mRNA mean. Except for its power in deriving analytical expressions for distributions and noise, our method is programmable and has apparent advantages in reducing computational cost.

  14. Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

    Science.gov (United States)

    Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

    2001-01-01

    Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

  15. 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

  16. Biochemical reaction engineering and process development in anaerobic wastewater treatment.

    Science.gov (United States)

    Aivasidis, Alexander; Diamantis, Vasileios

    2005-01-01

    Developments in production technology have frequently resulted in the concentrated local accumulation of highly organic-laden wastewaters. Anaerobic wastewater treatment, in industrial applications, constitutes an advanced method of synthesis by which inexpensive substrates are converted into valuable disproportionate products. A critical discussion of certain fundamental principles of biochemical reaction engineering relevant to the anaerobic mode of operation is made here, with special emphasis on the roles of thermodynamics, kinetics, mass and heat transfer, reactor design, biomass retention and recycling. The applications of the anaerobic processes are discussed, introducing the principles of an upflow anaerobic sludge bed reactor and a fixed-bed loop reactor. The merits of staging reactor systems are presented using selected examples based on two decades of research in the field of anaerobic fermentation and wastewater treatment at the Forschungszentrum Julich (Julich Research Center, Germany). Wastewater treatment is an industrial process associated with one of the largest levels of mass throughput known, and for this reason it provides a major impetus to further developments in bioprocess technology in general.

  17. Limits for Stochastic Reaction Networks

    DEFF Research Database (Denmark)

    Cappelletti, Daniele

    of reactions. Let the rates of degradation of the intermediate species be functions of a parameter N that tends to innity. We consider a reduced system where the intermediate species have been eliminated, and nd conditions on the degradation rate of the intermediates such that the behaviour of the reduced...... network tends to that of the original one. In particular, we prove a uniform punctual convergence in distribution and weak convergence of the integrals of continuous functions along the paths of the two models. Under some extra conditions, we also prove weak convergence of the two processes. The result....... 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...

  18. Molecular codes in biological and chemical reaction networks.

    Directory of Open Access Journals (Sweden)

    Dennis Görlich

    Full Text Available 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.

  19. Genetic Recombination as a Chemical Reaction Network

    OpenAIRE

    Müller, Stefan; Hofbauer, Josef

    2015-01-01

    The process of genetic recombination can be seen as a chemical reaction network with mass-action kinetics. We review the known results on existence, uniqueness, and global stability of an equilibrium in every compatibility class and for all rate constants, from both the population genetics and the reaction networks point of view.

  20. 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.

  1. Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices

    DEFF Research Database (Denmark)

    Schmidt, Karsten; Carlsen, Morten; Nielsen, Jens Bredal;

    1997-01-01

    has been implemented that describes label distribution in primary carbon metabolism, i.e., in a metabolic network including the Embden-Meyerhof-Parnas and pentose phosphate pathway, the tricarboxylic acid cycle, and selected anaplerotic reaction sequences. The model calculates the steady state label...

  2. A study for multiple steady states of biochemical reactions under substrate and product inhibition.

    Science.gov (United States)

    Chien

    2000-08-01

    This paper combines Sturm's method with the tangent analysis method to solve a biochemical reaction involving multiplicity. This method can easily derive the necessary conditions for multiplicity. In addition, we find a starting bifurcation point for multiplicity which cannot be obtained by the tangent method alone. Moreover, a start-up strategy is suggested to obtain a high conversion and unique steady state in four selected kinetic models of biochemical reactions, with inhibition.

  3. 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.

  4. A network dynamics approach to chemical reaction networks

    NARCIS (Netherlands)

    van der Schaft, Abraham; Rao, S.; Jayawardhana, B.

    2016-01-01

    A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a ve

  5. Stochastic Modeling and Simulation of Reaction-Diffusion Biochemical Systems

    OpenAIRE

    LI Fei

    2016-01-01

    Reaction Diffusion Master Equation (RDME) framework, characterized by the discretization of the spatial domain, is one of the most widely used methods in the stochastic simulation of reaction-diffusion systems. Discretization sizes for RDME have to be appropriately chosen such that each discrete compartment is "well-stirred" and the computational cost is not too expensive. An efficient discretization size based on the reaction-diffusion dynamics of each species is derived in this disserta...

  6. 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...

  7. 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...

  8. 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.

  9. Multilayer Network Analysis of Nuclear Reactions.

    Science.gov (United States)

    Zhu, Liang; Ma, Yu-Gang; 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 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. PMID:27558995

  10. Multilayer Network Analysis of Nuclear Reactions

    Science.gov (United States)

    Zhu, Liang; Ma, Yu-Gang; 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, 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. PMID:27558995

  11. 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.

  12. 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.

  13. Law of Localization in Chemical Reaction Networks

    Science.gov (United States)

    Okada, Takashi; Mochizuki, Atsushi

    2016-07-01

    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. Nonzero response patterns turn out to exhibit two characteristic features, 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.

  14. Trade-Offs in Delayed Information Transmission in Biochemical Networks

    Science.gov (United States)

    Mancini, F.; Marsili, M.; Walczak, A. M.

    2016-03-01

    In order to transmit biochemical signals, biological regulatory systems dissipate energy with concomitant entropy production. Additionally, signaling often takes place in challenging environmental conditions. In a simple model regulatory circuit given by an input and a delayed output, we explore the trade-offs between information transmission and the system's energetic efficiency. We determine the maximally informative network, given a fixed amount of entropy production and a delayed response, exploring both the case with and without feedback. We find that feedback allows the circuit to overcome energy constraints and transmit close to the maximum available information even in the dissipationless limit. Negative feedback loops, characteristic of shock responses, are optimal at high dissipation. Close to equilibrium positive feedback loops, known for their stability, become more informative. Asking how the signaling network should be constructed to best function in the worst possible environment, rather than an optimally tuned one or in steady state, we discover that at large dissipation the same universal motif is optimal in all of these conditions.

  15. Unveiling the hidden structure of complex stochastic biochemical networks

    Science.gov (United States)

    Valleriani, Angelo; Li, Xin; Kolomeisky, Anatoly B.

    2014-02-01

    Complex Markov models are widely used and powerful predictive tools to analyze stochastic biochemical processes. However, when the network of states is unknown, it is necessary to extract information from the data to partially build the network and estimate the values of the rates. The short-time behavior of the first-passage time distributions between two states in linear chains has been shown recently to behave as a power of time with an exponent equal to the number of intermediate states. For a general Markov model we derive the complete Taylor expansion of the first-passage time distribution between two arbitrary states. By combining algebraic methods and graph theory approaches it is shown that the first term of the Taylor expansion is determined by the shortest path from the initial state to the final state. When this path is unique, we prove that the coefficient of the first term can be written in terms of the product of the transition rates along the path. It is argued that the application of our results to first-return times may be used to estimate the dependence of rates on external parameters in experimentally measured time distributions.

  16. Temperature Control System for Biochemical Reactions in Microchip-Based Devices

    Institute of Scientific and Technical Information of China (English)

    荆高山; 张坚; 朱小山; 冯继宏; 谭智敏; 刘理天; 程京

    2001-01-01

    A silicon-glass chip based microreactor has been designed and fabricated for biochemical reactions such as polymerase chain reactions (PCR). The chip based microreactor has integrated resistive heating elements. The computer-controlled temperature control system is highly reliable with precise temperature control, excellent temperature uniformity, and rapid heating and cooling capabilities. The development of the microreaction system is an important step towards the construction of a lab-on-a-chip system.

  17. A network dynamics approach to chemical reaction networks

    Science.gov (United States)

    van der Schaft, A. J.; Rao, S.; Jayawardhana, B.

    2016-04-01

    A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.

  18. An updated nuclear reaction network for BBN

    OpenAIRE

    Serpico, P. D.

    2004-01-01

    The key Standard-Physics inputs of the Big Bang Nucleosynthesis (BBN) are the light nuclei reaction rates. Both the network and the nuclear rates have been recently reanalyzed and updated, and cosmological and New-Physics constraints (taking into account the WMAP Cosmic Microwave Background anisotropies measurement) obtained with a new code are presented.

  19. The alterations in biochemical signaling of hippocampal network activity in the autism brain The alterations in biochemical signaling of hippocampal network activity in the autism brain The alterations in biochemical signaling of hippocampal network activity in the autism brain

    Institute of Scientific and Technical Information of China (English)

    田允; 黄继云; 王锐; 陶蓉蓉; 卢应梅; 廖美华; 陆楠楠; 李静; 芦博; 韩峰

    2012-01-01

    Autism is a highly heritable neurodevelopmental condition characterized by impaired social interaction and communication. However, the role of synaptic dysfunction during development of autism remains unclear. In the present study, we address the alterations of biochemical signaling in hippocampal network following induction of the autism in experimental animals. Here, the an- imal disease model and DNA array being used to investigate the differences in transcriptome or- ganization between autistic and normal brain by gene co--expression network analysis.

  20. The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions

    Directory of Open Access Journals (Sweden)

    Thomas Philipp

    2012-05-01

    Full Text Available Abstract Background It is well known that the deterministic dynamics of biochemical reaction networks can be more easily studied if timescale separation conditions are invoked (the quasi-steady-state assumption. In this case the deterministic dynamics of a large network of elementary reactions are well described by the dynamics of a smaller network of effective reactions. Each of the latter represents a group of elementary reactions in the large network and has associated with it an effective macroscopic rate law. A popular method to achieve model reduction in the presence of intrinsic noise consists of using the effective macroscopic rate laws to heuristically deduce effective probabilities for the effective reactions which then enables simulation via the stochastic simulation algorithm (SSA. The validity of this heuristic SSA method is a priori doubtful because the reaction probabilities for the SSA have only been rigorously derived from microscopic physics arguments for elementary reactions. Results We here obtain, by rigorous means and in closed-form, a reduced linear Langevin equation description of the stochastic dynamics of monostable biochemical networks in conditions characterized by small intrinsic noise and timescale separation. The slow-scale linear noise approximation (ssLNA, as the new method is called, is used to calculate the intrinsic noise statistics of enzyme and gene networks. The results agree very well with SSA simulations of the non-reduced network of elementary reactions. In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations. Conclusions A new general method, the ssLNA, is derived and shown to correctly describe the statistics of intrinsic noise about the macroscopic concentrations under timescale separation conditions

  1. Engineering interpenetrating network hydrogels as biomimetic cell niche with independently tunable biochemical and mechanical properties.

    Science.gov (United States)

    Tong, Xinming; Yang, Fan

    2014-02-01

    Hydrogels have been widely used as artificial cell niche to mimic extracellular matrix with tunable properties. However, changing biochemical cues in hydrogels developed-to-date would often induce simultaneous changes in mechanical properties, which do not support mechanistic studies on stem cell-niche interactions. Here we report the development of a PEG-based interpenetrating network (IPN), which is composed of two polymer networks that can independently and simultaneously crosslink to form hydrogels in a cell-friendly manner. The resulting IPN hydrogel allows independently tunable biochemical and mechanical properties, as well as stable and more homogeneous presentation of biochemical ligands in 3D than currently available methods. We demonstrate the potential of our IPN platform for elucidating stem cell-niche interactions by modulating osteogenic differentiation of human adipose-derived stem cells. The versatility of such IPN hydrogels is further demonstrated using three distinct and widely used polymers to form the mechanical network while keeping the biochemical network constant.

  2. 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.

  3. Insights into the organization of biochemical regulatory networks using graph theory analyses.

    Science.gov (United States)

    Ma'ayan, Avi

    2009-02-27

    Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks. PMID:18940806

  4. Insights into the organization of biochemical regulatory networks using graph theory analyses.

    Science.gov (United States)

    Ma'ayan, Avi

    2009-02-27

    Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.

  5. Performance Improvements for Nuclear Reaction Network Integration

    CERN Document Server

    Longland, Richard; José, Jordi

    2014-01-01

    Aims: The aim of this work is to compare the performance of three reaction network integration methods used in stellar nucleosynthesis calculations. These are the Gear's backward differentiation method, Wagoner's method (a 2nd-order Runge-Kutta method), and the Bader-Deuflehard semi-implicit multi-step method. Methods: To investigate the efficiency of each of the integration methods considered here, a test suite of temperature and density versus time profiles is used. This suite provides a range of situations ranging from constant temperature and density to the dramatically varying conditions present in white dwarf mergers, novae, and x-ray bursts. Some of these profiles are obtained separately from full hydrodynamic calculations. The integration efficiencies are investigated with respect to input parameters that constrain the desired accuracy and precision. Results: Gear's backward differentiation method is found to improve accuracy, performance, and stability in integrating nuclear reaction networks. For te...

  6. Modified Step Variational Iteration Method for Solving Fractional Biochemical Reaction Model

    Directory of Open Access Journals (Sweden)

    R. Yulita Molliq

    2011-01-01

    Full Text Available A new method called the modification of step variational iteration method (MoSVIM is introduced and used to solve the fractional biochemical reaction model. The MoSVIM uses general Lagrange multipliers for construction of the correction functional for the problems, and it runs by step approach, which is to divide the interval into subintervals with time step, and the solutions are obtained at each subinterval as well adopting a nonzero auxiliary parameter ℏ to control the convergence region of series' solutions. The MoSVIM yields an analytical solution of a rapidly convergent infinite power series with easily computable terms and produces a good approximate solution on enlarged intervals for solving the fractional biochemical reaction model. The accuracy of the results obtained is in a excellent agreement with the Adam Bashforth Moulton method (ABMM.

  7. Ubiquitous "glassy" relaxation in catalytic reaction networks

    OpenAIRE

    Awazu, Akinori; Kaneko, Kunihiko

    2009-01-01

    Study of reversible catalytic reaction networks is important not only as an issue for chemical thermodynamics but also for protocells. From extensive numerical simulations and theoretical analysis, slow relaxation dynamics to sustain nonequlibrium states are commonly observed. These dynamics show two types of salient behaviors that are reminiscent of glassy behavior: slow relaxation along with the logarithmic time dependence of the correlation function and the emergence of plateaus in the rel...

  8. 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

  9. From Catalytic Reaction Networks to Protocells

    Science.gov (United States)

    Kaneko, Kunihiko

    2013-12-01

    In spite of recent advances, there still remains a large gape between a set of chemical reactions and a biological cell. Here we discuss several theoretical efforts to fill in the gap. The topics cover (i) slow relaxation to equilibrium due to glassy behavior in catalytic reaction networks (ii) consistency between molecule replication and cell growth, as well as energy metabolism (iii) control of a system by minority molecules in mutually catalytic system, which work as a carrier of genetic information, and leading to evolvability (iv) generation of a compartmentalized structure as a cluster of molecules centered around the minority molecule, and division of the cluster accompanied by the replication of minority molecule (v) sequential, logical process over several states from concurrent reaction dynamics, by taking advantage of discreteness in molecule number.

  10. Metabolic control analysis of biochemical pathways based on a thermokinetic description of reaction rates

    DEFF Research Database (Denmark)

    Nielsen, Jens Bredal

    1997-01-01

    of the thermokinetic description of reaction rates to include the influence of effecters. Here the reaction rate is written as a linear function of the logarithm of the metabolite concentrations. With this type of rate function it is shown that the approach of Delgado and Liao [Biochem. J. (1992) 282......, 919-927] can be much more widely applied, although it was originally based on linearized kinetics. The methodology of determining elasticity coefficients directly from pool levels is illustrated with an analysis of the first two steps of the biosynthetic pathway of penicillin. The results compare well...

  11. A new computational method to split large biochemical networks into coherent subnets

    Directory of Open Access Journals (Sweden)

    Verwoerd Wynand S

    2011-02-01

    Full Text Available Abstract Background Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation. The method proposed here (Netsplitter allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning. Results Partitioning of a genome scale network of 1348 metabolites and 1468 reactions for Arabidopsis thaliana encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called efficacy is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species. Conclusions For the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of

  12. A computational approach to persistence, permanence, and endotacticity of biochemical reaction systems.

    Science.gov (United States)

    Johnston, Matthew D; Pantea, Casian; Donnell, Pete

    2016-01-01

    We introduce a mixed-integer linear programming (MILP) framework capable of determining whether a chemical reaction network possesses the property of being endotactic or strongly endotactic. The network property of being strongly endotactic is known to lead to persistence and permanence of chemical species under genetic kinetic assumptions, while the same result is conjectured but as yet unproved for general endotactic networks. The algorithms we present are the first capable of verifying endotacticity of chemical reaction networks for systems with greater than two constituent species. We implement the algorithms in the open-source online package CoNtRol and apply them to a large sample of networks from the European Bioinformatics Institute's BioModels Database. We use strong endotacticity to establish for the first time the permanence of a well-studied circadian clock mechanism.

  13. Constrictor: Flux Balance Analysis Constraint Modification Provides Insight for Design of Biochemical Networks

    Science.gov (United States)

    Erickson, Keesha; Chatterjee, Anushree

    2014-03-01

    The use of in silico methods has become standard practice to correlate the structure of a biochemical network to the expression of a desired phenotype. Flux balance analysis (FBA) is one of the most prevalent techniques for modeling metabolism. FBA models have been successfully applied to obtain growth predictions, theoretical product yields from heterologous pathways, and genome engineering targets. We take inspiration from high-throughput recombineering techniques, which show that combinatorial exploration can reveal optimal mutants, and apply the advantages of computational techniques to analyze these combinations. We introduce Constrictor, an in silico tool for FBA that allows gene mutations to be analyzed in a combinatorial fashion, by applying simulated constraints accounting for regulation of gene expression. We apply this algorithm to study ethylene production in E. coli through the addition of the heterologous ethylene-forming enzyme from P. syringae. Targeting individual reactions as well as sets of reactions results in theoretical ethylene yields that are as much 65% greater than yields calculated using typical FBA. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants & select gene expression levels.

  14. A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions.

    Science.gov (United States)

    Samal, Satya Swarup; Grigoriev, Dima; Fröhlich, Holger; Weber, Andreas; Radulescu, Ovidiu

    2015-12-01

    Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We provide a geometric method, based on the Newton polytope, to identify slow variables of a biochemical network with polynomial rate functions. The gist of the method is the notion of tropical equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic and utilizes orders of magnitude instead of precise values of the model parameters. Application of this method to a large collection of biochemical network models supports the idea that the number of dynamical variables in minimal models of cell physiology can be small, in spite of the large number of molecular regulatory actors.

  15. Open complex-balanced mass action chemical reaction networks

    NARCIS (Netherlands)

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

    2014-01-01

    We consider open chemical reaction networks, i.e. ones with inflows and outflows. We assume that all the inflows to the network are constant and all outflows obey the mass action kinetics rate law. We define a complex-balanced open reaction network as one that admits a complex-balanced steady state.

  16. Computational analysis of the roles of biochemical reactions in anomalous diffusion dynamics

    Science.gov (United States)

    Naruemon, Rueangkham; Charin, Modchang

    2016-04-01

    Most biochemical processes in cells are usually modeled by reaction–diffusion (RD) equations. In these RD models, the diffusive process is assumed to be Gaussian. However, a growing number of studies have noted that intracellular diffusion is anomalous at some or all times, which may result from a crowded environment and chemical kinetics. This work aims to computationally study the effects of chemical reactions on the diffusive dynamics of RD systems by using both stochastic and deterministic algorithms. Numerical method to estimate the mean-square displacement (MSD) from a deterministic algorithm is also investigated. Our computational results show that anomalous diffusion can be solely due to chemical reactions. The chemical reactions alone can cause anomalous sub-diffusion in the RD system at some or all times. The time-dependent anomalous diffusion exponent is found to depend on many parameters, including chemical reaction rates, reaction orders, and chemical concentrations. Project supported by the Thailand Research Fund and Mahidol University (Grant No. TRG5880157), the Thailand Center of Excellence in Physics (ThEP), CHE, Thailand, and the Development Promotion of Science and Technology.

  17. Proton mediated control of biochemical reactions with bioelectronic pH modulation

    Science.gov (United States)

    Deng, Yingxin; Miyake, Takeo; Keene, Scott; Josberger, Erik E.; Rolandi, Marco

    2016-01-01

    In Nature, protons (H+) can mediate metabolic process through enzymatic reactions. Examples include glucose oxidation with glucose dehydrogenase to regulate blood glucose level, alcohol dissolution into carboxylic acid through alcohol dehydrogenase, and voltage-regulated H+ channels activating bioluminescence in firefly and jellyfish. Artificial devices that control H+ currents and H+ concentration (pH) are able to actively influence biochemical processes. Here, we demonstrate a biotransducer that monitors and actively regulates pH-responsive enzymatic reactions by monitoring and controlling the flow of H+ between PdHx contacts and solution. The present transducer records bistable pH modulation from an “enzymatic flip-flop” circuit that comprises glucose dehydrogenase and alcohol dehydrogenase. The transducer also controls bioluminescence from firefly luciferase by affecting solution pH. PMID:27052724

  18. Proton mediated control of biochemical reactions with bioelectronic pH modulation

    Science.gov (United States)

    Deng, Yingxin; Miyake, Takeo; Keene, Scott; Josberger, Erik E.; Rolandi, Marco

    2016-04-01

    In Nature, protons (H+) can mediate metabolic process through enzymatic reactions. Examples include glucose oxidation with glucose dehydrogenase to regulate blood glucose level, alcohol dissolution into carboxylic acid through alcohol dehydrogenase, and voltage-regulated H+ channels activating bioluminescence in firefly and jellyfish. Artificial devices that control H+ currents and H+ concentration (pH) are able to actively influence biochemical processes. Here, we demonstrate a biotransducer that monitors and actively regulates pH-responsive enzymatic reactions by monitoring and controlling the flow of H+ between PdHx contacts and solution. The present transducer records bistable pH modulation from an “enzymatic flip-flop” circuit that comprises glucose dehydrogenase and alcohol dehydrogenase. The transducer also controls bioluminescence from firefly luciferase by affecting solution pH.

  19. 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.

  20. 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.

  1. SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks

    Directory of Open Access Journals (Sweden)

    Schröder Adrian

    2008-04-01

    Full Text Available Abstract Background The development of complex biochemical models has been facilitated through the standardization of machine-readable representations like SBML (Systems Biology Markup Language. This effort is accompanied by the ongoing development of the human-readable diagrammatic representation SBGN (Systems Biology Graphical Notation. The graphical SBML editor CellDesigner allows direct translation of SBGN into SBML, and vice versa. For the assignment of kinetic rate laws, however, this process is not straightforward, as it often requires manual assembly and specific knowledge of kinetic equations. Results SBMLsqueezer facilitates exactly this modeling step via automated equation generation, overcoming the highly error-prone and cumbersome process of manually assigning kinetic equations. For each reaction the kinetic equation is derived from the stoichiometry, the participating species (e.g., proteins, mRNA or simple molecules as well as the regulatory relations (activation, inhibition or other modulations of the SBGN diagram. Such information allows distinctions between, for example, translation, phosphorylation or state transitions. The types of kinetics considered are numerous, for instance generalized mass-action, Hill, convenience and several Michaelis-Menten-based kinetics, each including activation and inhibition. These kinetics allow SBMLsqueezer to cover metabolic, gene regulatory, signal transduction and mixed networks. Whenever multiple kinetics are applicable to one reaction, parameter settings allow for user-defined specifications. After invoking SBMLsqueezer, the kinetic formulas are generated and assigned to the model, which can then be simulated in CellDesigner or with external ODE solvers. Furthermore, the equations can be exported to SBML, LaTeX or plain text format. Conclusion SBMLsqueezer considers the annotation of all participating reactants, products and regulators when generating rate laws for reactions. Thus, for

  2. A structured approach for the engineering of biochemical network models, illustrated for signalling pathways

    NARCIS (Netherlands)

    Breitling, Rainer; Gilbert, David; Heiner, Monika; Orton, Richard

    2008-01-01

    Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted

  3. Efficient Characterization of Parametric Uncertainty of Complex (Biochemical Networks.

    Directory of Open Access Journals (Sweden)

    Claudia Schillings

    2015-08-01

    Full Text Available Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.

  4. Hysteresis-driven structure formation in biochemical networks

    Science.gov (United States)

    Klein

    1998-09-21

    A mechanism of structure formation, based on hysteresis behaviour is presented. A bisubstrate kinetic system with substrate inhibition, discussed previously in the context of Turing structure formation, may show hysteresis behaviour, when embedded in a metabolic network: the system may possess multiple steady states and may be switched from one stable fixpoint to the other. When cells containing this type of system are diffusively coupled, under certain conditions patterns result, which, as is demonstrated, are not of the Turing type. The main difference to diffusion-driven (Turing) structures is the fact that the hysteresis-driven patterns emerge under diffusive conditions, under which both the homogeneous and the asymmetrical steady state is stable. The resulting special properties and biological implications are discussed.Copyright 1998 Academic Press Limited PMID:9778438

  5. Deterministic Function Computation with Chemical Reaction Networks

    CERN Document Server

    Chen, Ho-Lin; Soloveichik, David

    2012-01-01

    We study the deterministic computation of functions on tuples of natural numbers by chemical reaction networks (CRNs). CRNs have been shown to be efficiently Turing-universal when allowing for a small probability of error. CRNs that are guaranteed to converge on a correct answer, on the other hand, have been shown to decide only the semilinear predicates. We introduce the notion of function, rather than predicate, computation by representing the output of a function f:N^k --> N^l by a count of some molecular species, i.e., if the CRN starts with n_1,...,n_k molecules of some "input" species X_1,...,X_k, the CRN is guaranteed to converge to having f(n_1,...,n_k) molecules of the "output" species Y_1,...,Y_l. We show that a function f:N^k --> N^l is deterministically computed by a CRN if and only if its graph {(x,y) \\in N^k x N^l | f(x) = y} is a semilinear set. Finally, we show that each semilinear function f can be computed on input x in expected time O(polylog |x|).

  6. CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

    Science.gov (United States)

    Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

    2014-09-01

    Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

  7. 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

  8. 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

  9. Fast stochastic simulation of biochemical reaction systems by alternative formulations of the chemical Langevin equation

    KAUST Repository

    Mélykúti, Bence

    2010-01-01

    The Chemical Langevin Equation (CLE), which is a stochastic differential equation driven by a multidimensional Wiener process, acts as a bridge between the discrete stochastic simulation algorithm and the deterministic reaction rate equation when simulating (bio)chemical kinetics. The CLE model is valid in the regime where molecular populations are abundant enough to assume their concentrations change continuously, but stochastic fluctuations still play a major role. The contribution of this work is that we observe and explore that the CLE is not a single equation, but a parametric family of equations, all of which give the same finite-dimensional distribution of the variables. On the theoretical side, we prove that as many Wiener processes are sufficient to formulate the CLE as there are independent variables in the equation, which is just the rank of the stoichiometric matrix. On the practical side, we show that in the case where there are m1 pairs of reversible reactions and m2 irreversible reactions there is another, simple formulation of the CLE with only m1 + m2 Wiener processes, whereas the standard approach uses 2 m1 + m2. We demonstrate that there are considerable computational savings when using this latter formulation. Such transformations of the CLE do not cause a loss of accuracy and are therefore distinct from model reduction techniques. We illustrate our findings by considering alternative formulations of the CLE for a human ether a-go-go related gene ion channel model and the Goldbeter-Koshland switch. © 2010 American Institute of Physics.

  10. 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...

  11. Weber's Law in Autocatalytic Reaction Networks

    CERN Document Server

    Inoue, Masayo

    2011-01-01

    Biological responses often obey Weber's law, according to which the magnitude of the response depends only on the fold change in the external input. In this study, we demonstrate that a system involving a simple autocatalytic reaction shows such response when a chemical is slowly synthesized by the reaction from a faster influx process. We also show that an autocatalytic reaction process occurring in series or in parallel can obey Weber's law with an oscillatory adaptive response. Considering the simplicity and ubiquity of the autocatalytic process, our proposed mechanism is thought to be commonly observed in biological reactions.

  12. 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.

  13. Nonlinear stochastic dynamics of mesoscopic homogeneous biochemical reaction systems—an analytical theory

    International Nuclear Information System (INIS)

    The nonlinear dynamics of biochemical reactions in a small-sized system on the order of a cell are stochastic. Assuming spatial homogeneity, the populations of n molecular species follow a multi-dimensional birth-and-death process on Zn. We introduce the Delbrück–Gillespie process, a continuous-time Markov jump process, whose Kolmogorov forward equation has been known as the chemical master equation, and whose stochastic trajectories can be computed via the Gillespie algorithm. Using simple models, we illustrate that a system of nonlinear ordinary differential equations on Rn emerges in the infinite system size limit. For finite system size, transitions among multiple attractors of the nonlinear dynamical system are rare events with exponentially long transit times. There is a separation of time scales between the deterministic ODEs and the stochastic Markov jumps between attractors. No diffusion process can provide a global representation that is accurate on both short and long time scales for the nonlinear, stochastic population dynamics. On the short time scale and near deterministic stable fixed points, Ornstein–Uhlenbeck Gaussian processes give linear stochastic dynamics that exhibit time-irreversible circular motion for open, driven chemical systems. Extending this individual stochastic behaviour-based nonlinear population theory of molecular species to other biological systems is discussed. (invited article)

  14. 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

  15. Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach

    Directory of Open Access Journals (Sweden)

    Oliveira Rui

    2010-09-01

    Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.

  16. A microfluidic platform for controlled biochemical stimulation of twin neuronal networks.

    Science.gov (United States)

    Biffi, Emilia; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Ferrigno, Giancarlo; Redaelli, Alberto; Menegon, Andrea; Rasponi, Marco

    2012-06-01

    Spatially and temporally resolved delivery of soluble factors is a key feature for pharmacological applications. In this framework, microfluidics coupled to multisite electrophysiology offers great advantages in neuropharmacology and toxicology. In this work, a microfluidic device for biochemical stimulation of neuronal networks was developed. A micro-chamber for cell culturing, previously developed and tested for long term neuronal growth by our group, was provided with a thin wall, which partially divided the cell culture region in two sub-compartments. The device was reversibly coupled to a flat micro electrode array and used to culture primary neurons in the same microenvironment. We demonstrated that the two fluidically connected compartments were able to originate two parallel neuronal networks with similar electrophysiological activity but functionally independent. Furthermore, the device allowed to connect the outlet port to a syringe pump and to transform the static culture chamber in a perfused one. At 14 days invitro, sub-networks were independently stimulated with a test molecule, tetrodotoxin, a neurotoxin known to block action potentials, by means of continuous delivery. Electrical activity recordings proved the ability of the device configuration to selectively stimulate each neuronal network individually. The proposed microfluidic approach represents an innovative methodology to perform biological, pharmacological, and electrophysiological experiments on neuronal networks. Indeed, it allows for controlled delivery of substances to cells, and it overcomes the limitations due to standard drug stimulation techniques. Finally, the twin network configuration reduces biological variability, which has important outcomes on pharmacological and drug screening. PMID:22655017

  17. A microfluidic platform for controlled biochemical stimulation of twin neuronal networks.

    Science.gov (United States)

    Biffi, Emilia; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Ferrigno, Giancarlo; Redaelli, Alberto; Menegon, Andrea; Rasponi, Marco

    2012-06-01

    Spatially and temporally resolved delivery of soluble factors is a key feature for pharmacological applications. In this framework, microfluidics coupled to multisite electrophysiology offers great advantages in neuropharmacology and toxicology. In this work, a microfluidic device for biochemical stimulation of neuronal networks was developed. A micro-chamber for cell culturing, previously developed and tested for long term neuronal growth by our group, was provided with a thin wall, which partially divided the cell culture region in two sub-compartments. The device was reversibly coupled to a flat micro electrode array and used to culture primary neurons in the same microenvironment. We demonstrated that the two fluidically connected compartments were able to originate two parallel neuronal networks with similar electrophysiological activity but functionally independent. Furthermore, the device allowed to connect the outlet port to a syringe pump and to transform the static culture chamber in a perfused one. At 14 days invitro, sub-networks were independently stimulated with a test molecule, tetrodotoxin, a neurotoxin known to block action potentials, by means of continuous delivery. Electrical activity recordings proved the ability of the device configuration to selectively stimulate each neuronal network individually. The proposed microfluidic approach represents an innovative methodology to perform biological, pharmacological, and electrophysiological experiments on neuronal networks. Indeed, it allows for controlled delivery of substances to cells, and it overcomes the limitations due to standard drug stimulation techniques. Finally, the twin network configuration reduces biological variability, which has important outcomes on pharmacological and drug screening.

  18. Scalable rule-based modelling of allosteric proteins and biochemical networks.

    Directory of Open Access Journals (Sweden)

    Julien F Ollivier

    Full Text Available Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.

  19. PIERO ontology for analysis of biochemical transformations: effective implementation of reaction information in the IUBMB enzyme list.

    Science.gov (United States)

    Kotera, Masaaki; Nishimura, Yosuke; Nakagawa, Zen-ichi; Muto, Ai; Moriya, Yuki; Okamoto, Shinobu; Kawashima, Shuichi; Katayama, Toshiaki; Tokimatsu, Toshiaki; Kanehisa, Minoru; Goto, Susumu

    2014-12-01

    Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.

  20. Synthesis and design of optimal biorefinery using an expanded network with thermochemical and biochemical biomass conversion platforms

    DEFF Research Database (Denmark)

    Cheali, Peam; Gernaey, Krist; Sin, Gürkan

    2013-01-01

    This study presents the development of an expanded biorefinery processing network for producing biofuels that combines biochemical and thermochemical conversion platforms. The expanded network is coupled to a framework that uses a superstructure based optimization approach to generate and compare...... of 72 processing intervals . This superstructure was integrated with an earlier developed superstructure for biochemical conversion routes thereby forming a formidable number of biorefinery alternatives. The expanded network was demonstrated to be versatile and useful as a decision support tool...... for identifying at early stage optimal biorefinery concept with respect to technical, economic and environmental criteria....

  1. An efficient algorithm for computing fixed length attractors based on bounded model checking in synchronous Boolean networks with biochemical applications.

    Science.gov (United States)

    Li, X Y; Yang, G W; Zheng, D S; Guo, W S; Hung, W N N

    2015-01-01

    Genetic regulatory networks are the key to understanding biochemical systems. One condition of the genetic regulatory network under different living environments can be modeled as a synchronous Boolean network. The attractors of these Boolean networks will help biologists to identify determinant and stable factors. Existing methods identify attractors based on a random initial state or the entire state simultaneously. They cannot identify the fixed length attractors directly. The complexity of including time increases exponentially with respect to the attractor number and length of attractors. This study used the bounded model checking to quickly locate fixed length attractors. Based on the SAT solver, we propose a new algorithm for efficiently computing the fixed length attractors, which is more suitable for large Boolean networks and numerous attractors' networks. After comparison using the tool BooleNet, empirical experiments involving biochemical systems demonstrated the feasibility and efficiency of our approach.

  2. 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.

  3. Modeling and Robustness Analysis of Biochemical Networks of Glycerol Metabolism by Klebsiella Pneumoniae

    Science.gov (United States)

    Ye, Jianxiong; Feng, Enmin; Wang, Lei; Xiu, Zhilong; Sun, Yaqin

    Glycerol bioconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulatory. To date, there still exist some uncertain factors in this complex network because of the limitation in bio-techniques, especially in measuring techniques for intracellular substances. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of glycerol and 1,3-PD across the cell membrane, which have received intensive interest in recent years. On the basis of different inferences of the transport mechanisms, we reconstruct various metabolic networks correspondingly and subsequently develop their dynamical systems (S-systems). To determine the most reasonable metabolic network from all possible ones, we establish a quantitative definition of biological robustness and undertake parameter identification and robustness analysis for each system. Numerical results show that it is most possible that both glycerol and 1,3-PD pass the cell membrane by active transport and passive diffusion.

  4. Discriminating between rival biochemical network models: three approaches to optimal experiment design

    Directory of Open Access Journals (Sweden)

    August Elias

    2010-04-01

    Full Text Available Abstract Background The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures. Results In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ. Conclusions Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems

  5. 生化反应的五行归属%Anfive elements classification of bio-chemical reactions

    Institute of Scientific and Technical Information of China (English)

    徐天成

    2015-01-01

    五行学说提供了对事物进行系统分类的合理方法。人类体内充满了复杂的生物化学反应体系,能否用五行学说的基本规律归纳生物化学反应体系值得探讨。在以往相关研究的基础上,本文利用五行思想对生化反应的物质和反应类型进行分类,用简洁的中医学规律研究复杂的生化反应过程,这种新思路对药物研究、中西医结合学科的发展等具有挖掘价值。%Thefive elements theory provides us a system classification of all things so we can do the research in a much reasonable way. Further more, our bodies are full of complicated bio-chemical reactions and thefive elements theory should also be applicable to those reaction systems. This paper will try to make use of thefive elements theory to carry on an innovative classification of different types of bio-chemical reactions with the help of the former foundation of related researches, to make the complicated bio-chemical reactions easy to understand, and point out that this kind of lately academic thought is of great significance to the medicine researches as well as the integrated traditional Chinese and western medicine.

  6. 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

  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. On the steady states of weakly reversible chemical reaction networks

    OpenAIRE

    Deng, Jian; Jones, Christopher; Feinberg, Martin; Nachman, Adrian

    2011-01-01

    A natural condition on the structure of the underlying chemical reaction network, namely weak reversibility, is shown to guarantee the existence of an equilibrium (steady state) in each positive stoichiometric compatibility class for the associated mass-action system. Furthermore, an index formula is given for the set of equilibria in a given stoichiometric compatibility class.

  9. Complex and detailed balancing of chemical reaction networks revisited

    NARCIS (Netherlands)

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

    2015-01-01

    The characterization of the notions of complex and detailed balancing for mass action kinetics chemical reaction networks is revisited from the perspective of algebraic graph theory, in particular Kirchhoff’s Matrix Tree theorem for directed weighted graphs. This yields an elucidation of previously

  10. Distinct configurations of protein complexes and biochemical pathways revealed by epistatic interaction network motifs

    LENUS (Irish Health Repository)

    Casey, Fergal

    2011-08-22

    Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.

  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. Stochastic modeling of biochemical systems with multistep reactions using state-dependent time delay.

    Science.gov (United States)

    Wu, Qianqian; Tian, Tianhai

    2016-01-01

    To deal with the growing scale of molecular systems, sophisticated modelling techniques have been designed in recent years to reduce the complexity of mathematical models. Among them, a widely used approach is delayed reaction for simplifying multistep reactions. However, recent research results suggest that a delayed reaction with constant time delay is unable to describe multistep reactions accurately. To address this issue, we propose a novel approach using state-dependent time delay to approximate multistep reactions. We first use stochastic simulations to calculate time delay arising from multistep reactions exactly. Then we design algorithms to calculate time delay based on system dynamics precisely. To demonstrate the power of proposed method, two processes of mRNA degradation are used to investigate the function of time delay in determining system dynamics. In addition, a multistep pathway of metabolic synthesis is used to explore the potential of the proposed method to simplify multistep reactions with nonlinear reaction rates. Simulation results suggest that the state-dependent time delay is a promising and accurate approach to reduce model complexity and decrease the number of unknown parameters in the models. PMID:27553753

  13. Biochemical reactions in crowded environments: Revisiting the effects of volume exclusion with simulations

    Directory of Open Access Journals (Sweden)

    David eGomez

    2015-06-01

    Full Text Available Molecular crowding is ubiquitous within cells and affects many biological processes including protein-protein binding, enzyme activities and gene regulation. Here we revisit some generic effects of crowding using a combination of lattice simulations and reaction-diffusion simulations with the program ReaDDy. Specifically, we implement three reactions, simple binding, a diffusion-limited reaction and a reaction with Michaelis-Menten kinetics. Histograms of binding and unbinding times provide a detailed picture how crowding affects these reactions and how the separate effects of crowding on binding equilibrium and on diffusion act together. In addition, we discuss how crowding affects processes related to gene expression such as RNA polymerase-promoter binding and translation elongation.

  14. 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.

  15. Application of approximate pattern matching in two dimensional spaces to grid layout for biochemical network maps.

    Directory of Open Access Journals (Sweden)

    Kentaro Inoue

    Full Text Available BACKGROUND: For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. RESULTS: We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. CONCLUSIONS: Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.

  16. 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...... network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from...... Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome...

  17. Some biochemical reactions of strawberry plants to infection with Botrytis cinerea and salicylic acid treatment

    Directory of Open Access Journals (Sweden)

    Urszula Małolepsza

    2013-12-01

    Full Text Available The reactions of strawberry plants to infection with B. cinerea and treatment with salicylic acid has been studied. Infection of leaves with B. cinerea resulted in early increases in active oxygen species generation, superoxide dismutase and peroxidase activities and phenolic compounds content. Some increases of the above reactions were noticed in plants treated with salicylic acid but not in the plants treated with SA and then later infected with B. cinerea.

  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. A chemical reaction network solver for the astrophysics code NIRVANA

    Science.gov (United States)

    Ziegler, U.

    2016-02-01

    Context. Chemistry often plays an important role in astrophysical gases. It regulates thermal properties by changing species abundances and via ionization processes. This way, time-dependent cooling mechanisms and other chemistry-related energy sources can have a profound influence on the dynamical evolution of an astrophysical system. Modeling those effects with the underlying chemical kinetics in realistic magneto-gasdynamical simulations provide the basis for a better link to observations. Aims: The present work describes the implementation of a chemical reaction network solver into the magneto-gasdynamical code NIRVANA. For this purpose a multispecies structure is installed, and a new module for evolving the rate equations of chemical kinetics is developed and coupled to the dynamical part of the code. A small chemical network for a hydrogen-helium plasma was constructed including associated thermal processes which is used in test problems. Methods: Evolving a chemical network within time-dependent simulations requires the additional solution of a set of coupled advection-reaction equations for species and gas temperature. Second-order Strang-splitting is used to separate the advection part from the reaction part. The ordinary differential equation (ODE) system representing the reaction part is solved with a fourth-order generalized Runge-Kutta method applicable for stiff systems inherent to astrochemistry. Results: A series of tests was performed in order to check the correctness of numerical and technical implementation. Tests include well-known stiff ODE problems from the mathematical literature in order to confirm accuracy properties of the solver used as well as problems combining gasdynamics and chemistry. Overall, very satisfactory results are achieved. Conclusions: The NIRVANA code is now ready to handle astrochemical processes in time-dependent simulations. An easy-to-use interface allows implementation of complex networks including thermal processes

  20. Spreading out of perturbations in reversible reaction networks.

    Science.gov (United States)

    Maslov, Sergei; Sneppen, Kim; Ispolatov, I

    2007-08-17

    Using an example of physical interactions between proteins, we study how a perturbation propagates in the equilibrium of a network of reversible reactions governed by the law of mass action. We introduce a matrix formalism to describe the linear response of all equilibrium concentrations to shifts in total abundances of individual reactants, and reveal its heuristic analogy to the flow of electric current in a network of resistors. Our main conclusion is that, on average, the induced changes in equilibrium concentrations decay exponentially as a function of network distance from the source of perturbation. We analyze how this decay is influenced by such factors as the topology of a network, binding strength, and correlations between concentrations of neighboring nodes. We find that the minimal branching of the network, small values of dissociation constants, and low equilibrium free (unbound) concentrations of reacting substances all decrease the decay constant and thus increase the range of propagation. Exact analytic expressions for the decay constant are obtained for the case of equally strong interactions and uniform as well as oscillating concentrations on the Bethe lattice. Our general findings are illustrated using a real network of protein-protein interactions in baker's yeast with experimentally determined protein concentrations. PMID:18046464

  1. Spreading out of perturbations in reversible reaction networks

    Energy Technology Data Exchange (ETDEWEB)

    Maslov, Sergei [Department of Condensed Matter Physics, Brookhaven National Laboratory, Upton, NY 11973 (United States); Sneppen, Kim [Niels Bohr Institute, University of Copenhagen, Blegdamsvej 17, 2100 Copenhagen Oe (Denmark); Ispolatov, I [Ariadne Genomics Inc., 9430 Key West Ave. 113 Rockville, MD 20850 (United States)

    2007-08-15

    Using an example of physical interactions between proteins, we study how a perturbation propagates in the equilibrium of a network of reversible reactions governed by the law of mass action. We introduce a matrix formalism to describe the linear response of all equilibrium concentrations to shifts in total abundances of individual reactants, and reveal its heuristic analogy to the flow of electric current in a network of resistors. Our main conclusion is that, on average, the induced changes in equilibrium concentrations decay exponentially as a function of network distance from the source of perturbation. We analyze how this decay is influenced by such factors as the topology of a network, binding strength, and correlations between concentrations of neighboring nodes. We find that the minimal branching of the network, small values of dissociation constants, and low equilibrium free (unbound) concentrations of reacting substances all decrease the decay constant and thus increase the range of propagation. Exact analytic expressions for the decay constant are obtained for the case of equally strong interactions and uniform as well as oscillating concentrations on the Bethe lattice. Our general findings are illustrated using a real network of protein-protein interactions in baker's yeast with experimentally determined protein concentrations.

  2. Noise transmission and delay-induced stochasticoscillations in biochemical network motifs

    Institute of Scientific and Technical Information of China (English)

    Liu Sheng-Jun; Wang Qi; Liu Bo; Yan Shi-Wei; Fumihiko Sakata

    2011-01-01

    With the aid of stochastic delayed-feedback differential equations,we derive an analytic expression for the power spectra of reacting molecules included in a generic biological network motif that is incorporated with a feedback mechanism and time delays in gene regulation.We systematically analyse the effects of time delays,the feedback mechanism,and biological stochasticity on the power spectra.It has been clarified that the time delays together with the feedback mechanism can induce stochastic oscillations at the molecular level and invalidate the noise addition rule for a modular description of the noise propagator.Delay-induced stochastic resonance can be expected,which is related to the stability loss of the reaction systems and Hopf bifurcation occurring for solutions of the corresponding deterministic reaction equations.Through the analysis of the power spectrum,a new approach is proposed to estimate the oscillation period.

  3. Bosonic reaction-diffusion processes on scale-free networks

    Science.gov (United States)

    Baronchelli, Andrea; Catanzaro, Michele; Pastor-Satorras, Romualdo

    2008-07-01

    Reaction-diffusion processes can be adopted to model a large number of dynamics on complex networks, such as transport processes or epidemic outbreaks. In most cases, however, they have been studied from a fermionic perspective, in which each vertex can be occupied by at most one particle. While still useful, this approach suffers from some drawbacks, the most important probably being the difficulty to implement reactions involving more than two particles simultaneously. Here we develop a general framework for the study of bosonic reaction-diffusion processes on complex networks, in which there is no restriction on the number of interacting particles that a vertex can host. We describe these processes theoretically by means of continuous-time heterogeneous mean-field theory and divide them into two main classes: steady-state and monotonously decaying processes. We analyze specific examples of both behaviors within the class of one-species processes, comparing the results (whenever possible) with the corresponding fermionic counterparts. We find that the time evolution and critical properties of the particle density are independent of the fermionic or bosonic nature of the process, while differences exist in the functional form of the density of occupied vertices in a given degree class k . We implement a continuous-time Monte Carlo algorithm, well suited for general bosonic simulations, which allows us to confirm the analytical predictions formulated within mean-field theory. Our results, at both the theoretical and numerical levels, can be easily generalized to tackle more complex, multispecies, reaction-diffusion processes and open a promising path for a general study and classification of this kind of dynamical systems on complex networks.

  4. Patterns of Stochastic Behavior in Dynamically Unstable High-Dimensional Biochemical Networks

    Directory of Open Access Journals (Sweden)

    Simon Rosenfeld

    2009-01-01

    Full Text Available The question of dynamical stability and stochastic behavior of large biochemical networks is discussed. It is argued that stringent conditions of asymptotic stability have very little chance to materialize in a multidimensional system described by the differential equations of chemical kinetics. The reason is that the criteria of asymptotic stability (Routh- Hurwitz, Lyapunov criteria, Feinberg’s Deficiency Zero theorem would impose the limitations of very high algebraic order on the kinetic rates and stoichiometric coefficients, and there are no natural laws that would guarantee their unconditional validity. Highly nonlinear, dynamically unstable systems, however, are not necessarily doomed to collapse, as a simple Jacobian analysis would suggest. It is possible that their dynamics may assume the form of pseudo-random fluctuations quite similar to a shot noise, and, therefore, their behavior may be described in terms of Langevin and Fokker-Plank equations. We have shown by simulation that the resulting pseudo-stochastic processes obey the heavy-tailed Generalized Pareto Distribution with temporal sequence of pulses forming the set of constituent-specific Poisson processes. Being applied to intracellular dynamics, these properties are naturally associated with burstiness, a well documented phenomenon in the biology of gene expression.

  5. Patterns of stochastic behavior in dynamically unstable high-dimensional biochemical networks.

    Science.gov (United States)

    Rosenfeld, Simon

    2009-01-29

    The question of dynamical stability and stochastic behavior of large biochemical networks is discussed. It is argued that stringent conditions of asymptotic stability have very little chance to materialize in a multidimensional system described by the differential equations of chemical kinetics. The reason is that the criteria of asymptotic stability (Routh-Hurwitz, Lyapunov criteria, Feinberg's Deficiency Zero theorem) would impose the limitations of very high algebraic order on the kinetic rates and stoichiometric coefficients, and there are no natural laws that would guarantee their unconditional validity. Highly nonlinear, dynamically unstable systems, however, are not necessarily doomed to collapse, as a simple Jacobian analysis would suggest. It is possible that their dynamics may assume the form of pseudo-random fluctuations quite similar to a shot noise, and, therefore, their behavior may be described in terms of Langevin and Fokker-Plank equations. We have shown by simulation that the resulting pseudo-stochastic processes obey the heavy-tailed Generalized Pareto Distribution with temporal sequence of pulses forming the set of constituent-specific Poisson processes. Being applied to intracellular dynamics, these properties are naturally associated with burstiness, a well documented phenomenon in the biology of gene expression.

  6. The influence of stressors on biochemical reactions--a review of present scientific findings with noise.

    Science.gov (United States)

    Maschke, C; Rupp, T; Hecht, K

    2000-03-01

    For every faculty of perception there is, according to the degree of irritation, a biochemical or psychobiological activation. This is also true for the perception of sound or noise. Initially, these processes allow for the adjustment of the organism to a changed situation (eustress). Prolonged effects of stressors may ultimately lead to regulatory disturbances and induce pathological processes (distress). The pathogenetic concept that psychobiological stresses (e.g. noise) may be connected with the well-known risk factors of cardiovascular diseases, through excitation of the central nervous system, is based on the known stress models. The central connective factors are the activation hormones of the adrenal gland, also referred to as stress hormones. From blood and urine parameters recorded in epidemiological and experimental studies under the influence of acute or chronic noise, a simplified model of the pathogenetic mechanism has been developed. Fundamental conditions for future assessing the "stress hormones" have been derived, by means of which premorbid conditions can be determined on a population or group basis.

  7. A new method for studying caffeine's antioxygenic property: Peroxidase-Oxidase biochemical reaction

    Institute of Scientific and Technical Information of China (English)

    WANG Jun; CAI Ruxiu; LIN Zhixin; LIU Zhihong

    2003-01-01

    The effect of Caffeine on Peroxidase-Oxidase (PO) reaction was studied systematically in this paper. We proved that the valley of PO oscillation is the best phase angle which was used to research antioxygenic property by the Analyte Pulse Perturbation Technique (APP), based on investigating the mechanism. Area integral calculus was proposed to use in quantitative analysis for the first time. There is a good linear relationship (R = 0.9950) between the ratio of amplitude changes of PO oscillation and the concentration of caffeine in the range 4.61×10-7 mol/L-1.84×10-5 mol/L. A new method for analysis by PO oscillation was set up. We also investigated two-dimensional projections and Fourier spectrum of nonlinear complicate system--PO reaction which was perturbed by caffeine, in order to provide a theoretical basis for studying effects of kinds of antioxidants on life system.

  8. Recent developments in research on catalytic reaction networks

    Directory of Open Access Journals (Sweden)

    Roberto Serra

    2013-09-01

    Full Text Available Over the last years, analyses performed on a stochastic model of catalytic reaction networks have provided some indications about the reasons why wet-lab experiments hardly ever comply with the phase transition typically predicted by theoretical models with regard to the emergence of collectively self-replicating sets of molecule (also defined as autocatalytic sets, ACSs, a phenomenon that is often observed in nature and that is supposed to have played a major role in the emergence of the primitive forms of life. The model at issue has allowed to reveal that the emerging ACSs are characterized by a general dynamical fragility, which might explain the difficulty to observe them in lab experiments. In this work, the main results of the various analyses are reviewed, with particular regard to the factors able to affect the generic properties of catalytic reactions network, for what concerns, not only the probability of ACSs to be observed, but also the overall activity of the system, in terms of production of new species, reactions and matter.

  9. Review of computer simulations of isotope effects on biochemical reactions: From the Bigeleisen equation to Feynman's path integral.

    Science.gov (United States)

    Wong, Kin-Yiu; Xu, Yuqing; Xu, Liang

    2015-11-01

    Enzymatic reactions are integral components in many biological functions and malfunctions. The iconic structure of each reaction path for elucidating the reaction mechanism in details is the molecular structure of the rate-limiting transition state (RLTS). But RLTS is very hard to get caught or to get visualized by experimentalists. In spite of the lack of explicit molecular structure of the RLTS in experiment, we still can trace out the RLTS unique "fingerprints" by measuring the isotope effects on the reaction rate. This set of "fingerprints" is considered as a most direct probe of RLTS. By contrast, for computer simulations, oftentimes molecular structures of a number of TS can be precisely visualized on computer screen, however, theoreticians are not sure which TS is the actual rate-limiting one. As a result, this is an excellent stage setting for a perfect "marriage" between experiment and theory for determining the structure of RLTS, along with the reaction mechanism, i.e., experimentalists are responsible for "fingerprinting", whereas theoreticians are responsible for providing candidates that match the "fingerprints". In this Review, the origin of isotope effects on a chemical reaction is discussed from the perspectives of classical and quantum worlds, respectively (e.g., the origins of the inverse kinetic isotope effects and all the equilibrium isotope effects are purely from quantum). The conventional Bigeleisen equation for isotope effect calculations, as well as its refined version in the framework of Feynman's path integral and Kleinert's variational perturbation (KP) theory for systematically incorporating anharmonicity and (non-parabolic) quantum tunneling, are also presented. In addition, the outstanding interplay between theory and experiment for successfully deducing the RLTS structures and the reaction mechanisms is demonstrated by applications on biochemical reactions, namely models of bacterial squalene-to-hopene polycyclization and RNA 2'-O

  10. Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks.

    Science.gov (United States)

    Singh, Shalini; Samal, Areejit; Giri, Varun; Krishna, Sandeep; Raghuram, Nandula; Jain, Sanjay

    2013-05-01

    Unraveling the structure of complex biological networks and relating it to their functional role is an important task in systems biology. Here we attempt to characterize the functional organization of the large-scale metabolic networks of three microorganisms. We apply flux balance analysis to study the optimal growth states of these organisms in different environments. By investigating the differential usage of reactions across flux patterns for different environments, we observe a striking bimodal distribution in the activity of reactions. Motivated by this, we propose a simple algorithm to decompose the metabolic network into three subnetworks. It turns out that our reaction classifier, which is blind to the biochemical role of pathways, leads to three functionally relevant subnetworks that correspond to input, output, and intermediate parts of the metabolic network with distinct structural characteristics. Our decomposition method unveils a functional bow-tie organization of metabolic networks that is different from the bow-tie structure determined by graph-theoretic methods that do not incorporate functionality. PMID:23767567

  11. Attractor for a Reaction-Diffusion System Modeling Cancer Network

    Directory of Open Access Journals (Sweden)

    Xueyong Chen

    2014-01-01

    Full Text Available A reaction-diffusion cancer network regulated by microRNA is considered in this paper. We study the asymptotic behavior of solution and show the existence of global uniformly bounded solution to the system in a bounded domain Ω⊂Rn. Some estimates and asymptotic compactness of the solutions are proved. As a result, we establish the existence of the global attractor in L2(Ω×L2(Ω and prove that the solution converges to stable steady states. These results can help to understand the dynamical character of cancer network and propose a new insight to study the mechanism of cancer. In the end, the numerical simulation shows that the analytical results agree with numerical simulation.

  12. Hierarchical feedback modules and reaction hubs in cell signaling networks.

    Directory of Open Access Journals (Sweden)

    Jianfeng Xu

    Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.

  13. Can shoulder joint reaction forces be estimated by neural networks?

    Science.gov (United States)

    de Vries, W H K; Veeger, H E J; Baten, C T M; van der Helm, F C T

    2016-01-01

    To facilitate the development of future shoulder endoprostheses, a long term load profile of the shoulder joint is desired. A musculoskeletal model using 3D kinematics and external forces as input can estimate the mechanical load on the glenohumeral joint, in terms of joint reaction forces. For long term ambulatory measurements, these 3D kinematics can be measured by means of Inertial Magnetic Measurement Systems. Recording of external forces under daily conditions is not feasible; estimations of joint loading should preferably be independent of this input. EMG signals reflect the musculoskeletal response and can easily be measured under daily conditions. This study presents the use of a neural network for the prediction of glenohumeral joint reaction forces based upon arm kinematics and shoulder muscle EMG. Several setups were examined for NN training, with varying combinations of type of input, type of motion, and handled weights. When joint reaction forces are predicted by a trained NN, for motion data independent of the training data, results show a high intraclass correlation (ICC up to 0.98) and relative SEM as low as 3%, compared to similar output of a musculoskeletal model. A convenient setup in which kinematics and only one channel of EMG were used as input for the NN׳s showed comparable predictive power as more complex setups. These results are promising and enable long term estimation of shoulder joint reaction forces outside the motion lab, independent of external forces. PMID:26654109

  14. Physiological and biochemical reactions of Hordeum vulgare seedlings to the action of silver nanoparticles

    Directory of Open Access Journals (Sweden)

    N. O. Khromykh

    2015-07-01

    Full Text Available Morphometrical indexes, and spectrophotometrically measured protein and glutathione (GSH, GSSG contents and activity of peroxidase (POD, EC 1.11.1.7, glutathione-reductase (GR, EC 1.6.4.2 and glutathione S-transferase (GST, EС 2.5.1.18 were examined in Hordeum vulgare L. seedlings after 0.01 and 0.1 mg/l AgNPs treatment during 24 h. We tested the hypothesis that the action of nanoparticles has a stressful effect on the physiological and biochemical processes of seedlings. Growth of roots was inhibited and fresh weight decreased by 29% and 21% under low and high concentrations respectively. Conversely, leaf growth was intensified, and leaf length (16% and 18% and fresh weight (35% and 44% increased at low and high concentrations respectively. POD activity in roots increased by 26% and 7%, and decreased in leaves to 57% and 81% of control at low and high concentrations respectively. GSH content changed insignificantly, but GSSG content increased in roots (2 and 2.5-fold and in leaves (13% and 30% at both AgNPs concentrations. GSH/GSSG-ratio decreased in roots (1.9 and 2.6-fold and in leaves (1.1 and 1.3-fold at low and high concentrations respectively. GR activity decreased at a concentration of 0.01 mg/l (7% in roots and 17% in leaves respectively and increased at 0.1 mg/l (52% in roots and 6% in leaves. GST activity increased in leaves (52% and 78% at low and high concentrations but decreased by 17% in roots under high concentration of nanosilver. Thus, the action of AgNPs on barley seedlings had a dose-dependent and organ-specific character. The various directions of changes in growth, metabolic processes and activity of antioxidant defense systems appear to be a stress response of barley seedlings to the impact of AgNPs, which underlines the necessity of detailed study of plant intracellular processes exposed to the action of nanomaterial.

  15. 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.

  16. Effect of Maillard reaction on biochemical properties of peanut 7S globulin (Ara h 1) and its interaction with a human colon cancer cell line (Caco-2)

    NARCIS (Netherlands)

    Teodorowicz, M.; Fiedorowicz, E.; Kostyra, H.; Wichers, H.J.; Kostyra, E.

    2013-01-01

    Purpose The purpose of this study was to determine the influence of Maillard reaction (MR, glycation) on biochemical and biological properties of the major peanut allergen Ara h 1. Methods Three different time/temperature conditions of treatment were applied (37, 60, and 145 °C). The extent of MR wa

  17. Determination of Kinetic Parameters and Metal Ions in Urea-Urease System Based on the Biochemical Reaction Heat Induced Laser Beam Deflection

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A new analytical method for the determination of urea-urease system based on biochemical reaction heat induced laser beam deflection is presented in this paper. With the method, the Michaelis constant (Km) of urease and apparent inhibition constant (Ki) of some metal ion inhibitors were measured respectively. This method was also used for the quantitative determination of metal ions with satisfactory result.

  18. Experimental (Network) and Evaluated Nuclear Reaction Data at NDS

    International Nuclear Information System (INIS)

    Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.

  19. 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

  20. Network analysis of biochemical logic for noise reduction and stability: a system of three coupled enzymatic and gates.

    Science.gov (United States)

    Privman, Vladimir; Arugula, Mary A; Halámek, Jan; Pita, Marcos; Katz, Evgeny

    2009-04-16

    We develop an approach aimed at optimizing the parameters of a network of biochemical logic gates for reduction of the "analog" noise buildup. Experiments for three coupled enzymatic AND gates are reported, illustrating our procedure. Specifically, starch, one of the controlled network inputs, is converted to maltose by beta-amylase. With the use of phosphate (another controlled input), maltose phosphorylase then produces glucose. Finally, nicotinamide adenine dinucleotide (NAD(+)), the third controlled input, is reduced under the action of glucose dehydrogenase to yield the optically detected signal. Network functioning is analyzed by varying selective inputs and fitting standardized few-parameters "response-surface" functions assumed for each gate. This allows a certain probe of the individual gate quality, but primarily yields information on the relative contribution of the gates to noise amplification. The derived information is then used to modify our experimental system to put it in a regime of a less noisy operation.

  1. Multiplexing oscillatory biochemical signals.

    Science.gov (United States)

    de Ronde, Wiet; ten Wolde, Pieter Rein

    2014-04-01

    In recent years it has been increasingly recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that the protein signaling network of living cells has a bow-tie structure and that components are often shared between different signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals simultaneously through a common signaling pathway, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals. PMID:24685537

  2. Influence of site and age on biochemical characteristics of the collagen network of equine articular cartilage

    NARCIS (Netherlands)

    Brama, P.A.J.; TeKoppele, J.M.; Bank, R.A.; Weeren, P.R. van; Barneveld, A.

    1999-01-01

    Objective - To determine variations in biochemical characteristics of equine articular cartilage in relation to age and the degree of predisposition for osteochondral disease at a specific site. Sample Population - Articular cartilage specimens from 53 horses 4 to 30 years old. Procedure - Healthy s

  3. Microfluidic technology platforms for synthesizing, labeling and measuring the kinetics of transport and biochemical reactions for developing molecular imaging probes

    Energy Technology Data Exchange (ETDEWEB)

    Phelps, Michael E. [Univ. of California, Los Angeles, CA (United States)

    2009-09-01

    Radiotracer techniques are used in environmental sciences, geology, biology and medicine. Radiotracers with Positron Emission Tomography (PET) provided biological examinations of ~3 million patients 2008. Despite the success of positron labeled tracers in many sciences, there is limited access in an affordable and convenient manner to develop and use new tracers. Integrated microfluidic chips are a new technology well matched to the concentrations of tracers. Our goal is to develop microfluidic chips and new synthesis approaches to enable wide dissemination of diverse types of tracers at low cost, and to produce new generations of radiochemists for which there are many unfilled jobs. The program objectives are to: 1. Develop an integrated microfluidic platform technology for synthesizing and 18F-labeling diverse arrays of different classes of molecules. 2. Incorporate microfluidic chips into small PC controlled devices (“Synthesizer”) with a platform interfaced to PC for electronic and fluid input/out control. 3. Establish a de-centralized model with Synthesizers for discovering and producing molecular imaging probes, only requiring delivery of inexpensive [18F]fluoride ion from commercial PET radiopharmacies vs the centralized approach of cyclotron facilities synthesizing and shipping a few different types of 18F-probes. 4. Develop a position sensitive avalanche photo diode (PSAPD) camera for beta particles embedded in a microfluidic chip for imaging and measuring transport and biochemical reaction rates to valid new 18F-labeled probes in an array of cell cultures. These objectives are met within a research and educational program integrating radio-chemistry, synthetic chemistry, biochemistry, engineering and biology in the Crump Institute for Molecular Imaging. The Radiochemistry Training Program exposes PhD and post doctoral students to molecular imaging in vitro in cells and microorganisms in microfluidic chips and in vivo with PET, from new technologies

  4. 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

  5. Biochemical switching device: biomimetic approach and application to neural network study.

    Science.gov (United States)

    Okamoto, M

    1992-06-01

    There are many examples of enzymes that share substrates or cofactors in a cyclic manner. Techniques have been developed that use cyclic enzyme systems to assay quantitatively small amounts of biochemical substances (cofactor, substrate), however, only a few studies of the control of these systems have been published. The author previously showed with computer simulations that cyclic enzyme systems have the reliability of ON-OFF types of operation (McCulloch-Pitts' neuronic equation) capable of storing short-memory, and the applicability for a switching circuit in a biocomputer. This paper introduces a unique switching mechanism of cyclic enzyme system (basic switching element), and next, building the integrated biochemical switching system being composed of the basic switching element, shows the physiological phenomenon termed 'selective elimination of synapses' generally produced as a result of low-frequency train of electrical stimuli to the synapses (Kuroda, Y. 1989) Neurochem. Int. 14, 309-319). PMID:1368350

  6. Properties of Random Complex Chemical Reaction Networks and Their Relevance to Biological Toy Models

    OpenAIRE

    Bigan, Erwan; Steyaert, Jean-Marc; Douady, Stéphane

    2013-01-01

    We investigate the properties of large random conservative chemical reaction networks composed of elementary reactions endowed with either mass-action or saturating kinetics, assigning kinetic parameters in a thermodynamically-consistent manner. We find that such complex networks exhibit qualitatively similar behavior when fed with external nutrient flux. The nutrient is preferentially transformed into one specific chemical that is an intrinsic property of the network. We propose a self-consi...

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

    OpenAIRE

    der Schaft, Arjan van; Rao, Shodhan; 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 ...

  8. 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 ...

  9. 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

  10. An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks

    Directory of Open Access Journals (Sweden)

    Michael Gormley

    2011-01-01

    Full Text Available Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.

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

    DEFF Research Database (Denmark)

    Feliu, Elisenda; Wiuf, C.

    2012-01-01

    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...... precludes the existence of degenerate steady states. Further, we relate injectivity of a network to that of the network obtained by adding outflow, or degradation, reactions for all species....

  12. Unravelling the Maillard reaction network by multiresponse kinetic modelling

    OpenAIRE

    Martins, S.I.F.S.

    2003-01-01

    The Maillard reaction is an important reaction in food industry. It is responsible for the formation of colour and aroma, as well as toxic compounds as the recent discovered acrylamide. The knowledge of kinetic parameters, such as rate constants and activation energy, is necessary to predict its extent and, consequently, to optimise it. Each of the chapters presented in this thesis can be seen as a necessary step to succeed in applying multiresponse kinetic modelling in a complex reaction, su...

  13. 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....

  14. Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

    OpenAIRE

    Claudia Schillings; Mikael Sunnåker; Jörg Stelling; Christoph Schwab

    2015-01-01

    Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternat...

  15. Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks

    OpenAIRE

    Claudia Schillings; Mikael Sunnåker; Jörg Stelling; Christoph Schwab

    2015-01-01

    Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternat...

  16. 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.

  17. Boundedness and exponential stability for nonautonomous cellular neural networks with reaction-diffusion terms

    Energy Technology Data Exchange (ETDEWEB)

    Lou Xuyang [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China); Cui Baotong [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)]. E-mail: btcui@sohu.com

    2007-07-15

    Employing Lyapunov functional method, we analyze the ultimate boundedness and global exponential stability of a class of reaction-diffusion cellular neural networks with time-varying delays. Some new criteria are obtained to ensure ultimate boundedness and global exponential stability of delayed reaction-diffusion cellular neural networks (DRCNNs). Without assuming that the activation functions f {sub ijl}(.) are bounded, the results extend and improve the earlier publications.

  18. Effect of Maillard reaction on biochemical properties of peanut 7S globulin (Ara h 1) and its interaction with a human colon cancer cell line (Caco-2)

    OpenAIRE

    Teodorowicz, M.; Fiedorowicz, E.; Kostyra, H.; Wichers, H J; Kostyra, E.

    2013-01-01

    Purpose The purpose of this study was to determine the influence of Maillard reaction (MR, glycation) on biochemical and biological properties of the major peanut allergen Ara h 1. Methods Three different time/temperature conditions of treatment were applied (37, 60, and 145 °C). The extent of MR was assessed by SDS-PAGE, loss of free amino groups, fluorescence intensity, content of bound sugar and fructosamine. The Caco-2 model system was applied to study effects of hydrolysed and non-hydrol...

  19. Stability for delayed reaction-diffusion neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Allegretto, W. [Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Alberta, T6G 2G1 (Canada)]. E-mail: wallegre@math.ualberta.ca; Papini, D. [Dipartimento di Ingegneria dell' Informazione, Universita degli Studi di Siena, via Roma 56, 53100 Siena (Italy)]. E-mail: papini@dii.unisi.it

    2007-01-15

    We consider a Hopfield neural network model with diffusive terms, non-decreasing and discontinuous neural activation functions, time-dependent delays and time-periodic coefficients. We provide conditions on interconnection matrices and delays which guarantee that for each periodic input the model has a unique periodic solution that is globally exponentially stable. Even in the case without diffusion, such conditions improve recent results on classical delayed Hopfield neural networks with discontinuous activation functions. Numerical examples illustrate the results.

  20. 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...

  1. 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

  2. Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

    Science.gov (United States)

    Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

    2015-08-01

    Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design. PMID:26317784

  3. Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.

    Science.gov (United States)

    Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph

    2015-08-01

    Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.

  4. Landauer in the Age of Synthetic Biology: Energy Consumption and Information Processing in Biochemical Networks

    Science.gov (United States)

    Mehta, Pankaj; Lang, Alex H.; Schwab, David J.

    2016-03-01

    A central goal of synthetic biology is to design sophisticated synthetic cellular circuits that can perform complex computations and information processing tasks in response to specific inputs. The tremendous advances in our ability to understand and manipulate cellular information processing networks raises several fundamental physics questions: How do the molecular components of cellular circuits exploit energy consumption to improve information processing? Can one utilize ideas from thermodynamics to improve the design of synthetic cellular circuits and modules? Here, we summarize recent theoretical work addressing these questions. Energy consumption in cellular circuits serves five basic purposes: (1) increasing specificity, (2) manipulating dynamics, (3) reducing variability, (4) amplifying signal, and (5) erasing memory. We demonstrate these ideas using several simple examples and discuss the implications of these theoretical ideas for the emerging field of synthetic biology. We conclude by discussing how it may be possible to overcome these limitations using "post-translational" synthetic biology that exploits reversible protein modification.

  5. 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

  6. Different biochemical mechanisms ensure network-wide balancing of reducing equivalents in microbial metabolism.

    Science.gov (United States)

    Fuhrer, Tobias; Sauer, Uwe

    2009-04-01

    To sustain growth, the catabolic formation of the redox equivalent NADPH must be balanced with the anabolic demand. The mechanisms that ensure such network-wide balancing, however, are presently not understood. Based on 13C-detected intracellular fluxes, metabolite concentrations, and cofactor specificities for all relevant central metabolic enzymes, we have quantified catabolic NADPH production in Agrobacterium tumefaciens, Bacillus subtilis, Escherichia coli, Paracoccus versutus, Pseudomonas fluorescens, Rhodobacter sphaeroides, Sinorhizobium meliloti, and Zymomonas mobilis. For six species, the estimated NADPH production from glucose catabolism exceeded the requirements for biomass synthesis. Exceptions were P. fluorescens, with balanced rates, and E. coli, with insufficient catabolic production, in which about one-third of the NADPH is supplied via the membrane-bound transhydrogenase PntAB. P. versutus and B. subtilis were the only species that appear to rely on transhydrogenases for balancing NADPH overproduction during growth on glucose. In the other four species, the main but not exclusive redox-balancing mechanism appears to be the dual cofactor specificities of several catabolic enzymes and/or the existence of isoenzymes with distinct cofactor specificities, in particular glucose 6-phosphate dehydrogenase. An unexpected key finding for all species, except E. coli and B. subtilis, was the lack of cofactor specificity in the oxidative pentose phosphate pathway, which contrasts with the textbook view of the pentose phosphate pathway dehydrogenases as being NADP+ dependent.

  7. Reaction-Diffusion Processes on Random and Scale-Free Networks

    Science.gov (United States)

    Banerjee, Subhasis; Mallick, Shrestha Basu; Bose, Indrani

    We study the discrete Gierer-Meinhardt model of reaction-diffusion on three different types of networks: regular, random and scale-free. The model dynamics lead to the formation of stationary Turing patterns in the steady state in certain parameter regions. Some general features of the patterns are studied through numerical simulation. The results for the random and scale-free networks show a marked difference from those in the case of the regular network. The difference may be ascribed to the small world character of the first two types of networks.

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

    International Nuclear Information System (INIS)

    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

  10. Integrated microfluidic system enabling (bio)chemical reactions with on-line MALDI-TOF mass spectrometry

    NARCIS (Netherlands)

    Brivio, Monica; Fokkens, Roel H.; Verboom, Willem; Reinhoudt, David N.; Tas, Niels R.; Goedbloed, Martijn; Berg, van den Albert

    2002-01-01

    A continuous flow micro total analysis system (μ-TAS) consisting of an on-chip microfluidic device connected to a matrix assisted laser desorption ionization [MALDI] time-of-flight [TOF] mass spectrometer (MS) as an analytical screening system is presented. Reaction microchannels and inlet/outlet re

  11. Automatic Verification of Biochemical Network Using Model Checking Method%基于模型校核的生化网络自动辨别方法

    Institute of Scientific and Technical Information of China (English)

    Jinkyung Kim; Younghee Lee; Il Moon

    2008-01-01

    This study focuses on automatic searching and verifying methods for the reachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a consid-erable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for veri-fying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed ina temporal logic, such as computational tree logic (CTL). This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.

  12. 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.

  13. 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...

  14. Approaches to Chemical and Biochemical Information and Signal Processing

    Science.gov (United States)

    Privman, Vladimir

    2012-02-01

    We outline models and approaches for error control required to prevent buildup of noise when ``gates'' and other ``network elements'' based on (bio)chemical reaction processes are utilized to realize stable, scalable networks for information and signal processing. We also survey challenges and possible future research. [4pt] [1] Control of Noise in Chemical and Biochemical Information Processing, V. Privman, Israel J. Chem. 51, 118-131 (2010).[0pt] [2] Biochemical Filter with Sigmoidal Response: Increasing the Complexity of Biomolecular Logic, V. Privman, J. Halamek, M. A. Arugula, D. Melnikov, V. Bocharova and E. Katz, J. Phys. Chem. B 114, 14103-14109 (2010).[0pt] [3] Towards Biosensing Strategies Based on Biochemical Logic Systems, E. Katz, V. Privman and J. Wang, in: Proc. Conf. ICQNM 2010 (IEEE Comp. Soc. Conf. Publ. Serv., Los Alamitos, California, 2010), pages 1-9.

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

    OpenAIRE

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

    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 can characterize the space of equilibrium points and provide simple dynamical analysis on the state space modulo the space of equilibrium points.

  16. New Approach to the Stability of Chemical Reaction Networks: Piecewise Linear in Rates Lyapunov Functions

    OpenAIRE

    Al-Radhawi, M. Ali; Angeli, David

    2014-01-01

    Piecewise-Linear in Rates (PWLR) Lyapunov functions are introduced for a class of Chemical Reaction Networks (CRNs). In addition to their simple structure, these functions are robust with respect to arbitrary monotone reaction rates, of which mass-action is a special case. The existence of such functions ensures the convergence of trajectories towards equilibria, and guarantee their asymptotic stability with respect to the corresponding stoichiometric compatibility class. We give the definiti...

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

    OpenAIRE

    Aikawa, M.; Arnould, 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 c...

  18. Reconfigurable neuromorphic computation in biochemical systems.

    Science.gov (United States)

    Chiang, Hui-Ju Katherine; Jiang, Jie-Hong R; Fages, Francois

    2015-08-01

    Implementing application-specific computation and control tasks within a biochemical system has been an important pursuit in synthetic biology. Most synthetic designs to date have focused on realizing systems of fixed functions using specifically engineered components, thus lacking flexibility to adapt to uncertain and dynamically-changing environments. To remedy this limitation, an analog and modularized approach to realize reconfigurable neuromorphic computation with biochemical reactions is presented. We propose a biochemical neural network consisting of neuronal modules and interconnects that are both reconfigurable through external or internal control over the concentrations of certain molecular species. Case studies on classification and machine learning applications using the DNA strain displacement technology demonstrate the effectiveness of our design in both reconfiguration and autonomous adaptation. PMID:26736417

  19. Toward a self-consistent and unitary reaction network for big bang nucleosynthesis

    International Nuclear Information System (INIS)

    Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the 9B compound nuclear system in the resonant destruction of 7Li during primordial nucleosynthesis. We have studied reactions in the 9B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic 6Li(3He,3He)6Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for 6Li(3He,p)8Be* and from 0.4 to 5.0 MeV for the 6Li(3He,γ)7Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for 6Li(3He,γ)9B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)

  20. Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms

    Energy Technology Data Exchange (ETDEWEB)

    Sheng Li [School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China); Yang Huizhong [School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)], E-mail: victory8209@yahoo.com.cn; Lou Xuyang [School of Communication and Control Engineering, Jiangnan University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)

    2009-04-30

    This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.

  1. 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...

  2. Summary report on [IAEA] technical meeting of the International Network of Nuclear Reaction Data Centres

    International Nuclear Information System (INIS)

    An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at the IAEA Headquarters in Vienna from 25 to 26 May 2009. The meeting was attended by 23 participants from 13 cooperating data centres. A summary of the meeting is given in this report, along with the conclusions, actions, and status report of the participating data centres. (author)

  3. 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...

  4. Global asymptotic stability of BAM neural networks with distributed delays and reaction-diffusion terms

    Energy Technology Data Exchange (ETDEWEB)

    Cui Baotong [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Rd., Wuxi, Jiangsu 214122 (China)] e-mail: btcui@sohu.com; Lou Xuyang [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Rd., Wuxi, Jiangsu 214122 (China)

    2006-03-01

    The global asymptotic stability of bi-directional associative memory neural networks with distributed delays and reaction-diffusion terms are studied by using the analysis technique and Lyapunov functional. A sufficient condition is proposed. Two numerical examples are given to show the correctness of our analysis.

  5. MSU SINP CDFE nuclear data activities in the nuclear reaction data centres network

    International Nuclear Information System (INIS)

    This paper is the progress report of the Centre for Photonuclear Experiments Data, Moscow. It is a short review of the works carried out by the CDFE concerning the IAEA nuclear reaction data centers network activities from May 2001 until May 2002. and the description of the main results obtained. (a.n.)

  6. 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.

  7. Toward a self-consistent and unitary reaction network for big-bang nucleosynthesis

    Directory of Open Access Journals (Sweden)

    Paris Mark W.

    2014-04-01

    Full Text Available Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a “unitary reaction network” for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the 9B compound nuclear system in the resonant destruction of 7Li during primordial nucleosynthesis. We have studied reactions in the 9B compound system with a multichannel, two-body unitary R-matrix code (EDA using the known elastic and reaction data, in a four-channel treatment. The data include elastic 6Li(3He,3He6Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for 6Li(3He,p8Be* and from 0.4 to 5.0 MeV for the 6Li(3He,d7Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for 6Li(3He,γ9B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized.

  8. 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.

  9. Water formation at low temperatures by surface O2 hydrogenation II: the reaction network

    CERN Document Server

    Cuppen, H M; Romanzin, C; Linnartz, H; 10.1039/C0CP00251H

    2010-01-01

    Water is abundantly present in the Universe. It is the main component of interstellar ice mantles and a key ingredient for life. Water in space is mainly formed through surface reactions. Three formation routes have been proposed in the past: hydrogenation of surface O, O2, and O3. In a previous paper [Ioppolo et al., Astrophys. J., 2008, 686, 1474] we discussed an unexpected non-standard zeroth-order H2O2 production behaviour in O2 hydrogenation experiments, which suggests that the proposed reaction network is not complete, and that the reaction channels are probably more interconnected than previously thought. In this paper we aim to derive the full reaction scheme for O2 surface hydrogenation and to constrain the rates of the individual reactions. This is achieved through simultaneous H-atom and O2 deposition under ultra-high vacuum conditions for astronomically relevant temperatures. Different H/O2 ratios are used to trace different stages in the hydrogenation network. The chemical changes in the forming ...

  10. Report on the IAEA technical meeting on network of nuclear reaction data centres

    International Nuclear Information System (INIS)

    This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)

  11. Global exponential stability of Hopfield reaction-diffusion neural networks with time-varying delays

    Institute of Scientific and Technical Information of China (English)

    王林山; 徐道义

    2003-01-01

    The authors analyze the existence of the equilibrium point and global exponential stability for Hopfield reaction-diffusion neural networks with time-varying delays by means of the topological degree theory and generalized Halanay inequality. Since the diffusion phenomena and time delay could not be ignored in neural networks and electric circuits, the model presented here is close to the actual systems, and the sufficient conditions on global exponential stability established in this paper,which are easily verifiable, have a wider adaptive range.

  12. 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.

  13. 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.

  14. Report on the IAEA technical meeting of the International Network of Nuclear Reaction Data Centres

    International Nuclear Information System (INIS)

    An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at IAEA Headquarters, Vienna, Austria, from 8 to 10 October 2007. The meeting was attended by 19 participants from 11 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

  15. 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....

  16. Report on the IAEA advisory group meeting on network of nuclear reaction data centres

    International Nuclear Information System (INIS)

    This report summarizes the IAEA Advisory Group Meeting (AGM) on Network of Nuclear Reaction Data Centres, hold at the Institute of Physics and Power Engineering, Obninsk, Russia, 15 to 19 May 2000. The meeting was attended by 28 participants from 13 co-operating data centres from seven Member States and two International Organizations. The report contains a meeting summary, the conclusions and actions, progress and status reports of the participating data centres and working papers considered at the meeting. (author)

  17. Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres

    International Nuclear Information System (INIS)

    This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the IAEA Headquarters in Vienna, Austria from 23 to 25 April 2013. The meeting was attended by 24 participants representing 13 cooperative centres from 8 Member States and 2 International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)

  18. Report on the IAEA technical meeting on network of nuclear reaction data centres

    International Nuclear Information System (INIS)

    An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (and the biennial Data Centre Heads' Meeting) was held at the OECD Nuclear Energy Agency, Issy-les-Moulineaux (near Paris), France, from 27 to 30 May 2002. The meeting was attended by 21 participants from 12 co-operating data centres of six Member States and two international organizations. This report contains the meeting summary, conclusions and actions, status reports of the participating data centres, and working papers considered. (author)

  19. Report on the IAEA technical meeting on network of nuclear reaction data centres

    International Nuclear Information System (INIS)

    An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)

  20. 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.

  1. 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.

  2. Product-form stationary distributions for deficiency zero chemical reaction networks

    OpenAIRE

    Anderson, David F.; Craciun, Gheorghe; Kurtz, Thomas G.

    2008-01-01

    We consider stochastically modeled chemical reaction systems with mass-action kinetics and prove that a product-form stationary distribution exists for each closed, irreducible subset of the state space if an analogous deterministically modeled system with mass-action kinetics admits a complex balanced equilibrium. Feinberg's deficiency zero theorem then implies that such a distribution exists so long as the corresponding chemical network is weakly reversible and has a deficiency of zero. The...

  3. Report on the IAEA technical meeting on the network of nuclear reaction data centres

    International Nuclear Information System (INIS)

    Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)

  4. Modeling the Differences in Biochemical Capabilities of Pseudomonas Species by Flux Balance Analysis: How Good Are Genome-Scale Metabolic Networks at Predicting the Differences?

    Directory of Open Access Journals (Sweden)

    Parizad Babaei

    2014-01-01

    Full Text Available To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.

  5. 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

  6. Pattern formation on networks with reactions: A continuous-time random-walk approach

    Science.gov (United States)

    Angstmann, C. N.; Donnelly, I. C.; Henry, B. I.

    2013-03-01

    We derive the generalized master equation for reaction-diffusion on networks from an underlying stochastic process, the continuous time random walk (CTRW). The nontrivial incorporation of the reaction process into the CTRW is achieved by splitting the derivation into two stages. The reactions are treated as birth-death processes and the first stage of the derivation is at the single particle level, taking into account the death process, while the second stage considers an ensemble of these particles including the birth process. Using this model we have investigated different types of pattern formation across the vertices on a range of networks. Importantly, the CTRW defines the Laplacian operator on the network in a non-ad hoc manner and the pattern formation depends on the structure of this Laplacian. Here we focus attention on CTRWs with exponential waiting times for two cases: one in which the rate parameter is constant for all vertices and the other where the rate parameter is proportional to the vertex degree. This results in nonsymmetric and symmetric CTRW Laplacians, respectively. In the case of symmetric Laplacians, pattern formation follows from the Turing instability. However in nonsymmetric Laplacians, pattern formation may be possible with or without a Turing instability.

  7. 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

  8. On the role of conserved moieties in shaping the robustness and production capabilities of reaction networks

    Science.gov (United States)

    DeMartino, A.; Martelli, C.; Massucci, F. A.

    2009-02-01

    We study a simplified, solvable model of a fully connected metabolic network with constrained quenched disorder to mimic the conservation laws imposed by stoichiometry on chemical reactions. Within a spin-glass type of approach, we show that in the presence of a conserved metabolic pool the flux state corresponding to maximal growth is stationary independently of the pool size. In addition, and at odds with the case of unconstrained networks, the volume of optimal flux configurations remains finite, indicating that the frustration imposed by stoichiometric constraints, while reducing growth capabilities, confers robustness and flexibility to the system. These results have a clear biological interpretation and provide a basic, fully analytical explanation to features recently observed in real metabolic networks.

  9. Detection of Hopf bifurcations in chemical reaction networks using convex coordinates

    Science.gov (United States)

    Errami, Hassan; Eiswirth, Markus; Grigoriev, Dima; Seiler, Werner M.; Sturm, Thomas; Weber, Andreas

    2015-06-01

    We present efficient algorithmic methods to detect Hopf bifurcation fixed points in chemical reaction networks with symbolic rate constants, thereby yielding information about the oscillatory behavior of the networks. Our methods use the representations of the systems on convex coordinates that arise from stoichiometric network analysis. One of our methods then reduces the problem of determining the existence of Hopf bifurcation fixed points to a first-order formula over the ordered field of the reals that can be solved using computational logic packages. The second method uses ideas from tropical geometry to formulate a more efficient method that is incomplete in theory but worked very well for the examples that we have attempted; we have shown it to be able to handle systems involving more than 20 species.

  10. A unified biological modeling and simulation system for analyzing biological reaction networks

    Science.gov (United States)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2013-12-01

    In order to understand the biological response in a cell, a researcher has to create a biological network and design an experiment to prove it. Although biological knowledge has been accumulated, we still don't have enough biological models to explain complex biological phenomena. If a new biological network is to be created, integrated modeling software supporting various biological models is required. In this research, we design and implement a unified biological modeling and simulation system, called ezBioNet, for analyzing biological reaction networks. ezBioNet designs kinetic and Boolean network models and simulates the biological networks using a server-side simulation system with Object Oriented Parallel Accelerator Library framework. The main advantage of ezBioNet is that a user can create a biological network by using unified modeling canvas of kinetic and Boolean models and perform massive simulations, including Ordinary Differential Equation analyses, sensitivity analyses, parameter estimates and Boolean network analysis. ezBioNet integrates useful biological databases, including the BioModels database, by connecting European Bioinformatics Institute servers through Web services Application Programming Interfaces. In addition, we employ Eclipse Rich Client Platform, which is a powerful modularity framework to allow various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool and a simulation system for understanding the control mechanism by monitoring the change of each component in a biological network. The simulation result can be managed and visualized on ezBioNet, which is available free of charge at http://ezbionet.sourceforge.net or http://ezbionet.cbnu.ac.kr.

  11. 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). PMID:21230659

  12. 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.

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

    Science.gov (United States)

    Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf

    2008-01-01

    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 way both as a stand

  14. 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

  15. Noise and critical phenomena in biochemical signaling cycles at small molecule numbers

    Science.gov (United States)

    Metzner, C.; Sajitz-Hermstein, M.; Schmidberger, M.; Fabry, B.

    2009-08-01

    Biochemical reaction networks in living cells usually involve reversible covalent modification of signaling molecules, such as protein phosphorylation. Under conditions of small molecule numbers, as is frequently the case in living cells, mass-action theory fails to describe the dynamics of such systems. Instead, the biochemical reactions must be treated as stochastic processes that intrinsically generate concentration fluctuations of the chemicals. We investigate the stochastic reaction kinetics of covalent modification cycles (CMCs) by analytical modeling and numerically exact Monte Carlo simulation of the temporally fluctuating concentration. Depending on the parameter regime, we find for the probability density of the concentration qualitatively distinct classes of distribution functions including power-law distributions with a fractional and tunable exponent. These findings challenge the traditional view of biochemical control networks as deterministic computational systems and suggest that CMCs in cells can function as versatile and tunable noise generators.

  16. ezBioNet: A modeling and simulation system for analyzing biological reaction networks

    Science.gov (United States)

    Yu, Seok Jong; Tung, Thai Quang; Park, Junho; Lim, Jongtae; Yoo, Jaesoo

    2012-10-01

    To achieve robustness against living environments, a living organism is composed of complicated regulatory mechanisms ranging from gene regulations to signal transduction. If such life phenomena are to be understand, an integrated analysis tool that should have modeling and simulation functions for biological reactions, as well as new experimental methods for measuring biological phenomena, is fundamentally required. We have designed and implemented modeling and simulation software (ezBioNet) for analyzing biological reaction networks. The software can simultaneously perform an integrated modeling of various responses occurring in cells, ranging from gene expressions to signaling processes. To support massive analysis of biological networks, we have constructed a server-side simulation system (VCellSim) that can perform ordinary differential equations (ODE) analysis, sensitivity analysis, and parameter estimates. ezBioNet integrates the BioModel database by connecting the european bioinformatics institute (EBI) servers through Web services APIs and supports the handling of systems biology markup language (SBML) files. In addition, we employed eclipse RCP (rich client platform) which is a powerful modularity framework allowing various functional expansions. ezBioNet is intended to be an easy-to-use modeling tool, as well as a simulation system, to understand the control mechanism by monitoring the change of each component in a biological network. A researcher may perform the kinetic modeling and execute the simulation. The simulation result can be managed and visualized on ezBioNet, which is freely available at http://ezbionet.cbnu.ac.kr.

  17. 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.

  18. 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.

  19. 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. PMID:22163620

  20. A Reaction-Diffusion-Based Coding Rate Control Mechanism for Camera Sensor Networks

    Directory of Open Access Journals (Sweden)

    Naoki Wakamiya

    2010-08-01

    Full Text Available 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.

  1. 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

  2. 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.

  3. Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network

    DEFF Research Database (Denmark)

    Förster, Jochen; Famili, I.; Fu, P.;

    2003-01-01

    and the environment were included. A total of 708 structural open reading frames (ORFs) were accounted for in the reconstructed network, corresponding to 1035 metabolic reactions. Further, 140 reactions were included on the basis of biochemical evidence resulting in a genome-scale reconstructed metabolic network...... with Escherichia coli. The reconstructed metabolic network is the first comprehensive network for a eukaryotic organism, and it may be used as the basis for in silico analysis of phenotypic functions....

  4. Elucidation of Diels-Alder Reaction Network of 2,5-Dimethylfuran and Ethylene on HY Zeolite Catalyst

    Energy Technology Data Exchange (ETDEWEB)

    Do, Phuong T. M. [Univ. of Delaware, Newark, DE (United States); McAtee, Jesse R. [Univ. of Delaware, Newark, DE (United States); Watson, Donald A. [Univ. of Delaware, Newark, DE (United States); Lobo, Raul F. [Univ. of Delaware, Newark, DE (United States)

    2012-12-12

    The reaction of 2,5-dimethylfuran and ethylene to produce p-xylene represents a potentially important route for the conversion of biomass to high-value organic chemicals. Current preparation methods suffer from low selectivity and produce a number of byproducts. Using modern separation and analytical techniques, the structures of many of the byproducts produced in this reaction when HY zeolite is employed as a catalyst have been identified. From these data, a detailed reaction network is proposed, demonstrating that hydrolysis and electrophilic alkylation reactions compete with the desired Diels–Alder/dehydration sequence. This information will allow the rational identification of more selective catalysts and more selective reaction conditions.

  5. Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic Acid as determined by constraint based metabolic network analysis

    DEFF Research Database (Denmark)

    Rohatgi, Neha; Nielsen, Tine Kragh; Bjørn, Sara Petersen;

    2014-01-01

    The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role of gluco......, the results highlight that little is known of the mechanism of gluconate metabolism in humans despite its widespread use in medicine and consumer products.......The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role...... of gluconate in humans. Here we report the recombinant expression, purification and biochemical characterization of isoform I of human gluconokinase alongside substrate specificity and kinetic assays of the enzyme catalyzed reaction. The enzyme, shown to be a dimer, had ATP dependent phosphorylation activity...

  6. Gene network inference and biochemical assessment delineates GPCR pathways and CREB targets in small intestinal neuroendocrine neoplasia.

    Science.gov (United States)

    Drozdov, Ignat; Svejda, Bernhard; Gustafsson, Bjorn I; Mane, Shrikant; Pfragner, Roswitha; Kidd, Mark; Modlin, Irvin M

    2011-01-01

    Small intestinal (SI) neuroendocrine tumors (NET) are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE) transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations). All were up-regulated (psystem, confirmed that transcriptional effects are signaled through the cAMP/PKA/pCREB signaling pathway and that a SI NET cell line was most sensitive to a D(2) and 5-HT(2) receptor agonist BIM-53061. PMID:21853033

  7. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Das, Biswajit; Gangopadhyay, Gautam, E-mail: gautam@bose.res.in [S. N. Bose National Centre For Basic Sciences, Block-JD, Sector-III, Salt Lake, Kolkata 700 098 (India); Banerjee, Kinshuk [Department of Chemistry, University of Calcutta, 92 A.P.C. Road, Kolkata 700 009 (India)

    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.

  9. Identification of weakly beta-hemolytic porcine spirochetes by biochemical reactions, PCR-based restriction fragment length polymorphism analysis and species-specific PCR.

    Science.gov (United States)

    Ohya, Tatsuo; Araki, Hiroshi; Sueyoshi, Masuo

    2008-08-01

    We examined the usefulness of PCR-based restriction fragment length polymorphism (PCR-RFLP) and species-specific PCR combined with a newly devised rapid biochemical test using microplates for identifying weakly beta-hemolytic intestinal spirochetes (WBHIS) isolated from pigs. WBHIS strains showing atypical biochemical characteristics were decisively identified at the species level by PCR-RFLP and species-specific PCR. Identification of WBHIS at the species level in routine diagnostic work will certainly contribute to clarifying the pathogenicity of WBHIS.

  10. Gene network inference and biochemical assessment delineates GPCR pathways and CREB targets in small intestinal neuroendocrine neoplasia.

    Directory of Open Access Journals (Sweden)

    Ignat Drozdov

    Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional

  11. 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.

  12. A model for lignin alteration - Part I: A kinetic reaction-network model

    Science.gov (United States)

    Payne, D.F.; Ortoleva, P.J.

    2001-01-01

    A new quantitative model is presented which simulates the maturation of lignin-derived sedimentary organic matter under geologic conditions. In this model, compositionally specific reactants evolve to specific intermediate and mobile products through balanced, nth order processes, by way of a network of sequential and parallel reactions. The chemical kinetic approach is based primarily on published observed structural transformations of naturally matured, lignin-derived, sedimentary organic matter. Assuming that Upper Cretaceous Williams Fork coal in the Piceance Basin is primarily lignin-derived, the model is calibrated for the Multi-Well Experiment(MWX) Site in this basin. This kind of approach may be applied to other selectively preserved chemical components of sedimentary organic matter. ?? 2001 Elsevier Science Ltd. All rights reserved.

  13. Flux-based classification of reactions reveals a functional bow-tie organization of complex metabolic networks

    OpenAIRE

    Singh, Shalini; Samal, Areejit; Giri, Varun; Krishna, Sandeep; Raghuram, Nandula; Jain, Sanjay

    2012-01-01

    Unraveling the structure of complex biological networks and relating it to their functional role is an important task in systems biology. Here we attempt to characterize the functional organization of the large-scale metabolic networks of three microorganisms. We apply flux balance analysis to study the optimal growth states of these organisms in different environments. By investigating the differential usage of reactions across flux patterns for different environments, we observe a striking ...

  14. 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.

  15. Network of vascular diseases, death and biochemical characteristics in a set of 4,197 patients with type 1 diabetes (The FinnDiane Study

    Directory of Open Access Journals (Sweden)

    Wadén Johan

    2009-10-01

    Full Text Available Background Cardiovascular disease is the main cause of premature death in patients with type 1 diabetes. Patients with diabetic kidney disease have an increased risk of heart attack or stroke. Accurate knowledge of the complex inter-dependencies between the risk factors is critical for pinpointing the best targets for research and treatment. Therefore, the aim of this study was to describe the association patterns between clinical and biochemical features of diabetic complications. Methods Medical records and serum and urine samples of 4,197 patients with type 1 diabetes were collected from health care centers in Finland. At baseline, the mean diabetes duration was 22 years, 52% were male, 23% had kidney disease (urine albumin excretion over 300 mg/24 h or end-stage renal disease and 8% had a history of macrovascular events. All-cause mortality was evaluated after an average of 6.5 years of follow-up (25,714 patient years. The dataset comprised 28 clinical and 25 biochemical variables that were regarded as the nodes of a network to assess their mutual relationships. Results The networks contained cliques that were densely inter-connected (r > 0.6, including cliques for high-density lipoprotein (HDL markers, for triglycerides and cholesterol, for urinary excretion and for indices of body mass. The links between the cliques showed biologically relevant interactions: an inverse relationship between HDL cholesterol and the triglyceride clique (r P -16, a connection between triglycerides and body mass via C-reactive protein (r > 0.3, P -16 and intermediate-density cholesterol as the connector between lipoprotein metabolism and albuminuria (r > 0.3, P -16. Aging and macrovascular disease were linked to death via working ability and retinopathy. Diabetic kidney disease, serum creatinine and potassium, retinopathy and blood pressure were inter-connected. Blood pressure correlations indicated accelerated vascular aging in individuals with kidney disease

  16. Identification of efflux proteins using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

    Science.gov (United States)

    Ou, Yu-Yen; Chen, Shu-An; Chang, Yun-Min; Velmurugan, Devadasan; Fukui, Kazuhiko; Michael Gromiha, M

    2013-09-01

    Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.

  17. Classification of transporters using efficient radial basis function networks with position-specific scoring matrices and biochemical properties.

    Science.gov (United States)

    Ou, Yu-Yen; Chen, Shu-An; Gromiha, M Michael

    2010-05-15

    Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Transporters are generally classified into channels/pores, electrochemical transporters, and active transporters. Discriminating the specific class of transporters and their subfamilies are essential tasks in computational biology for the advancement of structural and functional genomics. We have systematically analyzed the amino acid composition, residue pair preference and amino acid properties in six different families of transporters. Utilizing the information, we have developed a radial basis function (RBF) network method based on profiles obtained with position specific scoring matrices for discriminating transporters belonging to three different classes and six families. Our method showed a fivefold cross validation accuracy of 76%, 73%, and 69% for discriminating transporters and nontransporters, three different classes and six different families of transporters, respectively. Further, the method was tested with independent datasets, which showed similar level of accuracy. A web server has been developed for discriminating transporters based on three classes and six families, and it is available at http://rbf.bioinfo.tw/ approximately sachen/tcrbf.html. We suggest that our method could be effectively used to identify transporters and discriminating them into different classes and families.

  18. 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 oxidize the NO produced by AOB through reactions catalyzed by oxidative NirK. PMID:26551878

  19. 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."

  20. 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."

  1. 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.

  2. 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

  3. Reaction Network of Layer-to-Tunnel Transition of MnO2.

    Science.gov (United States)

    Li, Ye-Fei; Zhu, Sheng-Cai; Liu, Zhi-Pan

    2016-04-27

    As a model system of 2-D oxide material, layered δ-MnO2 has important applications in Li ion battery systems. δ-MnO2 is also widely utilized as a precursor to synthesize other stable structure variants in the MnO2 family, such as α-, β-, R-, and γ-phases, which are 3-D interlinked structures with different tunnels. By utilizing the stochastic surface walking (SSW) pathway sampling method, we here for the first time resolve the atomistic mechanism and the kinetics of the layer-to-tunnel transition of MnO2, that is, from δ-MnO2 to the α-, β-, and R-phases. The SSW sampling determines the lowest-energy pathway from thousands of likely pathways that connects different phases. The reaction barriers of layer-to-tunnel phase transitions are found to be low, being 0.2-0.3 eV per formula unit, which suggests a complex competing reaction network toward different tunnel phases. All the transitions initiate via a common shearing and buckling movement of the MnO2 layer that leads to the breaking of the Mn-O framework and the formation of Mn(3+) at the transition state. Important hints are thus gleaned from these lowest-energy pathways: (i) the large pore size product is unfavorable for the entropic reason; (ii) cations are effective dopants to control the kinetics and selectivity in layer-to-tunnel transitions, which in general lowers the phase transition barrier and facilitates the creation of larger tunnel size; (iii) the phase transition not only changes the electronic structure but also induces the macroscopic morphology changes due to the interfacial strain. PMID:27054525

  4. 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

  5. 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.

  6. Stochastic bifurcation, slow fluctuations, and bistability as an origin of biochemical complexity.

    Science.gov (United States)

    Qian, Hong; Shi, Pei-Zhe; Xing, Jianhua

    2009-06-28

    We present a simple, unifying theory for stochastic biochemical systems with multiple time-scale dynamics that exhibit noise-induced bistability in an open-chemical environment, while the corresponding macroscopic reaction is unistable. Nonlinear stochastic biochemical systems like these are fundamentally different from classical systems in equilibrium or near-equilibrium steady state whose fluctuations are unimodal following Einstein-Onsager-Lax-Keizer theory. We show that noise-induced bistability in general arises from slow fluctuations, and a pitchfork bifurcation occurs as the rate of fluctuations decreases. Since an equilibrium distribution, due to detailed balance, has to be independent of changes in time-scale, the bifurcation is necessarily a driven phenomenon. As examples, we analyze three biochemical networks of currently interest: self-regulating gene, stochastic binary decision, and phosphorylation-dephosphorylation cycle with fluctuating kinase. The implications of bistability to biochemical complexity are discussed.

  7. Biochemical aspects on adverse reactions to contrast media. Changes of kininogen levels in dog plasma after intravenous injections of iohexol, iopamidol, and iothalamate.

    Science.gov (United States)

    Tanaka, T; Katayama, H; Shirakata, A; Takahasi, H

    1988-09-01

    The adverse reactions to contrast media have been investigated by several authors but the exact mechanisms have not yet been established. To study whether kinin-releasing systems are involved in these adverse reactions, we determined total plasma kininogen levels at intervals up to 30 minutes after the intravenous injections of contrast media in dogs. Injections of iohexol, iopamidol, and iothalamate decreased total plasma kininogen levels. This effect increased with increasing dose of the media and suggests that they activated the kinin-releasing systems in the plasma.

  8. Interfacial reaction of silver ultra-thin film deposited on interpenetrating polymer network substrate by liquor-phase reduction

    International Nuclear Information System (INIS)

    The interfacial reaction, metal transformations, and nonmetal bond types of silver ultra-thin film deposited on polyurethane (PU) based interpenetrating polymer networks (IPN) substrate by the liquor-phase reduction at room temperatures were studied by atomic force microscope (AFM), X-ray photoelectron spectroscopy (XPS) and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR). The IPN substrate was prepared by dip-pulling precursors onto a silicon wafer or a glass plate, followed by solidification at room temperature. The interpenetrate structures of IPN with two crosslinked networks restricted the aggregation of silver during the reduction and deposition. The devised -OH terminal group in PU simplified the determination of reactive site in IPN and reinforced the adhesion between IPN and silver through interfacial reaction. The XPS results, which matched well with the ATR-FTIR results, verified the chemical reactive site of PU in IPN with silver in the oxide state.

  9. Interfacial reaction of silver ultra-thin film deposited on interpenetrating polymer network substrate by liquor-phase reduction

    Energy Technology Data Exchange (ETDEWEB)

    Tang Dongyan, E-mail: dytang@hit.edu.cn [Department of Chemistry, School of Science, Harbin Institute of Technology, Harbin 150001 (China); Guo Yudi [Department of Chemistry, School of Science, Harbin Institute of Technology, Harbin 150001 (China); Zhang Xiaohong [College of Materials Science and Chemical Engineering, Harbin Engineering University, Harbin 150001 (China); Yin Yuelong [Department of Chemistry, School of Science, Harbin Institute of Technology, Harbin 150001 (China)

    2010-08-01

    The interfacial reaction, metal transformations, and nonmetal bond types of silver ultra-thin film deposited on polyurethane (PU) based interpenetrating polymer networks (IPN) substrate by the liquor-phase reduction at room temperatures were studied by atomic force microscope (AFM), X-ray photoelectron spectroscopy (XPS) and attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR). The IPN substrate was prepared by dip-pulling precursors onto a silicon wafer or a glass plate, followed by solidification at room temperature. The interpenetrate structures of IPN with two crosslinked networks restricted the aggregation of silver during the reduction and deposition. The devised -OH terminal group in PU simplified the determination of reactive site in IPN and reinforced the adhesion between IPN and silver through interfacial reaction. The XPS results, which matched well with the ATR-FTIR results, verified the chemical reactive site of PU in IPN with silver in the oxide state.

  10. Biochemical Hypermedia: Galactose Metabolism.

    Directory of Open Access Journals (Sweden)

    J.K. Sugai

    2013-05-01

    Full Text Available Introduction: Animations of biochemical processes and virtual laboratory environments lead to true molecular simulations. The use of interactive software’s in education can improve cognitive capacity, better learning and, mainly, it makes information acquisition easier. Material and Methods: This work presents the development of a biochemical hypermedia to understanding of the galactose metabolism. It was developed with the help of concept maps, ISIS Draw, ADOBE Photoshop and FLASH MX Program. Results and Discussion: A step by step animation process shows the enzymatic reactions of galactose conversion to glucose-1-phosphate (to glycogen synthesis, glucose-6-phosphate (glycolysis intermediary, UDP-galactose (substrate to mucopolysaccharides synthesis and collagen’s glycosylation. There are navigation guide that allow scrolling the mouse over the names of the components of enzymatic reactions of via the metabolism of galactose. Thus, explanatory text box, chemical structures and animation of the actions of enzymes appear to navigator. Upon completion of the module, the user’s response to the proposed exercise can be checked immediately through text box with interactive content of the answer. Conclusion: This hypermedia was presented for undergraduate students (UFSC who revealed that it was extremely effective in promoting the understanding of the theme.

  11. Weber's law for biological responses in autocatalytic networks of chemical reactions.

    Science.gov (United States)

    Inoue, Masayo; Kaneko, Kunihiko

    2011-07-22

    Biological responses often obey Weber's law, according to which the magnitude of the response depends only on the fold change in the external input. In this study, we demonstrate that a system involving a simple autocatalytic reaction shows such a response when a chemical is slowly synthesized by the reaction from a faster influx process. We also show that an autocatalytic reaction process occurring in series or in parallel can obey Weber's law with an oscillatory adaptive response. Considering the simplicity and ubiquity of the autocatalytic process, our proposed mechanism is thought to be commonly observed in biological reactions. PMID:21867048

  12. 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.

  13. Rational design of functional and tunable oscillating enzymatic networks

    Science.gov (United States)

    Semenov, Sergey N.; Wong, Albert S. Y.; van der Made, R. Martijn; Postma, Sjoerd G. J.; Groen, Joost; van Roekel, Hendrik W. H.; de Greef, Tom F. A.; Huck, Wilhelm T. S.

    2015-02-01

    Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.

  14. 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.

  15. 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.

  16. Delay-induced Turing-like waves for one-species reaction-diffusion model on a network

    Science.gov (United States)

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

    2015-09-01

    A one-species time-delay reaction-diffusion system defined on a complex network is studied. Traveling waves are predicted to occur following a symmetry-breaking instability of a homogeneous stationary stable solution, subject to an external nonhomogeneous perturbation. These are generalized Turing-like waves that materialize in a single-species populations dynamics model, as the unexpected byproduct of the imposed delay in the diffusion part. Sufficient conditions for the onset of the instability are mathematically provided by performing a linear stability analysis adapted to time-delayed differential equations. The method here developed exploits the properties of the Lambert W-function. The prediction of the theory are confirmed by direct numerical simulation carried out for a modified version of the classical Fisher model, defined on a Watts-Strogatz network and with the inclusion of the delay.

  17. 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;

    carbonate (DMC) [2]. In this work, through a computer-aided framework for process network synthesis-design, a network of conversion processes that all use emitted CO2 is investigated. CO2 is emitted into the environment from various sources: power generation, industrial processes, transportation...... and commercial processes. Within these there are high-purity emissions and low-purity emissions. Rather than sending these to the atmosphere, it is possible to collect them and use them for other purposes. Targeting some of the largest contributors: power generation, manufacturing, chemical industry...... through the reactions. Studies and detailed simulations have been performed on CO2 conversion to methanol, synthesis gas processes, dimethyl carbonate production, and other processes. The detailed simulations are performed on the paths that are selected based on basic calculations on each path. Then...

  18. 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.

  19. Facile: a command-line network compiler for systems biology

    OpenAIRE

    Ollivier Julien F; Siso-Nadal Fernando; Swain Peter S

    2007-01-01

    Abstract Background A goal of systems biology is the quantitative modelling of biochemical networks. Yet for many biochemical systems, parameter values and even the existence of interactions between some chemical species are unknown. It is therefore important to be able to easily investigate the effects of adding or removing reactions and to easily perform a bifurcation analysis, which shows the qualitative dynamics of a model for a range of parameter values. Results We present Facile, a Perl...

  20. Report on the IAEA technical meeting on co-ordination of the network of nuclear reaction data centres

    International Nuclear Information System (INIS)

    Results of the IAEA Technical Meeting on the Co-ordination of the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 17 to 19 June 2003, are summarised in this report. The meeting was attended by 14 participants from 9 cooperating data centres of five member states and two International Organizations. A meeting summary, the conclusions and actions, progress and status reports of the participating data centres, and working papers considered at the meeting, are given in the relevant sections. (author)

  1. 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 ...

  2. 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.

  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. 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

    to allow design of generalized fault estimation filters, which do not need a priori information about the faults internal model. Simulation results with a detailed nonlinear spacecraft model, which includes disturbances, show that the proposed diagnosis scheme can deal with faults affecting both reaction......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...

  5. 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

    , a fraction (quantified by the stress transfer function gif" border="0">) of the second-stage cross-links contribute to the effective first-stage cross-link density. The stress transfer functions extracted from the MD simulations of the reacting networks are found to be in very...

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

    Energy Technology Data Exchange (ETDEWEB)

    Mlynarczyk, Paul J.; Pullen, Robert H.; Abel, Steven M., E-mail: abel@utk.edu [Department of Chemical and Biomolecular Engineering, University of Tennessee, Knoxville, Tennessee 37996 (United States)

    2016-01-07

    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.

  8. Cellerator: extending a computer algebra system to include biochemical arrows for signal transduction simulations

    Science.gov (United States)

    Shapiro, Bruce E.; Levchenko, Andre; Meyerowitz, Elliot M.; Wold, Barbara J.; Mjolsness, Eric D.

    2003-01-01

    Cellerator describes single and multi-cellular signal transduction networks (STN) with a compact, optionally palette-driven, arrow-based notation to represent biochemical reactions and transcriptional activation. Multi-compartment systems are represented as graphs with STNs embedded in each node. Interactions include mass-action, enzymatic, allosteric and connectionist models. Reactions are translated into differential equations and can be solved numerically to generate predictive time courses or output as systems of equations that can be read by other programs. Cellerator simulations are fully extensible and portable to any operating system that supports Mathematica, and can be indefinitely nested within larger data structures to produce highly scaleable models.

  9. High-resolution mapping of bifurcations in nonlinear biochemical circuits.

    Science.gov (United States)

    Genot, A J; Baccouche, A; Sieskind, R; Aubert-Kato, N; Bredeche, N; Bartolo, J F; Taly, V; Fujii, T; Rondelez, Y

    2016-08-01

    Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.

  10. High-resolution mapping of bifurcations in nonlinear biochemical circuits

    Science.gov (United States)

    Genot, A. J.; Baccouche, A.; Sieskind, R.; Aubert-Kato, N.; Bredeche, N.; Bartolo, J. F.; Taly, V.; Fujii, T.; Rondelez, Y.

    2016-08-01

    Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.

  11. High-resolution mapping of bifurcations in nonlinear biochemical circuits

    Science.gov (United States)

    Genot, A. J.; Baccouche, A.; Sieskind, R.; Aubert-Kato, N.; Bredeche, N.; Bartolo, J. F.; Taly, V.; Fujii, T.; Rondelez, Y.

    2016-08-01

    Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator–prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.

  12. Automatic reaction to a chemical event detected by a low-cost wireless chemical sensing network

    OpenAIRE

    Beirne, Stephen; Lau, King-Tong; Corcoran, Brian; Diamond, Dermot

    2009-01-01

    A test-scale wireless chemical sensor network (WCSN) has been deployed within a controlled Environmental Chamber (EC). The combined signals from the WCSN were used to initiate a controllable response to the detected chemical event. When a particular sensor response pattern was obtained, a purging cycle was initiated. Sensor data were continuously checked against user-defined action limits, to determine if a chemical event had occurred. An acidic contaminant was used to demonstrate the respons...

  13. 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.

  14. A computational model for the identification of biochemical pathways in the krebs cycle.

    Science.gov (United States)

    Oliveira, Joseph S; Bailey, Colin G; Jones-Oliveira, Janet B; Dixon, David A; Gull, Dean W; Chandler, Mary L

    2003-01-01

    We have applied an algorithmic methodology which provably decomposes any complex network into a complete family of principal subcircuits to study the minimal circuits that describe the Krebs cycle. Every operational behavior that the network is capable of exhibiting can be represented by some combination of these principal subcircuits and this computational decomposition is linearly efficient. We have developed a computational model that can be applied to biochemical reaction systems which accurately renders pathways of such reactions via directed hypergraphs (Petri nets). We have applied the model to the citric acid cycle (Krebs cycle). The Krebs cycle, which oxidizes the acetyl group of acetyl CoA to CO(2) and reduces NAD and FAD to NADH and FADH(2), is a complex interacting set of nine subreaction networks. The Krebs cycle was selected because of its familiarity to the biological community and because it exhibits enough complexity to be interesting in order to introduce this novel analytic approach. This study validates the algorithmic methodology for the identification of significant biochemical signaling subcircuits, based solely upon the mathematical model and not upon prior biological knowledge. The utility of the algebraic-combinatorial model for identifying the complete set of biochemical subcircuits as a data set is demonstrated for this important metabolic process.

  15. Reproduction of a Protocell by Replication of Minority Molecule in Catalytic Reaction Network

    OpenAIRE

    Kamimura, Atsushi; Kaneko, Kunihiko

    2010-01-01

    For understanding the origin of life, it is essential to explain the development of a compartmentalized structure, which undergoes growth and division, from a set of chemical reactions. In this study, a hypercycle with two chemicals that mutually catalyze each other is considered in order to show that the reproduction of a protocell with a growth-division process naturally occurs when the replication speed of one chemical is considerably slower than that of the other chemical. It is observed ...

  16. Compartmentalization and Cell Division through Molecular Discreteness and Crowding in a Catalytic Reaction Network

    OpenAIRE

    Atsushi Kamimura; Kunihiko Kaneko

    2014-01-01

    Explanation of the emergence of primitive cellular structures from a set of chemical reactions is necessary to unveil the origin of life and to experimentally synthesize protocells. By simulating a cellular automaton model with a two-species hypercycle, we demonstrate the reproduction of a localized cluster; that is, a protocell with a growth-division process emerges when the replication and degradation speeds of one species are respectively slower than those of the other species, because of ...

  17. Stochastic innovation as a mechanism by which catalysts might self-assemble into chemical reaction networks

    OpenAIRE

    Bradford, Justin A; Dill, Ken A.

    2007-01-01

    We develop a computer model for how two different chemical catalysts in solution, A and B, could be driven to form AB complexes, based on the concentration gradients of a substrate or product that they share in common. If A's product is B's substrate, B will be attracted to A, mediated by a common resource that is not otherwise plentiful in the environment. By this simple physicochemical mechanism, chemical reactions could spontaneously associate to become chained together in solution. Accord...

  18. Effect of gel network on pattern formation in the ferrocyanide-iodate-sulfite reaction.

    Science.gov (United States)

    Ueno, Tomonaga; Yoshida, Ryo

    2011-06-01

    Stationary patterns have been researched experimentally since the discovery of the Turing pattern in the chlorite-iodide-malonic acid (CIMA) reaction and the self-replicating spot pattern in the ferrocyanide-iodate-sulfite (FIS) reaction. In this study, we reproduced the pattern formation in the FIS reaction by using poly(acrylamide) gels. Gels with different swelling ratios were prepared to use as a medium. The effect of the swelling ratio was compared with the effect of thickness. It was found that the swelling ratio greatly influenced pattern formation. Oscillating spot patterns appeared at high swelling ratios, and lamellar patterns appeared at a low swelling ratio. Self-replicating spot patterns appeared in between the two areas. The front velocities, which were observed in the initial stage of pattern formation, depended on the swelling ratio. Furthermore, this dependence obeys the free volume theory of diffusion. These results provide evidence that the change in front velocities is caused by a change in diffusion. Pattern formation can be controlled not only by thickness but also by swelling ratio, which may be useful for creating novel pattern templates. PMID:21557556

  19. 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.

  20. 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. PMID:26763107

  1. Global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays

    Institute of Scientific and Technical Information of China (English)

    Zhang Wei-Yuan; Li Jun-Min

    2011-01-01

    This paper investigates the global exponential stability of reaction-diffusion neural networks with discrete and distributed time-varying delays. By constructing a more general type of Lyapunov-Krasovskii functional combined with a free-weighting matrix approach and analysis techniques, delay-dependent exponential stability criteria are derived in the form of linear matrix inequalities. The obtained results are dependent on the size of the time-varying delays and the measure of the space, which are usually less conservative than delay-independent and space-independent ones. These results are easy to check, and improve upon the existing stability results. Some remarks are given to show the advantages of the obtained results over the previous results. A numerical example has been presented to show the usefulness of the derived linear matrix inequality (LMI)-based stability conditions.

  2. 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...

  3. Photochemical Production of Interpenetrating Polymer Networks; Simultaneous Initiation of Radical and Cationic Polymerization Reactions

    Directory of Open Access Journals (Sweden)

    Jean Pierre Fouassier

    2014-10-01

    Full Text Available In this paper, we propose to review the ways to produce, through photopolymerization, interpenetrating polymer networks (IPN based, e.g., on acrylate/epoxide or acrylate/vinylether blends and to outline the recent developments that allows a one-step procedure (concomitant radical/cationic polymerization, under air or in laminate, under various irradiation conditions (UV/visible/near IR; high/low intensity sources; monochromatic/polychromatic sources; household lamps/laser diodes/Light Emitting Diodes (LEDs. The paper illustrates the encountered mechanisms and the polymerization profiles. A short survey on the available monomer systems and some brief examples of the attained final properties of the IPNs is also provided.

  4. Compartmentalization and Cell Division through Molecular Discreteness and Crowding in a Catalytic Reaction Network

    Directory of Open Access Journals (Sweden)

    Atsushi Kamimura

    2014-10-01

    Full Text Available Explanation of the emergence of primitive cellular structures from a set of chemical reactions is necessary to unveil the origin of life and to experimentally synthesize protocells. By simulating a cellular automaton model with a two-species hypercycle, we demonstrate the reproduction of a localized cluster; that is, a protocell with a growth-division process emerges when the replication and degradation speeds of one species are respectively slower than those of the other species, because of overcrowding of molecules as a natural outcome of the replication. The protocell exhibits synchrony between its division process and replication of the minority molecule. We discuss the effects of the crowding molecule on the formation of primitive structures. The generality of this result is demonstrated through the extension of our model to a hypercycle with three molecular species, where a localized layered structure of molecules continues to divide, triggered by the replication of a minority molecule at the center.

  5. Reproduction of a Protocell by Replication of a Minority Molecule in a Catalytic Reaction Network

    Science.gov (United States)

    Kamimura, Atsushi; Kaneko, Kunihiko

    2010-12-01

    For understanding the origin of life, it is essential to explain the development of a compartmentalized structure, which undergoes growth and division, from a set of chemical reactions. In this study, a hypercycle with two chemicals that mutually catalyze each other is considered in order to show that the reproduction of a protocell with a growth-division process naturally occurs when the replication speed of one chemical is considerably slower than that of the other chemical, and molecules are crowded as a result of replication. It is observed that the protocell divides after a minority molecule is replicated at a slow synthesis rate, and thus, a synchrony between the reproduction of a cell and molecule replication is achieved. The robustness of such protocells against the invasion of parasitic molecules is also demonstrated.

  6. Symmetry breaking in a bulk-surface reaction-diffusion model for signalling networks

    Science.gov (United States)

    Rätz, Andreas; Röger, Matthias

    2014-08-01

    Signalling molecules play an important role for many cellular functions. We investigate here a general system of two membrane reaction-diffusion equations coupled to a diffusion equation inside the cell by a Robin-type boundary condition and a flux term in the membrane equations. A specific model of this form was recently proposed by the authors for the GTPase cycle in cells. We investigate here a putative role of diffusive instabilities in cell polarization. By a linearized stability analysis, we identify two different mechanisms. The first resembles a classical Turing instability for the membrane subsystem and requires (unrealistically) large differences in the lateral diffusion of activator and substrate. On the other hand, the second possibility is induced by the difference in cytosolic and lateral diffusion and appears much more realistic. We complement our theoretical analysis by numerical simulations that confirm the new stability mechanism and allow us to investigate the evolution beyond the regime where the linearization applies.

  7. 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.

  8. On global exponential stability and existence of periodic solutions for BAM neural networks with distributed delays and reaction-diffusion terms

    Energy Technology Data Exchange (ETDEWEB)

    Lou Xuyang [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)], E-mail: louxuyang28945@163.com; Cui Baotong [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)], E-mail: btcui@sohu.com; Wu Wei [Research Center of Control Science and Engineering, Southern Yangtze University, 1800 Lihu Road, Wuxi, Jiangsu 214122 (China)

    2008-05-15

    Both exponential stability and existence of periodic solutions are considered for a class of bi-directional associative memory (BAM) neural networks with distributed delays and reaction-diffusion terms by constructing suitable Lyapunov functional and Young inequality technique. The general sufficient conditions are given ensuring the global exponential stability and existence of periodic solutions of BAM neural networks with distributed delays and reaction-diffusion terms. The earlier results are extended and improved, and an illustrative example is given to demonstrate the effectiveness of the results in this paper.

  9. Adaptative biochemical pathways and regulatory networks in Klebsiella oxytoca BAS-10 producing a biotechnologically relevant exopolysaccharide during Fe(III-citrate fermentation

    Directory of Open Access Journals (Sweden)

    Gallo Giuseppe

    2012-11-01

    Full Text Available Abstract Background A bacterial strain previously isolated from pyrite mine drainage and named BAS-10 was tentatively identified as Klebsiella oxytoca. Unlikely other enterobacteria, BAS-10 is able to grow on Fe(III-citrate as sole carbon and energy source, yielding acetic acid and CO2 coupled with Fe(III reduction to Fe(II and showing unusual physiological characteristics. In fact, under this growth condition, BAS-10 produces an exopolysaccharide (EPS having a high rhamnose content and metal-binding properties, whose biotechnological applications were proven as very relevant. Results Further phylogenetic analysis, based on 16S rDNA sequence, definitively confirmed that BAS-10 belongs to K. oxytoca species. In order to rationalize the biochemical peculiarities of this unusual enterobacteriun, combined 2D-Differential Gel Electrophoresis (2D-DIGE analysis and mass spectrometry procedures were used to investigate its proteomic changes: i under aerobic or anaerobic cultivation with Fe(III-citrate as sole carbon source; ii under anaerobic cultivations using Na(I-citrate or Fe(III-citrate as sole carbon source. Combining data from these differential studies peculiar levels of outer membrane proteins, key regulatory factors of carbon and nitrogen metabolism and enzymes involved in TCA cycle and sugar biosynthesis or required for citrate fermentation and stress response during anaerobic growth on Fe(III-citrate were revealed. The protein differential regulation seems to ensure efficient cell growth coupled with EPS production by adapting metabolic and biochemical processes in order to face iron toxicity and to optimize energy production. Conclusion Differential proteomics provided insights on the molecular mechanisms necessary for anaeorobic utilization of Fe(III-citrate in a biotechnologically promising enterobacteriun, also revealing genes that can be targeted for the rational design of high-yielding EPS producer strains.

  10. Levoglucosan formation from crystalline cellulose: importance of a hydrogen bonding network in the reaction.

    Science.gov (United States)

    Hosoya, Takashi; Sakaki, Shigeyoshi

    2013-12-01

    Levoglucosan (1,6-anhydro-β-D-glucopyranose) formation by the thermal degradation of native cellulose was investigated by MP4(SDQ)//DFT(B3LYP) and DFT(M06-2X)//DFT(B3LYP) level computations. The computational results of dimer models lead to the conclusion that the degradation occurs by a concerted mechanism similar to the degradation of methyl β-D-glucoside reported in our previous study. One-chain models of glucose hexamer, in which the interchain hydrogen bonds of real cellulose crystals are absent, do not exhibit the correct reaction behavior of levoglucosan formation; for instance, the activation enthalpy (Ea =≈38 kcal mol(-1) ) is considerably underestimated compared to the experimental value (48-60 kcal mol(-1) ). This problem is solved with the use of two-chain models that contain interchain hydrogen bonds. The theoretical study of this model clearly shows that the degradation of the internal glucosyl residue leads to the formation of a levoglucosan precursor at the chain end and levoglucosan is selectively formed from this levoglucosan end. The calculated Ea (56-62 kcal mol(-1) ) agrees well with the experimental value. The computational results of three-chain models indicate that this degradation occurs selectively on the crystalline surface. All these computational results provide a comprehensive understanding of several experimental facts, the mechanisms of which have not yet been elucidated.

  11. 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.

  12. Selected hematologic and biochemical measurements in African HIV-infected and uninfected pregnant women and their infants: the HIV Prevention Trials Network 024 protocol

    Directory of Open Access Journals (Sweden)

    Urassa Willy

    2009-08-01

    Full Text Available Abstract Background Reference values for hematological and biochemical assays in pregnant women and in newborn infants are based primarily on Caucasian populations. Normative data are limited for populations in sub-Saharan Africa, especially comparing women with and without HIV infection, and comparing infants with and without HIV infection or HIV exposure. Methods We determined HIV status and selected hematological and biochemical measurements in women at 20–24 weeks and at 36 weeks gestation, and in infants at birth and 4–6 weeks of age. All were recruited within a randomized clinical trial of antibiotics to prevent chorioamnionitis-associated mother-to-child transmission of HIV (HPTN024. We report nearly complete laboratory data on 2,292 HIV-infected and 367 HIV-uninfected pregnant African women who were representative of the public clinics from which the women were recruited. Nearly all the HIV-infected mothers received nevirapine prophylaxis at the time of labor, as did their infants after birth (always within 72 hours of birth, but typically within just a few hours at the four study sites in Malawi (2 sites, Tanzania, and Zambia. Results HIV-infected pregnant women had lower red blood cell counts, hemoglobin, hematocrit, and white blood cell counts than HIV-uninfected women. Platelet and monocyte counts were higher among HIV-infected women at both time points. At the 4–6-week visit, HIV-infected infants had lower hemoglobin, hematocrit and white blood cell counts than uninfected infants. Platelet counts were lower in HIV-infected infants than HIV-uninfected infants, both at birth and at 4–6 weeks of age. At 4–6 weeks, HIV-infected infants had higher alanine aminotransferase measures than uninfected infants. Conclusion Normative data in pregnant African women and their newborn infants are needed to guide the large-scale HIV care and treatment programs being scaled up throughout the continent. These laboratory measures will help

  13. 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

  14. 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

  15. 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

  16. 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

  17. A master equation and moment approach for biochemical systems with creation-time-dependent bimolecular rate functions

    Science.gov (United States)

    Chevalier, Michael W.; El-Samad, Hana

    2014-12-01

    Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation times of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled.

  18. Electrospun interconnected Fe-N/C nanofiber networks as efficient electrocatalysts for oxygen reduction reaction in acidic media

    Science.gov (United States)

    Wu, Nan; Wang, Yingde; Lei, Yongpeng; Wang, Bing; Han, Cheng; Gou, Yanzi; Shi, Qi; Fang, Dong

    2015-11-01

    One-dimensional electrospun nanofibers have emerged as a potential candidate for high-performance oxygen reduction reaction (ORR) catalysts. However, contact resistance among the neighbouring nanofibers hinders the electron transport. Here, we report the preparation of interconnected Fe-N/C nanofiber networks (Fe-N/C NNs) with low electrical resistance via electrospinning followed by maturing and pyrolysis. The Fe-N/C NNs show excellent ORR activity with onset and half-wave potential of 55 and 108 mV less than those of Pt/C catalyst in 0.5 M H2SO4. Intriguingly, the resulting Fe-N/C NNs exhibit 34% higher peak current density and superior durability than generic Fe-N/C ones with similar microstructure and chemical compositions. Additionally, it also displays much better durability and methanol tolerance than Pt/C catalyst. The higher electroactivity is mainly due to the more effective electron transport between the interconnected nanofibers. Thus, our findings provide a novel insight into the design of functional electrospun nanofibers for the application in energy storage and conversion fields.

  19. Recent abstracts in biochemical technology

    OpenAIRE

    R R Siva Kiran; Brijesh P

    2008-01-01

    “Recent abstracts in biochemical technology” is a collection of interesting research articles published in “List of biochemical technology journals” (Table 1). The abstracts are most likely to report significant results in biochemical technology.

  20. Efficient, sparse biological network determination

    Directory of Open Access Journals (Sweden)

    Papachristodoulou Antonis

    2009-02-01

    Full Text Available Abstract Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational and uncertainties in the data are taken into account. It also produces a network structure that can

  1. 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

  2. Ouroboros - Playing A Biochemical

    Directory of Open Access Journals (Sweden)

    D. T. Rodrigues

    2014-08-01

    Full Text Available Ouroboros: Playing A Biochemical RODRIGUES,D.T.1,2;GAYER, M.C.1,2; ESCOTO, D.F.1; DENARDIN, E.L.G.2, ROEHRS, R.1,2 1Interdisciplinary Research Group on Teaching Practice, Graduate Program in Biochemistry, Unipampa, RS, Brazil 2Laboratory of Physicochemical Studies and Natural Products, Post Graduate Program in Biochemistry, Unipampa, RS, Brazil Introduction: Currently, teachers seek different alternatives to enhance the teaching-learning process. Innovative teaching methodologies are increasingly common tools in educational routine. The use of games, electronic or conventional, is an effective tool to assist in learning and also to raise the social interaction between students. Objective: In this sense our work aims to evaluate the card game and "Ouroboros" board as a teaching and learning tool in biochemistry for a graduating class in Natural Sciences. Materials and methods: The class gathered 22 students of BSc in Natural Sciences. Each letter contained a question across the board that was drawn to a group to answer within the allotted time. The questions related concepts of metabolism, organic and inorganic chemical reactions, bioenergetics, etc.. Before the game application, students underwent a pre-test with four issues involving the content that was being developed. Soon after, the game was applied. Then again questions were asked. Data analysis was performed from the ratio of the number of correct pre-test and post-test answers. Results and discussion: In the pre-test 18.1% of the students knew all issues, 18.1% got 3 correct answers, 40.9% answered only 2 questions correctly and 22.7% did not hit any. In post-test 45.4% answered all the questions right, 31.8% got 3 questions and 22.7% got 2 correct answers. The results show a significant improvement of the students about the field of content taught through the game. Conclusion: Generally, traditional approaches of chemistry and biochemistry are abstract and complex. Thus, through games

  3. In situ construction of three anion-dependent cu(i) coordination networks as promising heterogeneous catalysts for azide-alkyne "click" reactions.

    Science.gov (United States)

    Xu, Zhenghu; Han, Lu Lu; Zhuang, Gui Lin; Bai, Jing; Sun, Di

    2015-05-18

    Three Cu(I) coordination networks, namely, {[Cu2(bpz)2(CN)X]·CH3CN}n, (X = Cl, 1; I, 3), {[Cu6(bpz)6(CH3CN)3(CN)3Br]·2OH·14CH3CN}n, (2, bpz = 3,3',5,5'-tetramethyl-4,4'-bipyrazole), were prepared by using solvothermal method. The cyanide ligands in these networks were generated in situ by cleavage of C-C bond of MeCN under solvothermal condition. The structures of these networks are dependent on halogen anions. Complex 1 is a ladderlike structure with μ2-CN(-) as rung and μ2-bpz as armrest. The Cl(-) in 1 is at terminal position but does not extend the one-dimensional (1D) ladder to higher dimensionalities. Complex 2 is a three-dimensional (3D) framework comprised of novel planar [Cu3Br] triangle and single Cu nodes, which are extended by μ2-bpz and μ2-CN(-) to form a novel (3,9)-connected gfy network. Density functional theory calculations showed that single-electron delocalization of Br atom induces the plane structure of [Cu3Br]. Complex 3 also possesses a similar ladderlike subunit as in 1, but the I(-) acts as bidentate bridge to extend the ladder to 3D framework with a four-connected sra topology. The three networks show notable catalytic activity on the click reaction. The compared catalytic results demonstrate that complex 2 possesses the best catalysis performance among three complexes, which is ascribed to the largest solvent-accessible void (porosity: 2 (29.4%) > 1 (25.7%) > 3 (17.6%)) and the more Cu(I) active sites in 2. The present combined structure-property studies provide not only a new synthetic route to obtain a new kind of catalyst for click reaction but also the new insights on catalyst structure-function relationships. PMID:25941881

  4. 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

  5. Cytologic-Biochemical Radiation Dosimeters in Man

    International Nuclear Information System (INIS)

    The result of radiation interacting with living tissue is the deposition of energy therein. This energy triggers numerous chemical reactions within the molecules of the target tissues. We have measured in man the results of some of these reactions at doses up to 300 rads: chromosome aberrations; alterations in the kinetics of specific human cell populations; changes in 37 biochemical constituents of serum and/or urine. The utilization of chromosomes as a biological dosimeter is partially perfected but there are numerous discrepancies in data between different laboratories. Etiocholanolone can be used to evaluate marrow injury before the white-cell count falls below 5000/mm3. Most biochemical dosimeters evaluated gave negative or inconsistent results. However, salivary amylase is a promising indicator of human radiation injury from doses as low as 100 rads. (author)

  6. Reconstruction and Use of Microbial Metabolic Networks: the Core Escherichia coli Metabolic Model as an Educational Guide.

    Science.gov (United States)

    Orth, Jeffrey D; Fleming, R M T; Palsson, Bernhard Ø

    2010-09-01

    Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core Escherichia coli metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core E. coli model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.

  7. Measures of Biochemical Sociology

    Science.gov (United States)

    Snell, Joel; Marsh, Mitchell

    2008-01-01

    In a previous article, the authors introduced a new sub field in sociology that we labeled "biochemical sociology." We introduced the definition of a sociology that encompasses sociological measures, psychological measures, and biological indicators Snell & Marsh (2003). In this article, we want to demonstrate a research strategy that would assess…

  8. Biochemical Education in Brazil.

    Science.gov (United States)

    Vella, F.

    1988-01-01

    Described are discussions held concerning the problems of biochemical education in Brazil at a meeting of the Sociedade Brazileira de Bioquimica in April 1988. Also discussed are other visits that were made to universities in Brazil. Three major recommendations to improve the state of biochemistry education in Brazil are presented. (CW)

  9. The stochastic dynamics of biochemical systems

    OpenAIRE

    Challenger, Joseph Daniel

    2013-01-01

    The topic of this thesis is the stochastic dynamics of biochemical reaction systems. The importance of the intrinsic fluctuations in these systems has become more widely appreciated in recent years, and should be accounted for when modelling such systems mathematically. These models are described as continuous time Markov processes and their dynamics defined by a master equation. Analytical progress is made possible by the use of the van Kampen system-size expansion, which splits the dynamics...

  10. Forward design of a complex enzyme cascade reaction

    Science.gov (United States)

    Hold, Christoph; Billerbeck, Sonja; Panke, Sven

    2016-01-01

    Enzymatic reaction networks are unique in that one can operate a large number of reactions under the same set of conditions concomitantly in one pot, but the nonlinear kinetics of the enzymes and the resulting system complexity have so far defeated rational design processes for the construction of such complex cascade reactions. Here we demonstrate the forward design of an in vitro 10-membered system using enzymes from highly regulated biological processes such as glycolysis. For this, we adapt the characterization of the biochemical system to the needs of classical engineering systems theory: we combine online mass spectrometry and continuous system operation to apply standard system theory input functions and to use the detailed dynamic system responses to parameterize a model of sufficient quality for forward design. This allows the facile optimization of a 10-enzyme cascade reaction for fine chemical production purposes. PMID:27677244

  11. Reaction time impairments in decision-making networks as a diagnostic marker for traumatic brain injuries and neurodegenerative diseases

    OpenAIRE

    Maia, Pedro D; Kutz, J Nathan

    2016-01-01

    The presence of diffuse Focal Axonal Swellings (FAS) is a hallmark cellular feature in many neurodegenerative diseases and traumatic brain injury. Among other things, the FAS have a significant impact on spike-train encodings that propagate through the affected neurons, leading to compromised signal processing on a neuronal network level. This work merges, for the first time, three fields of study: (i) signal processing in excitatory-inhibitory (EI) networks of neurons via population codes, (...

  12. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; de Fries, Louise Skovlund

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social...

  13. 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.

  14. 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.

  15. Chemical networks*

    OpenAIRE

    Thi Wing-Fai

    2015-01-01

    This chapter discusses the fundamental ideas of how chemical networks are build, their strengths and limitations. The chemical reactions that occur in disks combine the cold phase reactions used to model cold molecular clouds with the hot chemistry applied to planetary atmosphere models. With a general understanding of the different types of reactions that can occur, one can proceed in building a network of chemical reactions and use it to explain the abundance of species seen in disks. One o...

  16. The solution space of metabolic networks: Producibility, robustness and fluctuations

    Energy Technology Data Exchange (ETDEWEB)

    Martino, A De [CNR/IPCF, UoS Roma-Sapienza (Italy); Marinari, E, E-mail: andrea.demartino@roma1.infn.i, E-mail: enzo.marinari@roma1.infn.i [Dipartimento di Fisica, Sapienza Universita di Roma, p.le A. Moro 2, 00185 Roma (Italy)

    2010-06-01

    By flux analysis one generically indicates a class of constraint-based approaches to the study of biochemical reaction networks concerned with the calculation of the flux configurations compatible with given stoichiometric and thermodynamic constraints. One of its main areas of application is the study of cellular metabolic networks. We briefly and selectively review the main approaches to this problem and then, building on recent work, we provide a characterization of the productive capabilities of the metabolic network of the bacterium E.coli in a specified growth medium in terms of the producible biochemical species. While a robust and physiologically meaningful production profile clearly emerges, the underlying constraints still allow for significant fluctuations in the net production even for key metabolites like ATP and, as a consequence, apparently lay the ground for different growth scenarios.

  17. A Theory of Decomposition of Complex Chemical Networks using the Hill Functions

    CERN Document Server

    Chikayama, Eisuke

    2014-01-01

    The design and synthesis of complex and large mimicked biochemical networks de novo is an unsolved problem in synthetic biology. To address this limitation without resorting to ad hoc computations and experiments, a predictive mathematical theory is required to reduce these complex chemical networks into natural physico-chemical expressions. Here we provide a mathematical theory that offers a physico-chemical expression for a large chemical network that is almost arbitrarily both nonlinear and complex. Unexpectedly, the theory demonstrates that such networks can be decomposed into reactions based solely on the Hill equation, a simple chemical logic gate. This theory, analogous to implemented electrical logic gates or functional algorithms in a computer, is proposed for implementing regulated sequences of functional chemical reactions, such as mimicked genes, transcriptional regulation, signal transduction, protein interaction, and metabolic networks, into an artificial designed chemical network.

  18. Temporal motifs in time-dependent networks

    International Nuclear Information System (INIS)

    Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as telecommunication, neural signal processing, biochemical reaction and human social interaction networks. We introduce the framework of temporal motifs to study the mesoscale topological–temporal structure of temporal networks in which the events of nodes do not overlap in time. Temporal motifs are classes of similar event sequences, where the similarity refers not only to topology but also to the temporal order of the events. We provide a mapping from event sequences to coloured directed graphs that enables an efficient algorithm for identifying temporal motifs. We discuss some aspects of temporal motifs, including causality and null models, and present basic statistics of temporal motifs in a large mobile call network

  19. Induced biochemical conversions of heavy crude oils

    International Nuclear Information System (INIS)

    Products formed during multiple interactions of microorganisms with oils fall into two major categories: those formed due to the action of indigenous microorganisms under reservoir conditions over geological periods of time and those products which are generated by the action of introduced organisms. The extreme end product of the first category is the production of heavy 'biodegraded' crudes. The extreme end product of the second category is the production of reduced sulfates due to the introduction of sulfate-reducing bacteria which may lead to the souring of a field. There is, however, a select group of microorganisms whose action on the crudes is beneficial. The interactions between such microorganisms and different crude oils occur through complex biochemical and chemical reactions. These reactions depend on multiple variables within and at the interface of a multicomponent system consisting of organic, aqueous, and inorganic components. Studies, carried out in this laboratory (BNL) of biochemical and chemical reactions in crude oils which involve extremophilic organisms (organisms which thrive in extreme environments), have shown that the reactions are not random and follow distinct trends. These trends can be categorized. The use of a group of characteristic chemical markers, such as mass spectrometric fragmentation patterns of light and heavy hydrocarbons, heterocyclic and organometallic compounds, as well as total trace metal and heteroatom contents of crude oils before and after the biochemical treatment allows to follow the type and the extent of chemical changes which occur during the biochemical conversion of heavy crude oils by microorganisms. The bioconversion involves multiple, simultaneous, and/or concurrent chemical reactions in which the microorganisms serve as biocatalysts. In this sense, the biocatalysts are active in a reaction medium which depends on the chemical composition of the crude and the selectivity of the biocatalyst. Thus, the

  20. Cellular Biology in Terms of Stochastic Nonlinear Biochemical Dynamics: Emergent Properties, Isogenetic Variations and Chemical System Inheritability

    Science.gov (United States)

    Qian, Hong

    2010-12-01

    Based on a stochastic, nonlinear, open biochemical reaction system perspective, we present an analytical theory for cellular biochemical processes. The chemical master equation (CME) approach provides a unifying mathematical framework for cellular modeling. We apply this theory to both self-regulating gene networks and phosphorylation-dephosphorylation signaling modules with feedbacks. Two types of bistability are illustrated in mesoscopic biochemical systems: one that has a macroscopic, deterministic counterpart and another that does not. In certain cases, the latter stochastic bistability is shown to be a "ghost" of the extinction phenomenon. We argue the thermal fluctuations inherent in molecular processes do not disappear in mesoscopic cell-sized nonlinear systems; rather they manifest themselves as isogenetic variations on a different time scale. Isogenetic biochemical variations in terms of the stochastic attractors can have extremely long lifetime. Transitions among discrete stochastic attractors spend most of the time in "waiting", exhibit punctuated equilibria. It can be naturally passed to "daughter cells" via a simple growth and division process. The CME system follows a set of nonequilibrium thermodynamic laws that include non-increasing free energy F( t) with external energy drive Q hk ≥0, and total entropy production rate e p =- dF/ dt+ Q hk ≥0. In the thermodynamic limit, with a system's size being infinitely large, the nonlinear bistability in the CME exhibits many of the characteristics of macroscopic equilibrium phase transition.

  1. Biochemical fuel cell

    Energy Technology Data Exchange (ETDEWEB)

    Weidlich, E.; Richter, G.

    1978-03-30

    Until now, biochemical fuel cells have suffered a reduction of capacity in operation due to omission of internal contact between the electrodes and the diaphragm. This disadvantage is remedied by the invention by connecting the oxygen electrode with a rigid electrode frame and providing means for pressing the fuel electrode to the diaphragm and the diaphragm to the oxygen electrode on the side of the fuel electrode away from the diaphragm. The means of exerting pressure can be metal springs, but preferably elastomers, particularly silicon rubber, or springy gels are used.

  2. On the Reaction Diffusion Master Equation in the Microscopic Limit

    CERN Document Server

    Hellander, Stefan; Petzold, Linda

    2011-01-01

    Stochastic modeling of reaction-diffusion kinetics has emerged as a powerful theoretical tool in the study of biochemical reaction networks. Two frequently employed models are the particle-tracking Smoluchowski framework and the on-lattice Reaction-Diffusion Master Equation (RDME) framework. As the mesh size goes from coarse to fine, the RDME initially becomes more accurate. However, recent developments have shown that it will become increasingly inaccurate compared to the Smoluchowski model as the lattice spacing becomes very fine. In this paper we give a new, general and simple argument for why the RDME breaks down. Our analysis reveals a hard limit on the voxel size for which no local RDME can agree with the Smoluchowski model.

  3. Reaction-diffusion master equation in the microscopic limit

    Science.gov (United States)

    Hellander, Stefan; Hellander, Andreas; Petzold, Linda

    2012-04-01

    Stochastic modeling of reaction-diffusion kinetics has emerged as a powerful theoretical tool in the study of biochemical reaction networks. Two frequently employed models are the particle-tracking Smoluchowski framework and the on-lattice reaction-diffusion master equation (RDME) framework. As the mesh size goes from coarse to fine, the RDME initially becomes more accurate. However, recent developments have shown that it will become increasingly inaccurate compared to the Smoluchowski model as the lattice spacing becomes very fine. Here we give a general and simple argument for why the RDME breaks down. Our analysis reveals a hard limit on the voxel size for which no local RDME can agree with the Smoluchowski model and lets us quantify this limit in two and three dimensions. In this light we review and discuss recent work in which the RDME has been modified in different ways in order to better agree with the microscale model for very small voxel sizes.

  4. Physiological and biochemical basis of salmon young ifshes migratory behavior

    Institute of Scientific and Technical Information of China (English)

    Vladimir Ivanovich Martemyanov

    2016-01-01

    The review presents data on structural changes, physiological and biochemical reactions occurring at salmon young fishes during smoltification. It is shown, that young salmon fishes located in fresh water, in the process of smoltification undergo a complex of structural, physiological and biochemical changes directed on preparation of the organism for living in the sea. These changes cause stress reaction which excites young fishes to migrate down the river towards the sea. Measures to improve reproduction of young salmon fishes at fish farms are offered.

  5. Exact hybrid particle/population simulation of rule-based models of biochemical systems.

    Science.gov (United States)

    Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

    2014-04-01

    Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings

  6. Exact hybrid particle/population simulation of rule-based models of biochemical systems.

    Directory of Open Access Journals (Sweden)

    Justin S Hogg

    2014-04-01

    Full Text Available Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that

  7. N,P-Codoped Carbon Networks as Efficient Metal-free Bifunctional Catalysts for Oxygen Reduction and Hydrogen Evolution Reactions.

    Science.gov (United States)

    Zhang, Jintao; Qu, Liangti; Shi, Gaoquan; Liu, Jiangyong; Chen, Jianfeng; Dai, Liming

    2016-02-01

    The high cost and scarcity of noble metal catalysts, such as Pt, have hindered the hydrogen production from electrochemical water splitting, the oxygen reduction in fuel cells and batteries. Herein, we developed a simple template-free approach to three-dimensional porous carbon networks codoped with nitrogen and phosphorus by pyrolysis of a supermolecular aggregate of self-assembled melamine, phytic acid, and graphene oxide (MPSA/GO). The pyrolyzed MPSA/GO acted as the first metal-free bifunctional catalyst with high activities for both oxygen reduction and hydrogen evolution. Zn-air batteries with the pyrolyzed MPSA/GO air electrode showed a high peak power density (310 W g(-1) ) and an excellent durability. Thus, the pyrolyzed MPSA/GO is a promising bifunctional catalyst for renewable energy technologies, particularly regenerative fuel cells. PMID:26709954

  8. Environmental versatility promotes modularity in genome-scale metabolic networks

    Directory of Open Access Journals (Sweden)

    Wagner Andreas

    2011-08-01

    Full Text Available Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional

  9. 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.

  10. 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.

  11. 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...

  12. Biochemical synthesis with stable isotopes

    International Nuclear Information System (INIS)

    Descriptions of the biochemical synthesis of glucose-13C6 from Agmenellum quadruplication; the biochemical labelling of [13C, 15N] Chlorella and [13C] E. coli, [15N] E. coli, and the production of lactic-13C3 acid utilizing Lactobacillus casei are discussed

  13. EVALUATING BIOCHEMICAL INTERNET RESOURCES

    Directory of Open Access Journals (Sweden)

    R.M. Lima

    2007-05-01

    Full Text Available Many people fail to properly evaluate INTERNET information. This is often due to alack of understanding of the issues, by responsible authorities, and, morespecifically, a lack of understanding of the structure and modis operandi of theINTERNET tool. The aim of this project was to analyze biochemical issuesavailable in WEB pages, evaluating contents quality, coverage, accuracy, authorityand currency. Twenty three sites were analyzed for their contents, presence ofbibliographical references, authorship, titles responsibility and adequacy to targetpublic. The great majority (95% did not mention bibliographic references andtarget public. Less than half divulged names and/or graduation status ofresponsibles. Some sites contained critical conceptual errors, such as: oxygen isessential for anaerobic respiration; presence of H2O in photosynthesis dark phase;yeast is a pluricellular fungal; the overall equation of photosynthesis with errors;NADH2 instead NAD+; etc. None of the analyzed sites was thus consideredexcellent. Although the use of the internet is expanding rapidly on collegecampuses, little is known about students usage; how they perceive the reality ofinternet information and how successful they are in searching through it. Our datastrenghthen the need for rigorous evaluation concerning to educational research ofbiochemical themes on the WEB.

  14. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance

    Science.gov (United States)

    Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema

    2014-01-01

    Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

  15. Metabolic network reconstruction and flux variability analysis of storage synthesis in developing oilseed rape (Brassica napus L.) embryos

    Energy Technology Data Exchange (ETDEWEB)

    Hay, J.; Schwender, J.

    2011-08-01

    Computational simulation of large-scale biochemical networks can be used to analyze and predict the metabolic behavior of an organism, such as a developing seed. Based on the biochemical literature, pathways databases and decision rules defining reaction directionality we reconstructed bna572, a stoichiometric metabolic network model representing Brassica napus seed storage metabolism. In the highly compartmentalized network about 25% of the 572 reactions are transport reactions interconnecting nine subcellular compartments and the environment. According to known physiological capabilities of developing B. napus embryos, four nutritional conditions were defined to simulate heterotrophy or photoheterotrophy, each in combination with the availability of inorganic nitrogen (ammonia, nitrate) or amino acids as nitrogen sources. Based on mathematical linear optimization the optimal solution space was comprehensively explored by flux variability analysis, thereby identifying for each reaction the range of flux values allowable under optimality. The range and variability of flux values was then categorized into flux variability types. Across the four nutritional conditions, approximately 13% of the reactions have variable flux values and 10-11% are substitutable (can be inactive), both indicating metabolic redundancy given, for example, by isoenzymes, subcellular compartmentalization or the presence of alternative pathways. About one-third of the reactions are never used and are associated with pathways that are suboptimal for storage synthesis. Fifty-seven reactions change flux variability type among the different nutritional conditions, indicating their function in metabolic adjustments. This predictive modeling framework allows analysis and quantitative exploration of storage metabolism of a developing B. napus oilseed.

  16. In Silico Genome-Scale Reconstruction and Validation of the Corynebacterium glutamicum Metabolic Network

    DEFF Research Database (Denmark)

    Kjeldsen, Kjeld Raunkjær; Nielsen, J.

    2009-01-01

    A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...

  17. 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

  18. 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.

  19. Weighting schemes in metabolic graphs for identifying biochemical routes.

    Science.gov (United States)

    Ghosh, S; Baloni, P; Vishveshwara, S; Chandra, N

    2014-03-01

    Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes [Formula: see text] fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks.

  20. The application of information theory to biochemical signaling systems.

    Science.gov (United States)

    Rhee, Alex; Cheong, Raymond; Levchenko, Andre

    2012-08-01

    Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.

  1. 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

  2. Comparative analysis of Salmonella genomes identifies a metabolic network for escalating growth in the inflamed gut.

    Science.gov (United States)

    Nuccio, Sean-Paul; Bäumler, Andreas J

    2014-03-18

    The Salmonella genus comprises a group of pathogens associated with illnesses ranging from gastroenteritis to typhoid fever. We performed an in silico analysis of comparatively reannotated Salmonella genomes to identify genomic signatures indicative of disease potential. By removing numerous annotation inconsistencies and inaccuracies, the process of reannotation identified a network of 469 genes involved in central anaerobic metabolism, which was intact in genomes of gastrointestinal pathogens but degrading in genomes of extraintestinal pathogens. This large network contained pathways that enable gastrointestinal pathogens to utilize inflammation-derived nutrients as well as many of the biochemical reactions used for the enrichment and biochemical discrimination of Salmonella serovars. Thus, comparative genome analysis identifies a metabolic network that provides clues about the strategies for nutrient acquisition and utilization that are characteristic of gastrointestinal pathogens. IMPORTANCE While some Salmonella serovars cause infections that remain localized to the gut, others disseminate throughout the body. Here, we compared Salmonella genomes to identify characteristics that distinguish gastrointestinal from extraintestinal pathogens. We identified a large metabolic network that is functional in gastrointestinal pathogens but decaying in extraintestinal pathogens. While taxonomists have used traits from this network empirically for many decades for the enrichment and biochemical discrimination of Salmonella serovars, our findings suggest that it is part of a "business plan" for growth in the inflamed gastrointestinal tract. By identifying a large metabolic network characteristic of Salmonella serovars associated with gastroenteritis, our in silico analysis provides a blueprint for potential strategies to utilize inflammation-derived nutrients and edge out competing gut microbes.

  3. Large-scale networks in engineering and life sciences

    CERN Document Server

    Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai

    2014-01-01

    This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines.  The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...

  4. 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.

  5. Astronomy with Radioactivities: Chapter 9, Nuclear Reactions

    OpenAIRE

    Wiescher, M.; Rauscher, T.

    2010-01-01

    Nuclear reaction rates determine the abundances of isotopes in stellar burning processes. A multitude of reactions determine the reaction flow pattern which is described in terms of reaction network simulations. The reaction rates are determined by laboratory experiments supplemented by nuclear reaction and structure theory. We will discuss the experimental approach as well as the theoretical tools for obtaining the stellar reaction rates. A detailed analysis of a reaction is only possible fo...

  6. Physiological and biochemical aspects of the effect of ionizing radiations on the lung parenchyma

    International Nuclear Information System (INIS)

    Concerning the biochemical reactions of the lung parenchyma to irradiation the following points have been developed. Role of biochemically active substances (histamine, serotonin, kinins, catecholamines, prostaglandins) in the early reaction of the lung to irradiation, their common feature being their vascular impact point. Lung irradiation and lipids (fatty acids and lipid metabolism in general); irradiation, by raising the proportion of unsaturated at the expense of saturated fatty acids, may give rise to serious physiological respiratory disorders. Lung irradiation and blood fluidity (fibrinolytic activity, heparin, platelet factors). Pulmonary interstitium and irradiation (of the three interstitium components collagen plays a preferential part). Irradiation and immunological lung reaction (reasons behind the immunological theory, immunological assistance, immunological mechanism of pulmonary reactions towards pollutants). Enzymatic lung radiolesion indicators. Three kinds of physiological changes have been considered. Vascular physiology disturbances caused by the initial biochemical reactions; anomalies of physiological or functional trials, images of the lesion formed; disorders of the cell physiology of carcinogenesis

  7. Biochemical research elucidating metabolic pathways in Pneumocystis*

    Directory of Open Access Journals (Sweden)

    Kaneshiro E.S.

    2010-12-01

    Full Text Available Advances in sequencing the Pneumocystis carinii genome have helped identify potential metabolic pathways operative in the organism. Also, data from characterizing the biochemical and physiological nature of these organisms now allow elucidation of metabolic pathways as well as pose new challenges and questions that require additional experiments. These experiments are being performed despite the difficulty in doing experiments directly on this pathogen that has yet to be subcultured indefinitely and produce mass numbers of cells in vitro. This article reviews biochemical approaches that have provided insights into several Pneumocystis metabolic pathways. It focuses on 1 S-adenosyl-L-methionine (AdoMet; SAM, which is a ubiquitous participant in numerous cellular reactions; 2 sterols: focusing on oxidosqualene cyclase that forms lanosterol in P. carinii; SAM:sterol C-24 methyltransferase that adds methyl groups at the C-24 position of the sterol side chain; and sterol 14α-demethylase that removes a methyl group at the C-14 position of the sterol nucleus; and 3 synthesis of ubiquinone homologs, which play a pivotal role in mitochondrial inner membrane and other cellular membrane electron transport.

  8. BEST: Biochemical Engineering Simulation Technology

    Energy Technology Data Exchange (ETDEWEB)

    1996-01-01

    The idea of developing a process simulator that can describe biochemical engineering (a relatively new technology area) was formulated at the National Renewable Energy Laboratory (NREL) during the late 1980s. The initial plan was to build a consortium of industrial and U.S. Department of Energy (DOE) partners to enhance a commercial simulator with biochemical unit operations. DOE supported this effort; however, before the consortium was established, the process simulator industry changed considerably. Work on the first phase of implementing various fermentation reactors into the chemical process simulator, ASPEN/SP-BEST, is complete. This report will focus on those developments. Simulation Sciences, Inc. (SimSci) no longer supports ASPEN/SP, and Aspen Technology, Inc. (AspenTech) has developed an add-on to its ASPEN PLUS (also called BioProcess Simulator [BPS]). This report will also explain the similarities and differences between BEST and BPS. ASPEN, developed by the Massachusetts Institute of Technology for DOE in the late 1970s, is still the state-of-the-art chemical process simulator. It was selected as the only simulator with the potential to be easily expanded into the biochemical area. ASPEN/SP, commercially sold by SimSci, was selected for the BEST work. SimSci completed work on batch, fed-batch, and continuous fermentation reactors in 1993, just as it announced it would no longer commercially support the complete ASPEN/SP product. BEST was left without a basic support program. Luckily, during this same time frame, AspenTech was developing a biochemical simulator with its version of ASPEN (ASPEN PLUS), which incorporates most BEST concepts. The future of BEST will involve developing physical property data and models appropriate to biochemical systems that are necessary for good biochemical process design.

  9. Toward the automated generation of genome-scale metabolic networks in the SEED

    Directory of Open Access Journals (Sweden)

    Gould John

    2007-04-01

    Full Text Available Abstract Background Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. Results We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis. We have implemented our tools and database within the SEED, an open-source software environment for comparative

  10. Irreversible thermodynamics of open chemical networks I: Emergent cycles and broken conservation laws

    CERN Document Server

    Polettini, Matteo

    2014-01-01

    In this and a companion paper we outline a general framework for the thermodynamic description of open chemical reaction networks, with special regard to metabolic networks regulating cellular physiology and biochemical functions. We first introduce closed networks ``in a box'', whose thermodynamics is subjected to strict physical constraints: the mass-action law, elementarity of processes, and detailed balance. We further digress on the role of solvents and on the seemingly unacknowledged property of network independence of free energy landscapes. We then open the system by assuming that the concentrations of certain substrate species (the chemostats) are fixed, whether because promptly regulated by the environment via contact with reservoirs, or because nearly constant in a time window. As a result, the system is driven out of equilibrium. A rich algebraic and topological structure ensues in the network of internal species: Emergent irreversible cycles are associated to nonvanishing affinities, whose symmet...

  11. 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.

  12. 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.

  13. Noise Filtering and Prediction in Biological Signaling Networks

    CERN Document Server

    Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael

    2016-01-01

    Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...

  14. Impact of flow velocity on biochemical processes - a laboratory experiment

    Science.gov (United States)

    Boisson, A.; Roubinet, D.; Aquilina, L.; Bour, O.; Davy, P.

    2014-08-01

    Understanding and predicting hydraulic and chemical properties of natural environments are current crucial challenges. It requires considering hydraulic, chemical and biological processes and evaluating how hydrodynamic properties impact on biochemical reactions. In this context, an original laboratory experiment to study the impact of flow velocity on biochemical reactions along a one-dimensional flow streamline has been developed. Based on the example of nitrate reduction, nitrate-rich water passes through plastic tubes at several flow velocities (from 6.2 to 35 mm min-1), while nitrate concentration at the tube outlet is monitored for more than 500 h. This experimental setup allows assessing the biologically controlled reaction between a mobile electron acceptor (nitrate) and an electron donor (carbon) coming from an immobile phase (tube) that produces carbon during its degradation by microorganisms. It results in observing a dynamic of the nitrate transformation associated with biofilm development which is flow-velocity dependent. It is proposed that the main behaviors of the reaction rates are related to phases of biofilm development through a simple analytical model including assimilation. Experiment results and their interpretation demonstrate a significant impact of flow velocity on reaction performance and stability and highlight the relevance of dynamic experiments over static experiments for understanding biogeochemical processes.

  15. Model-Based Design of Biochemical Microreactors.

    Science.gov (United States)

    Elbinger, Tobias; Gahn, Markus; Neuss-Radu, Maria; Hante, Falk M; Voll, Lars M; Leugering, Günter; Knabner, Peter

    2016-01-01

    Mathematical modeling of biochemical pathways is an important resource in Synthetic Biology, as the predictive power of simulating synthetic pathways represents an important step in the design of synthetic metabolons. In this paper, we are concerned with the mathematical modeling, simulation, and optimization of metabolic processes in biochemical microreactors able to carry out enzymatic reactions and to exchange metabolites with their surrounding medium. The results of the reported modeling approach are incorporated in the design of the first microreactor prototypes that are under construction. These microreactors consist of compartments separated by membranes carrying specific transporters for the input of substrates and export of products. Inside the compartments of the reactor multienzyme complexes assembled on nano-beads by peptide adapters are used to carry out metabolic reactions. The spatially resolved mathematical model describing the ongoing processes consists of a system of diffusion equations together with boundary and initial conditions. The boundary conditions model the exchange of metabolites with the neighboring compartments and the reactions at the surface of the nano-beads carrying the multienzyme complexes. Efficient and accurate approaches for numerical simulation of the mathematical model and for optimal design of the microreactor are developed. As a proof-of-concept scenario, a synthetic pathway for the conversion of sucrose to glucose-6-phosphate (G6P) was chosen. In this context, the mathematical model is employed to compute the spatio-temporal distributions of the metabolite concentrations, as well as application relevant quantities like the outflow rate of G6P. These computations are performed for different scenarios, where the number of beads as well as their loading capacity are varied. The computed metabolite distributions show spatial patterns, which differ for different experimental arrangements. Furthermore, the total output of G6P

  16. Hyponatraemia: biochemical and clinical perspectives.

    OpenAIRE

    Gill, G; Leese, G

    1998-01-01

    Hyponatraemia is a common bio-chemical abnormality, occurring in about 15% of hospital inpatients. It is often associated with severe illness and relatively poor outcome. Pathophysiologically, hyponatraemia may be spurious, dilutional, depletional or redistributional. Particularly difficult causes and concepts of hyponatraemia are the syndrome of inappropriate antidiuresis and the sick cell syndrome, which are discussed here in detail. Therapy should always be targeted at the underlying disea...

  17. Cellular automata modelling of biomolecular networks dynamics.

    Science.gov (United States)

    Bonchev, D; Thomas, S; Apte, A; Kier, L B

    2010-01-01

    The modelling of biological systems dynamics is traditionally performed by ordinary differential equations (ODEs). When dealing with intracellular networks of genes, proteins and metabolites, however, this approach is hindered by network complexity and the lack of experimental kinetic parameters. This opened the field for other modelling techniques, such as cellular automata (CA) and agent-based modelling (ABM). This article reviews this emerging field of studies on network dynamics in molecular biology. The basics of the CA technique are discussed along with an extensive list of related software and websites. The application of CA to networks of biochemical reactions is exemplified in detail by the case studies of the mitogen-activated protein kinase (MAPK) signalling pathway, the FAS-ligand (FASL)-induced and Bcl-2-related apoptosis. The potential of the CA method to model basic pathways patterns, to identify ways to control pathway dynamics and to help in generating strategies to fight with cancer is demonstrated. The different line of CA applications presented includes the search for the best-performing network motifs, an analysis of importance for effective intracellular signalling and pathway cross-talk. PMID:20373215

  18. Biochemical markers of bone turnover

    International Nuclear Information System (INIS)

    Biochemical markers of bone turnover has received increasing attention over the past few years, because of the need for sensitivity and specific tool in the clinical investigation of osteoporosis. Bone markers should be unique to bone, reflect changes of bone less, and should be correlated with radiocalcium kinetics, histomorphometry, or changes in bone mass. The markers also should be useful in monitoring treatment efficacy. Although no bone marker has been established to meet all these criteria, currently osteocalcin and pyridinium crosslinks are the most efficient markers to assess the level of bone turnover in the menopausal and senile osteoporosis. Recently, N-terminal telopeptide (NTX), C-terminal telopeptide (CTX) and bone specific alkaline phosphatase are considered as new valid markers of bone turnover. Recent data suggest that CTX and free deoxypyridinoline could predict the subsequent risk of hip fracture of elderly women. Treatment of postmenopausal women with estrogen, calcitonin and bisphosphonates demonstrated rapid decrease of the levels of bone markers that correlated with the long-term increase of bone mass. Factors such as circadian rhythms, diet, age, sex, bone mass and renal function affect the results of biochemical markers and should be appropriately adjusted whenever possible. Each biochemical markers of bone turnover may have its own specific advantages and limitations. Recent advances in research will provide more sensitive and specific assays

  19. Biochemical adaptation to ocean acidification.

    Science.gov (United States)

    Stillman, Jonathon H; Paganini, Adam W

    2015-06-01

    The change in oceanic carbonate chemistry due to increased atmospheric PCO2  has caused pH to decline in marine surface waters, a phenomenon known as ocean acidification (OA). The effects of OA on organisms have been shown to be widespread among diverse taxa from a wide range of habitats. The majority of studies of organismal response to OA are in short-term exposures to future levels of PCO2 . From such studies, much information has been gathered on plastic responses organisms may make in the future that are beneficial or harmful to fitness. Relatively few studies have examined whether organisms can adapt to negative-fitness consequences of plastic responses to OA. We outline major approaches that have been used to study the adaptive potential for organisms to OA, which include comparative studies and experimental evolution. Organisms that inhabit a range of pH environments (e.g. pH gradients at volcanic CO2 seeps or in upwelling zones) have great potential for studies that identify adaptive shifts that have occurred through evolution. Comparative studies have advanced our understanding of adaptation to OA by linking whole-organism responses with cellular mechanisms. Such optimization of function provides a link between genetic variation and adaptive evolution in tuning optimal function of rate-limiting cellular processes in different pH conditions. For example, in experimental evolution studies of organisms with short generation times (e.g. phytoplankton), hundreds of generations of growth under future conditions has resulted in fixed differences in gene expression related to acid-base regulation. However, biochemical mechanisms for adaptive responses to OA have yet to be fully characterized, and are likely to be more complex than simply changes in gene expression or protein modification. Finally, we present a hypothesis regarding an unexplored area for biochemical adaptation to ocean acidification. In this hypothesis, proteins and membranes exposed to the

  20. Motif analysis for small-number effects in chemical reaction dynamics

    Science.gov (United States)

    Saito, Nen; Sughiyama, Yuki; Kaneko, Kunihiko

    2016-09-01

    The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a "mesoscopic" level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration.

  1. 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.

  2. 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...

  3. Biochemical Markers of Myocardial Damage.

    Science.gov (United States)

    Bodor, Geza S

    2016-04-01

    Heart diseases, especially coronary artery diseases (CAD), are the leading causes of morbidity and mortality in developed countries. Effective therapy is available to ensure patient survival and to prevent long term sequelae after an acute ischemic event caused by CAD, but appropriate therapy requires rapid and accurate diagnosis. Research into the pathology of CAD have demonstrated the usefulness of measuring concentrations of chemicals released from the injured cardiac muscle can aid the diagnosis of diseases caused by myocardial ischemia. Since the mid-1950s successively better biochemical markers have been described in research publications and applied for the clinical diagnosis of acute ischemic myocardial injury. Aspartate aminotransferase of the 1950s was replaced by other cytosolic enzymes such as lactate dehydrogenase, creatine kinase and their isoenzymes that exhibited better cardiac specificity. With the availability of immunoassays, other muscle proteins, that had no enzymatic activity, were also added to the diagnostic arsenal but their limited tissue specificity and sensitivity lead to suboptimal diagnostic performance. After the discovery that cardiac troponins I and T have the desired specificity, they have replaced the cytosolic enzymes in the role of diagnosing myocardial ischemia and infarction. The use of the troponins provided new knowledge that led to revision and redefinition of ischemic myocardial injury as well as the introduction of biochemicals for estimation of the probability of future ischemic myocardial events. These markers, known as cardiac risk markers, evolved from the diagnostic markers such as CK-MB or troponins, but markers of inflammation also belong to these groups of diagnostic chemicals. This review article presents a brief summary of the most significant developments in the field of biochemical markers of cardiac injury and summarizes the most recent significant recommendations regarding the use of the cardiac markers in

  4. Enzyme and biochemical producing fungi

    DEFF Research Database (Denmark)

    Lübeck, Peter Stephensen; Lübeck, Mette; Nilsson, Lena;

    2010-01-01

    We are developing a biorefinery concept for biological production of chemicals, drugs, feed and fuels using plant biomass as raw material in well-defined cell-factories. Among the important goals is the discovery of new biocatalysts for production of enzymes, biochemicals and fuels and already our...... screening of a large collection of fungal strains isolated from natural habitats have resulted in identification of strains with high production of hydrolytic enzymes and excretion of organic acids. Our research focuses on creating a fungal platform based on synthetic biology for developing new cell...

  5. Computer Simulations of Mechano-Chemical Networks Choreographing Actin Dynamics in Cell Motility

    Science.gov (United States)

    Zhuravlev, Pavel I.; Hu, Longhua; Papoian, Garegin A.

    In eukaryotic cells, cell motility is largely driven by self-assembly and growth of filamentous networks comprised of actin. Numerous proteins regulate actin network dynamics either biochemically, or through mechanical interactions. This regulation is rather complex, intricately coordinated both spatially and temporally. Although experiments in vivo and in vitro have provided a trove of structural and biochemical information about actin-based cell motility processes, experimental data is not always easy to interpret unambiguously, sometimes various interpretations being in contradiction with each other. Hence, mathematical modeling approaches are necessary for providing a physical foundation for interpreting and guiding experiments. In particular, computer simulations based on physicochemical interactions provide a systems-level description of protrusion dynamics. In this contribution, we review recent progress in modeling actin-based cell motility using detailed computer simulations. We elaborate on the way actin network dynamics is determined by the interplay between chemical reactions, mechanical feedbacks, and transport bottlenecks. We also discuss the role of inherent randomness of elementary chemical reactions in determining the dynamical behavior of the mechano-chemical network controlling actin polymerization and growth.

  6. The biochemical basis for thermoregulation in heat-producing flowers.

    Science.gov (United States)

    Umekawa, Yui; Seymour, Roger S; Ito, Kikukatsu

    2016-01-01

    Thermoregulation (homeothermy) in animals involves a complex mechanism involving thermal receptors throughout the body and integration in the hypothalamus that controls shivering and non-shivering thermogenesis. The flowers of some ancient families of seed plants show a similar degree of physiological thermoregulation, but by a different mechanism. Here, we show that respiratory control in homeothermic spadices of skunk cabbage (Symplocarpus renifolius) is achieved by rate-determining biochemical reactions in which the overall thermodynamic activation energy exhibits a negative value. Moreover, NADPH production, catalyzed by mitochondrial isocitrate dehydrogenase in a chemically endothermic reaction, plays a role in the pre-equilibrium reaction. We propose that a law of chemical equilibrium known as Le Châtelier's principle governs the homeothermic control in skunk cabbage. PMID:27095582

  7. The carbon assimilation network in Escherichia coli is densely connected and largely sign-determined by directions of metabolic fluxes.

    Directory of Open Access Journals (Sweden)

    Valentina Baldazzi

    2010-06-01

    Full Text Available Gene regulatory networks consist of direct interactions but also include indirect interactions mediated by metabolites and signaling molecules. We describe how these indirect interactions can be derived from a model of the underlying biochemical reaction network, using weak time-scale assumptions in combination with sensitivity criteria from metabolic control analysis. We apply this approach to a model of the carbon assimilation network in Escherichia coli. Our results show that the derived gene regulatory network is densely connected, contrary to what is usually assumed. Moreover, the network is largely sign-determined, meaning that the signs of the indirect interactions are fixed by the flux directions of biochemical reactions, independently of specific parameter values and rate laws. An inversion of the fluxes following a change in growth conditions may affect the signs of the indirect interactions though. This leads to a feedback structure that is at the same time robust to changes in the kinetic properties of enzymes and that has the flexibility to accommodate radical changes in the environment.

  8. Integration of Metabolic Modeling with Gene Co-expression Reveals Transcriptionally Programmed Reactions Explaining Robustness in Mycobacterium tuberculosis.

    Science.gov (United States)

    Puniya, Bhanwar Lal; Kulshreshtha, Deepika; Mittal, Inna; Mobeen, Ahmed; Ramachandran, Srinivasan

    2016-01-01

    Robustness of metabolic networks is accomplished by gene regulation, modularity, re-routing of metabolites and plasticity. Here, we probed robustness against perturbations of biochemical reactions of M. tuberculosis in the form of predicting compensatory trends. In order to investigate the transcriptional programming of genes associated with correlated fluxes, we integrated with gene co-expression network. Knock down of the reactions NADH2r and ATPS responsible for producing the hub metabolites, and Central carbon metabolism had the highest proportion of their associated genes under transcriptional co-expression with genes of their flux correlated reactions. Reciprocal gene expression correlations were observed among compensatory routes, fresh activation of alternative routes and in the multi-copy genes of Cysteine synthase and of Phosphate transporter. Knock down of 46 reactions caused the activation of Isocitrate lyase or Malate synthase or both reactions, which are central to the persistent state of M. tuberculosis. A total of 30 new freshly activated routes including Cytochrome c oxidase, Lactate dehydrogenase, and Glycine cleavage system were predicted, which could be responsible for switching into dormant or persistent state. Thus, our integrated approach of exploring transcriptional programming of flux correlated reactions has the potential to unravel features of system architecture conferring robustness. PMID:27000948

  9. Cellular reactions to patterned biointerfaces

    OpenAIRE

    Schulte, Vera Antonie

    2012-01-01

    The subject of this thesis is to study cellular reactions to topographically, mechanically and biochemically tunable polymeric biomaterials. Different aspects of in vitro cell-biomaterial interactions were systematically studied with the murine fibroblast cell line NIH L929 and primary human dermal fibroblasts (HDFs). Besides a general cytocompatibility assessment of the applied materials and the quantification of cell adhesion per se, cell morphological changes (e.g. cell spreading) and intr...

  10. Amplification and detection of single-molecule conformational fluctuation through a protein interaction network with bimodal distributions.

    Science.gov (United States)

    Wu, Zhanghan; Elgart, Vlad; Qian, Hong; Xing, Jianhua

    2009-09-10

    A protein undergoes conformational dynamics with multiple time scales, which results in fluctuating enzyme activities. Recent studies in single-molecule enzymology have observe this "age-old" dynamic disorder phenomenon directly. However, the single-molecule technique has its limitation. To be able to observe this molecular effect with real biochemical functions in situ, we propose to couple the fluctuations in enzymatic activity to noise propagations in small protein interaction networks such as a zeroth-order ultrasensitive phosphorylation-dephosphorylation cycle. We show that enzyme fluctuations can indeed be amplified by orders of magnitude into fluctuations in the level of substrate phosphorylation, a quantity of wide interest in cellular biology. Enzyme conformational fluctuations sufficiently slower than the catalytic reaction turnover rate result in a bimodal concentration distribution of the phosphorylated substrate. In return, this network-amplified single-enzyme fluctuation can be used as a novel biochemical "reporter" for measuring single-enzyme conformational fluctuation rates.

  11. Stiffness detection and reduction in discrete stochastic simulation of biochemical systems

    Science.gov (United States)

    Pu, Yang; Watson, Layne T.; Cao, Yang

    2011-02-01

    Typical multiscale biochemical models contain fast-scale and slow-scale reactions, where "fast" reactions fire much more frequently than "slow" ones. This feature often causes stiffness in discrete stochastic simulation methods such as Gillespie's algorithm and the Tau-Leaping method leading to inefficient simulation. This paper proposes a new strategy to automatically detect stiffness and identify species that cause stiffness for the Tau-Leaping method, as well as two stiffness reduction methods. Numerical results on a stiff decaying dimerization model and a heat shock protein regulation model demonstrate the efficiency and accuracy of the proposed methods for multiscale biochemical systems.

  12. Spatio-temporal patterns with hyperchaotic dynamics in diffusively coupled biochemical oscillators

    Directory of Open Access Journals (Sweden)

    Gerold Baier

    1997-01-01

    Full Text Available We present three examples how complex spatio-temporal patterns can be linked to hyperchaotic attractors in dynamical systems consisting of nonlinear biochemical oscillators coupled linearly with diffusion terms. The systems involved are: (a a two-variable oscillator with two consecutive autocatalytic reactions derived from the Lotka–Volterra scheme; (b a minimal two-variable oscillator with one first-order autocatalytic reaction; (c a three-variable oscillator with first-order feedback lacking autocatalysis. The dynamics of a finite number of coupled biochemical oscillators may account for complex patterns in compartmentalized living systems like cells or tissue, and may be tested experimentally in coupled microreactors.

  13. Network Biology (http://www.iaees.org/publications/journals/nb/online-version.asp

    Directory of Open Access Journals (Sweden)

    networkbiology@iaees.org

    Full Text Available Network Biology ISSN 2220-8879 URL: http://www.iaees.org/publications/journals/nb/online-version.asp RSS: http://www.iaees.org/publications/journals/nb/rss.xml E-mail: networkbiology@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope NETWORK BIOLOGY (ISSN 2220-8879; CODEN NBEICS is an open access, peer-reviewed international journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology. It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: •Theories, algorithms and programs of network analysis •Innovations and applications of biological networks •Ecological networks, food webs and natural equilibrium •Co-evolution, co-extinction, biodiversity conservation •Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs •Physiological networksNetwork regulation of metabolic processes, human diseases and ecological systems •Social networks, epidemiological networks •System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc. We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc. Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal

  14. 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...

  15. 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.

  16. A network perspective on metabolic inconsistency

    Directory of Open Access Journals (Sweden)

    Sonnenschein Nikolaus

    2012-05-01

    Full Text Available Abstract Background Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. Results Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. Conclusions The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical

  17. Mathematical treatment of isotopologue and isotopomer speciation and fractionation in biochemical kinetics

    Energy Technology Data Exchange (ETDEWEB)

    Maggi, F.M.; Riley, W.J.

    2009-11-01

    We present a mathematical treatment of the kinetic equations that describe isotopologue and isotopomer speciation and fractionation during enzyme-catalyzed biochemical reactions. These equations, presented here with the name GEBIK (general equations for biochemical isotope kinetics) and GEBIF (general equations for biochemical isotope fractionation), take into account microbial biomass and enzyme dynamics, reaction stoichiometry, isotope substitution number, and isotope location within each isotopologue and isotopomer. In addition to solving the complete GEBIK and GEBIF, we also present and discuss two approximations to the full solutions under the assumption of biomass-free and enzyme steady-state, and under the quasi-steady-state assumption as applied to the complexation rate. The complete and approximate approaches are applied to observations of biological denitrification in soils. Our analysis highlights that the full GEBIK and GEBIF provide a more accurate description of concentrations and isotopic compositions of substrates and products throughout the reaction than do the approximate forms. We demonstrate that the isotopic effects of a biochemical reaction depend, in the most general case, on substrate and complex concentrations and, therefore, the fractionation factor is a function of time. We also demonstrate that inverse isotopic effects can occur for values of the fractionation factor smaller than 1, and that reactions that do not discriminate isotopes do not necessarily imply a fractionation factor equal to 1.

  18. Cellular Signaling Networks Function as Generalized Wiener-Kolmogorov Filters to Suppress Noise

    Science.gov (United States)

    Hinczewski, Michael; Thirumalai, D.

    2014-10-01

    Cellular signaling involves the transmission of environmental information through cascades of stochastic biochemical reactions, inevitably introducing noise that compromises signal fidelity. Each stage of the cascade often takes the form of a kinase-phosphatase push-pull network, a basic unit of signaling pathways whose malfunction is linked with a host of cancers. We show that this ubiquitous enzymatic network motif effectively behaves as a Wiener-Kolmogorov optimal noise filter. Using concepts from umbral calculus, we generalize the linear Wiener-Kolmogorov theory, originally introduced in the context of communication and control engineering, to take nonlinear signal transduction and discrete molecule populations into account. This allows us to derive rigorous constraints for efficient noise reduction in this biochemical system. Our mathematical formalism yields bounds on filter performance in cases important to cellular function—such as ultrasensitive response to stimuli. We highlight features of the system relevant for optimizing filter efficiency, encoded in a single, measurable, dimensionless parameter. Our theory, which describes noise control in a large class of signal transduction networks, is also useful both for the design of synthetic biochemical signaling pathways and the manipulation of pathways through experimental probes such as oscillatory input.

  19. Biochemical Analysis of Microbial Rhodopsins.

    Science.gov (United States)

    Maresca, Julia A; Keffer, Jessica L; Miller, Kelsey J

    2016-01-01

    Ion-pumping rhodopsins transfer ions across the microbial cell membrane in a light-dependent fashion. As the rate of biochemical characterization of microbial rhodopsins begins to catch up to the rate of microbial rhodopsin identification in environmental and genomic sequence data sets, in vitro analysis of their light-absorbing properties and in vivo analysis of ion pumping will remain critical to characterizing these proteins. As we learn more about the variety of physiological roles performed by microbial rhodopsins in different cell types and environments, observing the localization patterns of the rhodopsins and/or quantifying the number of rhodopsin-bearing cells in natural environments will become more important. Here, we provide protocols for purification of rhodopsin-containing membranes, detection of ion pumping, and observation of functional rhodopsins in laboratory and environmental samples using total internal reflection fluorescence microscopy. © 2016 by John Wiley & Sons, Inc. PMID:27153387

  20. 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.

  1. The network of antigen-antibody reactions in adult women with breast cancer or benign breast pathology or without breast pathology.

    Directory of Open Access Journals (Sweden)

    Tania Romo-González

    Full Text Available The Immunoglobulin G (IgG antibody response to different protein antigens of the mammary ductal carcinoma by adult women affected by Breast Cancer (BC distinguishes at least 103 proteins that differ in their molecular weights (MW. The IgG producing cell clones (nodes coexist with each other in each individual organism and share energy resources among themselves, as well as factors that control the level of expression and Specificity of their IgG antibodies. So, it can be proposed that among them there is a Network of interconnections (links unveiled by the antigens, which specifically react with the IgG antibodies produced by the clones. This Network possibly regulates IgG antibodies' activity and effectiveness. We describe the Network of nodes and links that exists between the different antigens and their respective IgG producing cell clones against the extracted protein antigens from the cells of the T47D Cell-Line, in 50 women with BC, 50 women with Benign Breast Pathology (BBP and 50 women without breast pathology (H. We have found that women with BBP have the highest number of Links, followed by the H group and, lastly, the women with BC, a finding which suggests that cancer interferes with the Connectivity between the IgG producing cell clones and blocks the expression of 322 links in women with BBP and 32 links in women with H. It is also plausible that the largest number of links in the women with BBP indicates the Network's state of arousal that provides protection against BC. On the other hand, there were many missing links in the BC group of women; the clone which lost more links in the BC group was the hub 24, which point to some of the antigens of T47D as potentially useful as vaccines, as the immune system of women with BBP is well aware of them.

  2. Noise Control in Gene Regulatory Networks with Negative Feedback.

    Science.gov (United States)

    Hinczewski, Michael; Thirumalai, D

    2016-07-01

    Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.

  3. TrypanoCyc : a community-led biochemical pathways database for Trypanosoma brucei

    NARCIS (Netherlands)

    Shameer, Sanu; Logan-Klumpler, Flora J; Vinson, Florence; Cottret, Ludovic; Merlet, Benjamin; Achcar, Fiona; Boshart, Michael; Berriman, Matthew; Breitling, Rainer; Bringaud, Frédéric; Bütikofer, Peter; Cattanach, Amy M; Bannerman-Chukualim, Bridget; Creek, Darren J; Crouch, Kathryn; de Koning, Harry P; Denise, Hubert; Ebikeme, Charles; Fairlamb, Alan H; Ferguson, Michael A J; Ginger, Michael L; Hertz-Fowler, Christiane; Kerkhoven, Eduard J; Mäser, Pascal; Michels, Paul A M; Nayak, Archana; Nes, David W; Nolan, Derek P; Olsen, Christian; Silva-Franco, Fatima; Smith, Terry K; Taylor, Martin C; Tielens, Aloysius G M; Urbaniak, Michael D; van Hellemond, Jaap J; Vincent, Isabel M; Wilkinson, Shane R; Wyllie, Susan; Opperdoes, Fred R; Barrett, Michael P; Jourdan, Fabien

    2015-01-01

    The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about

  4. TrypanoCyc: A community-led biochemical pathways database for Trypanosoma brucei

    NARCIS (Netherlands)

    S. Shameer (Sanu); F.J. Logan-Klumpler (Flora J.); F. Vinson (Florence); L. Cottret (Ludovic); B. Merlet (Benjamin); F. Achcar (Fiona); M. Boshart (Michael); M. Berriman (Matthew); R. Breitling (Rainer); F. Bringaud (Frédéric); P. Bütikofer (Peter); A.M. Cattanach (Amy M.); B. Bannerman-Chukualim (Bridget); D.J. Creek (Darren J.); K. Crouch (Kathryn); H.P. De Koning (Harry P.); H. Denise (Hubert); C. Ebikeme (Charles); A.H. Fairlamb (Alan H.); M.A.J. Ferguson (Michael A. J.); M.L. Ginger (Michael L.); C. Hertz-Fowler (Christiane); E.J. Kerkhoven (Eduard); P. Mäser (Pascal); P.A.M. Michels (Paul); A. Nayak (Archana); D. Nes (DavidW.); D.P. Nolan (Derek P.); C. Olsen (Christian); F. Silva-Franco (Fatima); T.K. Smith (Terry K.); M.C. Taylor (Martin C.); A.G.M. Tielens (Aloysius); M.D. Urbaniak (Michael D.); J.J. van Hellemond (Jaap); I.M. Vincent (Isabel M.); S.R. Wilkinson (Shane R.); S. Wyllie (Susan); F.R. Opperdoes (Fred); M.P. Barrett (Michael P.); F. Jourdan (Fabien)

    2015-01-01

    textabstractThe metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individualmetabolic networks is increasing as we learn

  5. Metabolic constraint-based refinement of transcriptional regulatory networks.

    Science.gov (United States)

    Chandrasekaran, Sriram; Price, Nathan D

    2013-01-01

    There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10(-172)), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10(-14)) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to

  6. Design of a biochemical circuit motif for learning linear functions.

    Science.gov (United States)

    Lakin, Matthew R; Minnich, Amanda; Lane, Terran; Stefanovic, Darko

    2014-12-01

    Learning and adaptive behaviour are fundamental biological processes. A key goal in the field of bioengineering is to develop biochemical circuit architectures with the ability to adapt to dynamic chemical environments. Here, we present a novel design for a biomolecular circuit capable of supervised learning of linear functions, using a model based on chemical reactions catalysed by DNAzymes. To achieve this, we propose a novel mechanism of maintaining and modifying internal state in biochemical systems, thereby advancing the state of the art in biomolecular circuit architecture. We use simulations to demonstrate that the circuit is capable of learning behaviour and assess its asymptotic learning performance, scalability and robustness to noise. Such circuits show great potential for building autonomous in vivo nanomedical devices. While such a biochemical system can tell us a great deal about the fundamentals of learning in living systems and may have broad applications in biomedicine (e.g. autonomous and adaptive drugs), it also offers some intriguing challenges and surprising behaviours from a machine learning perspective. PMID:25401175

  7. Biochemical bases of mineral waters genesis

    Directory of Open Access Journals (Sweden)

    D. D. Zhernosekov

    2005-02-01

    Full Text Available This work directs data about mineral water genesis. The accent on balneological sense is done. We suggest the criteria of biochemical processes estimation which take part in mineral water compounds creation. These criteria can be used for illustration of dependence between waters medical properties and biochemical processes of their genesis.

  8. Topographical mapping of biochemical properties of articular cartilage in the equine fetlock joint

    NARCIS (Netherlands)

    Brama, P.A.J.; Tekoppele, J.M.; Bank, R.A.; Karssenberg, D.; Barneveld, A.; Weeren, P.R. van

    2000-01-01

    The aim of this study was to evaluate topographical differences in the biochemical composition of the extracellular matrix of articular cartilage of the normal equine fetlock joint. Water content, DNA content, glycosaminoglycan (GAG) content and a number of characteristics of the collagen network (t

  9. Reactions of charged and neutral recoil particles following nuclear transformations

    International Nuclear Information System (INIS)

    The status of the following programs is reported: study of the stereochemistry of halogen atom or ion reactions produced via (eta,γ) or (IT) nuclear reactions with diastereomeric molecules; study of nuclear decay induced reactions of halogen species with organic compounds in the gas phase; decay-induced labelling of compounds of biochemical interest; energetics and mechanisms involved in the reactions of highly energetic carbon-11 atoms with simple organic molecules; and chemistry of the positronium. (LK)

  10. Kinetic modeling of reactions in Foods

    NARCIS (Netherlands)

    Boekel, van M.A.J.S.

    2008-01-01

    The level of quality that food maintains as it travels down the production-to-consumption path is largely determined by the chemical, biochemical, physical, and microbiological changes that take place during its processing and storage. Kinetic Modeling of Reactions in Foods demonstrates how to effec

  11. Gray box modeling of MSW degradation: Revealing its dominant (bio)chemical mechanism

    NARCIS (Netherlands)

    Van Turnhout, A.G.; Heimovaara, T.J.; Kleerebezem, R.

    2013-01-01

    In this paper we present an approach to describe organic degradation within immobile water regions of Municipal Solid Waste (MSW) landfills which is best described by the term “gray box” model. We use a simplified set of dominant (bio)chemical and physical reactions and realistic environmental condi

  12. Modeling integrated cellular machinery using hybrid Petri-Boolean networks.

    Directory of Open Access Journals (Sweden)

    Natalie Berestovsky

    Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them

  13. Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution

    Science.gov (United States)

    Mannakee, Brian K.; Gutenkunst, Ryan N.

    2016-01-01

    The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein’s rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces. PMID:27380265

  14. Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.

    Science.gov (United States)

    Mannakee, Brian K; Gutenkunst, Ryan N

    2016-07-01

    The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

  15. Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.

    Directory of Open Access Journals (Sweden)

    Brian K Mannakee

    2016-07-01

    Full Text Available The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.

  16. Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network

    Directory of Open Access Journals (Sweden)

    Heavner Benjamin D

    2012-06-01

    Full Text Available Abstract Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Additional file 1 Function testYeastModel.m.m. Click here for file Additional file 2 Function model

  17. Simulation methods with extended stability for stiff biochemical Kinetics

    Directory of Open Access Journals (Sweden)

    Rué Pau

    2010-08-01

    Full Text Available Abstract Background With increasing computer power, simulating the dynamics of complex systems in chemistry and biology is becoming increasingly routine. The modelling of individual reactions in (biochemical systems involves a large number of random events that can be simulated by the stochastic simulation algorithm (SSA. The key quantity is the step size, or waiting time, τ, whose value inversely depends on the size of the propensities of the different channel reactions and which needs to be re-evaluated after every firing event. Such a discrete event simulation may be extremely expensive, in particular for stiff systems where τ can be very short due to the fast kinetics of some of the channel reactions. Several alternative methods have been put forward to increase the integration step size. The so-called τ-leap approach takes a larger step size by allowing all the reactions to fire, from a Poisson or Binomial distribution, within that step. Although the expected value for the different species in the reactive system is maintained with respect to more precise methods, the variance at steady state can suffer from large errors as τ grows. Results In this paper we extend Poisson τ-leap methods to a general class of Runge-Kutta (RK τ-leap methods. We show that with the proper selection of the coefficients, the variance of the extended τ-leap can be well-behaved, leading to significantly larger step sizes. Conclusions The benefit of adapting the extended method to the use of RK frameworks is clear in terms of speed of calculation, as the number of evaluations of the Poisson distribution is still one set per time step, as in the original τ-leap method. The approach paves the way to explore new multiscale methods to simulate (biochemical systems.

  18. 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.

  19. 利用回声状态网络建立管式聚合反应的灰箱模型%An approach of grey-box modeling with echo state network for tubular polymerization reaction

    Institute of Scientific and Technical Information of China (English)

    秦松; 曹柳林

    2014-01-01

    提出一种利用回声状态网络(echo state network, ESN)建立复杂分布参数系统模型的灰箱建模方法。此建模方法可以充分利用已知机理模型的结构信息和回声状态网络的逼近能力,可更好地描述和解释出系统各变量之间的因果关系,使模型的“灰箱”化程度更高。首先,根据系统方程和先验知识将初始系统特征团引入ESN储备池中,赋予网络节点实际物理意义,并以此建立结构逼近神经网络模型;然后,通过逐步回归分析方法,结合递归最小二乘算法选择最优系统特征团,并对网络结构进行优化,建立起描述系统特性关系的灰箱模型。本文以实验室规模的管式聚合反应过程作为实验对象,建立以温度分布为输出的数学模型,结果表明所提出的灰箱建模方法行之有效。%An approach of grey-box modeling with Echo State Network (ESN) is developed for modeling dynamic processes with nonlinear characteristics. This method can take full advantage of the already known structural information of the mechanism model at the early stage of modeling and make better use of the approximation ability of neural networks, thus resulting in higher accuracy of grey-box modeling. By combination the prior knowledge and systematic equations into ESN state pool, structure approaching neural network (SAAN) is established based on system feature block, and it is given actual significance. Then the optimal fundamental genes were chosen through recursive least square method with stepwise regression analysis to optimize the structure of SANN, so as to get the grey-box model. Detailed process of modeling was described in modeling of tubular polymerization reaction in laboratory scale. The simulation result proves that the approach is effective.ocesses heat exchanger network synthesis by taking place.

  20. BIOCHEMICAL SCREENING OF DIABETIC NEPHROPATHY

    Directory of Open Access Journals (Sweden)

    Vivek

    2016-01-01

    Full Text Available Diabetic nephropathy is a clinical syndrome characterized by the following- Persistent albuminuria (>300mg/d or >200μg/min, that is confirmed on at least 2 occasions 3-6 months apart diabetic, progressive decline in the Glomerular Filtration Rate (GFR, elevated arterial blood pressure. The earliest biochemical criteria for the diagnosis of diabetic nephropathy is the presence of micro-albumin in the urine, which if left untreated will eventually lead to End-Stage Renal Disease (ESRD. Micro-albuminuria refers to the excretion of albumin in the urine at a rate that exceeds normal limits. The current study was conducted to establish the prevalence of micro-albuminuria in a sequential sample of diabetic patients attending hospital and OPD Clinic to determine its relationship with known and putative risk factors to identify micro- and normo-albuminuric patients in their sample for subsequent comparison in different age, sex, weight and creatinine clearance of the micro- and normo-albuminuric patients. This cross-sectional analytical study was conducted in one hundred patients at Saraswathi Institute of Medical Sciences, Anwarpur, Hapur, U. P. Patients having diabetes mellitus in different age group ranging from 30 to 70 years were selected. Data was analysed by SPSS software. Micro-albuminuria was observed in 35% in patients with type 2 diabetes mellitus. It was observed that 65% patients were free from any type of albuminuria. Also micro-albuminuria was present in 10% of the patients less than 50 yrs. of age, while 15% of the patients more than 50 yrs. of age were having micro-albuminuria. There was a statistically significant correlation of micro-albuminuria with duration of diabetes. Incidence of micro-albuminuria increases with age as well as increased duration of diabetes mellitus. Our study shows that only 5% patients developed macro-albuminuria. Glycosylated haemoglobin and fasting plasma glucose was significantly raised among all these

  1. Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.

    Directory of Open Access Journals (Sweden)

    Christian L Barrett

    2006-05-01

    Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.

  2. Genome-scale metabolic network reconstruction.

    Science.gov (United States)

    Fondi, Marco; Liò, Pietro

    2015-01-01

    Bacterial metabolism is an important source of novel products/processes for everyday life and strong efforts are being undertaken to discover and exploit new usable substances of microbial origin. Computational modeling and in silico simulations are powerful tools in this context since they allow the exploration and a deeper understanding of bacterial metabolic circuits. Many approaches exist to quantitatively simulate chemical reaction fluxes within the whole microbial metabolism and, regardless of the technique of choice, metabolic model reconstruction is the first step in every modeling pipeline. Reconstructing a metabolic network consists in drafting the list of the biochemical reactions that an organism can carry out together with information on cellular boundaries, a biomass assembly reaction, and exchange fluxes with the external environment. Building up models able to represent the different functional cellular states is universally recognized as a tricky task that requires intensive manual effort and much additional information besides genome sequence. In this chapter we present a general protocol for metabolic reconstruction in bacteria and the main challenges encountered during this process. PMID:25343869

  3. Evaluation of Pore Networks in Caprocks at Geologic Storage Sites: A Combined Study using High Temperature and Pressure Reaction Experiments, Small Angle Neutron Scattering, and Focused Ion Beam-Scanning Electron Microscopy

    Science.gov (United States)

    Mouzakis, K. M.; Sitchler, A.; Wang, X.; McCray, J. E.; Kaszuba, J. P.; Rother, G.; Dewers, T. A.; Heath, J. E.

    2011-12-01

    Low permeability rock units, often shales or mudstones, that overlie geologic formations under consideration for CO2 sequestration will help contain injected CO2. CO2 that does flow through these rocks will dissolve into the porewaters, creating carbonic acid lowering the pH. This perturbation of the system may result in mineral dissolution or precipitation, which can change the pore structure and impact the flow properties of the caprocks. In order to investigate the impacts that reaction can have on caprock pore structure, we performed a combination of high pressure high temperature reaction experiments, small angle neutron scattering (SANS) experiments and high resolution focused ion beam-scanning electron microscope (FIB-SEM) imaging on samples from the Gothic shale and Marine Tuscaloosa Group. Small angle neutron scattering was performed on unreacted and reacted caprocks at the High Flux Isotope Reactor at Oak Ridge National Laboratory. New precipitates and pores are observed in high-resolution images of the reacted samples. The precipitates have been preliminarily identified as gypsum or anhydrite, and sulfide minerals. Results from small angle neutron scattering, a technique that provides information about pores and pore/mineral interfaces at scales ~ 5 to 300 nm, show an increased porosity and specific surface area after reaction with brine and CO2. However, there appear to be differences in how the pore networks change between the two samples that are related to sample mineralogy and original pore network structure. Changes to pores and formation of new pores may lead to different capillary sealing behavior and permeability. This combination of controlled laboratory experiments, neutron scattering and high-resolution imaging provides detailed information about the geochemical processes that occur at the pore scale as CO2 reacts with rocks underground. Such information is integral to the evaluation of large-scale CO2 sequestration as a feasible technology

  4. A Program on Biochemical and Biomedical Engineering.

    Science.gov (United States)

    San, Ka-Yiu; McIntire, Larry V.

    1989-01-01

    Presents an introduction to the Biochemical and Biomedical Engineering program at Rice University. Describes the development of the academic and enhancement programs, including organizational structure and research project titles. (YP)

  5. 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 ...

  6. Towards biochemical filters with a sigmoidal response to pH changes: buffered biocatalytic signal transduction

    Science.gov (United States)

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

    We realize a biochemical filtering process by introducing a buffer in a biocatalytic signal-transduction logic system based on the function of an enzyme, esterase. The input, ethyl butyrate, is converted into butyric acid-the output signal, which in turn is measured by the drop in the pH value. The developed approach offers a versatile "network element" for increasing the complexity of biochemical information processing systems. Evaluation of an optimal regime for quality filtering is accomplished in the framework of a kinetic rate-equation model.

  7. Towards biochemical filters with a sigmoidal response to pH changes: buffered biocatalytic signal transduction.

    Science.gov (United States)

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

    2011-03-14

    We realize a biochemical filtering process by introducing a buffer in a biocatalytic signal-transduction logic system based on the function of an enzyme, esterase. The input, ethyl butyrate, is converted into butyric acid--the output signal, which in turn is measured by the drop in the pH value. The developed approach offers a versatile "network element" for increasing the complexity of biochemical information processing systems. Evaluation of an optimal regime for quality filtering is accomplished in the framework of a kinetic rate-equation model.

  8. Nonequilibrium steady state of biochemical cycle kinetics under non-isothermal conditions

    CERN Document Server

    Jin, Xiao

    2016-01-01

    Nonequilibrium steady state of isothermal biochemical cycle kinetics has been extensively studied, but much less investigated under non-isothermal conditions. However, once the heat exchange between subsystems is rather slow, the isothermal assumption of the whole system meets great challenge, which is indeed the case inside many kinds of living organisms. Here we generalize the nonequilibrium steady-state theory of isothermal biochemical cycle kinetics, in the master-equation models, to the situation in which the temperatures of subsystems can be far from uniform. We first obtain a new thermodynamic relation between the chemical reaction rates and thermodynamic potentials under such a non-isothermal circumstances, which immediately implies simply applying the isothermal transition-state rate formula for each chemical reaction in terms of only the reactants' temperature, is not thermodynamically consistent. Therefore, we mathematically derive several revised reaction-rate formulas which not only obey the new ...

  9. Biochemical software: Carbohydrates on Laboratory

    Directory of Open Access Journals (Sweden)

    D.N. Heidrich

    2005-07-01

    Full Text Available Educators around  the  world  are  being  challenged  to  develop  and  design  better and  more  effective strategies for student learning  using a variety  of modern  resources.  In this  present  work, an educa- tional  hypermedia  software  was constructed as a support tool to biochemistry teaching.  Occurrence, structure, main  characteristics and  biological  function  of the  biomolecule  Carbohydrates were pre- sented  through  modules.  The  software was developed  using concept  maps,  ISIS-Draw,  and  FLASH- MX animation program.  The chapter  Carbohydrates on Laboratory illustrates experimental methods of carbohydrates characterization, through  animation of a laboratory scenery.   The  subject was de- veloped showing reactions  as Bial, Benedict, Selliwanoff, Barfoed, Phenol  Sulphuric,  and Iodines, and also enzymatic  reactions  as glucose oxidase and amylase.  There are also links with short texts  in order to help the understanding of the contents  and principles of laboratory practice  as well as background reactions. Application of the software to undergraduate students and high school teachers  showed an excellent  acceptance.   All of them  considered  the  software  a very good learning  tool.  Both  teachers and students welcomed this program  as it is more flexible, and allows the learning in a more individual rhythm. In addition, application of the software would be suitable  to a more effective learning  and it is less expensive than conventional experimental teaching.

  10. Irreversible thermodynamics of open chemical networks. I. Emergent cycles and broken conservation laws.

    Science.gov (United States)

    Polettini, Matteo; Esposito, Massimiliano

    2014-07-14

    In this paper and Paper II, we outline a general framework for the thermodynamic description of open chemical reaction networks, with special regard to metabolic networks regulating cellular physiology and biochemical functions. We first introduce closed networks "in a box", whose thermodynamics is subjected to strict physical constraints: the mass-action law, elementarity of processes, and detailed balance. We further digress on the role of solvents and on the seemingly unacknowledged property of network independence of free energy landscapes. We then open the system by assuming that the concentrations of certain substrate species (the chemostats) are fixed, whether because promptly regulated by the environment via contact with reservoirs, or because nearly constant in a time window. As a result, the system is driven out of equilibrium. A rich algebraic and topological structure ensues in the network of internal species: Emergent irreversible cycles are associated with nonvanishing affinities, whose symmetries are dictated by the breakage of conservation laws. These central results are resumed in the relation a + b = s(Y) between the number of fundamental affinities a, that of broken conservation laws b and the number of chemostats s(Y). We decompose the steady state entropy production rate in terms of fundamental fluxes and affinities in the spirit of Schnakenberg's theory of network thermodynamics, paving the way for the forthcoming treatment of the linear regime, of efficiency and tight coupling, of free energy transduction, and of thermodynamic constraints for network reconstruction. PMID:25028009

  11. Piezonuclear Reactions

    CERN Document Server

    Cardone, Fabio; Petrucci, Andrea

    2010-01-01

    In this paper, we deal with the subject of piezonuclear reactions, namely nuclear reactions (of new type) triggered by pressure waves. We discuss the experimental evidences obtained in the last two decades, which can be summarized essentially as follows: experiments in cavitation of liquids, where transmutation of elements, creation of elements and emission of neutrons have been observed; emission of neutrons in brittle failure of solids subjected to mechanical pressure; alteration of the lifetime of un unstable element (thorium) subjected to cavitation. A theoretical model to explain these facts is proposed. Future perspectives of these experimental and theoretical investigations are also underlined.

  12. Stochastic analysis of biochemical systems

    CERN Document Server

    Anderson, David F

    2015-01-01

    This book focuses on counting processes and continuous-time Markov chains motivated by examples and applications drawn from chemical networks in systems biology.  The book should serve well as a supplement for courses in probability and stochastic processes.  While the material is presented in a manner most suitable for students who have studied stochastic processes up to and including martingales in continuous time, much of the necessary background material is summarized in the Appendix. Students and Researchers with a solid understanding of calculus, differential equations, and elementary probability and who are well-motivated by the applications will find this book of interest.    David F. Anderson is Associate Professor in the Department of Mathematics at the University of Wisconsin and Thomas G. Kurtz is Emeritus Professor in the Departments of Mathematics and Statistics at that university. Their research is focused on probability and stochastic processes with applications in biology and other ar...

  13. Networks in cognitive science.

    Science.gov (United States)

    Baronchelli, Andrea; Ferrer-i-Cancho, Ramon; Pastor-Satorras, Romualdo; Chater, Nick; Christiansen, Morten H

    2013-07-01

    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, and 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.

  14. 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.

  15. 40 CFR 158.2010 - Biochemical pesticides data requirements.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Biochemical pesticides data...) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Biochemical Pesticides § 158.2010 Biochemical pesticides... required to support registration of biochemical pesticides. Sections 158.2080 through 158.2084 identify...

  16. 40 CFR 158.2000 - Biochemical pesticides definition and applicability.

    Science.gov (United States)

    2010-07-01

    ... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Biochemical pesticides definition and...) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Biochemical Pesticides § 158.2000 Biochemical pesticides definition and applicability. This subpart applies to all biochemical pesticides as defined in paragraphs...

  17. Computational study of noise in a large signal transduction network

    Directory of Open Access Journals (Sweden)

    Ruohonen Keijo

    2011-06-01

    Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.

  18. A graphical user interface for a method to infer kinetics and network architecture (MIKANA.

    Directory of Open Access Journals (Sweden)

    Márcio A Mourão

    Full Text Available One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1.

  19. Improved methods for the mathematically controlled comparison of biochemical systems

    Directory of Open Access Journals (Sweden)

    Schwacke John H

    2004-06-01

    Full Text Available Abstract The method of mathematically controlled comparison provides a structured approach for the comparison of alternative biochemical pathways with respect to selected functional effectiveness measures. Under this approach, alternative implementations of a biochemical pathway are modeled mathematically, forced to be equivalent through the application of selected constraints, and compared with respect to selected functional effectiveness measures. While the method has been applied successfully in a variety of studies, we offer recommendations for improvements to the method that (1 relax requirements for definition of constraints sufficient to remove all degrees of freedom in forming the equivalent alternative, (2 facilitate generalization of the results thus avoiding the need to condition those findings on the selected constraints, and (3 provide additional insights into the effect of selected constraints on the functional effectiveness measures. We present improvements to the method and related statistical models, apply the method to a previously conducted comparison of network regulation in the immune system, and compare our results to those previously reported.

  20. DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.

    Energy Technology Data Exchange (ETDEWEB)

    MASLOV,S.SNEPPEN,K.

    2003-11-17

    Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An

  1. Networks in cognitive science

    OpenAIRE

    Andrea Baronchelli; Ramon Ferrer-i-Cancho; Romualdo Pastor-Satorras; Nick Chater; Christiansen, Morten H.

    2013-01-01

    Networks of interconnected nodes have long played a key role in Cognitive Science, from artificial neural net- works to spreading activation models of semantic mem- ory. 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, and collaborations among scientists. Today, the inclusion of network theory into Cognitive Sciences, and the expansion of complex- s...

  2. Artificial neural networks in medicine

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-07-01

    This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.

  3. In vitro biochemical characterization of all barley endosperm starch synthases

    DEFF Research Database (Denmark)

    Cuesta-Seijo, Jose A.; Nielsen, Morten M.; Ruzanski, Christian;

    2016-01-01

    Starch is the main storage polysaccharide in cereals and the major source of calories in the human diet. It is synthesized by a panel of enzymes including five classes of starch synthases (SSs). While the overall starch synthase (SS) reaction is known, the functional differences between the five SS...... classes are poorly understood. Much of our knowledge comes from analyzing mutant plants with altered SS activities, but the resulting data are often difficult to interpret as a result of pleitropic effects, competition between enzymes, overlaps in enzyme activity and disruption of multi-enzyme complexes....... Here we provide a detailed biochemical study of the activity of all five classes of SSs in barley endosperm. Each enzyme was produced recombinantly in E. coli and the properties and modes of action in vitro were studied in isolation from other SSs and other substrate modifying activities. Our results...

  4. Final Technical Report "Multiscale Simulation Algorithms for Biochemical Systems"

    Energy Technology Data Exchange (ETDEWEB)

    Petzold, Linda R.

    2012-10-25

    Biochemical systems are inherently multiscale and stochastic. In microscopic systems formed by living cells, the small numbers of reactant molecules can result in dynamical behavior that is discrete and stochastic rather than continuous and deterministic. An analysis tool that respects these dynamical characteristics is the stochastic simulation algorithm (SSA, Gillespie, 1976), a numerical simulation procedure that is essentially exact for chemical systems that are spatially homogeneous or well stirred. Despite recent improvements, as a procedure that simulates every reaction event, the SSA is necessarily inefficient for most realistic problems. There are two main reasons for this, both arising from the multiscale nature of the underlying problem: (1) stiffness, i.e. the presence of multiple timescales, the fastest of which are stable; and (2) the need to include in the simulation both species that are present in relatively small quantities and should be modeled by a discrete stochastic process, and species that are present in larger quantities and are more efficiently modeled by a deterministic differential equation (or at some scale in between). This project has focused on the development of fast and adaptive algorithms, and the fun- damental theory upon which they must be based, for the multiscale simulation of biochemical systems. Areas addressed by this project include: (1) Theoretical and practical foundations for ac- celerated discrete stochastic simulation (tau-leaping); (2) Dealing with stiffness (fast reactions) in an efficient and well-justified manner in discrete stochastic simulation; (3) Development of adaptive multiscale algorithms for spatially homogeneous discrete stochastic simulation; (4) Development of high-performance SSA algorithms.

  5. Biochemical evolution II: origin of life in tubular microstructures on weathered feldspar surfaces.

    Science.gov (United States)

    Parsons, I; Lee, M R; Smith, J V

    1998-12-22

    Mineral surfaces were important during the emergence of life on Earth because the assembly of the necessary complex biomolecules by random collisions in dilute aqueous solutions is implausible. Most silicate mineral surfaces are hydrophilic and organophobic and unsuitable for catalytic reactions, but some silica-rich surfaces of partly dealuminated feldspars and zeolites are organophilic and potentially catalytic. Weathered alkali feldspar crystals from granitic rocks at Shap, north west England, contain abundant tubular etch pits, typically 0.4-0.6 microm wide, forming an orthogonal honeycomb network in a surface zone 50 microm thick, with 2-3 x 10(6) intersections per mm2 of crystal surface. Surviving metamorphic rocks demonstrate that granites and acidic surface water were present on the Earth's surface by approximately 3.8 Ga. By analogy with Shap granite, honeycombed feldspar has considerable potential as a natural catalytic surface for the start of biochemical evolution. Biomolecules should have become available by catalysis of amino acids, etc. The honeycomb would have provided access to various mineral inclusions in the feldspar, particularly apatite and oxides, which contain phosphorus and transition metals necessary for energetic life. The organized environment would have protected complex molecules from dispersion into dilute solutions, from hydrolysis, and from UV radiation. Sub-micrometer tubes in the honeycomb might have acted as rudimentary cell walls for proto-organisms, which ultimately evolved a lipid lid giving further shelter from the hostile outside environment. A lid would finally have become a complete cell wall permitting detachment and flotation in primordial "soup." Etch features on weathered alkali feldspar from Shap match the shape of overlying soil bacteria. PMID:9860941

  6. On the Green's function of the partially diffusion-controlled reversible ABCD reaction for radiation chemistry codes

    Energy Technology Data Exchange (ETDEWEB)

    Plante, Ianik, E-mail: ianik.plante-1@nasa.gov [Wyle Science, Technology & Engineering, 1290 Hercules, Houston, TX 77058 (United States); Devroye, Luc, E-mail: lucdevroye@gmail.com [School of Computer Science, McGill University, 3480 University Street, Montreal H3A 0E9 (Canada)

    2015-09-15

    Several computer codes simulating chemical reactions in particles systems are based on the Green's functions of the diffusion equation (GFDE). Indeed, many types of chemical systems have been simulated using the exact GFDE, which has also become the gold standard for validating other theoretical models. In this work, a simulation algorithm is presented to sample the interparticle distance for partially diffusion-controlled reversible ABCD reaction. This algorithm is considered exact for 2-particles systems, is faster than conventional look-up tables and uses only a few kilobytes of memory. The simulation results obtained with this method are compared with those obtained with the independent reaction times (IRT) method. This work is part of our effort in developing models to understand the role of chemical reactions in the radiation effects on cells and tissues and may eventually be included in event-based models of space radiation risks. However, as many reactions are of this type in biological systems, this algorithm might play a pivotal role in future simulation programs not only in radiation chemistry, but also in the simulation of biochemical networks in time and space as well.

  7. On the Green's function of the partially diffusion-controlled reversible ABCD reaction for radiation chemistry codes

    Science.gov (United States)

    Plante, Ianik; Devroye, Luc

    2015-09-01

    Several computer codes simulating chemical reactions in particles systems are based on the Green's functions of the diffusion equation (GFDE). Indeed, many types of chemical systems have been simulated using the exact GFDE, which has also become the gold standard for validating other theoretical models. In this work, a simulation algorithm is presented to sample the interparticle distance for partially diffusion-controlled reversible ABCD reaction. This algorithm is considered exact for 2-particles systems, is faster than conventional look-up tables and uses only a few kilobytes of memory. The simulation results obtained with this method are compared with those obtained with the independent reaction times (IRT) method. This work is part of our effort in developing models to understand the role of chemical reactions in the radiation effects on cells and tissues and may eventually be included in event-based models of space radiation risks. However, as many reactions are of this type in biological systems, this algorithm might play a pivotal role in future simulation programs not only in radiation chemistry, but also in the simulation of biochemical networks in time and space as well.

  8. Dynamics of the ethanolamine glycerophospholipid remodeling network.

    Directory of Open Access Journals (Sweden)

    Lu Zhang

    Full Text Available Acyl chain remodeling in lipids is a critical biochemical process that plays a central role in disease. However, remodeling remains poorly understood, despite massive increases in lipidomic data. In this work, we determine the dynamic network of ethanolamine glycerophospholipid (PE remodeling, using data from pulse-chase experiments and a novel bioinformatic network inference approach. The model uses a set of ordinary differential equations based on the assumptions that (1 sn1 and sn2 acyl positions are independently remodeled; (2 remodeling reaction rates are constant over time; and (3 acyl donor concentrations are constant. We use a novel fast and accurate two-step algorithm to automatically infer model parameters and their values. This is the first such method applicable to dynamic phospholipid lipidomic data. Our inference procedure closely fits experimental measurements and shows strong cross-validation across six independent experiments with distinct deuterium-labeled PE precursors, demonstrating the validity of our assumptions. In contrast, fits of randomized data or fits using random model parameters are worse. A key outcome is that we are able to robustly distinguish deacylation and reacylation kinetics of individual acyl chain types at the sn1 and sn2 positions, explaining the established prevalence of saturated and unsaturated chains in the respective positions. The present study thus demonstrates that dynamic acyl chain remodeling processes can be reliably determined from dynamic lipidomic data.

  9. 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...

  10. 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.

  11. Geometric universality of currents in an open network of interacting particles

    Energy Technology Data Exchange (ETDEWEB)

    Sinitsyn, Nikolai A [Los Alamos National Laboratory; Chernyak, Vladimir Y [Los Alamos National Laboratory; Chertkov, Michael [Los Alamos National Laboratory

    2010-01-01

    We discuss a non-equilibrium statistical system on a graph or network. Identical particles are injected, interact with each other, traverse, and leave the graph in a stochastic manner described in terms of Poisson rates, possibly dependent on time and instantaneous occupation numbers at the nodes of the graph. We show that under the assumption of the relative rates constancy, the system demonstrates a profound statistical symmetry, resulting in geometric universality of the particle currents statistics. The phenomenon applies broadly to many man-made and natural open stochastic systems, such as queuing of packages over internet, transport of electrons and quasi-particles in mesoscopic systems, and chains of reactions in bio-chemical networks. We illustrate the utility of the general approach using two enabling examples from the two latter disciplines.

  12. Predictive biochemical assays for late radiation effects

    Energy Technology Data Exchange (ETDEWEB)

    Rubin, P.; Finkelstein, J.N.; Siemann, D.W.; Shapiro, D.L.; Van Houtte, P.; Penney, D.P.

    1986-04-01

    Surfactant precursors or other products of Type II pneumocytes have the potential to be the first biochemical marker for late radiation effects. This is particularly clinically important in the combined modality era because of the frequent occurrence of pneumonitis and pulmonary fibrosis secondary to radiation or chemotherapy. Accordingly, correlative studies have been pursued with the Type II pneumocyte as a beginning point to understand the complex pathophysiology of radiation pneumonitis and fibrosis. From our ultrastructural and biochemical studies, it is evident that Type II pneumocytes are an early target of radiation and the release of surfactant into the alveolus shortly after exposure persists for days and weeks. Through the use of lavaging techniques, alveolar surfactant has been elevated after pulmonary irradiation. In three murine strains and in the rabbit, there is a strong correlation with surfactant release at 7 and/or 28 days in vivo with later lethality in months. In vitro studies using cultures of type II pneumocytes also demonstrate dose response and tolerance factors that are comparable to the in vivo small and large animal diagnostic models. New markers are being developed to serve as a predictive index for later lethal pneumonopathies. With the development of these techniques, the search for early biochemical markers in man has been undertaken. Through the use of biochemical, histological, and ultrastructural techniques, a causal relationship between radiation effects on type II pneumocytes, pulmonary cells, endothelial cells of blood vessels, and their roles in the production of pneumonitis and fibrosis will evolve.

  13. 2009 Biochemical Conversion Platform Review Report

    Energy Technology Data Exchange (ETDEWEB)

    Ferrell, John [Office of Energy Efficiency and Renewable Energy (EERE), Washington, DC (United States)

    2009-12-01

    This document summarizes the recommendations and evaluations provided by an independent external panel of experts at the U.S. Department of Energy Biomass Program’s Biochemical Conversion platform review meeting, held on April 14-16, 2009, at the Sheraton Denver Downtown, Denver, Colorado.

  14. Biochemical Thermodynamics under near Physiological Conditions

    Science.gov (United States)

    Mendez, Eduardo

    2008-01-01

    The recommendations for nomenclature and tables in Biochemical Thermodynamics approved by IUBMB and IUPAC in 1994 can be easily introduced after the chemical thermodynamic formalism. Substitution of the usual standard thermodynamic properties by the transformed ones in the thermodynamic equations, and the use of appropriate thermodynamic tables…

  15. Biochemical Applications in the Analytical Chemistry Lab

    Science.gov (United States)

    Strong, Cynthia; Ruttencutter, Jeffrey

    2004-01-01

    An HPLC and a UV-visible spectrophotometer are identified as instruments that helps to incorporate more biologically-relevant experiments into the course, in order to increase the students understanding of selected biochemistry topics and enhances their ability to apply an analytical approach to biochemical problems. The experiment teaches…

  16. Biochemical applications of FT-IR spectroscopy

    NARCIS (Netherlands)

    Pistorius, A.M.A.

    1996-01-01

    This thesis describes the use of (FT-)IR spectroscopy in general biochemical research. In chapter 3, IR spectroscopy is used in the quantitation of residual detergent after reconstitution of an integral membrane protein in a pre-defined lipid matrix. This chapter discusses the choice of the vibratio

  17. Predictive biochemical assays for late radiation effects

    International Nuclear Information System (INIS)

    Surfactant precursors or other products of Type II pneumocytes have the potential to be the first biochemical marker for late radiation effects. This is particularly clinically important in the combined modality era because of the frequent occurrence of pneumonitis and pulmonary fibrosis secondary to radiation or chemotherapy. Accordingly, correlative studies have been pursued with the Type II pneumocyte as a beginning point to understand the complex pathophysiology of radiation pneumonitis and fibrosis. From our ultrastructural and biochemical studies, it is evident that Type II pneumocytes are an early target of radiation and the release of surfactant into the alveolus shortly after exposure persists for days and weeks. Through the use of lavaging techniques, alveolar surfactant has been elevated after pulmonary irradiation. In three murine strains and in the rabbit, there is a strong correlation with surfactant release at 7 and/or 28 days in vivo with later lethality in months. In vitro studies using cultures of type II pneumocytes also demonstrate dose response and tolerance factors that are comparable to the in vivo small and large animal diagnostic models. New markers are being developed to serve as a predictive index for later lethal pneumonopathies. With the development of these techniques, the search for early biochemical markers in man has been undertaken. Through the use of biochemical, histological, and ultrastructural techniques, a causal relationship between radiation effects on type II pneumocytes, pulmonary cells, endothelial cells of blood vessels, and their roles in the production of pneumonitis and fibrosis will evolve

  18. Metabolic modeling of muscle metabolism identifies key reactions linked to insulin resistance phenotypes

    Directory of Open Access Journals (Sweden)

    Christopher Nogiec

    2015-03-01

    Conclusions: Our results indicate that complex interactions between multiple biochemical reactions contribute to metabolic perturbations observed in human IR, and that the PDH complex plays a key role in these metabolic phenotypes.

  19. Physiological, Biochemical, and Molecular Mechanisms of Heat Stress Tolerance in Plants

    OpenAIRE

    Masayuki Fujita; Md. Mahabub Alam; Rajib Roychowdhury; Mirza Hasanuzzaman; Kamrun Nahar

    2013-01-01

    High temperature (HT) stress is a major environmental stress that limits plant growth, metabolism, and productivity worldwide. Plant growth and development involve numerous biochemical reactions that are sensitive to temperature. Plant responses to HT vary with the degree and duration of HT and the plant type. HT is now a major concern for crop production and approaches for sustaining high yields of crop plants under HT stress are important agricultural goals. Plants possess a number of adapt...

  20. Gray box modeling of MSW degradation: Revealing its dominant (bio)chemical mechanism

    OpenAIRE

    Van Turnhout, A.G.; Heimovaara, T.J.; Kleerebezem, R.

    2013-01-01

    In this paper we present an approach to describe organic degradation within immobile water regions of Municipal Solid Waste (MSW) landfills which is best described by the term “gray box” model. We use a simplified set of dominant (bio)chemical and physical reactions and realistic environmental conditions. All equations, relationships and inhibitions are based on semi-empirical or fundamental relationships which have proven to be applicable in the peer reviewed literature. As much as possible ...

  1. Functional adaptation of equine articular cartilage: The formation of regional biochemical characteristics up to age one year

    NARCIS (Netherlands)

    Brama, P.A.J.; Tekoppele, J.M.; Bank, R.A.; Barneveld, A.; Weeren, P.R. van

    2000-01-01

    Biochemical heterogeneity of cartilage within a joint is well known in mature individuals. It has recently been reported that heterogeneity for proteoglycan content and chondrocyte metabolism in sheep develops postnatally under the influence of loading. No data exist on the collagen network in gener

  2. Teaching Biochemical Pathways Using Concept Maps

    Directory of Open Access Journals (Sweden)

    Simon Brown

    2013-08-01

    Full Text Available The interesting paper by Dinarvand and Vaisi-Raygan (1 makes valuable points about a particularly challenging aspect of biochemistry learning and teaching. Their work prompts me to ask two questions and make a comment. First, what do the authors mean by a concept map (CM? A pathway map could be considered a CM, but a CM could cover modes of regulation and kinetics in relation to particular reactions or pathways and there are many other possibilities. Irrespective of this, a CM can get extremely complex if more than a few concepts are involved (2, as can be seen in examples given by Novak (3. This is the fundamental problem of teaching and learning biochemistry (4, which combines the network of pathways, compartmentation, macromol¬ecular structure, regulation, kinetics and some fairly sophisticated chemical concepts.Second, how did the students go about preparing CMs? My experience is that students prefer to use a computer for most tasks, but standard CM software (5 may not be suitable. For example, they often struggle unnec¬essarily to use software to prepare a graphical summary of the structural features of a protein, its precursors and the gene encoding it. This distracts them from the material. My suggestions that pencil and paper might be sufficient are usually met with amazement. Third, as Dinarvand and Vaisi-Raygan (1 make clear, a coherent summary of the metabolism considered in a course in metabolic biochemistry is crucial if students are to appreciate the pathways and their interconn-ection and regulation. For many years I have used an approach in which students collaborate in tutorials to achieve this. The sessions are usually initiated by me drawing the plasma membrane and the mitochondrial membranes on a large board and inviting the students to fill in the blanks (I provide large sheets of paper so that students can make copies. With coaxing, someone volunteers and I explain that the volunteer is not alone because everyone is

  3. Impact of flow velocity on biochemical processes – a laboratory experiment

    Directory of Open Access Journals (Sweden)

    A. Boisson

    2014-08-01

    Full Text Available Understanding and predicting hydraulic and chemical properties of natural environments are current crucial challenges. It requires considering hydraulic, chemical and biological processes and evaluating how hydrodynamic properties impact on biochemical reactions. In this context, an original laboratory experiment to study the impact of flow velocity on biochemical reactions along a one-dimensional flow streamline has been developed. Based on the example of nitrate reduction, nitrate-rich water passes through plastic tubes at several flow velocities (from 6.2 to 35 mm min−1, while nitrate concentration at the tube outlet is monitored for more than 500 h. This experimental setup allows assessing the biologically controlled reaction between a mobile electron acceptor (nitrate and an electron donor (carbon coming from an immobile phase (tube that produces carbon during its degradation by microorganisms. It results in observing a dynamic of the nitrate transformation associated with biofilm development which is flow-velocity dependent. It is proposed that the main behaviors of the reaction rates are related to phases of biofilm development through a simple analytical model including assimilation. Experiment results and their interpretation demonstrate a significant impact of flow velocity on reaction performance and stability and highlight the relevance of dynamic experiments over static experiments for understanding biogeochemical processes.

  4. Induced biochemical interactions in immature and biodegraded heavy crude oils

    Energy Technology Data Exchange (ETDEWEB)

    Premuzic, E.T.; Lin, M.S.; Bohenek, M.; Joshi-Tope, G.; Shelenkova, L.; Zhou, W.M.

    1998-11-01

    Studies in which selective chemical markers have been used to explore the mechanisms by which biocatalysts interact with heavy crude oils have shown that the biochemical reactions follow distinct trends. The term biocatalyst refers to a group of extremophilic microorganisms which, under the experimental conditions used, interact with heavy crude oils to (1) cause a redistribution of hydrocarbons, (2) cause chemical changes in oil fractions containing sulfur compounds and lower the sulfur content, (3) decrease organic nitrogen content, and (4) decrease the concentration of trace metals. Current data indicate that the overall effect is due to simultaneous reactions yielding products with relatively higher concentration of saturates and lower concentrations of aromatics and resins. The compositional changes depend on the microbial species and the chemistry of the crudes. Economic analysis of a potential technology based on the available data indicate that such a technology, used in a pre-refinery mode, may be cost efficient and promising. In the present paper, the background of oil biocatalysis and some recent results will be discussed.

  5. INDUCED BIOCHEMICAL INTERACTIONS IN IMMATURE AND BIODEGRADED HEAVY CRUDE OILS

    Energy Technology Data Exchange (ETDEWEB)

    PREMUZIC,E.T.; LIN,M.S.; BOHENEK,M.; JOSHI-TOPE,G.; SHELENKOVA,L.; ZHOU,W.M.

    1998-10-27

    Studies in which selective chemical markers have been used to explore the mechanisms by which biocatalysts interact with heavy crude oils have shown that the biochemical reactions follow distinct trends. The term biocatalyst refers to a group of extremophilic microorganisms which, under the experimental conditions used, interact with heavy crude oils to (1) cause a redistribution of hydrocarbons, (2) cause chemical changes in oil fractions containing sulfur compounds and lower the sulfur content, (3) decrease organic nitrogen content, and (4) decrease the concentration of trace metals. Current data indicate that the overall effect is due to simultaneous reactions yielding products with relatively higher concentration of saturates and lower concentrations of aromatics and resins. The compositional changes depend on the microbial species and the chemistry of the crudes. Economic analysis of a potential technology based on the available data indicate that such a technology, used in a pre-refinery mode, may be cost efficient and promising. In the present paper, the background of oil biocatalysis and some recent results will be discussed.

  6. Combining molecular dynamics with mesoscopic Green’s function reaction dynamics simulations

    International Nuclear Information System (INIS)

    In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. We propose a novel approach that combines GFRD for simulating the system at the mesoscopic scale where particles are far apart, with a microscopic technique such as Langevin dynamics or Molecular Dynamics (MD), for simulating the system at the microscopic scale where reactants are in close proximity. This scheme defines the regions where the particles are close together and simulated with high microscopic resolution and those where they are far apart and simulated with lower mesoscopic resolution, adaptively on the fly. The new multi-scale scheme, called MD-GFRD, is generic and can be used to efficiently simulate reaction-diffusion systems at the particle level

  7. Soil reaction; 1 : 1 000 000

    International Nuclear Information System (INIS)

    Soil reaction (pH) indicates the character of the acid base reactions in soils. It determines the course of numerous chemical and biochemical reactions, which influence the decay and transformation of organic and mineral substances, creation of clayey minerals, mobilisation of elements, and thus their accessibility to plants. It characterises the state of the acidifying effect on soils through natural and anthropic factors (acid rains). The map facilitates the visual perception of the nature and extent of soil acidification and its possible negative impact on other environmental components. The map of soil reaction was compiled by the geo-statistical methods relying on 7,172 pH measuring operations. Samples of forest and agricultural soils were taken in the 1995 - 1999 period in the framework of geochemical mapping of Slovak's soils. (authors)

  8. 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

  9. 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.

  10. Optical Biochemical Platforms for Nanoparticles Detection

    CERN Document Server

    Campanella, Clarissa Martina

    2014-01-01

    In the biochemical sensing field, a fervent research activity related to the development of real time, low cost, compact and high throughput devices for the detection and characterization of natural or synthetic nanoparticles NPs actually exists. In this research scenario, different platforms for biosensing purposes have been developed according to the huge amount of physical effects involved in the transduction of the biochemical-signal into a measurable output signal. In the present work two different optical platforms for NP detection have been investigated, one based on integrated optics and the other based on microscopy. Both the approaches rely on the study of the interaction of an electromagnetic wave with a small particle in the hypothesis of dealing with a Rayleigh scatterer, i.e. a nanoparticle having a size really smaller than the one of the wavelength of the incident light and scattering light elastically.

  11. Advancement in biochemical assays in andrology

    Institute of Scientific and Technical Information of China (English)

    Wolf-BernhardSchill; RaftHenkel

    1999-01-01

    Determination of maikers of sperm function, accessory sex gland secretion and silent male genital tract inflammation is of considerable diagnostic value in the evaluation of male infertility. The introduction of biochemical tests into the analysis of male factor has the advantage that standardized assays with a coefficient of variafion characteristic of clinical chemistry are performed, in contrast to biological test systems with a large variability .Biochemical parameters may be used in clinical practice to evaluate the sperm fertitizing capacity (acrosin, aniline blue,ROS), to characterize male accessory sex gland secretinns (fructose, a-glucosidase, PSA), and to identify men with silent genital tract inflammation (elastase, C'3 complement component, coeruloplasmin, IgA, IgG, ROS). (As/an J Androl 1999 Jun; 1: 45-51)

  12. Prions: the danger of biochemical weapons

    OpenAIRE

    Eric Almeida Xavier

    2014-01-01

    The knowledge of biotechnology increases the risk of using biochemical weapons for mass destruction. Prions are unprecedented infectious pathogens that cause a group of fatal neurodegenerative diseases by a novel mechanism. They are transmissible particles that are devoid of nucleic acid. Due to their singular characteristics, Prions emerge as potential danger since they can be used in the development of such weapons. Prions cause fatal infectious diseases, and to date there is no therapeutic...

  13. Chain reaction

    International Nuclear Information System (INIS)

    Chain Reaction is a work of recent American political history. It seeks to explain how and why America came to depend so heavily on its experts after World War II, how those experts translated that authority into political clout, and why that authority and political discretion declined in the 1970s. The author's research into the internal memoranda of the Atomic Energy Commission substantiates his argument in historical detail. It was not the ravages of American anti-intellectualism, as so many scholars have argued, that brought the experts back down to earth. Rather, their decline can be traced to the very roots of their success after World War II. The need to over-state anticipated results in order to garner public support, incessant professional and bureaucratic specialization, and the sheer proliferation of expertise pushed arcane and insulated debates between experts into public forums at the same time that a broad cross section of political participants found it easier to gain access to their own expertise. These tendencies ultimately undermined the political influence of all experts. (author)

  14. Electronic modulation of biochemical signal generation

    Science.gov (United States)

    Gordonov, Tanya; Kim, Eunkyoung; Cheng, Yi; Ben-Yoav, Hadar; Ghodssi, Reza; Rubloff, Gary; Yin, Jun-Jie; Payne, Gregory F.; Bentley, William E.

    2014-08-01

    Microelectronic devices that contain biological components are typically used to interrogate biology rather than control biological function. Patterned assemblies of proteins and cells have, however, been used for in vitro metabolic engineering, where coordinated biochemical pathways allow cell metabolism to be characterized and potentially controlled on a chip. Such devices form part of technologies that attempt to recreate animal and human physiological functions on a chip and could be used to revolutionize drug development. These ambitious goals will, however, require new biofabrication methodologies that help connect microelectronics and biological systems and yield new approaches to device assembly and communication. Here, we report the electrically mediated assembly, interrogation and control of a multi-domain fusion protein that produces a bacterial signalling molecule. The biological system can be electrically tuned using a natural redox molecule, and its biochemical response is shown to provide the signalling cues to drive bacterial population behaviour. We show that the biochemical output of the system correlates with the electrical input charge, which suggests that electrical inputs could be used to control complex on-chip biological processes.

  15. Haematological and biochemical analysis in canine enteritis

    Directory of Open Access Journals (Sweden)

    Abid Ali Bhat

    Full Text Available Aim: The present investigation screened eighteen clinical cases of canine enteritis for haematological and biochemical analyses. Materials and Methods: Eighteen dogs suffering from enteritis were selected and detailed clinical manifestations were noted. Hematological and biochemical parameters were estimated by using various kits. Blood was also collected from twelve healthy dogs for establishing control values and data obtained were subjected to statistical analysis. Results: The affected dogs showed anorexia, diarrhoea, depression, varying degree of dehydration and tachycardia. There were significant changes in packed cell volume, neutrophils, lymphocytes and mean corpuscular haemoglobin concentration. Biochemical investigation revealed significant decrease in plasma glucose, total plasma protein, albumin and albumin:globulin ratio (A:G ratio. The level of potassium and chloride was markedly decreased. Significant increase in alanine aminotransferase (ALT and blood urea nitrogen (BUN was observed. Conclusion: Packed Cell Volume (PCV and Total Erythrocyte Count (TEC remained almost similar between healthy dogs and dogs affected with diarrhoea. Mean Total Leukocyte Count (TLC value was significantly higher as compared to the control group. Hypoglycemia, hypoproteinemia, hypokalemia, hypochloremia and increase in blood urea nitrogen was observed in dogs suffering from enteritis. [Vet World 2013; 6(7.000: 380-383

  16. [Biochemical antenatal screening for fetal anomalies.].

    Science.gov (United States)

    Torfadóttir, G; Jónsson, J J

    2001-05-01

    Biochemical antenatal screening started 30 years ago. Initially, the goal was to detect neural tube defects by measuring a-fetoprotein in maternal serum (MS-AFP) and amniotic fluid (AF-AFP). The serendipitous discovery of an association between low AFP maternal serum concentration and chromosomal anomalies resulted in increased research interest in biochemical screening in pregnancy. Subsequently double, triple or quadruple tests in 2nd trimester of pregnancy became widely used in combination with fetal chromosome determination in at risk individuals. In Iceland, antenatal screening for chromosomal anomalies has essentially been based on fetal chromosome studies offered to pregnant women 35 years or older. This strategy needs to be revised. Recently first trimester biochemical screening based on maternal serum pregnancy associated plasma protein A (MS-PAPP-A) and free b-human chorionic gonadotropin (MS-free b-hCG) and multivariate risk assessment has been developed. This screening test can be improved if done in conjunction with nuchal translucency measurements in an early sonography scan. PMID:17018982

  17. Hydrogel-based piezoresistive biochemical microsensors

    Science.gov (United States)

    Guenther, Margarita; Schulz, Volker; Gerlach, Gerald; Wallmersperger, Thomas; Solzbacher, Florian; Magda, Jules J.; Tathireddy, Prashant; Lin, Genyao; Orthner, Michael P.

    2010-04-01

    This work is motivated by a demand for inexpensive, robust and reliable biochemical sensors with high signal reproducibility and long-term-stable sensitivity, especially for medical applications. Micro-fabricated sensors can provide continuous monitoring and on-line control of analyte concentrations in ambient aqueous solutions. The piezoresistive biochemical sensor containing a special biocompatible polymer (hydrogel) with a sharp volume phase transition in the neutral physiological pH range near 7.4 can detect a specific analyte, for example glucose. Thereby the hydrogel-based biochemical sensors are useful for the diagnosis and monitoring of diabetes. The response of the glucosesensitive hydrogel was studied at different regimes of the glucose concentration change and of the solution supply. Sensor response time and accuracy with which a sensor can track gradual changes in glucose was estimated. Additionally, the influence of various recommended sterilization methods on the gel swelling properties and on the mechano-electrical transducer of the pH-sensors has been evaluated in order to choose the most optimal sterilization method for the implantable sensors. It has been shown that there is no negative effect of gamma irradiation with a dose of 25.7 kGy on the hydrogel sensitivity. In order to achieve an optimum between sensor signal amplitude and sensor response time, corresponding calibration and measurement procedures have been proposed and evaluated for the chemical sensors.

  18. Study on Disproportionation Reaction of FCC Gasoline on Acid Catalyst

    Institute of Scientific and Technical Information of China (English)

    Xu Youhao; Wang Xieqing

    2004-01-01

    Based on the experimental data relating to the reaction of FCC gasoline on acid catalyst the analysis of product distribution, and composition of gasoline and diesel fractions have been analyzed. The occurrence of disproportionation reaction of FCC gasoline on acid catalyst and the network of disproportionation reaction have been identified. Study has also shown that different reaction temperatures can result in different pathways of disproportionation reactions on acid catalyst.

  19. Inhibition of zinc-dependent peptidases by Maillard reaction products

    OpenAIRE

    Missagia de Marco, Leticia

    2015-01-01

    The Maillard reaction is a network of different non-enzymatic reactions between carbonyl groups of reducing sugars and amino groups from amino acids, peptides, or proteins, which progresses in three major stages and originates a very heterogeneous mixture of reaction products. It is also known as non-enzymatic browning, due to the brown macromolecular pigments formed in the final stage of the reaction. The chemistry underlying the Maillard reaction is complex. It encloses not only one reactio...

  20. Biochemical Hypermedia: Glucose as a Central Molecule in Metabolism

    Directory of Open Access Journals (Sweden)

    J.K. Sugai

    2008-05-01

    Full Text Available The technologies of information, together with education resources, have been pointed out as a solution to the improvement of teaching approach, but they still claim for programs to fulfill the demands of didactic materials. So, a biochemical software was developed aiming to contribute for the better understanding of the glycolysis. It was prepared with the help of concept maps, ISIS Draw, ADOBE Photoshop and FLASH MX Program. The introduction screen shows a teacher in a theater presenting glucose as a central molecule in the metabolism of animals, plants and many microorganisms. She invites for a better knowledge of glucose through a view of its discovery and its metabolism. A step by step animation process shows the interaction of glucose in aerobic conditions with the enzymes of the glycolytic pathways and its products. An explanation text of each enzyme catalytic process is provided by links. A static pathway is always available through a link. The fates of pyruvate yielding lactic acid and ethanol under anaerobic conditions are shown as well. The overall reactions of gluconeogenesis and the functional significance of this pathway are presented. The experimental treatment involved the presentation of this hypermedia for Nutrition undergraduate students (UFSC as a tool for better comprehension of the theme. The students revealed that it was extremely effective in promoting the understanding of the enzymatic mechanisms involved in glycolysis. This suggests that there is a significant added value in employing the software as an instructional effort to enhance student’s abilities to understand biochemical pathways.

  1. Physiological, Biochemical, and Molecular Mechanisms of Heat Stress Tolerance in Plants

    Directory of Open Access Journals (Sweden)

    Masayuki Fujita

    2013-05-01

    Full Text Available High temperature (HT stress is a major environmental stress that limits plant growth, metabolism, and productivity worldwide. Plant growth and development involve numerous biochemical reactions that are sensitive to temperature. Plant responses to HT vary with the degree and duration of HT and the plant type. HT is now a major concern for crop production and approaches for sustaining high yields of crop plants under HT stress are important agricultural goals. Plants possess a number of adaptive, avoidance, or acclimation mechanisms to cope with HT situations. In addition, major tolerance mechanisms that employ ion transporters, proteins, osmoprotectants, antioxidants, and other factors involved in signaling cascades and transcriptional control are activated to offset stress-induced biochemical and physiological alterations. Plant survival under HT stress depends on the ability to perceive the HT stimulus, generate and transmit the signal, and initiate appropriate physiological and biochemical changes. HT-induced gene expression and metabolite synthesis also substantially improve tolerance. The physiological and biochemical responses to heat stress are active research areas, and the molecular approaches are being adopted for developing HT tolerance in plants. This article reviews the recent findings on responses, adaptation, and tolerance to HT at the cellular, organellar, and whole plant levels and describes various approaches being taken to enhance thermotolerance in plants.

  2. Asian collaboration on nuclear reaction data compilation

    International Nuclear Information System (INIS)

    Nuclear reaction data are essential for research and development in nuclear engineering, radiation therapy, nuclear physics and astrophysics. Experimental data must be compiled in a database and be accessible to nuclear data users. One of the nuclear reaction databases is the EXFOR database maintained by the International Network of Nuclear Reaction Data Centres (NRDC) under the auspices of the International Atomic Energy Agency. Recently, collaboration among the Asian NRDC members is being further developed under the support of the Asia-Africa Science Platform Program of the Japan Society for the Promotion of Science. We report the activity for three years to develop the Asian collaboration on nuclear reaction data compilation. (author)

  3. Network Potentials

    OpenAIRE

    Chakrabarti, Subhadip; Gilles, Robert Paul

    2005-01-01

    A network payoff function assigns a utility to all participants in a (social) network. In this paper we discuss properties of such network payoff functions that guarantee the existence of certain types of pairwise stable networks and the convergence of certain network formation processes. In particular we investigate network payoff functions that admit an exact network potential or an ordinal network potential. We relate these network potentials to exact and ordinal potentials of a non-cooper...

  4. Complex networks: Patterns of complexity

    Science.gov (United States)

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2010-07-01

    The Turing mechanism provides a paradigm for the spontaneous generation of patterns in reaction-diffusion systems. A framework that describes Turing-pattern formation in the context of complex networks should provide a new basis for studying the phenomenon.

  5. Biochemical Disincentives to Fertilizing Cellulosic Ethanol Crops

    Science.gov (United States)

    Gallagher, M. E.; Hockaday, W. C.; Snapp, S.; McSwiney, C.; Baldock, J.

    2010-12-01

    Corn grain biofuel crops produce the highest yields when the cropping ecosystem is not nitrogen (N)-limited, achieved by application of fertilizer. There are environmental consequences for excessive fertilizer application to crops, including greenhouse gas emissions, hypoxic “dead zones,” and health problems from N runoff into groundwater. The increase in corn acreage in response to demand for alternative fuels (i.e. ethanol) could exacerbate these problems, and divert food supplies to fuel production. A potential substitute for grain ethanol that could reduce some of these impacts is cellulosic ethanol. Cellulosic ethanol feedstocks include grasses (switchgrass), hardwoods, and crop residues (e.g. corn stover, wheat straw). It has been assumed that these feedstocks will require similar N fertilization rates to grain biofuel crops to maximize yields, but carbohydrate yield versus N application has not previously been monitored. We report the biochemical stocks (carbohydrate, protein, and lignin in Mg ha-1) of a corn ecosystem grown under varying N levels. We measured biochemical yield in Mg ha-1 within the grain, leaf and stem, and reproductive parts of corn plants grown at seven N fertilization rates (0-202 kg N ha-1), to evaluate the quantity and quality of these feedstocks across a N fertilization gradient. The N fertilization rate study was performed at the Kellogg Biological Station-Long Term Ecological Research Site (KBS-LTER) in Michigan. Biochemical stocks were measured using 13C nuclear magnetic resonance spectroscopy (NMR), combined with a molecular mixing model (Baldock et al. 2004). Carbohydrate and lignin are the main biochemicals of interest in ethanol production since carbohydrate is the ethanol feedstock, and lignin hinders the carbohydrate to ethanol conversion process. We show that corn residue carbohydrate yields respond only weakly to N fertilization compared to grain. Grain carbohydrate yields plateau in response to fertilization at

  6. Radiation treatment of drugs, biochemicals and vaccines

    International Nuclear Information System (INIS)

    The concise and tabulated review reports experimental results on the effects of radiation treatment on drugs, vaccines, biochemicals and adjuvants including enzymes as well. Irradiation was mostly performed by γ-radiation using 60Co and to a lesser extent by 137Cs, 182Ta, X-rays and accelerators. Ionizing radiation proved to be a useful tool for sterilization and inactivation in producing drugs, vaccines, and bioactive agents and will contribute to realize procedures difficultly solvable as to engineering and economy, respectively. 124 refs

  7. An electrodynamic preconcentrator integrated thermoelectric biosensor chip for continuous monitoring of biochemical process

    International Nuclear Information System (INIS)

    This paper proposes an integrated sensor chip for continuous monitoring of a biochemical process. It is composed of a preconcentrator and a thermoelectric biosensor. In the preconcentrator, the concentration of the injected biochemical sample is electrodynamically condensed. Then, in the downstream thermoelectric biosensor, the preconcentrated target molecules react with sequentially injected capture molecules and generate reaction heat. The reaction heat is detected based on the thermoelectric effect, and an integrated split-flow microchannel improves the sensor stability by providing ability to self-compensate thermal noise. These sequential preconcentration and detection processes are performed in completely label-free and continuous conditions and consequently enhance the sensor sensitivity. The performance of the integrated biosensor chip was evaluated at various flow rates and applied voltages. First, in order to verify characteristics of the fabricated preconcentrator, 10 µm -diameter polystyrene (PS) particles were used. The particles were concentrated by applying ac voltage from 0 to 16 Vpp at 3 MHz at various flow rates. In the experimental result, approximately 92.8% of concentration efficiency was achieved at a voltage over 16 Vpp and at a flow rate below 100 µl h−1. The downstream thermoelectric biosensor was characterized by measuring reaction heat of biotin–streptavidin interaction. The preconcentrated streptavidin-coated PS particles flow into the reaction chamber and react with titrated biotin. The measured output voltage was 288.2 µV at a flow rate of 100 µl h−1 without preconcentration. However, by using proposed preconcentrator, an output voltage of 812.3 µV was achieved with a 16 Vpp-applied preconcentration in the same given sample and flow rate. According to these results, the proposed label-free biomolecular preconcentration and detection technique can be applied in continuous and high-throughput biochemical applications. (paper)

  8. Network Competition with Local Network

    OpenAIRE

    Oystein Fjeldstad; Moen, Espen R; Christian Riis

    2007-01-01

    Local network externalities are present when the network externalities associated with entering a certain network depends not only on the total number of agents in the network, but on the identity of the agents in the network. We explore the consequences of local network externalities within a framework where two networks compete on the Hotelling circle. We first show that local network externalities, in contrast to global network externalities, do not sharpen competition. Then we show that t...

  9. Biochemical Control With Radiotherapy Improves Overall Survival in Intermediate and High-Risk Prostate Cancer Patients Who Have an Estimated 10-Year Overall Survival of >90%

    International Nuclear Information System (INIS)

    Purpose: To identify subgroups of patients with carcinoma of the prostate treated with radical radiotherapy that have improved overall survival when disease is biochemically controlled. Methods and Materials: A cohort of 1,060 prostate cancer patients treated with radical radiotherapy was divided into nine subgroups based on National Comprehensive Cancer Network risk category and estimated 10-year overall survival (eOS 10y) derived from the age adjusted Charlson Comorbidity Index. Patients with and without biochemical control were compared with respect to overall survival. Actuarial estimates of overall survival were calculated using the Kaplan-Meier method. Univariate and multivariate Cox proportional hazards models were used for analysis of overall survival. Results: Median follow-up was 125 months (range, 51–176 months). Only the subgroups with high or intermediate risk disease and an eOS 10y of >90% had a statistically significantly improved overall survival when prostate cancer was biochemically controlled. In all other groups, biochemical control made no significant difference to overall survival. In the subgroup with high-risk disease and eOS 10y >90%, actuarial overall survival was 86.3% (95% confidence interval [CI] 78.5%–94.1%) and 62.1% (95% CI 52.9%–71.3%) for patients with biochemical control and biochemical relapse respectively (p = 0.002). In the intermediate risk group with eOS >90%, actuarial overall survival was 95.3% (95% CI 89.0%–100%) and 79.8% (95% CI 68.0%–91.6%) for biochemically controlled and biochemically relapsed patients (p = 0.033). On multivariate analysis, National Comprehensive Cancer Network risk group (p = 0.005), biochemical control (p = 0.033) and eOS 10y (p 90%.

  10. Textural and biochemical changes during ripening of old-fashioned salted herrings

    DEFF Research Database (Denmark)

    Christensen, Mette; Andersen, Eva; Christensen, Line;

    2011-01-01

    BACKGROUND: Understanding of the biochemical reactions taking place during ripening of salted herring is still rather limited. Therefore, salted herrings were traditionally produced and the impact of the brine composition was evaluated in relation to the development of the characteristic texture of...... salted herrings. The aim of this study was to measure the texture changes during ripening using two differentmethods and to correlate the texture changeswith brine composition andwith biochemical modifications at themolecular level. RESULTS: During ripening (up to 151 days), hardness was higher in salted...... the ripening period could be explained by free-radical-induced cross-linking of myosin and the formation of aggregates. In addition, degradation of these aggregates correlated with the decrease in hardness observed at 371 days. CONCLUSIONS: Texture changes during ripening of salted herrings can be...

  11. BIOCHEMICAL PROCESSES FOR GEOTHERMAL BRINE TREATMENT

    Energy Technology Data Exchange (ETDEWEB)

    PREMUZIC,E.T.; LIN,M.S.; BOHENEK,M.; JOSHI-TOPE,G.; ZHOU,W.; SHELENKOVA,L.; WILKE,R.

    1998-09-20

    As part of the DOE Geothermal Energy Program, BNL's Advanced Biochemical Processes for Geothermal Brines (ABPGB) project is aimed at the development of cost-efficient and environmentally acceptable technologies for the disposal of geothermal wastes. Extensive chemical studies of high and low salinity brines and precipitates have indicated that in addition to trace quantities of regulated substances, e.g., toxic metals such as arsenic and mercury, there are significant concentrations of valuable metals, including gold, silver and platinum. Further chemical and physical studies of the silica product have also shown that the produced silica is a valuable material with commercial potential. A combined biochemical and chemical technology is being developed which (1) solubilizes, separates, and removes environmentally regulated constituents in geothermal precipitates and brines (2) generates an amorphous silica product which may be used as feedstock for the production of revenue generating materials, (3) recover economically valuable trace metals and salts. Geothermal power resources which utilize low salinity brines and use the Stretford process for hydrogen sulfide abatement generate a contaminated sulfur cake. Combined technology converts such sulfur to a commercial grade sulfur, suitable for agricultural use. The R and D activities at BNL are conducted jointly with industrial parties in an effort focused on field applications.

  12. Biochemical processes for geothermal brine treatment

    Energy Technology Data Exchange (ETDEWEB)

    Premuzic, E.T.; Lin, M.S.; Bohenek, M.; Joshi-Tope, G.; Zhou, W.; Shelenkova, L.; Wilke, R.

    1998-08-01

    As part of the DOE Geothermal Energy Program, BNL`s Advanced Biochemical Processes for Geothermal Brines (ABPGB) project is aimed at the development of cost-efficient and environmentally acceptable technologies for the disposal of geothermal wastes. Extensive chemical studies of high and low salinity brines and precipitates have indicated that in addition to trace quantities of regulated substances, e.g., toxic metals such as arsenic and mercury, there are significant concentrations of valuable metals, including gold, silver and platinum. Further chemical and physical studies of the silica product have also shown that the produced silica is a valuable material with commercial potential. A combined biochemical and chemical technology is being developed which (1) solubilizes, separates, and removes environmentally regulated constituents in geothermal precipitates and brines, (2) generates an amorphous silica product which may be used as feedstock for the production of revenue generating materials, (3) recover economically valuable trace metals and salts. Geothermal power resources which utilize low salinity brines and use the Stretford process for hydrogen sulfide abatement generate a contaminated sulfur cake. Combined technology converts such sulfur to a commercial grade sulfur, suitable for agricultural use. The R and D activities at BNL are conducted jointly with industrial parties in an effort focused on field applications.

  13. Serum biochemical markers in carcinoma breast.

    Directory of Open Access Journals (Sweden)

    Seth R

    2003-08-01

    Full Text Available BACKGROUND: Despite the extensive research for many years throughout the world, the etiopathogenesis of cancer still remains obscure. For the early detection of carcinoma of various origins, a number of biochemical markers have been studied to evaluate the malignancy. AIM: To analyse serum gamma glutamyl transpeptidase (GGTP, lactate dehydrogenase (LDH and superoxide dismutase (SOD in carcinoma breast patients. SETTINGS & DESIGN: The serum biochemical markers were estimated in twenty five histopathologically confirmed patients with carcinoma breast and equal number of healthy age- matched individuals served as control. MATERIAL & METHODS: Serum gamma glutamyl transpeptidase (GGTP, lactate dehydrogenase (LDH and superoxide dismutase (SOD were estimated and their sensitivity determined. Statistics: Data was analysed with student′s ′t′-test and sensitivity score of these markers was determined. RESULTS & CONCLUSIONS: The mean serum GGTP, LDH and SOD activities in patients with carcinoma breast were tremendously increased as compared to controls, and a steady increase was observed in their activities from stage I through stage IV as well as following distant metastasis. Serum GGTP, LDH and SOD might prove to be most sensitive biomarkers in carcinoma breast in early detection of the disease.

  14. Quality, microstructure, biochemical and immunochemical characteristics of hypoallergenic pasta.

    Science.gov (United States)

    Susanna, S; Prabhasankar, P

    2012-08-01

    Celiac disease is an immune-mediated enteropathy, characterized by lifelong intolerance to gluten in genetically susceptible individuals. This study aims to develop hypoallergenic pasta using blends of Triticum durum semolina, 40% of other non-wheat flours and additives. Formulated pasta samples were evaluated for product quality characteristics and also subjected to biochemical analysis. Results showed that cooking loss ranged from 6.9% to 7.4%, which were within the acceptable range of 8%. Color change was low and in vitro protein digestibility of the pasta was found to be insignificant. Pasting characteristics of the hypoallergenic flour showed the increased peak viscosity and decreased gelatinization temperature. The scanning electron microscopy results demonstrated less-affected microstructure of gluten network. Texture profile analysis and descriptive sensory analysis revealed that optimized hypoallergenic pasta with xanthan gum as additive was acceptable and comparable with control. SDS-PAGE pattern showed distinct protein profile and decreased intensity, which was supported by Dot-Blot. In conclusion, the hypoallergenic pasta prepared by replacing T durum flour by 40% of other non-gluten flours could be useful for celiac patients because of its low antigenic activity.

  15. Hierarchical graphs for rule-based modeling of biochemical systems

    Directory of Open Access Journals (Sweden)

    Hu Bin

    2011-02-01

    Full Text Available Abstract Background In rule-based modeling, graphs are used to represent molecules: a colored vertex represents a component of a molecule, a vertex attribute represents the internal state of a component, and an edge represents a bond between components. Components of a molecule share the same color. Furthermore, graph-rewriting rules are used to represent molecular interactions. A rule that specifies addition (removal of an edge represents a class of association (dissociation reactions, and a rule that specifies a change of a vertex attribute represents a class of reactions that affect the internal state of a molecular component. A set of rules comprises an executable model that can be used to determine, through various means, the system-level dynamics of molecular interactions in a biochemical system. Results For purposes of model annotation, we propose the use of hierarchical graphs to represent structural relationships among components and subcomponents of molecules. We illustrate how hierarchical graphs can be used to naturally document the structural organization of the functional components and subcomponents of two proteins: the protein tyrosine kinase Lck and the T cell receptor (TCR complex. We also show that computational methods developed for regular graphs can be applied to hierarchical graphs. In particular, we describe a generalization of Nauty, a graph isomorphism and canonical labeling algorithm. The generalized version of the Nauty procedure, which we call HNauty, can be used to assign canonical labels to hierarchical graphs or more generally to graphs with multiple edge types. The difference between the Nauty and HNauty procedures is minor, but for completeness, we provide an explanation of the entire HNauty algorithm. Conclusions Hierarchical graphs provide more intuitive formal representations of proteins and other structured molecules with multiple functional components than do the regular graphs of current languages for

  16. The biochemical basis of antimicrobial responses in Manduca sexta

    Institute of Scientific and Technical Information of China (English)

    Haobo Jiang

    2008-01-01

    Innate immunity is essential for the wellbeing of vertebrates and invertebrates.Key components of this defense system include pattern recognition receptors that bind to infectious agents, extra-and intra-cellular proteins that relay signals, as well as molecules and cells that eliminate pathogens. We have been studying the defense mechanisms in a biochemical model insect, Manduca sexta. In this insect, hemolin, peptidoglycan recognition proteins, β-1,3-glucan recognition proteins and C-type lectins detect microbial surface molecules and induce immune responses such as phagocytosis, nodulation, encapsulation,melanization and production of antimicrobial peptides. Some of these responses are mediated by extracellular serine proteinase pathways. The proteolytic activation of prophenoloxidase (proPO) yields active phenoloxidase (PO) which catalyzes the formation of quinones and melanin for wound healing and microbe killing. M. sexta hemolymph proteinase 14 (HP 14) precursor interacts with peptidoglycan or β- 1,3-glucan, autoactivates,and leads to the activation of other HPs including HP21 and proPO-activating proteinases (PAPs). PAP-1, -2 and -3 cut proPO to generate active PO in the presence of two serine proteinase homologs. Inhibition of the proteinases by serpins and association of the proteinase homologs with bacteria ensure a localized defense reaction. M. sexta HP1, HP6,HP8, HP17 and other proteinases may also participate in proPO activation or processing of sp(a)tzle and plasmatocyte spreading peptide.

  17. Spongiosa Primary Development: A Biochemical Hypothesis by Turing Patterns Formations

    Science.gov (United States)

    López-Vaca, Oscar Rodrigo; Garzón-Alvarado, Diego Alexander

    2012-01-01

    We propose a biochemical model describing the formation of primary spongiosa architecture through a bioregulatory model by metalloproteinase 13 (MMP13) and vascular endothelial growth factor (VEGF). It is assumed that MMP13 regulates cartilage degradation and the VEGF allows vascularization and advances in the ossification front through the presence of osteoblasts. The coupling of this set of molecules is represented by reaction-diffusion equations with parameters in the Turing space, creating a stable spatiotemporal pattern that leads to the formation of the trabeculae present in the spongy tissue. Experimental evidence has shown that the MMP13 regulates VEGF formation, and it is assumed that VEGF negatively regulates MMP13 formation. Thus, the patterns obtained by ossification may represent the primary spongiosa formation during endochondral ossification. Moreover, for the numerical solution, we used the finite element method with the Newton-Raphson method to approximate partial differential nonlinear equations. Ossification patterns obtained may represent the primary spongiosa formation during endochondral ossification. PMID:23193429

  18. In Vitro Biochemical Characterization of All Barley Endosperm Starch Synthases

    Directory of Open Access Journals (Sweden)

    Jose Antonio Cuesta-Seijo

    2016-01-01

    Full Text Available Starch is the main storage polysaccharide in cereals and the major source of calories in the human diet. It is synthesized by a panel of enzymes including five classes of starch synthases (SSs. While the overall starch synthase (SS reaction is known, the functional differences between the five SS classes are poorly understood. Much of our knowledge comes from analyzing mutant plants with altered SS activities, but the resulting data are often difficult to interpret as a result of pleitropic effects, competition between enzymes, overlaps in enzyme activity and disruption of multi-enzyme complexes. Here we provide a detailed biochemical study of the activity of all five classes of SSs in barley endosperm. Each enzyme was produced recombinantly in E. coli and the properties and modes of action in vitro were studied in isolation from other SSs and other substrate modifying activities. Our results define the mode of action of each SS class in unprecedented detail; we analyze their substrate selection, temperature dependence and stability, substrate affinity and temporal abundance during barley development. Our results are at variance with some generally accepted ideas about starch biosynthesis and might lead to the reinterpretation of results obtained in planta. In particular, they indicate that granule bound SS is capable of processive action even in the absence of a starch matrix, that SSI has no elongation limit, and that SSIV, believed to be critical for the initiation of starch granules, has maltoligosaccharides and not polysaccharides as its preferred substrates.

  19. Spongiosa Primary Development: A Biochemical Hypothesis by Turing Patterns Formations

    Directory of Open Access Journals (Sweden)

    Oscar Rodrigo López-Vaca

    2012-01-01

    Full Text Available We propose a biochemical model describing the formation of primary spongiosa architecture through a bioregulatory model by metalloproteinase 13 (MMP13 and vascular endothelial growth factor (VEGF. It is assumed that MMP13 regulates cartilage degradation and the VEGF allows vascularization and advances in the ossification front through the presence of osteoblasts. The coupling of this set of molecules is represented by reaction-diffusion equations with parameters in the Turing space, creating a stable spatiotemporal pattern that leads to the formation of the trabeculae present in the spongy tissue. Experimental evidence has shown that the MMP13 regulates VEGF formation, and it is assumed that VEGF negatively regulates MMP13 formation. Thus, the patterns obtained by ossification may represent the primary spongiosa formation during endochondral ossification. Moreover, for the numerical solution, we used the finite element method with the Newton-Raphson method to approximate partial differential nonlinear equations. Ossification patterns obtained may represent the primary spongiosa formation during endochondral ossification.

  20. Reactions of charged and neutral recoil particles following nuclear transformations. Progress report No. 10

    International Nuclear Information System (INIS)

    The status of the following programs is reported: study of the stereochemistry of halogen atom reactions produced via (n,γ) nuclear reactions with diastereomeric molecules in the condensed phase; decay-induced labelling of compounds of biochemical interest; and chemistry of positronium

  1. Exploring biological network structure with clustered random networks

    Directory of Open Access Journals (Sweden)

    Bansal Shweta

    2009-12-01

    Full Text Available Abstract Background Complex biological systems are often modeled as networks of interacting units. Networks of biochemical interactions among proteins, epidemiological contacts among hosts, and trophic interactions in ecosystems, to name a few, have provided useful insights into the dynamical processes that shape and traverse these systems. The degrees of nodes (numbers of interactions and the extent of clustering (the tendency for a set of three nodes to be interconnected are two of many well-studied network properties that can fundamentally shape a system. Disentangling the interdependent effects of the various network properties, however, can be difficult. Simple network models can help us quantify the structure of empirical networked systems and understand the impact of various topological properties on dynamics. Results Here we develop and implement a new Markov chain simulation algorithm to generate simple, connected random graphs that have a specified degree sequence and level of clustering, but are random in all other respects. The implementation of the algorithm (ClustRNet: Clustered Random Networks provides the generation of random graphs optimized according to a local or global, and relative or absolute measure of clustering. We compare our algorithm to other similar methods and show that ours more successfully produces desired network characteristics. Finding appropriate null models is crucial in bioinformatics research, and is often difficult, particularly for biological networks. As we demonstrate, the networks generated by ClustRNet can serve as random controls when investigating the impacts of complex network features beyond the byproduct of degree and clustering in empirical networks. Conclusion ClustRNet generates ensembles of graphs of specified edge structure and clustering. These graphs allow for systematic study of the impacts of connectivity and redundancies on network function and dynamics. This process is a key step in

  2. Possibilities and methods for biochemical assessment of radiation injury

    International Nuclear Information System (INIS)

    An extensitive review (77 references) is made of the application of biochemical diagnostic methods for assessment of radiation diseases. A brief characteristics of several biochemical indicators is given: deoxycytidine, thymidine, ρ-aminoisocarboxylic acid, DNA-ase, nucleic acids. Influence of such factors as age, sex, season etc. is studied by means of functional biochemical indicators as: creatine, triptophanic metabolites, 5-hydroxy-indolacetic acid, biogenic amines, serum proteins, enzymes, etc

  3. Conceptual Aspects of Theory Appraisal: Some Biochemical Examples

    Directory of Open Access Journals (Sweden)

    F. Michael Akeroyd

    1997-11-01

    Full Text Available This paper considers papers on conceptual analysis by Laudan (1981 and Whitt (1989 and relates them to three biochemical episodes: (1 the modern 'biochemical explanation' of acupuncture; (2 the chemio-osmotic hypothesis of oxidative phosphorylation; (3 the theory of the complete digestion of proteins in the gut. The advantages of including philosophical debate in chemical/biochemical undergraduate courses is then discussed.

  4. Prebiotic metabolic networks?

    OpenAIRE

    Luisi, Pier Luigi

    2014-01-01

    A prebiotic origin of metabolism has been proposed as one of several scenarios for the origin of life. In their recent work, Ralser and colleagues (Keller et al, 2014) observe an enzyme‐free, metabolism‐like reaction network under conditions reproducing a possible prebiotic environment.

  5. Soya bean Gα proteins with distinct biochemical properties exhibit differential ability to complement Saccharomyces cerevisiae gpa1 mutant.

    Science.gov (United States)

    Roy Choudhury, Swarup; Wang, Yuqi; Pandey, Sona

    2014-07-01

    Signalling pathways mediated by heterotrimeric G-proteins are common to all eukaryotes. Plants have a limited number of each of the G-protein subunits, with the most elaborate G-protein network discovered so far in soya bean (Glycine max, also known as soybean) which has four Gα, four Gβ and ten Gγ proteins. Biochemical characterization of Gα proteins from plants suggests significant variation in their properties compared with the well-characterized non-plant proteins. Furthermore, the four soya bean Gα (GmGα) proteins exhibit distinct biochemical activities among themselves, but the extent to which such biochemical differences contribute to their in vivo function is also not known. We used the yeast gpa1 mutant which displays constitutive signalling and growth arrest in the pheromone-response pathway as an in vivo model to evaluate the effect of distinct biochemical activities of GmGα proteins. We showed that specific GmGα proteins can be activated during pheromone-dependent receptor-mediated signalling in yeast and they display different strengths towards complementation of yeast gpa1 phenotypes. We also identified amino acids that are responsible for differential complementation abilities of specific Gα proteins. These data establish that specific plant Gα proteins are functional in the receptor-mediated pheromone-response pathway in yeast and that the subtle biochemical differences in their activity are physiologically relevant.

  6. Catalysis of Photochemical Reactions.

    Science.gov (United States)

    Albini, A.

    1986-01-01

    Offers a classification system of catalytic effects in photochemical reactions, contrasting characteristic properties of photochemical and thermal reactions. Discusses catalysis and sensitization, examples of catalyzed reactions of excepted states, complexing ground state substrates, and catalysis of primary photoproducts. (JM)

  7. Local biochemical and morphological differences in human Achilles tendinopathy

    DEFF Research Database (Denmark)

    Pingel, Jessica; Fredberg, U.; Qvortrup, Klaus;

    2012-01-01

    The incidence of Achilles tendinopathy is high and underlying etiology as well as biochemical and morphological pathology associated with the disease is largely unknown. The aim of the present study was to describe biochemical and morphological differences in chronic Achilles tendinopathy. The ex....... The expressions of growth factors, inflammatory mediators and tendon morphology were determined in both chronically diseased and healthy tendon parts.......The incidence of Achilles tendinopathy is high and underlying etiology as well as biochemical and morphological pathology associated with the disease is largely unknown. The aim of the present study was to describe biochemical and morphological differences in chronic Achilles tendinopathy...

  8. Quantifying evolvability in small biological networks

    Energy Technology Data Exchange (ETDEWEB)

    Nemenman, Ilya [Los Alamos National Laboratory; Mugler, Andrew [COLUMBIA UNIV; Ziv, Etay [COLUMBIA UNIV; Wiggins, Chris H [COLUMBIA UNIV

    2008-01-01

    The authors introduce a quantitative measure of the capacity of a small biological network to evolve. The measure is applied to a stochastic description of the experimental setup of Guet et al. (Science 2002, 296, pp. 1466), treating chemical inducers as functional inputs to biochemical networks and the expression of a reporter gene as the functional output. The authors take an information-theoretic approach, allowing the system to set parameters that optimise signal processing ability, thus enumerating each network's highest-fidelity functions. All networks studied are highly evolvable by the measure, meaning that change in function has little dependence on change in parameters. Moreover, each network's functions are connected by paths in the parameter space along which information is not significantly lowered, meaning a network may continuously change its functionality without completely losing it along the way. This property further underscores the evolvability of the networks.

  9. Hemoglobin variants: biochemical properties and clinical correlates.

    Science.gov (United States)

    Thom, Christopher S; Dickson, Claire F; Gell, David A; Weiss, Mitchell J

    2013-03-01

    Diseases affecting hemoglobin synthesis and function are extremely common worldwide. More than 1000 naturally occurring human hemoglobin variants with single amino acid substitutions throughout the molecule have been discovered, mainly through their clinical and/or laboratory manifestations. These variants alter hemoglobin structure and biochemical properties with physiological effects ranging from insignificant to severe. Studies of these mutations in patients and in the laboratory have produced a wealth of information on hemoglobin biochemistry and biology with significant implications for hematology practice. More generally, landmark studies of hemoglobin performed over the past 60 years have established important paradigms for the disciplines of structural biology, genetics, biochemistry, and medicine. Here we review the major classes of hemoglobin variants, emphasizing general concepts and illustrative examples.

  10. The biochemical basis of hereditary fructose intolerance.

    Science.gov (United States)

    Bouteldja, Nadia; Timson, David J

    2010-04-01

    Hereditary fructose intolerance is a rare, but potentially lethal, inherited disorder of fructose metabolism, caused by mutation of the aldolase B gene. Treatment currently relies solely on dietary restriction of problematic sugars. Biochemical study of defective aldolase B enzymes is key to revealing the molecular basis of the disease and providing a stronger basis for improved treatment and diagnosis. Such studies have revealed changes in enzyme activity, stability and oligomerisation. However, linking these changes to disease phenotypes has not always been straightforward. This review gives a general overview of the features of hereditary fructose intolerance, then concentrates on the biochemistry of the AP variant (Ala149Pro variant of aldolase B) and molecular pathological consequences of mutation of the aldolase B gene.

  11. On thermonuclear reaction rates

    OpenAIRE

    Hans J. Haubold; Mathai, Arak Mathai

    1996-01-01

    Nuclear reactions govern major aspects of the chemical evolution of galaxies and stars. Analytic study of the reaction rates and reaction probability integrals is attempted here. Exact expressions for the reaction rates and reaction probability integrals for nuclear reactions in the cases of nonresonant, modified nonresonant, screened nonresonant and resonant cases are given. These are expressed in terms of H-functions, G-functions and in computable series forms. Computational aspects are als...

  12. Biochemically enhanced methane production from coal

    Science.gov (United States)

    Opara, Aleksandra

    For many years, biogas was connected mostly with the organic matter decomposition in shallow sediments (e.g., wetlands, landfill gas, etc.). Recently, it has been realized that biogenic methane production is ongoing in many hydrocarbon reservoirs. This research examined microbial methane and carbon dioxide generation from coal. As original contributions methane production from various coal materials was examined in classical and electro-biochemical bench-scale reactors using unique, developed facultative microbial consortia that generate methane under anaerobic conditions. Facultative methanogenic populations are important as all known methanogens are strict anaerobes and their application outside laboratory would be problematic. Additional testing examined the influence of environmental conditions, such as pH, salinity, and nutrient amendments on methane and carbon dioxide generation. In 44-day ex-situ bench-scale batch bioreactor tests, up to 300,000 and 250,000 ppm methane was generated from bituminous coal and bituminous coal waste respectively, a significant improvement over 20-40 ppm methane generated from control samples. Chemical degradation of complex hydrocarbons using environmentally benign reagents, prior to microbial biodegradation and methanogenesis, resulted in dissolution of up to 5% bituminous coal and bituminous coal waste and up to 25% lignite in samples tested. Research results confirm that coal waste may be a significant underutilized resource that could be converted to useful fuel. Rapid acidification of lignite samples resulted in low pH (below 4.0), regardless of chemical pretreatment applied, and did not generate significant methane amounts. These results confirmed the importance of monitoring and adjusting in situ and ex situ environmental conditions during methane production. A patented Electro-Biochemical Reactor technology was used to supply electrons and electron acceptor environments, but appeared to influence methane generation in a

  13. Transcription fluctuation effects on biochemical oscillations.

    Directory of Open Access Journals (Sweden)

    Ryota Nishino

    Full Text Available Some biochemical systems show oscillation. They often consist of feedback loops with repressive transcription regulation. Such biochemical systems have distinctive characteristics in comparison with ordinary chemical systems: i numbers of molecules involved are small, ii there are typically only a couple of genes in a cell with a finite regulation time. Due to the fluctuations caused by these features, the system behavior can be quite different from the one by deterministic rate equations, because the rate equations ignore molecular fluctuations and thus are exact only in the infinite molecular number limit. The molecular fluctuations on a free-running circadian system have been studied by Gonze et al. (2002 by introducing a scale parameter [Formula: see text] for the system size. They consider, however, only the first effect, assuming that the gene process is fast enough for the second effect to be ignored, but this has not been examined systematically yet. Here we study fluctuation effects due to the finite gene regulation time by introducing a new scale parameter [Formula: see text], which we take as the unbinding time of a nuclear protein from the gene. We focus on the case where the fluctuations due to small molecular numbers are negligible. In simulations on the same system studied by Gonze et al., we find the system is unexpectedly sensitive to the fluctuation in the transcription regulation; the period of oscillation fluctuates about 30 min even when the regulation time scale [Formula: see text] is around 30 s, that is even smaller than 1/1000 of its circadian period. We also demonstrate that the distribution width for the oscillation period and amplitude scales with [Formula: see text], and the correlation time scales with [Formula: see text] in the small [Formula: see text] regime. The relative fluctuations for the period are about half of that for the amplitude, namely, the periodicity is more stable than the amplitude.

  14. Declarative Networking

    CERN Document Server

    Loo, Boon Thau

    2012-01-01

    Declarative Networking is a programming methodology that enables developers to concisely specify network protocols and services, which are directly compiled to a dataflow framework that executes the specifications. Declarative networking proposes the use of a declarative query language for specifying and implementing network protocols, and employs a dataflow framework at runtime for communication and maintenance of network state. The primary goal of declarative networking is to greatly simplify the process of specifying, implementing, deploying and evolving a network design. In addition, decla

  15. Reconstruction and in silico analysis of metabolic network for an oleaginous yeast, Yarrowia lipolytica.

    Directory of Open Access Journals (Sweden)

    Pengcheng Pan

    Full Text Available With the emergence of energy scarcity, the use of renewable energy sources such as biodiesel is becoming increasingly necessary. Recently, many researchers have focused their minds on Yarrowia lipolytica, a model oleaginous yeast, which can be employed to accumulate large amounts of lipids that could be further converted to biodiesel. In order to understand the metabolic characteristics of Y. lipolytica at a systems level and to examine the potential for enhanced lipid production, a genome-scale compartmentalized metabolic network was reconstructed based on a combination of genome annotation and the detailed biochemical knowledge from multiple databases such as KEGG, ENZYME and BIGG. The information about protein and reaction associations of all the organisms in KEGG and Expasy-ENZYME database was arranged into an EXCEL file that can then be regarded as a new useful database to generate other reconstructions. The generated model iYL619_PCP accounts for 619 genes, 843 metabolites and 1,142 reactions including 236 transport reactions, 125 exchange reactions and 13 spontaneous reactions. The in silico model successfully predicted the minimal media and the growing abilities on different substrates. With flux balance analysis, single gene knockouts were also simulated to predict the essential genes and partially essential genes. In addition, flux variability analysis was applied to design new mutant strains that will redirect fluxes through the network and may enhance the production of lipid. This genome-scale metabolic model of Y. lipolytica can facilitate system-level metabolic analysis as well as strain development for improving the production of biodiesels and other valuable products by Y. lipolytica and other closely related oleaginous yeasts.

  16. A systematic evaluation of the compatibility of histones containing methyl-lysine analogues with biochemical reactions

    Institute of Scientific and Technical Information of China (English)

    Guangshuai Jia; Weixiang Wang; Hong Li; Zhuo Mao; Gaihong Cai; Jian Sun; Hui Wu; Mo Xu; Peng Yang; Wen Yuan; She Chen; Bing Zhu

    2009-01-01

    @@ Dear Editor, Histone lysine methylation has receoved a great deal of attention from the chromatin field over the past 10 years. To date, histone lysine methylations have been demonstrated to play pivotal roles in nearly all biological processes involving chromatin, including replication, transcription, DNA repair etc.

  17. Noise Control in Gene Regulatory Networks with Negative Feedback.

    Science.gov (United States)

    Hinczewski, Michael; Thirumalai, D

    2016-07-01

    Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise. PMID:27095600

  18. Possible Roles of Neural Electron Spin Networks in Memory and Consciousness

    CERN Document Server

    Hu, H P

    2004-01-01

    Spin is the origin of quantum effects in both Bohm and Hestenes quantum formulism and a fundamental quantum process associated with the structure of space-time. Thus, we have recently theorized that spin is the mind-pixel and developed a qualitative model of consciousness based on nuclear spins inside neural membranes and proteins. In this paper, we explore the possibility of unpaired electron spins being the mind-pixels. Besides free O2 and NO, the main sources of unpaired electron spins in neural membranes and proteins are transition metal ions and O2 and NO bound/absorbed to large molecules, free radicals produced through biochemical reactions and excited molecular triplet states induced by fluctuating internal magnetic fields. We show that unpaired electron spin networks inside neural membranes and proteins are modulated by action potentials through exchange and dipolar coupling tensors and spin-orbital coupling and g-factor tensors and perturbed by microscopically strong and fluctuating internal magnetic...

  19. Coeliac Disease: Background and biochemical aspects

    NARCIS (Netherlands)

    Hamer, R.J.

    2005-01-01

    Coeliac Disease has to be considered a main food related affliction, with life long consequences for the people having the disease. Coeliac Disease patients suffer from adverse effects that can be related to specific gluten peptide sequences that trigger a sequence of immune related reactions leadin

  20. Coeliac disease: Background and biochemical aspects

    NARCIS (Netherlands)

    Hamer, R.J.

    2005-01-01

    Coeliac Disease has to be considered a main food related affliction, with life long consequences for the people having the disease. Coeliac Disease patients suffer from adverse effects that can be related to specific gluten peptide sequences that trigger a sequence of immune related reactions leadin