Cellular automata modelling of biomolecular networks dynamics.
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
Duplication and Divergence Effect on Network Motifs in Undirected Bio-Molecular Networks.
Pei Wang; Jinhu Lu; Xinghuo Yu; Zengrong Liu
2015-06-01
Duplication and divergence are two basic evolutionary mechanisms of bio-molecular networks. Real-world bio-molecular networks and their statistical characteristics can be well mimicked by artificial algorithms based on the two mechanisms. Bio-molecular networks consist of network motifs, which act as building blocks of large-scale networks. A fundamental question is how network motifs are evolved from long time evolution and natural selection. By considering the effect of various duplication and divergence strategies, we find that the underlying duplication scheme of the real-world undirected bio-molecular networks would rather follow the anti-preference strategy than the random one. The anti-preference duplication mechanism and the dimerization processes can lead to the formation of various motifs, and robustly conserve proper quantities of motifs in the artificial networks as that in the real-world ones. Furthermore, the anti-preference mechanism and edge deletion divergence can robustly preserve the sparsity of the networks. The investigations reveal the possible evolutionary mechanisms of network motifs in real-world bio-molecular networks, and have potential implications in the design, synthesis and reengineering of biological networks for biomedical purpose. PMID:25203993
The interplay of intrinsic and extrinsic bounded noises in biomolecular networks.
Caravagna, Giulio; Mauri, Giancarlo; d'Onofrio, Alberto
2013-01-01
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. PMID:23437034
The interplay of intrinsic and extrinsic bounded noises in biomolecular networks.
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.
Soberano de Oliveira, Ana Paula; Patil, Kiran Raosaheb; Nielsen, Jens
2008-01-01
use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data. Results: Here we introduce a hypothesis-driven method that integrates bio-molecular network topology with...... transcriptome data, thereby allowing the identification of key biological features (Reporter Features) around which transcriptional changes are significantly concentrated. We have combined transcriptome data with different biological networks in order to identify Reporter Gene Ontologies, Reporter Transcription...... Factors, Reporter Proteins and Reporter Complexes, and use this to decipher the logic of regulatory circuits playing a key role in yeast glucose repression and human diabetes. Conclusion: Reporter Features offer the opportunity to identify regulatory hot-spots in bio-molecular interaction networks that...
Fei, Yiyan; Landry, James P.; Zhu, X. D., E-mail: xdzhu@physics.ucdavis.edu [Department of Physics, University of California, One Shields Avenue, Davis, California 95616 (United States); Li, Yanhong; Yu, Hai; Lau, Kam; Huang, Shengshu; Chokhawala, Harshal A.; Chen, Xi [Department of Chemistry, University of California, One Shields Avenue, Davis, California 95616 (United States)
2013-11-15
A biological state is equilibrium of multiple concurrent biomolecular reactions. The relative importance of these reactions depends on physiological temperature typically between 10 °C and 50 °C. Experimentally the temperature dependence of binding reaction constants reveals thermodynamics and thus details of these biomolecular processes. We developed a variable-temperature opto-fluidic system for real-time measurement of multiple (400–10 000) biomolecular binding reactions on solid supports from 10 °C to 60 °C within ±0.1 °C. We illustrate the performance of this system with investigation of binding reactions of plant lectins (carbohydrate-binding proteins) with 24 synthetic glycans (i.e., carbohydrates). We found that the lectin-glycan reactions in general can be enthalpy-driven, entropy-driven, or both, and water molecules play critical roles in the thermodynamics of these reactions.
Kim, J; Bates, D. G.; Postlethwaite, I.
2008-01-01
Abstract—Stochastic differential equations are now commonly used to model biomolecular networks in systems biology, and much recent research has been devoted to the development of methods to analyse their stability properties. Stability analysis of such systems may be performed using the Laplace transform, which requires the calculation of the exponential matrix involving time symbolically. However, the calculation of the symbolic exponential matrix is not feasible for problems of even mod...
Autocatalysis in reaction networks.
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. PMID:25245394
Modeling stochasticity in biochemical reaction networks
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.
Tsuji Shingo
2012-09-01
Full Text Available Abstract Background In the functional genomics analysis domain, various methodologies are available for interpreting the results produced by high-throughput biological experiments. These methods commonly use a list of genes as an analysis input, and most of them produce a more complicated list of genes or pathways as the results of the analysis. Although there are several network-based methods, which detect key nodes in the network, the results tend to include well-studied, major hub genes. Results To mine the molecules that have biological meaning but to fewer degrees than major hubs, we propose, in this study, a new network-based method for selecting these hidden key molecules based on virtual information flows circulating among the input list of genes. The human biomolecular network was constructed from the Pathway Commons database, and a calculation method based on betweenness centrality was newly developed. We validated the method with the ErbB pathway and applied it to practical cancer research data. We were able to confirm that the output genes, despite having fewer edges than major hubs, have biological meanings that were able to be invoked by the input list of genes. Conclusions The developed method, named NetHiKe (Network-based Hidden Key molecule miner, was able to detect potential key molecules by utilizing the human biomolecular network as a knowledge base. Thus, it is hoped that this method will enhance the progress of biological data analysis in the whole-genome research era.
Bend-twist-stretch model for coarse elastic network simulation of biomolecular motion
Stember, Joseph N.; Wriggers, Willy
2009-08-01
The empirical harmonic potential function of elastic network models (ENMs) is augmented by three- and four-body interactions as well as by a parameter-free connection rule. In the new bend-twist-stretch (BTS) model the complexity of the parametrization is shifted from the spatial level of detail to the potential function, enabling an arbitrary coarse graining of the network. Compared to distance cutoff-based Hookean springs, the approach yields a more stable parametrization of coarse-grained ENMs for biomolecular dynamics. Traditional ENMs give rise to unbounded zero-frequency vibrations when (pseudo)atoms are connected to fewer than three neighbors. A large cutoff is therefore chosen in an ENM (about twice the average nearest-neighbor distance), resulting in many false-positive connections that reduce the spatial detail that can be resolved. More importantly, the required three-neighbor connectedness also limits the coarse graining, i.e., the network must be dense, even in the case of low-resolution structures that exhibit few spatial features. The new BTS model achieves such coarse graining by extending the ENM potential to include three-and four-atom interactions (bending and twisting, respectively) in addition to the traditional two-atom stretching. Thus, the BTS model enables reliable modeling of any three-dimensional graph irrespective of the atom connectedness. The additional potential terms were parametrized using continuum elastic theory of elastic rods, and the distance cutoff was replaced by a competitive Hebb connection rule, setting all free parameters in the model. We validate the approach on a carbon-alpha representation of adenylate kinase and illustrate its use with electron microscopy maps of E. coli RNA polymerase, E. coli ribosome, and eukaryotic chaperonin containing T-complex polypeptide 1, which were difficult to model with traditional ENMs. For adenylate kinase, we find excellent reproduction (>90% overlap) of the ENM modes and B factors
Modeling of fluctuating reaction networks
Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press
Arjun Verma
2016-07-01
Full Text Available We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed.
Verma, Arjun; Fratto, Brian E; Privman, Vladimir; Katz, Evgeny
2016-01-01
We consider flow systems that have been utilized for small-scale biomolecular computing and digital signal processing in binary-operating biosensors. Signal measurement is optimized by designing a flow-reversal cuvette and analyzing the experimental data to theoretically extract the pulse shape, as well as reveal the level of noise it possesses. Noise reduction is then carried out numerically. We conclude that this can be accomplished physically via the addition of properly designed well-mixing flow-reversal cell(s) as an integral part of the flow system. This approach should enable improved networking capabilities and potentially not only digital but analog signal-processing in such systems. Possible applications in complex biocomputing networks and various sense-and-act systems are discussed. PMID:27399702
Modelling of biochemical reaction networks
Gloppen Jørgensen, Arne Gunnar
2011-01-01
This report investigates signalling in reaction kinetic networks. The main topic is signalling between a substance being controlled by another substance and how this can be related to control theory. Different types of so-called natural controllers are compared and certain properties are investigated. Natural controllers are models on how a catalyst enzyme controls, for example the concentration, of a substance. There are sixteen different combinations of signalling between these substanc...
Programming in Biomolecular Computation:
Hartmann, Lars; Jones, Neil; Simonsen, Jakob Grue;
2011-01-01
Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We identify a number of common features in programming that seem...... conspicuously absent from the literature on biomolecular computing; to partially redress this absence, we introduce a model of computation that is evidently programmable, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined...... by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only executable, but are also compilable and interpretable. It is universal: all computable functions can be computed (in natural ways...
Programmability of Chemical Reaction Networks
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.
The Interplay of Intrinsic and Extrinsic Bounded Noises in Biomolecular Networks
Caravagna, G; Mauri, G; D'Onofrio, A.
2013-01-01
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 genetic network. The influence of intrinsic and extrinsic noises on genetic 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 perturbation...
Modeling and Analysis of Abnormality Detection in Biomolecular Nano-Networks
Ghavami, Siavash; Lahouti, Farshad; Masoudi-Nejad, Ali
2012-01-01
A scheme for detection of abnormality in molecular nano-networks is proposed. This is motivated by the fact that early diagnosis, classification and detection of diseases such as cancer play a crucial role in their successful treatment. The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells as abnormality that is captured...
Recep Colak
Full Text Available BACKGROUND: Computational prediction of functionally related groups of genes (functional modules from large-scale data is an important issue in computational biology. Gene expression experiments and interaction networks are well studied large-scale data sources, available for many not yet exhaustively annotated organisms. It has been well established, when analyzing these two data sources jointly, modules are often reflected by highly interconnected (dense regions in the interaction networks whose participating genes are co-expressed. However, the tractability of the problem had remained unclear and methods by which to exhaustively search for such constellations had not been presented. METHODOLOGY/PRINCIPAL FINDINGS: We provide an algorithmic framework, referred to as Densely Connected Biclustering (DECOB, by which the aforementioned search problem becomes tractable. To benchmark the predictive power inherent to the approach, we computed all co-expressed, dense regions in physical protein and genetic interaction networks from human and yeast. An automatized filtering procedure reduces our output which results in smaller collections of modules, comparable to state-of-the-art approaches. Our results performed favorably in a fair benchmarking competition which adheres to standard criteria. We demonstrate the usefulness of an exhaustive module search, by using the unreduced output to more quickly perform GO term related function prediction tasks. We point out the advantages of our exhaustive output by predicting functional relationships using two examples. CONCLUSION/SIGNIFICANCE: We demonstrate that the computation of all densely connected and co-expressed regions in interaction networks is an approach to module discovery of considerable value. Beyond confirming the well settled hypothesis that such co-expressed, densely connected interaction network regions reflect functional modules, we open up novel computational ways to comprehensively analyze
Modeling and Analysis of Abnormality Detection in Biomolecular Nano-Networks
Ghavami, Siavash; Masoudi-Nejad, Ali
2012-01-01
A scheme for detection of abnormality in molecular nano-networks is proposed. This is motivated by the fact that early diagnosis, classification and detection of diseases such as cancer play a crucial role in their successful treatment. The proposed nano-abnormality detection scheme (NADS) comprises of a two-tier network of sensor nano-machines (SNMs) in the first tier and a data gathering node (DGN) at the sink. The SNMs detect the presence of competitor cells as abnormality that is captured by variations in parameters of a nano-communications channel. In the second step, the SNMs transmit micro-scale messages over a noisy micro communications channel (MCC) to the DGN, where a decision is made upon fusing the received signals. The detection performance of each SNM is analyzed by setting up a Neyman-Pearson test. Next, taking into account the effect of the MCC, the overall performance of the proposed NADS is quantified in terms of probabilities of misdetection and false alarm. A design problem is formulated, ...
Živković, J.; Mitrović, M.; Janssen, L.; Heus, H. A.; Tadić, B.; Speller, S.
2010-03-01
Investigations of bonds between single molecules and molecular complexes by dynamic force spectroscopy are subject to large fluctuations at nanoscale and possible aspecific binding, which mask the experimental output. Big efforts are devoted to develop methods for the effective selection of the relevant experimental data, before the quantitative analysis of bond parameters. Here we present a methodology which is based on the application of graph theory. The force-distance curves corresponding to repeated pulling events are mapped onto their correlation network (mathematical graph). On these graphs the groups of similar curves appear as topological modules, which are identified using the spectral analysis of graphs. We demonstrate the approach by analyzing a large ensemble of the force-distance curves measured on: ssDNA-ssDNA, peptide-RNA (from HIV1), and peptide-Au surface systems. Within our data sets the methodology systematically separates subgroups of curves which are related to different types of intermolecular interactions and to spatial arrangements in which the molecules are brought together and/or pulling speeds. This demonstrates the sensitivity of the method to the spatial degrees of freedom, suggesting potential applications in the case of large molecular complexes and situations with multiple binding sites.
Genetic Recombination as a Chemical Reaction Network
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.
Stochastic thermodynamics of chemical reaction networks
Schmiedl, Tim; Seifert, Udo
2006-01-01
For chemical reaction networks described by a master equation, we define energy and entropy on a stochastic trajectory and develop a consistent nonequilibrium thermodynamic description along a single stochastic trajectory of reaction events. A first-law like energy balance relates internal energy, applied (chemical) work and dissipated heat for every single reaction. Entropy production along a single trajectory involves a sum over changes in the entropy of the network itself and the entropy o...
A network dynamics approach to chemical reaction networks
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
Limits for Stochastic Reaction Networks
Cappelletti, Daniele
Reaction systems have been introduced in the 70s to model biochemical systems. Nowadays their range of applications has increased and they are fruitfully used in dierent elds. The concept is simple: some chemical species react, the set of chemical reactions form a graph and a rate function is...... associated with each reaction. Such functions describe the speed of the dierent reactions, or their propensities. Two modelling regimes are then available: the evolution of the dierent species concentrations can be deterministically modelled through a system of ODE, while the counts of the dierent species at...... a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along...
Multilayer Network Analysis of Nuclear Reactions.
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
Multilayer Network Analysis of Nuclear Reactions
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
Multilayer network analysis of nuclear reactions
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.
Programming in Biomolecular Computation
Hartmann, Lars; Jones, Neil; Simonsen, Jakob Grue
2010-01-01
Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We introduce a model of computation that is evidently programmable......, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only...... in a strong sense: a universal algorithm exists, that is able to execute any program, and is not asymptotically inefficient. A prototype model has been implemented (for now in silico on a conventional computer). This work opens new perspectives on just how computation may be specified at the biological level....
Law of Localization in Chemical Reaction Networks
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.
Law of localization in chemical reaction networks
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.
Markovian Dynamics on Complex Reaction Networks
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...
Markovian dynamics on complex reaction networks
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.
Markovian dynamics on complex reaction networks
Goutsias, J.; Jenkinson, G.
2013-08-01
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 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.
A network dynamics approach to chemical reaction networks
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.
Deciphering Time Scale Hierarchy in Reaction Networks.
Nagahata, Yutaka; Maeda, Satoshi; Teramoto, Hiroshi; Horiyama, Takashi; Taketsugu, Tetsuya; Komatsuzaki, Tamiki
2016-03-01
Markovian dynamics on complex reaction networks are one of the most intriguing subjects in a wide range of research fields including chemical reactions, biological physics, and ecology. To represent the global kinetics from one node (corresponding to a basin on an energy landscape) to another requires information on multiple pathways that directly or indirectly connect these two nodes through the entire network. In this paper we present a scheme to extract a hierarchical set of global transition states (TSs) over a discrete-time Markov chain derived from first-order rate equations. The TSs can naturally take into account the multiple pathways connecting any pair of nodes. We also propose a new type of disconnectivity graph (DG) to capture the hierarchical organization of different time scales of reactions that can capture changes in the network due to changes in the time scale of observation. The crux is the introduction of the minimum conductance cut (MCC) in graph clustering, corresponding to the dividing surface across the network having the "smallest" transition probability between two disjoint subnetworks (superbasins on the energy landscape) in the network. We present a new combinatorial search algorithm for finding this MCC. We apply our method to a reaction network of Claisen rearrangement of allyl vinyl ether that consists of 23 nodes and 66 links (saddles on the energy landscape) connecting them. We compare the kinetic properties of our DG to those of the transition matrix of the rate equations and show that our graph can properly reveal the hierarchical organization of time scales in a network. PMID:26641663
An updated nuclear reaction network for BBN
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.
Mean field interaction in biochemical reaction networks
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.
Catalytic reaction dynamics in inhomogeneous networks.
Watanabe, Akitomo; Yakubo, Kousuke
2014-05-01
Biochemical reactions in a cell can be modeled by a catalytic reaction network (CRN). It has been reported that catalytic chain reactions occur intermittently in the CRN with a homogeneous random-graph topology and its avalanche-size distribution obeys a power law with the exponent 4/3 [A. Awazu and K. Kaneko, Phys. Rev. E 80, 010902(R) (2009)]. This fact indicates that the catalytic reaction dynamics in homogeneous CRNs exhibits self-organized criticality (SOC). Structures of actual CRNs are, however, known to be highly inhomogeneous. We study the influence of various types of inhomogeneities found in real-world metabolic networks on the universality class of SOC. Our numerical results clarify that SOC keeps its universality class even for networks possessing structural inhomogeneities such as the scale-free property, community structures, and degree correlations. In contrast, if the CRN has inhomogeneous catalytic functionality, the universality class of SOC depends on how widely distributed the number of reaction paths catalyzed by a single chemical species is. PMID:25353843
Biomolecular Science (Fact Sheet)
2012-04-01
A brief fact sheet about NREL Photobiology and Biomolecular Science. The research goal of NREL's Biomolecular Science is to enable cost-competitive advanced lignocellulosic biofuels production by understanding the science critical for overcoming biomass recalcitrance and developing new product and product intermediate pathways. NREL's Photobiology focuses on understanding the capture of solar energy in photosynthetic systems and its use in converting carbon dioxide and water directly into hydrogen and advanced biofuels.
Performance Improvements for Nuclear Reaction Network Integration
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...
Neural Networks in Chemical Reaction Dynamics
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
Ubiquitous "glassy" relaxation in catalytic reaction networks
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...
Ubiquitous ``glassy'' relaxation in catalytic reaction networks
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.
From Catalytic Reaction Networks to Protocells
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.
Bartolini, Desirée; Galli, Francesco
2016-04-15
Glutathione S-transferase P (GSTP), and possibly other members of the subfamily of cytosolic GSTs, are increasingly proposed to have roles far beyond the classical GSH-dependent enzymatic detoxification of electrophilic metabolites and xenobiotics. Emerging evidence suggests that these are essential components of the redox sensing and signaling platform of cells. GSTP monomers physically interact with cellular proteins, such as other cytosolic GSTs, signaling kinases and the membrane peroxidase peroxiredoxin 6. Other interactions reported in literature include that with regulatory proteins such as Fanconi anemia complementation group C protein, transglutaminase 2 and several members of the keratin family of genes. Transcription factors downstream of inflammatory and oxidative stress pathways, namely STAT3 and Nrf2, were recently identified to be further components of this interactome. Together these pieces of evidence suggest the existence of a regulatory biomolecular network in which GSTP represents a node of functional convergence and coordination of signaling and transcription proteins, namely the "GSTP interactome", associated with key cellular processes such as cell cycle regulation and the stress response. These aspects and the methodological approach to explore the cellular interactome(s) are discussed in this review paper. PMID:26922696
Graphical reduction of reaction networks by linear elimination of species
Sáez, Meritxell; Wiuf, Carsten; Feliu, Elisenda
2015-01-01
We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties of the...
Discreteness-Induced Criticality in Random Catalytic Reaction Networks
Awazu, Akinori; Kaneko, Kunihiko
2009-01-01
Universal intermittent dynamics in a random catalytic reaction network, induced by smallness in the molecule number is reported. Stochastic simulations for a random catalytic reaction network subject to a flow of chemicals show that the system undergoes a transition from a stationary to an intermittent reaction phase when the flow rate is decreased. In the intermittent reaction phase, two temporal regimes with active and halted reactions alternate. The number frequency of reaction events at e...
Lu, Benzhuo; Zhou, Y C
2011-05-18
The effects of finite particle size on electrostatics, density profiles, and diffusion have been a long existing topic in the study of ionic solution. The previous size-modified Poisson-Boltzmann and Poisson-Nernst-Planck models are revisited in this article. In contrast to many previous works that can only treat particle species with a single uniform size or two sizes, we generalize the Borukhov model to obtain a size-modified Poisson-Nernst-Planck (SMPNP) model that is able to treat nonuniform particle sizes. The numerical tractability of the model is demonstrated as well. The main contributions of this study are as follows. 1), We show that an (arbitrarily) size-modified PB model is indeed implied by the SMPNP equations under certain boundary/interface conditions, and can be reproduced through numerical solutions of the SMPNP. 2), The size effects in the SMPNP effectively reduce the densities of highly concentrated counterions around the biomolecule. 3), The SMPNP is applied to the diffusion-reaction process for the first time, to our knowledge. In the case of low substrate density near the enzyme reactive site, it is observed that the rate coefficients predicted by SMPNP model are considerably larger than those by the PNP model, suggesting both ions and substrates are subject to finite size effects. 4), An accurate finite element method and a convergent Gummel iteration are developed for the numerical solution of the completely coupled nonlinear system of SMPNP equations. PMID:21575582
Lu, Benzhuo; Zhou, Y.C.
2011-01-01
The effects of finite particle size on electrostatics, density profiles, and diffusion have been a long existing topic in the study of ionic solution. The previous size-modified Poisson-Boltzmann and Poisson-Nernst-Planck models are revisited in this article. In contrast to many previous works that can only treat particle species with a single uniform size or two sizes, we generalize the Borukhov model to obtain a size-modified Poisson-Nernst-Planck (SMPNP) model that is able to treat nonuniform particle sizes. The numerical tractability of the model is demonstrated as well. The main contributions of this study are as follows. 1), We show that an (arbitrarily) size-modified PB model is indeed implied by the SMPNP equations under certain boundary/interface conditions, and can be reproduced through numerical solutions of the SMPNP. 2), The size effects in the SMPNP effectively reduce the densities of highly concentrated counterions around the biomolecule. 3), The SMPNP is applied to the diffusion-reaction process for the first time, to our knowledge. In the case of low substrate density near the enzyme reactive site, it is observed that the rate coefficients predicted by SMPNP model are considerably larger than those by the PNP model, suggesting both ions and substrates are subject to finite size effects. 4), An accurate finite element method and a convergent Gummel iteration are developed for the numerical solution of the completely coupled nonlinear system of SMPNP equations. PMID:21575582
Open complex-balanced mass action chemical reaction networks
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.
Guerette, Paul A; Hoon, Shawn; Ding, Dawei; Amini, Shahrouz; Masic, Admir; Ravi, Vydianathan; Venkatesh, Byrappa; Weaver, James C; Miserez, Ali
2014-07-22
The predatory efficiency of squid and cuttlefish (superorder Decapodiformes) is enhanced by robust Sucker Ring Teeth (SRT) that perform grappling functions during prey capture. Here, we show that SRT are composed entirely of related structural “suckerin” proteins whose modular designs enable the formation of nanoconfined β-sheet-reinforced polymer networks. Thirty-seven previously undiscovered suckerins were identified from transcriptomes assembled from three distantly related decapodiform cephalopods. Similarity in modular sequence design and exon–intron architecture suggests that suckerins are encoded by a multigene family. Phylogenetic analysis supports this view, revealing that suckerin genes originated in a common ancestor ~350 MYa and indicating that nanoconfined β-sheet reinforcement is an ancient strategy to create robust bulk biomaterials. X-ray diffraction, nanomechanical, and micro-Raman spectroscopy measurements confirm that the modular design of the suckerins facilitates the formation of β-sheets of precise nanoscale dimensions and enables their assembly into structurally robust supramolecular networks stabilized by cooperative hydrogen bonding. The suckerin gene family has likely played a key role in the evolutionary success of decapodiform cephalopods and provides a large molecular toolbox for biomimetic materials engineering. PMID:24911543
New techniques for integrating nuclear reaction networks
The development of high resolution stellar models in multiple dimensions has moved us one step closer to answering fundamental questions of how stars burn their fuel. These models rely on solving a computationally expensive set of conservation, transportation, and energy production equations, the latter of which is determined by a complex network of nuclear reactions. The size of this network is constrained by the nature of the stellar environment in question and whether detailed nucleosynthesis yields are required. Alternatively, the nucleosynthesis yields can be computed by decoupling the calculation into a post-processing nucleosynthesis part, which relies on the output of a simplified hydrodynamics-focused calculation. Here, we investigate improving post-processing calculation performance by using more advanced integration methods than those typically adopted in nuclear astrophysics research. These are the Bader-Deuflehard multi-step method and the Gear backward differentiation method, which are compared with the traditional Wagoner two-step method. Integration method performance is quantified for a range of stellar environment models including Novae, X-ray bursts, hydrostatic core helium burning, and explosive hydrogen-helium burning in white dwarf merger events. We show that by using these methods, nucleosynthesis integration accuracy can be greatly improved and computation time can be significantly reduced by up to two orders of magnitude. Applicability of the integration methods to full hydrodynamical models will also be outlined. (author)
Role of biomolecular logic systems in biosensors and bioactuators
Mailloux, Shay; Katz, Evgeny
2014-09-01
An overview of recent advances in biosensors and bioactuators based on biocomputing systems is presented. Biosensors digitally process multiple biochemical signals through Boolean logic networks of coupled biomolecular reactions and produce an output in the form of a YES/NO response. Compared to traditional single-analyte sensing devices, the biocomputing approach enables high-fidelity multianalyte biosensing, which is particularly beneficial for biomedical applications. Multisignal digital biosensors thus promise advances in rapid diagnosis and treatment of diseases by processing complex patterns of physiological biomarkers. Specifically, they can provide timely detection and alert medical personnel of medical emergencies together with immediate therapeutic intervention. Application of the biocomputing concept has been successfully demonstrated for systems performing logic analysis of biomarkers corresponding to different injuries, particularly as exemplified for liver injury. Wide-ranging applications of multianalyte digital biosensors in medicine, environmental monitoring, and homeland security are anticipated. "Smart" bioactuators, for signal-triggered drug release, for example, were designed by interfacing switchable electrodes with biocomputing systems. Integration of biosensing and bioactuating systems with biomolecular information processing systems advances the potential for further scientific innovations and various practical applications.
Biomolecular logic systems: applications to biosensors and bioactuators
Katz, Evgeny
2014-05-01
The paper presents an overview of recent advances in biosensors and bioactuators based on the biocomputing concept. Novel biosensors digitally process multiple biochemical signals through Boolean logic networks of coupled biomolecular reactions and produce output in the form of YES/NO response. Compared to traditional single-analyte sensing devices, biocomputing approach enables a high-fidelity multi-analyte biosensing, particularly beneficial for biomedical applications. Multi-signal digital biosensors thus promise advances in rapid diagnosis and treatment of diseases by processing complex patterns of physiological biomarkers. Specifically, they can provide timely detection and alert to medical emergencies, along with an immediate therapeutic intervention. Application of the biocomputing concept has been successfully demonstrated for systems performing logic analysis of biomarkers corresponding to different injuries, particularly exemplified for liver injury. Wide-ranging applications of multi-analyte digital biosensors in medicine, environmental monitoring and homeland security are anticipated. "Smart" bioactuators, for example for signal-triggered drug release, were designed by interfacing switchable electrodes and biocomputing systems. Integration of novel biosensing and bioactuating systems with the biomolecular information processing systems keeps promise for further scientific advances and numerous practical applications.
Deterministic Function Computation with Chemical Reaction Networks
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|).
Mixture of experts models to exploit global sequence similarity on biomolecular sequence labeling
2009-01-01
Background Identification of functionally important sites in biomolecular sequences has broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Experimental determination of such sites lags far behind the number of known biomolecular sequences. Hence, there is a need to develop reliable computational methods for identifying functionally important sites from biomolecular sequences. Results We present a mixture of experts approach to b...
Membrane-based biomolecular smart materials
Membrane-based biomolecular materials are a new class of smart material that feature networks of artificial lipid bilayers contained within durable synthetic substrates. Bilayers contained within this modular material platform provide an environment that can be tailored to host an enormous diversity of functional biomolecules, where the functionality of the global material system depends on the type(s) and organization(s) of the biomolecules that are chosen. In this paper, we review a series of biomolecular material platforms developed recently within the Leo Group at Virginia Tech and we discuss several novel coupling mechanisms provided by these hybrid material systems. The platforms developed demonstrate that the functions of biomolecules and the properties of synthetic materials can be combined to operate in concert, and the examples provided demonstrate how the formation and properties of a lipid bilayer can respond to a variety of stimuli including mechanical forces and electric fields
Hagen, Wilfred Raymond
2008-01-01
Comprehensive, Up-to-Date Coverage of Spectroscopy Theory and its Applications to Biological SystemsAlthough a multitude of books have been published about spectroscopy, most of them only occasionally refer to biological systems and the specific problems of biomolecular EPR (bioEPR). Biomolecular EPR Spectroscopy provides a practical introduction to bioEPR and demonstrates how this remarkable tool allows researchers to delve into the structural, functional, and analytical analysis of paramagnetic molecules found in the biochemistry of all species on the planet. A Must-Have Reference in an Intrinsically Multidisciplinary FieldThis authoritative reference seamlessly covers all important bioEPR applications, including low-spin and high-spin metalloproteins, spin traps and spin lables, interaction between active sites, and redox systems. It is loaded with practical tricks as well as do's and don'ts that are based on the author's 30 years of experience in the field. The book also comes with an unprecedented set of...
On the Complexity of Reconstructing Chemical Reaction Networks
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...
Weber's Law in Autocatalytic Reaction Networks
Inoue, Masayo; Kaneko, Kunihiko
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...
Neural networks for the prediction organic chemistry reactions
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.
Output-input ratio in thermally fluctuating biomolecular machines.
Kurzynski, Michal; Torchala, Mieczyslaw; Chelminiak, Przemyslaw
2014-01-01
Biological molecular machines are proteins that operate under isothermal conditions and hence are referred to as free energy transducers. They can be formally considered as enzymes that simultaneously catalyze two chemical reactions: the free energy-donating (input) reaction and the free energy-accepting (output) one. Most if not all biologically active proteins display a slow stochastic dynamics of transitions between a variety of conformational substates composing their native state. This makes the description of the enzymatic reaction kinetics in terms of conventional rate constants insufficient. In the steady state, upon taking advantage of the assumption that each reaction proceeds through a single pair (the gate) of transition conformational substates of the enzyme-substrates complex, the degree of coupling between the output and the input reaction fluxes has been expressed in terms of the mean first-passage times on a conformational transition network between the distinguished substates. The theory is confronted with the results of random-walk simulations on the five-dimensional hypercube. The formal proof is given that, for single input and output gates, the output-input degree of coupling cannot exceed unity. As some experiments suggest such exceeding, looking for the conditions for increasing the degree of coupling value over unity challenges the theory. Performed simulations of random walks on several model networks involving more extended gates indicate that the case of the degree of coupling value higher than 1 is realized in a natural way on critical branching trees extended by long-range shortcuts. Such networks are scale-free and display the property of the small world. For short-range shortcuts, the networks are scale-free and fractal, representing a reasonable model for biomolecular machines displaying tight coupling, i.e., the degree of coupling equal exactly to unity. A hypothesis is stated that the protein conformational transition networks, as
DNA reaction networks: Providing a panoramic view
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.
Analyzing biomolecular interactions by variable angle ellipsometry
Wu, Jiun-Yan; Lee, Chih-Kung; Lee, J. H.; Shiue, Shuen-Chen; Lee, Shu-Sheng; Lin, Shiming
2001-10-01
In this paper, an innovative ellipsometer is developed and applied to metrology of the biomolecular interaction on a protein biochip. Both the theory, optical and opto-mechanical configurations of this newly developed ellipsometer and methodologies adopted in system design to improve the system performance are presented. It will be shown that by measuring the ellipsometric parameters, the corresponding concentration variation in biochemical reaction can be calculated according to stoichiometry analysis. By applying the variable angle ellipsometry to analysis of a multi-layered sample, the thickness and concentration are resolved. It is believed that the newly developed ellipsometer biosensor is able to undertake an accurate measurement on biomedical interaction.
Cellular metabolic network analysis: discovering important reactions in Treponema pallidum.
Chen, Xueying; Zhao, Min; Qu, Hong
2015-01-01
T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pallidum, critical reactions are identified. As a comparison, we also apply the analytical approaches to the metabolic network of H. pylori to find coregulated drug targets and unique drug targets for different microorganisms. Based on the clustering results, all reactions are further classified into various roles. Therefore, the general picture of their metabolic network is obtained and two types of reactions, both of which are involved in nucleic acid metabolism, are found to be essential for T. pallidum. It is also discovered that both hubs of reactions and the isolated reactions in purine and pyrimidine metabolisms play important roles in T. pallidum. These reactions could be potential drug targets for treating syphilis. PMID:26495292
Turing instability in reaction-diffusion models on complex networks
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.
Integration of metabolome data with metabolic networks reveals reporter reactions
Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen; Ülgen, Kutlu Özergin; Kirdar, Betül; Nielsen, Jens
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...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...
Cellular Metabolic Network Analysis: Discovering Important Reactions in Treponema pallidum
Xueying Chen; Min Zhao; Hong Qu
2015-01-01
T. pallidum, the syphilis-causing pathogen, performs very differently in metabolism compared with other bacterial pathogens. The desire for safe and effective vaccine of syphilis requests identification of important steps in T. pallidum's metabolism. Here, we apply Flux Balance Analysis to represent the reactions quantitatively. Thus, it is possible to cluster all reactions in T. pallidum. By calculating minimal cut sets and analyzing topological structure for the metabolic network of T. pall...
ARWAR: A network approach for predicting Adverse Drug Reactions.
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. PMID:26638149
On the steady states of weakly reversible chemical reaction networks
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.
Injectivity, multiple zeros, and multistationarity in reaction networks
Feliu, Elisenda
2015-01-01
Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parametrized by many (unknown) parameters....
Molecular codes in biological and chemical reaction networks.
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.
Complex and detailed balancing of chemical reaction networks revisited
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
Chemical reaction network approaches to Biochemical Systems Theory.
Arceo, Carlene Perpetua P; Jose, Editha C; Marin-Sanguino, Alberto; Mendoza, Eduardo R
2015-11-01
This paper provides a framework to represent a Biochemical Systems Theory (BST) model (in either GMA or S-system form) as a chemical reaction network with power law kinetics. Using this representation, some basic properties and the application of recent results of Chemical Reaction Network Theory regarding steady states of such systems are shown. In particular, Injectivity Theory, including network concordance [36] and the Jacobian Determinant Criterion [43], a "Lifting Theorem" for steady states [26] and the comprehensive results of Müller and Regensburger [31] on complex balanced equilibria are discussed. A partial extension of a recent Emulation Theorem of Cardelli for mass action systems [3] is derived for a subclass of power law kinetic systems. However, it is also shown that the GMA and S-system models of human purine metabolism [10] do not display the reactant-determined kinetics assumed by Müller and Regensburger and hence only a subset of BST models can be handled with their approach. Moreover, since the reaction networks underlying many BST models are not weakly reversible, results for non-complex balanced equilibria are also needed. PMID:26363083
Stochastic analysis of complex reaction networks using binomial moment equations.
Barzel, Baruch; Biham, Ofer
2012-09-01
The stochastic analysis of complex reaction networks is a difficult problem because the number of microscopic states in such systems increases exponentially with the number of reactive species. Direct integration of the master equation is thus infeasible and is most often replaced by Monte Carlo simulations. While Monte Carlo simulations are a highly effective tool, equation-based formulations are more amenable to analytical treatment and may provide deeper insight into the dynamics of the network. Here, we present a highly efficient equation-based method for the analysis of stochastic reaction networks. The method is based on the recently introduced binomial moment equations [Barzel and Biham, Phys. Rev. Lett. 106, 150602 (2011)]. The binomial moments are linear combinations of the ordinary moments of the probability distribution function of the population sizes of the interacting species. They capture the essential combinatorics of the reaction processes reflecting their stoichiometric structure. This leads to a simple and transparent form of the equations, and allows a highly efficient and surprisingly simple truncation scheme. Unlike ordinary moment equations, in which the inclusion of high order moments is prohibitively complicated, the binomial moment equations can be easily constructed up to any desired order. The result is a set of equations that enables the stochastic analysis of complex reaction networks under a broad range of conditions. The number of equations is dramatically reduced from the exponential proliferation of the master equation to a polynomial (and often quadratic) dependence on the number of reactive species in the binomial moment equations. The aim of this paper is twofold: to present a complete derivation of the binomial moment equations; to demonstrate the applicability of the moment equations for a representative set of example networks, in which stochastic effects play an important role. PMID:23030885
Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry
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...
Graphical reduction of reaction networks by linear elimination of species
Sáez, Meritxell; Wiuf, Carsten; Feliu, Elisenda
2015-01-01
The quasi-steady state approximation and time-scale separation are commonly applied methods to simplify models of biochemical reaction networks based on ordinary differential equations (ODEs). The concentrations of the "fast" species are assumed effectively to be at steady state with respect to the "slow" species. Under this assumption the steady state equations can be used to eliminate the "fast" variables and a new ODE system with only the slow species can be obtained. We interpret a reduce...
Variable elimination in chemical reaction networks with mass action kinetics
Feliu, Elisenda; Wiuf, Carsten
2011-01-01
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 of variables. The procedure reduces the variables in the system to a set of "core" variables by eliminating variables corresponding to a set of non-interacting species. The steady states are paramet...
A chemical reaction network solver for the astrophysics code NIRVANA
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
Spreading out of perturbations in reversible reaction networks.
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
Spreading out of perturbations in reversible reaction networks
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.
Reduced models of networks of coupled enzymatic reactions
Kumar, Ajit
2011-01-01
The Michaelis-Menten equation has played a central role in our understanding of biochemical processes. It has long been understood how this equation approximates the dynamics of irreversible enzymatic reactions. However, a similar approximation in the case of networks, where the product of one reaction can act as an enzyme in another, has not been fully developed. Here we rigorously derive such an approximation in a class of coupled enzymatic networks where the individual interactions are of Michaelis-Menten type. We show that the sufficient conditions for the validity of the total quasi steady state assumption (tQSSA), obtained in a single protein case by Borghans, de Boer and Segel can be extended to sufficient conditions for the validity of the tQSSA in a large class of enzymatic networks. Secondly, we derive reduced equations that approximate the network's dynamics and involve only protein concentrations. This significantly reduces the number of equations necessary to model such systems. We prove the vali...
St Laurent Georges
2010-03-01
signaling network is capable of performing information processing in a robust manner, a functional property that is independent of the signaling task required to be executed. Nevertheless, it was found that the robust performance of the network is not solely determined by its design principle (topology, but this may be heavily dependent on the network's current position in biochemical reaction space. Ultimately, our results enabled us the identification of key rate limiting steps which most effectively control the performance of the system under diverse dynamical regimes. Conclusions Overall, our in silico study suggests that biologically relevant and non-intuitive aspects on the general behavior of a complex biomolecular network can be elucidated only when taking into account a wide spectrum of dynamical regimes attainable by the system. Most importantly, this strategy provides the means for a suitable assessment of the inherent variational constraints imposed by the structure of the system when systematically probing its parameter space.
Storing and analysing biomolecular contacts
Walter, Peter
2011-01-01
Biomolecular contacts play a crucial role in all areas of life. In particular, protein-protein (PP) interactions are essential for most processes in biological cells. Antigen-antibody recognition, enzyme substrate binding, hormone receptor binding, RNA splicing, DNA replication and signal transduction are just some examples for the rich variety of PP interactions. In the last years modern proteomic methods have helped to get a better understanding of the complexity within living cell and orga...
Models for solvated biomolecular structures
Cerutti, David
2007-01-01
Methods for estimating the structure and energetics of water around biomolecules are presented with the objective of improving the treatment of the biomolecular simulation environment as well as facilitating the use of complex, rigorous water models in the context of structure prediction problems that demand cheap solutions. Salt solutions around biomolecules are studied using an implicit solvent model with explicitly represented ions, revealing that the structure of the ion atmosphere is muc...
Bosonic reaction-diffusion processes on scale-free networks
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.
Recent developments in research on catalytic reaction networks
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.
A self-regulating biomolecular comparator for processing oscillatory signals.
Agrawal, Deepak K; Franco, Elisa; Schulman, Rebecca
2015-10-01
While many cellular processes are driven by biomolecular oscillators, precise control of a downstream on/off process by a biochemical oscillator signal can be difficult: over an oscillator's period, its output signal varies continuously between its amplitude limits and spends a significant fraction of the time at intermediate values between these limits. Further, the oscillator's output is often noisy, with particularly large variations in the amplitude. In electronic systems, an oscillating signal is generally processed by a downstream device such as a comparator that converts a potentially noisy oscillatory input into a square wave output that is predominantly in one of two well-defined on and off states. The comparator's output then controls downstream processes. We describe a method for constructing a synthetic biochemical device that likewise produces a square-wave-type biomolecular output for a variety of oscillatory inputs. The method relies on a separation of time scales between the slow rate of production of an oscillatory signal molecule and the fast rates of intermolecular binding and conformational changes. We show how to control the characteristics of the output by varying the concentrations of the species and the reaction rates. We then use this control to show how our approach could be applied to process different in vitro and in vivo biomolecular oscillators, including the p53-Mdm2 transcriptional oscillator and two types of in vitro transcriptional oscillators. These results demonstrate how modular biomolecular circuits could, in principle, be combined to build complex dynamical systems. The simplicity of our approach also suggests that natural molecular circuits may process some biomolecular oscillator outputs before they are applied downstream. PMID:26378119
Hierarchical feedback modules and reaction hubs in cell signaling networks.
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.
Structural parameter identifiability analysis for dynamic reaction networks
Davidescu, Florin Paul; Jørgensen, Sten Bay
2008-01-01
A fundamental problem in model identification is to investigate whether unknown parameters in a given model structure potentially can be uniquely recovered from experimental data. This issue of global or structural identifiability is essential during nonlinear first principles model development...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models. The...
Switching dynamics in reaction networks induced by molecular discreteness
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 concentrations on the system size, which is caused by transitions to discreteness-induced novel states
Adaptive hybrid simulations for multiscale stochastic reaction networks
The probability distribution describing the state of a Stochastic Reaction Network (SRN) evolves according to the Chemical Master Equation (CME). It is common to estimate its solution using Monte Carlo methods such as the Stochastic Simulation Algorithm (SSA). In many cases, these simulations can take an impractical amount of computational time. Therefore, many methods have been developed that approximate sample paths of the underlying stochastic process and estimate the solution of the CME. A prominent class of these methods include hybrid methods that partition the set of species and the set of reactions into discrete and continuous subsets. Such a partition separates the dynamics into a discrete and a continuous part. Simulating such a stochastic process can be computationally much easier than simulating the exact discrete stochastic process with SSA. Moreover, the quasi-stationary assumption to approximate the dynamics of fast subnetworks can be applied for certain classes of networks. However, as the dynamics of a SRN evolves, these partitions may have to be adapted during the simulation. We develop a hybrid method that approximates the solution of a CME by automatically partitioning the reactions and species sets into discrete and continuous components and applying the quasi-stationary assumption on identifiable fast subnetworks. Our method does not require any user intervention and it adapts to exploit the changing timescale separation between reactions and/or changing magnitudes of copy-numbers of constituent species. We demonstrate the efficiency of the proposed method by considering examples from systems biology and showing that very good approximations to the exact probability distributions can be achieved in significantly less computational time. This is especially the case for systems with oscillatory dynamics, where the system dynamics change considerably throughout the time-period of interest
COEL: A Cloud-based Reaction Network Simulator
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.
Exact results for noise power spectra in linear biochemical reaction networks
Warren, Patrick B.; Tanase-Nicola, Sorin; Wolde, Pieter Rein ten
2005-01-01
We present a simple method for determining the exact noise power spectra in linear chemical reaction networks. We apply the method to networks which are representative of biochemical processes such as gene expression and signal detection. Our results clarify how noise is transmitted by signal detection motifs, and indicate how to coarse-grain networks by the elimination of fast reactions.
Experimental (Network) and Evaluated Nuclear Reaction Data at NDS
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.
Variable elimination in chemical reaction networks with mass action kinetics
Feliu, Elisenda
2011-01-01
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 of variables. The procedure reduces the variables in the system to a set of "core" variables by eliminating variables corresponding to a set of non-interacting species. The steady states are parameterized algebraically by the core variables, and a graphical condition is given for when a steady state with positive core variables necessarily have all variables positive. Further, we characterize graphically the sets of eliminated variables that are constrained by a conservation law and show that this conservation law takes a specific form.
Integrative NMR for biomolecular research.
Lee, Woonghee; Cornilescu, Gabriel; Dashti, Hesam; Eghbalnia, Hamid R; Tonelli, Marco; Westler, William M; Butcher, Samuel E; Henzler-Wildman, Katherine A; Markley, John L
2016-04-01
NMR spectroscopy is a powerful technique for determining structural and functional features of biomolecules in physiological solution as well as for observing their intermolecular interactions in real-time. However, complex steps associated with its practice have made the approach daunting for non-specialists. We introduce an NMR platform that makes biomolecular NMR spectroscopy much more accessible by integrating tools, databases, web services, and video tutorials that can be launched by simple installation of NMRFAM software packages or using a cross-platform virtual machine that can be run on any standard laptop or desktop computer. The software package can be downloaded freely from the NMRFAM software download page ( http://pine.nmrfam.wisc.edu/download_packages.html ), and detailed instructions are available from the Integrative NMR Video Tutorial page ( http://pine.nmrfam.wisc.edu/integrative.html ). PMID:27023095
Microwave spectroscopy of biomolecular building blocks.
Alonso, José L; López, Juan C
2015-01-01
Microwave spectroscopy, considered as the most definitive gas phase structural probe, is able to distinguish between different conformational structures of a molecule, because they have unique spectroscopic constants and give rise to distinct individual rotational spectra.Previously, application of this technique was limited to molecular specimens possessing appreciable vapor pressures, thus discarding the possibility of studying many other molecules of biological importance, in particular those with high melting points, which had a tendency to undergo thermal reactions, and ultimately degradation, upon heating.Nowadays, the combination of laser ablation with Fourier transform microwave spectroscopy techniques, in supersonic jets, has enabled the gas-phase study of such systems. In this chapter, these techniques, including broadband spectroscopy, as well as results of their application into the study of the conformational panorama and structure of biomolecular building blocks, such as amino acids, nucleic bases, and monosaccharides, are briefly discussed, and with them, the tools for conformational assignation - rotational constants, nuclear quadrupole coupling interaction, and dipole moment. PMID:25721775
Biomolecular transport and separation in nanotubular networks.
Stachowiak, Jeanne C.; Stevens, Mark Jackson (Sandia National Laboratories, Albuquerque, NM); Robinson, David B.; Branda, Steven S.; Zendejas, Frank; Meagher, Robert J.; Sasaki, Darryl Yoshio; Bachand, George David (Sandia National Laboratories, Albuquerque, NM); Hayden, Carl C.; Sinha, Anupama; Abate, Elisa; Wang, Julia; Carroll-Portillo, Amanda (Sandia National Laboratories, Albuquerque, NM); Liu, Haiqing (Sandia National Laboratories, Albuquerque, NM)
2010-09-01
Cell membranes are dynamic substrates that achieve a diverse array of functions through multi-scale reconfigurations. We explore the morphological changes that occur upon protein interaction to model membrane systems that induce deformation of their planar structure to yield nanotube assemblies. In the two examples shown in this report we will describe the use of membrane adhesion and particle trajectory to form lipid nanotubes via mechanical stretching, and protein adsorption onto domains and the induction of membrane curvature through steric pressure. Through this work the relationship between membrane bending rigidity, protein affinity, and line tension of phase separated structures were examined and their relationship in biological membranes explored.
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks
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
[Advances in biomolecular machine: methane monooxygenases].
Lu, Jixue; Wang, Shizhen; Fang, Baishan
2015-07-01
Methane monooxygenases (MMO), regarded as "an amazing biomolecular machine", catalyze the oxidation of methane to methanol under aerobic conditions. MMO catalyze the oxidation of methane elaborately, which is a novel way to catalyze methane to methanol. Furthermore, MMO can inspire the biomolecular machine design. In this review, we introduced MMO including structure, gene and catalytic mechanism. The history and the taxonomy of MMO were also introduced. PMID:26647577
Defect reaction network in Si-doped InAs. Numerical predictions.
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
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 ...
Properties of Random Complex Chemical Reaction Networks and Their Relevance to Biological Toy Models
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...
Furusawa, Chikara; Kaneko, Kunihiko
2005-01-01
The evolutionary origin of universal statistics in biochemical reaction network is studied, to explain the power-law distribution of reaction links and the power-law distributions of chemical abundances. Using cell models with catalytic reaction network, we find evidence that the power-law distribution in abundances of chemicals emerges by the selection of cells with higher growth speeds. Through the further evolution, this inhomogeneity in chemical abundances is shown to be embedded in the d...
Ness, Robert O; Sachs, Karen; Vitek, Olga
2016-03-01
Causal inference, the task of uncovering regulatory relationships between components of biomolecular pathways and networks, is a primary goal of many high-throughput investigations. Statistical associations between observed protein concentrations can suggest an enticing number of hypotheses regarding the underlying causal interactions, but when do such associations reflect the underlying causal biomolecular mechanisms? The goal of this perspective is to provide suggestions for causal inference in large-scale experiments, which utilize high-throughput technologies such as mass-spectrometry-based proteomics. We describe in nontechnical terms the pitfalls of inference in large data sets and suggest methods to overcome these pitfalls and reliably find regulatory associations. PMID:26731284
Sensitivity of chemical reaction networks: a structural approach 3. Regular multimolecular systems
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 ...
Metabolome based reaction graphs of M. tuberculosis and M. leprae: a comparative network analysis.
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
Analysis of weblike network structures of directed graphs for chemical reactions in methane plasmas
Chemical reactions of molecular gases like methane are so complicated that a chart of decomposed and/or synthesized species originating from molecules in plasma resembles a weblike network in which we write down species and reactions among them. Here we consider properties of the network structures of chemical reactions in methane plasmas. In the network, atoms/molecules/radical species are assumed to form nodes and chemical reactions correspond to directed edges in the terminology of graph theory. Investigation of the centrality index reveals importance of CH3 in the global chemical reaction, and difference of an index for each radical species between cases with and without electrons clarifies that the electrons are at an influential position to tighten the network structure
Reduction of dynamical biochemical reactions networks in computational biology
Radulescu, O.; Gorban, A.N.; Zinovyev, A.; Noel, V.
2012-01-01
Biochemical networks are used in computational biology, to model mechanistic details of systems involved in cell signaling, metabolism, and regulation of gene expression. Parametric and structural uncertainty, as well as combinatorial explosion are strong obstacles against analyzing the dynamics of large models of this type. Multiscaleness, an important property of these networks, can be used to get past some of these obstacles. Networks with many well separated time scales, can be reduced to...
Conducting polymer based biomolecular electronic devices
B D Malhotra; Rahul Singhal
2003-08-01
Biomolecular electronics is rapidly evolving from physics, chemistry, biology, electronics and information technology. Organic materials such as proteins, pigments and conducting polymers have been considered as alternatives for carrying out the functions that are presently being performed by semiconductor silicon. Conducting polymers such as polypyrroles, polythiophenes and polyanilines have been projected for applications for a wide range of biomolecular electronic devices such as optical, electronic, drug-delivery, memory and biosensing devices. Our group has been actively working towards the application of conducting polymers to Schottky diodes, metal–insulator–semiconductor (MIS) devices and biosensors for the past 10 years. This paper is a review of some of the results obtained at our laboratory in the area of conducting polymer biomolecular electronics.
Unravelling the Maillard reaction network by multiresponse kinetic modelling
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...
Markov propagation of allosteric effects in biomolecular systems: application to GroEL–GroES
Chennubhotla, Chakra; Bahar, Ivet
2006-01-01
We introduce a novel approach for elucidating the potential pathways of allosteric communication in biomolecular systems. The methodology, based on Markov propagation of ‘information' across the structure, permits us to partition the network of interactions into soft clusters distinguished by their coherent stochastics. Probabilistic participation of residues in these clusters defines the communication patterns inherent to the network architecture. Application to bacterial chaperonin complex ...
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.
Ion irradiation and biomolecular radiation damage II. Indirect effect
Wang, Wei; Su, Wenhui
2010-01-01
It has been reported that damage of genome in a living cell by ionizing radiation is about one-third direct and two-thirds indirect. The former which has been introduced in our last paper, concerns direct energy deposition and ionizing reactions in the biomolecules; the latter results from radiation induced reactive species (mainly radicals) in the medium (mainly water) surrounding the biomolecules. In this review, a short description of ion implantation induced radical formation in water is presented. Then we summarize the aqueous radical reaction chemistry of DNA, protein and their components, followed by a brief introduction of biomolecular damage induced by secondary particles (ions and electron). Some downstream biological effects are also discussed.
On the graph and systems analysis of reversible chemical reaction networks with mass action kinetics
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
Perspective: Coarse-grained models for biomolecular systems
Noid, W. G.
2013-09-01
By focusing on essential features, while averaging over less important details, coarse-grained (CG) models provide significant computational and conceptual advantages with respect to more detailed models. Consequently, despite dramatic advances in computational methodologies and resources, CG models enjoy surging popularity and are becoming increasingly equal partners to atomically detailed models. This perspective surveys the rapidly developing landscape of CG models for biomolecular systems. In particular, this review seeks to provide a balanced, coherent, and unified presentation of several distinct approaches for developing CG models, including top-down, network-based, native-centric, knowledge-based, and bottom-up modeling strategies. The review summarizes their basic philosophies, theoretical foundations, typical applications, and recent developments. Additionally, the review identifies fundamental inter-relationships among the diverse approaches and discusses outstanding challenges in the field. When carefully applied and assessed, current CG models provide highly efficient means for investigating the biological consequences of basic physicochemical principles. Moreover, rigorous bottom-up approaches hold great promise for further improving the accuracy and scope of CG models for biomolecular systems.
Advances in integrative modeling of biomolecular complexes
Karaca, E.; Bonvin, A.M.J.J.
2013-01-01
High-resolution structural information is needed in order to unveil the underlying mechanistic of biomolecular function. Due to the technical limitations or the nature of the underlying complexes, acquiring atomic resolution information is difficult for many challenging systems, while, often, low-re
Flourescence from Gas-Phase Biomolecular Ions
Nielsen, Steen Brøndsted
2013-01-01
This chapter deals with measurements of fluorescence from electronically excited biomolecular ions where there are no interactions with an external environment. Biomolecules with no natural fluorophores are labelled with a dye for such experiments. First, some of the advantages, but also difficul...
Programming in Biomolecular Computation: Programs, Self-Interpretation and Visualisation
J.G. Simonsen
2011-01-01
Full Text Available Our goal is to provide a top-down approach to biomolecular computation. In spite of widespread discussion about connections between biology and computation, one question seems notable by its absence: Where are the programs? We identify a number of common features in programming that seem conspicuously absent from the literature on biomolecular computing; to partially redress this absence, we introduce a model of computation that is evidently programmable, by programs reminiscent of low-level computer machine code; and at the same time biologically plausible: its functioning is defined by a single and relatively small set of chemical-like reaction rules. Further properties: the model is stored-program: programs are the same as data, so programs are not only executable, but are also compilable and interpretable. It is universal: all computable functions can be computed (in natural ways and without arcane encodings of data and algorithm; it is also uniform: new ``hardware'' is not needed to solve new problems; and (last but not least it is Turing complete in a strong sense: a universal algorithm exists, that is able to execute any program, and is not asymptotically inefficient.
Turing-Hopf instability in biochemical reaction networks arising from pairs of subnetworks.
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
Preclusion of switch behavior in reaction networks with mass-action kinetics
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 of...... injective networks, that is, networks for which the species formation rate function is injective in the interior of the positive orthant within each stoichiometric class. We show that a network is injective if and only if the determinant of the Jacobian of a certain function does not vanish. The function...... 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....
Reaction-Diffusion Processes on Random and Scale-Free Networks
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.
Open chemical reaction networks, steady-state loads and Braess-like paradox
Banerjee, Kinshuk
2014-01-01
Open chemical reaction systems involve matter-exchange with the surroundings. As a result, species can accumulate inside a system during the course of the reaction. We study the role of network topology in governing the concentration build-up inside a fixed reaction volume at steady state, particularly focusing on the effect of additional paths. The problem is akin to that in traffic networks where an extra route, surprisingly, can increase the overall travel time. This is known as the Braess' paradox. Here, we report chemical analogues of such a paradox in suitably chosen reaction networks, where extra reaction step(s) can inflate the total concentration, denoted as `load', at steady state. It is shown that, such counter-intuitive behavior emerges in a qualitatively similar pattern in networks of varying complexities. We then explore how such extra routes affect the load in a biochemical scheme of uric acid degradation. From a thorough analysis of this network, we propose a functional role of some decomposit...
Minimal metabolic pathway structure is consistent with associated biomolecular interactions.
Bordbar, Aarash; Nagarajan, Harish; Lewis, Nathan E; Latif, Haythem; Ebrahim, Ali; Federowicz, Stephen; Schellenberger, Jan; Palsson, Bernhard O
2014-01-01
Pathways are a universal paradigm for functionally describing cellular processes. Even though advances in high-throughput data generation have transformed biology, the core of our biological understanding, and hence data interpretation, is still predicated on human-defined pathways. Here, we introduce an unbiased, pathway structure for genome-scale metabolic networks defined based on principles of parsimony that do not mimic canonical human-defined textbook pathways. Instead, these minimal pathways better describe multiple independent pathway-associated biomolecular interaction datasets suggesting a functional organization for metabolism based on parsimonious use of cellular components. We use the inherent predictive capability of these pathways to experimentally discover novel transcriptional regulatory interactions in Escherichia coli metabolism for three transcription factors, effectively doubling the known regulatory roles for Nac and MntR. This study suggests an underlying and fundamental principle in the evolutionary selection of pathway structures; namely, that pathways may be minimal, independent, and segregated. PMID:24987116
The US nuclear reaction data network. Summary of the first meeting, March 13 & 14 1996
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.
The US nuclear reaction data network. Summary of the first meeting, March 13 ampersand 14 1996
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
PREFACE: Radiation Damage in Biomolecular Systems (RADAM07)
McGuigan, Kevin G.
2008-03-01
, which include: theoretical, experimental, medical and computational physicists, radiation chemists, radiation biologists and microbiologists, among others. An important aspect of the previous 3 conferences in this series was to remove barriers between the different working groups and to encourage a more interdisciplinary approach to research collaborations. During RADAM_07 we could observe the success of these efforts. A large number of presentations were based on new collaborations, many funded under the COST STSM programme, and all presentations led to lively discussions. It is clear from the discussions following many of the presentations and at the poster sessions that Radiation Damage in Biomolecular Systems remains a topic of increasing interest, relevance and importance. The success of this conference as well as of the whole RADAM conference series reflects the growing international interest in the area of interactions of ionizing radiation with biomolecules. Despite the scheduled conclusion in September 2007 of COST Action P9 which has part-funded this, and previous RADAM meetings, the nature of the cross-disciplinary interactions and opportunities for collaborative research was deemed so successful and valuable by the assembled delegates that it was agreed that another such meeting should be held in Debrecen in Hungary from 13-15 June 2008 http://www.isa.au.dk/meetings/radam2008/programme.html. Additional information about RADAM07 programme is available on the conference web-page http://www.isa.au.dk/networks/cost/radam07/index.html. The Organizing Committee would like to thank all speakers, contributors, session chairs, referees and meeting staff for their efforts in making the RADAM07 successful. The local Organization Committee would like to thank Lorraine Monard and Margaret Nolan for their invaluable expertise in conference management. We also gratefully acknowledge the financial support of our sponsors - The Royal, College of Surgeons in Ireland (RCSI
Improvements in continuum modeling for biomolecular systems
Qiao, Yu
2015-01-01
Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson-Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of biological systems. Considerable efforts have been made to improve the continuum model to account for discrete particle interactions and to make progress in numerical methods to provide accurate and efficient simulation. This review will summarize recent main improvements in continuum modeling for biomolecular systems, with focus on the size-modified models, the coupling of the classical density functional theory and PNP equations, the coupling of polar and nonpolar interactions, and numerical progress.
A cascade reaction network mimicking the basic functional steps of adaptive immune response
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.
Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks
Anderson, David F.; Craciun, Gheorghe; Gopalkrishnan, Manoj; Wiuf, Carsten Henrik
2015-01-01
We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potent......We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non......-equilibrium potential of the stationary distribution of stochastically modeled complex balanced systems. We extend this result to general birth–death models and demonstrate via example that similar scaling limits can yield Lyapunov functions even for models that are not complex or detailed balanced, and may even have...
Report on the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres
This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. (author)
The origin of large molecules in primordial autocatalytic reaction networks
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...
Julio Michael Stern; Fabio Nakano
2014-01-01
This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of...
On the graph and systems analysis of reversible chemical reaction networks with mass action kinetics
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.
The cost and capacity of signaling in the Escherichia coli protein reaction network
In systems biology new ways are required to analyze the large amount of existing data on regulation of cellular processes. Recent work can be roughly classified into either dynamical models of well-described subsystems, or coarse-grained descriptions of the topology of the molecular networks at the scale of the whole organism. In order to bridge these two disparate approaches one needs to develop simplified descriptions of dynamics and topological measures which address the propagation of signals in molecular networks. Transmission of a signal across a reaction node depends on the presence of other reactants. It will typically be more demanding to transmit a signal across a reaction node with more input links. Sending signals along a path with several subsequent reaction nodes also increases the constraints on the presence of other proteins in the overall network. Therefore counting in and out links along reactions of a potential pathway can give insight into the signaling properties of a particular molecular network. Here, we consider the directed network of protein regulation in E. coli, characterizing its modularity in terms of its potential to transmit signals. We demonstrate that the simplest measure based on identifying subnetworks of strong components, within which each node could send a signal to every other node, does indeed partition the network into functional modules. We suggest that the total number of reactants needed to send a signal between two nodes in the network can be considered as the cost associated with transmitting this signal. Similarly we define spread as the number of reaction products that could be influenced by transmission of a successful signal. Our considerations open for a new class of network measures that implicitly utilize the constrained repertoire of chemical modifications of any biological molecule. The counting of cost and spread connects the topology of networks to the specificity of signaling across the network. Thereby, we
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...
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...
Growth states of catalytic reaction networks exhibiting energy metabolism
Kondo, Yohei; Kaneko, Kunihiko
2011-07-01
All cells derive nutrition by absorbing some chemical and energy resources from the environment; these resources are used by the cells to reproduce the chemicals within them, which in turn leads to an increase in their volume. In this study we introduce a protocell model exhibiting catalytic reaction dynamics, energy metabolism, and cell growth. Results of extensive simulations of this model show the existence of four phases with regard to the rates of both the influx of resources and cell growth. These phases include an active phase with high influx and high growth rates, an inefficient phase with high influx but low growth rates, a quasistatic phase with low influx and low growth rates, and a death phase with negative growth rate. A mean field model well explains the transition among these phases as bifurcations. The statistical distribution of the active phase is characterized by a power law, and that of the inefficient phase is characterized by a nearly equilibrium distribution. We also discuss the relevance of the results of this study to distinct states in the existing cells.
Toward a self-consistent and unitary reaction network for big bang nucleosynthesis
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)
Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms
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.
Coupling switches and oscillators as a means to shape cellular signals in biomolecular systems
To understand how a complex biomolecular network functions, a decomposition or a reconstruction process of the network is often needed so as to provide new insights into the regulatory mechanisms underlying various dynamical behaviors and also to gain qualitative knowledge of the network. Unfortunately, it seems that there are still no general rules on how to decompose a complex network into simple modules. An alternative resolution is to decompose a complex network into small modules or subsystems with specified functions such as switches and oscillators and then integrate them by analyzing the interactions between them. The main idea of this approach can be illustrated by considering a bidirectionally coupled network in this paper, i.e., coupled Toggle switch and Repressilator, and analyzing the occurrence of various dynamics, although the theoretical principle may hold for a general class of networks. We show that various biomolecular signals can be shaped by regulating the coupling between the subsystems. The approach presented here can be expected to simplify and analyze even more complex biological networks
A computational framework for the automated construction of glycosylation reaction networks.
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
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...
Integration of biomolecular logic gates with field-effect transducers
Highlights: → Enzyme-based AND/OR logic gates are integrated with a capacitive field-effect sensor. → The AND/OR logic gates compose of multi-enzyme system immobilised on sensor surface. → Logic gates were activated by different combinations of chemical inputs (analytes). → The logic output (pH change) produced by the enzymes was read out by the sensor. - Abstract: The integration of biomolecular logic gates with field-effect devices - the basic element of conventional electronic logic gates and computing - is one of the most attractive and promising approaches for the transformation of biomolecular logic principles into macroscopically useable electrical output signals. In this work, capacitive field-effect EIS (electrolyte-insulator-semiconductor) sensors based on a p-Si-SiO2-Ta2O5 structure modified with a multi-enzyme membrane have been used for electronic transduction of biochemical signals processed by enzyme-based OR and AND logic gates. The realised OR logic gate composes of two enzymes (glucose oxidase and esterase) and was activated by ethyl butyrate or/and glucose. The AND logic gate composes of three enzymes (invertase, mutarotase and glucose oxidase) and was activated by two chemical input signals: sucrose and dissolved oxygen. The developed integrated enzyme logic gates produce local pH changes at the EIS sensor surface as a result of biochemical reactions activated by different combinations of chemical input signals, while the pH value of the bulk solution remains unchanged. The pH-induced charge changes at the gate-insulator (Ta2O5) surface of the EIS transducer result in an electronic signal corresponding to the logic output produced by the immobilised enzymes. The logic output signals have been read out by means of a constant-capacitance method.
Variable elimination in chemical reaction networks with mass-action kinetics
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...
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.
MSU SINP CDFE nuclear data activities in the nuclear reaction data centres network
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.)
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)
Biomolecular Assembly of Gold Nanocrystals
Micheel, Christine Marya
2005-05-20
Over the past ten years, methods have been developed to construct discrete nanostructures using nanocrystals and biomolecules. While these frequently consist of gold nanocrystals and DNA, semiconductor nanocrystals as well as antibodies and enzymes have also been used. One example of discrete nanostructures is dimers of gold nanocrystals linked together with complementary DNA. This type of nanostructure is also known as a nanocrystal molecule. Discrete nanostructures of this kind have a number of potential applications, from highly parallel self-assembly of electronics components and rapid read-out of DNA computations to biological imaging and a variety of bioassays. My research focused in three main areas. The first area, the refinement of electrophoresis as a purification and characterization method, included application of agarose gel electrophoresis to the purification of discrete gold nanocrystal/DNA conjugates and nanocrystal molecules, as well as development of a more detailed understanding of the hydrodynamic behavior of these materials in gels. The second area, the development of methods for quantitative analysis of transmission electron microscope data, used computer programs written to find pair correlations as well as higher order correlations. With these programs, it is possible to reliably locate and measure nanocrystal molecules in TEM images. The final area of research explored the use of DNA ligase in the formation of nanocrystal molecules. Synthesis of dimers of gold particles linked with a single strand of DNA possible through the use of DNA ligase opens the possibility for amplification of nanostructures in a manner similar to polymerase chain reaction. These three areas are discussed in the context of the work in the Alivisatos group, as well as the field as a whole.
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. PMID:25955852
On a theory of stability for nonlinear stochastic chemical reaction networks
We present elements of a stability theory for small, stochastic, nonlinear chemical reaction networks. Steady state probability distributions are computed with zero-information (ZI) closure, a closure algorithm that solves chemical master equations of small arbitrary nonlinear reactions. Stochastic models can be linearized around the steady state with ZI-closure, and the eigenvalues of the Jacobian matrix can be readily computed. Eigenvalues govern the relaxation of fluctuation autocorrelation functions at steady state. Autocorrelation functions reveal the time scales of phenomena underlying the dynamics of nonlinear reaction networks. In accord with the fluctuation-dissipation theorem, these functions are found to be congruent to response functions to small perturbations. Significant differences are observed in the stability of nonlinear reacting systems between deterministic and stochastic modeling formalisms
Nanoarchitectonics of biomolecular assemblies for functional applications
Avinash, M. B.; Govindaraju, T.
2014-10-01
The stringent processes of natural selection and evolution have enabled extraordinary structure-function properties of biomolecules. Specifically, the archetypal designs of biomolecules, such as amino acids, nucleobases, carbohydrates and lipids amongst others, encode unparalleled information, selectivity and specificity. The integration of biomolecules either with functional molecules or with an embodied functionality ensures an eclectic approach for novel and advanced nanotechnological applications ranging from electronics to biomedicine, besides bright prospects in systems chemistry and synthetic biology. Given this intriguing scenario, our feature article intends to shed light on the emerging field of functional biomolecular engineering.
Nanotube-Based Chemical and Biomolecular Sensors
J.Koh; B.Kim; S.Hong; H.Lim; H.C.Choi
2008-01-01
We present a brief review about recent results regarding carbon nanotube (CNT)-based chemical and biomolecular sensors. For the fabrication of CNT-based sensors, devices containing CNT channels between two metal electrodes are first fabricated usually via chemical vapor deposition (CVD) process or "surface programmed assembly" method. Then, the CNT surfaces are often functionalized to enhance the selectivity of the sensors. Using this process, highly-sensitive CNT-based sensors can be fabricated for the selective detection of various chemical and biological molecules such as hydrogen, ammonia, carbon monoxide, chlorine gas, DNA, glucose, alcohol, and proteins.
Scalable Molecular Dynamics for Large Biomolecular Systems
Robert K. Brunner
2000-01-01
Full Text Available We present an optimized parallelization scheme for molecular dynamics simulations of large biomolecular systems, implemented in the production-quality molecular dynamics program NAMD. With an object-based hybrid force and spatial decomposition scheme, and an aggressive measurement-based predictive load balancing framework, we have attained speeds and speedups that are much higher than any reported in literature so far. The paper first summarizes the broad methodology we are pursuing, and the basic parallelization scheme we used. It then describes the optimizations that were instrumental in increasing performance, and presents performance results on benchmark simulations.
Micro and Nanotechnologies Enhanced Biomolecular Sensing
Tza-Huei Wang
2013-07-01
Full Text Available This editorial summarizes some of the recent advances of micro and nanotechnology-based tools and devices for biomolecular detection. These include the incorporation of nanomaterials into a sensor surface or directly interfacing with molecular probes to enhance target detection via more rapid and sensitive responses, and the use of self-assembled organic/inorganic nanocomposites that inhibit exceptional spectroscopic properties to enable facile homogenous assays with efficient binding kinetics. Discussions also include some insight into microfluidic principles behind the development of an integrated sample preparation and biosensor platform toward a miniaturized and fully functional system for point of care applications.
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.
Improvements in continuum modeling for biomolecular systems
Yu, Qiao; Ben-Zhuo, Lu
2016-01-01
Modeling of biomolecular systems plays an essential role in understanding biological processes, such as ionic flow across channels, protein modification or interaction, and cell signaling. The continuum model described by the Poisson- Boltzmann (PB)/Poisson-Nernst-Planck (PNP) equations has made great contributions towards simulation of these processes. However, the model has shortcomings in its commonly used form and cannot capture (or cannot accurately capture) some important physical properties of the biological systems. Considerable efforts have been made to improve the continuum model to account for discrete particle interactions and to make progress in numerical methods to provide accurate and efficient simulations. This review will summarize recent main improvements in continuum modeling for biomolecular systems, with focus on the size-modified models, the coupling of the classical density functional theory and the PNP equations, the coupling of polar and nonpolar interactions, and numerical progress. Project supported by the National Natural Science Foundation of China (Grant No. 91230106) and the Chinese Academy of Sciences Program for Cross & Cooperative Team of the Science & Technology Innovation.
Coassembly of aromatic dipeptides into biomolecular necklaces.
Yuran, Sivan; Razvag, Yair; Reches, Meital
2012-11-27
This paper describes the formation of complex peptide-based structures by the coassembly of two simple peptides, the diphenylalanine peptide and its tert-butyl dicarbonate (Boc) protected analogue. Each of these peptides can self-assemble into a distinct architecture: the diphenylalanine peptide into tubular structures and its analogue into spheres. Integrated together, these peptides coassemble into a construction of beaded strings, where spherical assemblies are connected by elongated elements. Electron and scanning force microscopy demonstrated the morphology of these structures, which we termed "biomolecular necklaces". Additional experiments indicated the reversibility of the coassembly process and the stability of the structures. Furthermore, we suggest a possible mechanism of formation for the biomolecular necklaces. Our suggestion is based on the necklace model for polyelectrolyte chains, which proposes that a necklace structure appears as a result of counterion condensation on the backbone of a polyelectrolyte. Overall, the approach of coassembly, demonstrated using aromatic peptides, can be adapted to any peptides and may lead to the development and discovery of new self-assembled architectures formed by peptides and other biomolecules. PMID:23061818
Water formation at low temperatures by surface O2 hydrogenation II: the reaction network
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 ...
Global exponential stability of Hopfield reaction-diffusion neural networks with time-varying delays
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.
Report on the IAEA technical meeting on network of nuclear reaction data centres
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)
Deterministic characterization of phase noise in biomolecular oscillators
On top of the many external perturbations, cellular oscillators also face intrinsic perturbations due the randomness of chemical kinetics. Biomolecular oscillators, distinct in their parameter sets or distinct in their architecture, show different resilience with respect to such intrinsic perturbations. Assessing this resilience can be done by ensemble stochastic simulations. These are computationally costly and do not permit further insights into the mechanistic cause of the observed resilience. For reaction systems operating at a steady state, the linear noise approximation (LNA) can be used to determine the effect of molecular noise. Here we show that methods based on LNA fail for oscillatory systems and we propose an alternative ansatz. It yields an asymptotic expression for the phase diffusion coefficient of stochastic oscillators. Moreover, it allows us to single out the noise contribution of every reaction in an oscillatory system. We test the approach on the one-loop model of the Drosophila circadian clock. Our results are consistent with those obtained through stochastic simulations with a gain in computational efficiency of about three orders of magnitude
Coupling sample paths to the partial thermodynamic limit in stochastic chemical reaction networks
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.
Pd-PEG-PU polymer networks: A convenient catalyst for hydrogenation and Suzuki coupling reactions
Polyethyleneglycol-polyurethane (PEG-PU) polymer network films with sodium tetrachloropalladate were fabricated by solution casting technique. The incorporated palladium ions were reduced using sodium borohydride to obtain zero-valent (metallic) palladium in the films. These films were characterized by X-ray diffraction, Fourier transform-infra red spectroscopy, atomic absorption spectroscopy and X-ray photoelectron spectroscopy for their structure and chemical composition. The films containing palladium metal are found to be highly efficient toward hydrogenation of a number of unsaturated organic compounds and Suzuki coupling reactions of haloarenes. The novelty of this catalyst system is the incorporation of palladium in the polymer network that renders the catalyst system's reusability because there is no leaching of the metal from the polymer matrix and as the catalyst system is in the form of a compact film it can be easily separated from the reaction mixture and can be reused.
Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction
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.
INFLUENCE OF NOISE AND DELAY ON REACTION-DIFFUSION RECURRENT NEURAL NETWORKS
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.
Report on the IAEA technical meeting of the International Network of Nuclear Reaction Data Centres
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)
Report on the IAEA technical meeting on the network of nuclear reaction data centres
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)
Product-form stationary distributions for deficiency zero chemical reaction networks
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...
Report on the IAEA advisory group meeting on network of nuclear reaction data centres
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)
Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres
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)
Report on the IAEA technical meeting on network of nuclear reaction data centres
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)
Report on the IAEA technical meeting on network of nuclear reaction data centres
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)
A reaction-diffusion model of ROS-induced ROS release in a mitochondrial network.
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
On the rejection-based algorithm for simulation and analysis of large-scale reaction networks
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
Pattern formation on networks with reactions: A continuous-time random-walk approach
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.
On the rejection-based algorithm for simulation and analysis of large-scale reaction networks
Thanh, Vo Hong; Zunino, Roberto; Priami, Corrado
2015-06-01
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.
Biomolecular Structure Determination with Divide and Concur
Kallus, Yoav; Elser, Veit
2009-03-01
Divide and concur (D-C) is a general computational approach, designed for the solution of highly frustrated problems. Recently applied to the problems of disk packing, the kissing number problem, and 3-SAT, it was competitive or outperformed special-purpose methods.ootnotetextS. Gravel and V. Elser, Phys. Rev. E 78, 036706 (2008) We present a method for applying the D-C framework to the problem of biomolecular structure determination. From a list of geometric constraints on groups of atoms in the molecule, we construct a deterministic iterative map that efficiently searches for structures simultaneously satisfying all constraints. As our method eschews an energy function and its minimization to focus on geometric constraints, it can very naturally integrate with the geometric constraints due to chemistry and physics, experimental constraints due to NMR data or many other experimental or biological hints. We present some results of our method.
Ensemble based convergence assessment of biomolecular trajectories
Lyman, E; Lyman, Edward; Zuckerman, Daniel M.
2006-01-01
Assessing the convergence of a biomolecular simulation is an essential part of any computational investigation. This is because many important quantities (e.g., free energy differences) depend on the relative populations of different conformers; insufficient convergence translates into systematic errors. Here we present a simple method to self-consistently assess the convergence of a simulation. Standard clustering methods first generate a set of reference structures to any desired precision. The trajectory is then classified by proximity to the reference structures, yielding a one-dimensional histogram of structurally distinct populations. Comparing ensembles of different trajectories (or different parts of the same trajectory) built with the same reference structures provides a sensitive, quantitative measure of convergence. Please note: this is a preliminary manuscript, and should be read as such. Comments are most welcome, especially regarding pertinent prior work.
Micro- and nanodevices integrated with biomolecular probes.
Alapan, Yunus; Icoz, Kutay; Gurkan, Umut A
2015-12-01
Understanding how biomolecules, proteins and cells interact with their surroundings and other biological entities has become the fundamental design criterion for most biomedical micro- and nanodevices. Advances in biology, medicine, and nanofabrication technologies complement each other and allow us to engineer new tools based on biomolecules utilized as probes. Engineered micro/nanosystems and biomolecules in nature have remarkably robust compatibility in terms of function, size, and physical properties. This article presents the state of the art in micro- and nanoscale devices designed and fabricated with biomolecular probes as their vital constituents. General design and fabrication concepts are presented and three major platform technologies are highlighted: microcantilevers, micro/nanopillars, and microfluidics. Overview of each technology, typical fabrication details, and application areas are presented by emphasizing significant achievements, current challenges, and future opportunities. PMID:26363089
Micro- and nanodevices integrated with biomolecular probes
Alapan, Yunus; Icoz, Kutay; Gurkan, Umut A.
2016-01-01
Understanding how biomolecules, proteins and cells interact with their surroundings and other biological entities has become the fundamental design criterion for most biomedical micro- and nanodevices. Advances in biology, medicine, and nanofabrication technologies complement each other and allow us to engineer new tools based on biomolecules utilized as probes. Engineered micro/nanosystems and biomolecules in nature have remarkably robust compatibility in terms of function, size, and physical properties. This article presents the state of the art in micro- and nanoscale devices designed and fabricated with biomolecular probes as their vital constituents. General design and fabrication concepts are presented and three major platform technologies are highlighted: microcantilevers, micro/nanopillars, and microfluidics. Overview of each technology, typical fabrication details, and application areas are presented by emphasizing significant achievements, current challenges, and future opportunities. PMID:26363089
Biomolecular Markers in Cancer of the Tongue
Daris Ferrari
2009-01-01
Full Text Available The incidence of tongue cancer is increasing worldwide, and its aggressiveness remains high regardless of treatment. Genetic changes and the expression of abnormal proteins have been frequently reported in the case of head and neck cancers, but the little information that has been published concerning tongue tumours is often contradictory. This review will concentrate on the immunohistochemical expression of biomolecular markers and their relationships with clinical behaviour and prognosis. Most of these proteins are associated with nodal stage, tumour progression and metastases, but there is still controversy concerning their impact on disease-free and overall survival, and treatment response. More extensive clinical studies are needed to identify the patterns of molecular alterations and the most reliable predictors in order to develop tailored anti-tumour strategies based on the targeting of hypoxia markers, vascular and lymphangiogenic factors, epidermal growth factor receptors, intracytoplasmatic signalling and apoptosis.
A unified biological modeling and simulation system for analyzing biological reaction networks
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.
FERN – a Java framework for stochastic simulation and evaluation of reaction networks
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
FERN – a Java framework for stochastic simulation and evaluation of reaction networks
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
Biomolecular detection employing the Interferometric Reflectance Imaging Sensor (IRIS).
Lopez, Carlos A; Daaboul, George G; Ahn, Sunmin; Reddington, Alexander P; Monroe, Margo R; Zhang, Xirui; Irani, Rostem J; Yu, Chunxiao; Genco, Caroline A; Cretich, Marina; Chiari, Marcella; Goldberg, Bennett B; Connor, John H; Ünlü, M Selim
2011-01-01
The sensitive measurement of biomolecular interactions has use in many fields and industries such as basic biology and microbiology, environmental/agricultural/biodefense monitoring, nanobiotechnology, and more. For diagnostic applications, monitoring (detecting) the presence, absence, or abnormal expression of targeted proteomic or genomic biomarkers found in patient samples can be used to determine treatment approaches or therapy efficacy. In the research arena, information on molecular affinities and specificities are useful for fully characterizing the systems under investigation. Many of the current systems employed to determine molecular concentrations or affinities rely on the use of labels. Examples of these systems include immunoassays such as the enzyme-linked immunosorbent assay (ELISA), polymerase chain reaction (PCR) techniques, gel electrophoresis assays, and mass spectrometry (MS). Generally, these labels are fluorescent, radiological, or colorimetric in nature and are directly or indirectly attached to the molecular target of interest. Though the use of labels is widely accepted and has some benefits, there are drawbacks which are stimulating the development of new label-free methods for measuring these interactions. These drawbacks include practical facets such as increased assay cost, reagent lifespan and usability, storage and safety concerns, wasted time and effort in labelling, and variability among the different reagents due to the labelling processes or labels themselves. On a scientific research basis, the use of these labels can also introduce difficulties such as concerns with effects on protein functionality/structure due to the presence of the attached labels and the inability to directly measure the interactions in real time. Presented here is the use of a new label-free optical biosensor that is amenable to microarray studies, termed the Interferometric Reflectance Imaging Sensor (IRIS), for detecting proteins, DNA, antigenic material
ezBioNet: A modeling and simulation system for analyzing biological reaction networks
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.
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
Garg, Sahil; Galstyan, Aram; Hermjakob, Ulf; Marcu, Daniel
2015-01-01
We advance the state of the art in biomolecular interaction extraction with three contributions: (i) We show that deep, Abstract Meaning Representations (AMR) significantly improve the accuracy of a biomolecular interaction extraction system when compared to a baseline that relies solely on surface- and syntax-based features; (ii) In contrast with previous approaches that infer relations on a sentence-by-sentence basis, we expand our framework to enable consistent predictions over sets of sen...
Kaihong Zhao
2010-01-01
Full Text Available The existence of equilibrium solutions to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales is proved by the topological degree theory and M-matrix method. Under some sufficient conditions, we obtain the uniqueness and global exponential stability of equilibrium solution to reaction-diffusion recurrent neural networks with Dirichlet boundary conditions on time scales by constructing suitable Lyapunov functional and inequality skills. One example is given to illustrate the effectiveness of our results.
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.
Leporace, Gustavo; Batista, Luiz Alberto; Metsavaht, Leonardo; Nadal, Jurandir
2015-08-01
The aim of the study was to analyze and compare the residuals obtained from ground reaction force (GRF) models developed using two different neural network configurations (one network with three outputs; and three networks with one output each), based on accelerometer data. Seventeen healthy subjects walked along a walkway, with a force plate embedded, with a three dimensional accelerometer attached to the shank. Multilayer perceptron networks (MLP) models were developed with the 3D accelerometer data as inputs to predict the GRF. The residuals of these models were evaluated graphically and numerically to verify the fitting. A visual analysis of the simulated signals suggests the model was able to adequately predict the GRF. The errors and correlations found in the MLP models for the 3D GRF is at least similar to other studies, although some of them showed higher errors. There was not difference between the two MLP configurations. However, despite the high correlation coefficient and closeness to a normal probability distribution, the residual analysis still presented a higher kurtosis and skewness, suggesting that the inclusion of other variables and the increase of the validation sample size could increase the fitting of the simulation. PMID:26736876
Biomolecular surface construction by PDE transform.
Zheng, Qiong; Yang, Siyang; Wei, Guo-Wei
2012-03-01
This work proposes a new framework for the surface generation based on the partial differential equation (PDE) transform. The PDE transform has recently been introduced as a general approach for the mode decomposition of images, signals, and data. It relies on the use of arbitrarily high-order PDEs to achieve the time-frequency localization, control the spectral distribution, and regulate the spatial resolution. The present work provides a new variational derivation of high-order PDE transforms. The fast Fourier transform is utilized to accomplish the PDE transform so as to avoid stringent stability constraints in solving high-order PDEs. As a consequence, the time integration of high-order PDEs can be done efficiently with the fast Fourier transform. The present approach is validated with a variety of test examples in two-dimensional and three-dimensional settings. We explore the impact of the PDE transform parameters, such as the PDE order and propagation time, on the quality of resulting surfaces. Additionally, we utilize a set of 10 proteins to compare the computational efficiency of the present surface generation method and a standard approach in Cartesian meshes. Moreover, we analyze the present method by examining some benchmark indicators of biomolecular surface, that is, surface area, surface-enclosed volume, solvation free energy, and surface electrostatic potential. A test set of 13 protein molecules is used in the present investigation. The electrostatic analysis is carried out via the Poisson-Boltzmann equation model. To further demonstrate the utility of the present PDE transform-based surface method, we solve the Poisson-Nernst-Planck equations with a PDE transform surface of a protein. Second-order convergence is observed for the electrostatic potential and concentrations. Finally, to test the capability and efficiency of the present PDE transform-based surface generation method, we apply it to the construction of an excessively large biomolecule, a
Baldi, P.; Blanke, Mogens; Castaldi, P.; Mimmo, N.; Simani, S.
2015-01-01
This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank of...... residual filters organized in a generalized scheme, followed by a fault estimation module consisting of a bank of adaptive estimation filters. The residuals are decoupled from aerodynamic disturbances thanks to the Nonlinear Geometric Approach. The use of Radial Basis Function Neural Networks is shown 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...
Elucidation of Diels-Alder Reaction Network of 2,5-Dimethylfuran and Ethylene on HY Zeolite Catalyst
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.
Integrated Spintronic Platforms for Biomolecular Recognition Detection
Martins, V. C.; Cardoso, F. A.; Loureiro, J.; Mercier, M.; Germano, J.; Cardoso, S.; Ferreira, R.; Fonseca, L. P.; Sousa, L.; Piedade, M. S.; Freitas, P. P.
2008-06-01
This paper covers recent developments in magnetoresistive based biochip platforms fabricated at INESC-MN, and their application to the detection and quantification of pathogenic waterborn microorganisms in water samples for human consumption. Such platforms are intended to give response to the increasing concern related to microbial contaminated water sources. The presented results concern the development of biological active DNA chips and protein chips and the demonstration of the detection capability of the present platforms. Two platforms are described, one including spintronic sensors only (spin-valve based or magnetic tunnel junction based), and the other, a fully scalable platform where each probe site consists of a MTJ in series with a thin film diode (TFD). Two microfluidic systems are described, for cell separation and concentration, and finally, the read out and control integrated electronics are described, allowing the realization of bioassays with a portable point of care unit. The present platforms already allow the detection of complementary biomolecular target recognition with 1 pM concentration.
A multiscale modeling approach for biomolecular systems
Bowling, Alan, E-mail: bowling@uta.edu; Haghshenas-Jaryani, Mahdi, E-mail: mahdi.haghshenasjaryani@mavs.uta.edu [The University of Texas at Arlington, Department of Mechanical and Aerospace Engineering (United States)
2015-04-15
This paper presents a new multiscale molecular dynamic model for investigating the effects of external interactions, such as contact and impact, during stepping and docking of motor proteins and other biomolecular systems. The model retains the mass properties ensuring that the result satisfies Newton’s second law. This idea is presented using a simple particle model to facilitate discussion of the rigid body model; however, the particle model does provide insights into particle dynamics at the nanoscale. The resulting three-dimensional model predicts a significant decrease in the effect of the random forces associated with Brownian motion. This conclusion runs contrary to the widely accepted notion that the motor protein’s movements are primarily the result of thermal effects. This work focuses on the mechanical aspects of protein locomotion; the effect ATP hydrolysis is estimated as internal forces acting on the mechanical model. In addition, the proposed model can be numerically integrated in a reasonable amount of time. Herein, the differences between the motion predicted by the old and new modeling approaches are compared using a simplified model of myosin V.
Biochemical Filter with Sigmoidal Response: Increasing the Complexity of Biomolecular Logic
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...
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
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.
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.
Biomolecular Modification of Inorganic Crystal Growth
De Yoreo, J J
2007-04-27
The fascinating shapes and hierarchical designs of biomineralized structures are an inspiration to materials scientists because of the potential they suggest for biomolecular control over materials synthesis. Conversely, the failure to prevent or limit tissue mineralization in the vascular, skeletal, and urinary systems is a common source of disease. Understanding the mechanisms by which organisms direct or limit crystallization has long been a central challenge to the biomineralization community. One prevailing view is that mineral-associated macromolecules are responsible for either inhibiting crystallization or initiating and stabilizing non-equilibrium crystal polymorphs and morphologies through interactions between anionic moieties and cations in solution or at mineralizing surfaces. In particular, biomolecules that present carboxyl groups to the growing crystal have been implicated as primary modulators of growth. Here we review the results from a combination of in situ atomic force microscopy (AFM) and molecular modeling (MM) studies to investigate the effect of specific interactions between carboxylate-rich biomolecules and atomic steps on crystal surfaces during the growth of carbonates, oxalates and phosphates of calcium. Specifically, we how the growth kinetics and morphology depend on the concentration of additives that include citrate, simple amino acids, synthetic Asp-rich polypeptides, and naturally occurring Asp-rich proteins found in both functional and pathological mineral tissues. The results reveal a consistent picture of shape modification in which stereochemical matching of modifiers to specific atomic steps drives shape modification. Inhibition and other changes in growth kinetics are shown to be due to a range of mechanisms that depend on chemistry and molecular size. Some effects are well described by classic crystal growth theories, but others, such as step acceleration due to peptide charge and hydrophylicity, were previously unrealized
Bridging Nano- and Microtribology in Mechanical and Biomolecular Layers
Tomala, Agnieszka; Göçerler, Hakan; Gebeshuber, Ille C.
The physical and chemical composition of surfaces determine various important properties of solids such as corrosion rates, adhesive properties, frictional properties, catalytic activity, wettability, contact potential and - finally and most importantly - failure mechanisms. Very thin, weak layers (of man-made and biological origin) on much harder substrates that reduce friction are the focus of the micro- and nanotribological investigations presented in this chapter.Biomolecular layers fulfil various functions in organs of the human body. Examples comprise the skin that provides a protective physical barrier between the body and the environment, preventing unwanted inward and outward passage of water and electrolytes, reducing penetration by destructive chemicals, arresting the penetration of microorganisms and external antigens and absorbing radiation from the sun, or the epithelium of the cornea that blocks the passage of foreign material, such as dust, water and bacteria, into the eye and that contributes to the lubrication layer that ensures smooth movement of the eyelids over the eyeballs.Monomolecular thin films, additive-derived reaction layers and hard coatings are widely used to tailor tribological properties of surfaces. Nanotribological investigations on these substrates can reveal novel properties regarding the orientation of chemisorbed additive layers, development of rubbing films with time and the relation of frictional properties to surface characteristics in diamond coatings.Depending on the questions to be answered with the tribological research, various micro- and nanotribological measurement methods are applied, including scanning probe microscopy (AFM, FFM), scanning electron microscopy, microtribometer investigations, angle-resolved photoelectron spectroscopy and optical microscopy. Thoughts on the feasibility of a unified approach to energy-dissipating systems and how it might be reached (touching upon new ways of scientific publishing
Alexander V Ratushny
2011-06-01
Full Text Available When living systems detect changes in their external environment their response must be measured to balance the need to react appropriately with the need to remain stable, ignoring insignificant signals. Because this is a fundamental challenge of all biological systems that execute programs in response to stimuli, we developed a generalized time-frequency analysis (TFA framework to systematically explore the dynamical properties of biomolecular networks. Using TFA, we focused on two well-characterized yeast gene regulatory networks responsive to carbon-source shifts and a mammalian innate immune regulatory network responsive to lipopolysaccharides (LPS. The networks are comprised of two different basic architectures. Dual positive and negative feedback loops make up the yeast galactose network; whereas overlapping positive and negative feed-forward loops are common to the yeast fatty-acid response network and the LPS-induced network of macrophages. TFA revealed remarkably distinct network behaviors in terms of trade-offs in responsiveness and noise suppression that are appropriately tuned to each biological response. The wild type galactose network was found to be highly responsive while the oleate network has greater noise suppression ability. The LPS network appeared more balanced, exhibiting less bias toward noise suppression or responsiveness. Exploration of the network parameter space exposed dramatic differences in system behaviors for each network. These studies highlight fundamental structural and dynamical principles that underlie each network, reveal constrained parameters of positive and negative feedback and feed-forward strengths that tune the networks appropriately for their respective biological roles, and demonstrate the general utility of the TFA approach for systems and synthetic biology.
A mechanical Turing machine: blueprint for a biomolecular computer.
Shapiro, Ehud
2012-08-01
We describe a working mechanical device that embodies the theoretical computing machine of Alan Turing, and as such is a universal programmable computer. The device operates on three-dimensional building blocks by applying mechanical analogues of polymer elongation, cleavage and ligation, movement along a polymer, and control by molecular recognition unleashing allosteric conformational changes. Logically, the device is not more complicated than biomolecular machines of the living cell, and all its operations are part of the standard repertoire of these machines; hence, a biomolecular embodiment of the device is not infeasible. If implemented, such a biomolecular device may operate in vivo, interacting with its biochemical environment in a program-controlled manner. In particular, it may 'compute' synthetic biopolymers and release them into its environment in response to input from the environment, a capability that may have broad pharmaceutical and biological applications. PMID:22649583
Falkner, Joshua Charles
The three projects within this work address the difficulties of controlling biomolecular crystal formats (i.e. size and shape), producing 3-D ordered composite materials from biomolecular crystal templates, and understanding the mechanism of a practical iron oxide synthesis. The unifying thread consistent throughout these three topics is the development of methods to manipulate nanomaterials using a bottom-up approach. Biomolecular crystals are nanometer to millimeter sized crystals that have well ordered mesoporous solvent channels. The overall physical dimensions of these crystals are highly dependent on crystallization conditions. The controlled growth of micro- and nanoprotein crystals was studied to provide new pathways for creating smaller crystalline protein materials. This method produced tetragonal hen egg-white lysozyme crystals (250--100,000 nm) with near monodisperse size distributions (membranes or templates. In this work, the porous structure of larger cowpea mosaic virus crystals was used to template metal nanoparticle growth within the body centered cubic crystalline network. The final composite material was found to have long range ordering of palladium and platinum nonocrystal aggregates (10nm) with symmetry consistent to the virus template. Nanoparticle synthesis itself is an immense field of study with an array of diverse applications. The final piece of this work investigates the mechanism behind a previously developed iron oxide synthesis to gain more understanding and direction to future synthesis strategies. The particle growth mechanism was found to proceed by the formation of a solvated iron(III)oleate complex followed by a reduction of iron (III) to iron (II). This unstable iron(II) nucleates to form a wustite (FeO) core which serves as an epitaxial surface for the magnetite (Fe3O4) shell growth. This method produces spherical particles (6-60nm) with relative size distributions of less than 15%.
Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks
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.
Evolution of Autocatalytic Sets in Computational Models of Chemical Reaction Networks.
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. PMID:26499126
A model for lignin alteration - Part I: A kinetic reaction-network model
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.
Parlett, Christopher M. A.; Isaacs, Mark A.; Beaumont, Simon K.; Bingham, Laura M.; Hondow, Nicole S.; Wilson, Karen; Lee, Adam F.
2016-02-01
The chemical functionality within porous architectures dictates their performance as heterogeneous catalysts; however, synthetic routes to control the spatial distribution of individual functions within porous solids are limited. Here we report the fabrication of spatially orthogonal bifunctional porous catalysts, through the stepwise template removal and chemical functionalization of an interconnected silica framework. Selective removal of polystyrene nanosphere templates from a lyotropic liquid crystal-templated silica sol-gel matrix, followed by extraction of the liquid crystal template, affords a hierarchical macroporous-mesoporous architecture. Decoupling of the individual template extractions allows independent functionalization of macropore and mesopore networks on the basis of chemical and/or size specificity. Spatial compartmentalization of, and directed molecular transport between, chemical functionalities affords control over the reaction sequence in catalytic cascades; herein illustrated by the Pd/Pt-catalysed oxidation of cinnamyl alcohol to cinnamic acid. We anticipate that our methodology will prompt further design of multifunctional materials comprising spatially compartmentalized functions.
A moment-convergence method for stochastic analysis of biochemical reaction networks
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.
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 ...
Engineering intracellular active transport systems as in vivo biomolecular tools.
Bachand, George David; Carroll-Portillo, Amanda
2006-11-01
Active transport systems provide essential functions in terms of cell physiology and metastasis. These systems, however, are also co-opted by invading viruses, enabling directed transport of the virus to and from the cell's nucleus (i.e., the site of virus replication). Based on this concept, fundamentally new approaches for interrogating and manipulating the inner workings of living cells may be achievable by co-opting Nature's active transport systems as an in vivo biomolecular tool. The overall goal of this project was to investigate the ability to engineer kinesin-based transport systems for in vivo applications, specifically the collection of effector proteins (e.g., transcriptional regulators) within single cells. In the first part of this project, a chimeric fusion protein consisting of kinesin and a single chain variable fragment (scFv) of an antibody was successfully produced through a recombinant expression system. The kinesin-scFv retained both catalytic and antigenic functionality, enabling selective capture and transport of target antigens. The incorporation of a rabbit IgG-specific scFv into the kinesin established a generalized system for functionalizing kinesin with a wide range of target-selective antibodies raised in rabbits. The second objective was to develop methods of isolating the intact microtubule network from live cells as a platform for evaluating kinesin-based transport within the cytoskeletal architecture of a cell. Successful isolation of intact microtubule networks from two distinct cell types was demonstrated using glutaraldehyde and methanol fixation methods. This work provides a platform for inferring the ability of kinesin-scFv to function in vivo, and may also serve as a three-dimensional scaffold for evaluating and exploiting kinesin-based transport for nanotechnological applications. Overall, the technology developed in this project represents a first-step in engineering active transport system for in vivo
From dynamics to structure and function of model biomolecular systems
Fontaine-Vive-Curtaz, F.
2007-01-01
The purpose of this thesis was to extend recent works on structure and dynamics of hydrogen bonded crystals to model biomolecular systems and biological processes. The tools that we have used are neutron scattering (NS) and density functional theory (DFT) and force field (FF) based simulation method
Electron-biomolecular ion collisions were studied using an electrostatic storage ring with a merging electron beam device. Biomolecular ions produced by an electrospray ion source and accelerated to 20 keV/charge were injected into the ring after being mass-analyzed. The circulating ion beam was then merged with an electron beam. Neutral reaction products in collisions of electrons with ions were detected by a micro-channel plate outside of the ring. Electron-ion collisions were studied for multiply-deprotonated oligonucleotide and peptide anions as well as singly protonated oligonucleotide and peptide cations. For peptide cations, neutrals were resonantly emitted at an electron energy of around 6.5 eV, which was almost independent of the ion masses. This is deduced to come from electron-ion recombination, resulting in the cleavage of a peptide bond. For DNA oligonucleotide cations, resonant neutral particle emission was also observed. In electron and DNA anion collisions, neutrals started to increase from definite threshold energies, where the threshold energies increased in proportion to the ion charge. The same was found for peptide anions. The origin of this phenomenon is discussed
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
Programming chemical kinetics: engineering dynamic reaction networks with DNA strand displacement
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
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.
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
Biomolecular self-defense and futility of high-specificity therapeutic targeting.
Rosenfeld, Simon
2011-01-01
Robustness has been long recognized to be a distinctive property of living entities. While a reasonably wide consensus has been achieved regarding the conceptual meaning of robustness, the biomolecular mechanisms underlying this systemic property are still open to many unresolved questions. The goal of this paper is to provide an overview of existing approaches to characterization of robustness in mathematically sound terms. The concept of robustness is discussed in various contexts including network vulnerability, nonlinear dynamic stability, and self-organization. The second goal is to discuss the implications of biological robustness for individual-target therapeutics and possible strategies for outsmarting drug resistance arising from it. Special attention is paid to the concept of swarm intelligence, a well studied mechanism of self-organization in natural, societal and artificial systems. It is hypothesized that swarm intelligence is the key to understanding the emergent property of chemoresistance. PMID:22272063
Advances in modeling of biomolecular interactions
Cong-zhongCAI; Ze-rongLI; Wan-luWANG; Yu-zongCHEN
2004-01-01
Modeling of molecular interactions is increasingly used in life science research and biotechnology development.Examples are computer aided drug design, prediction of protein interactions with other molecules, and simulation of networks of biomolecules in a particular process in human body. This article reviews recent progress in the related fields and provides a brief overview on the methods used in molecular modeling of biological systems.
Drift-Implicit Multi-Level Monte Carlo Tau-Leap Methods for Stochastic Reaction Networks
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
LiHe{sup +} IN THE EARLY UNIVERSE: A FULL ASSESSMENT OF ITS REACTION NETWORK AND FINAL ABUNDANCES
Bovino, Stefano; Tacconi, Mario; Gianturco, Francesco A. [Department of Chemistry, Universita degli Studi di Roma ' La Sapienza' , Piazzale A. Moro 5, 00185 Roma (Italy); Curik, Roman [J. Heyrovsky Institute of Physical Chemistry, Dolejskova 3, Prague (Czech Republic); Galli, Daniele, E-mail: fa.gianturco@caspur.it [INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze (Italy)
2012-06-10
We present the results of quantum calculations based on entirely ab initio methods for a variety of molecular processes and chemical reactions involving the LiHe{sup +} ionic polar molecule. With the aid of these calculations, we derive accurate reaction rates and fitting expressions valid over a range of gas temperatures representative of the typical conditions of the pregalactic gas. With the help of a full chemical network, we then compute the evolution of the abundance of LiHe{sup +} as function of redshift in the early universe. Finally, we compare the relative abundance of LiHe{sup +} with that of other polar cations formed in the same redshift interval.
Bauß, Anna; Langenmaier, Michael; Strittmatter, Eric; Plattner, Dietmar A; Koslowski, Thorsten
2016-06-01
We present a computer simulation study of the thermodynamics and kinetics of charge transfer reactions within the fungal peroxidase AauDyPI from Auricularia auriculae-judae. Driving forces and reorganization energies are obtained from a thermodynamic integration scheme based upon molecular dynamics simulations. To enhance the numerical accuracy, the free energies are analyzed within a least-squares scheme of a closely knit thermodynamic network. We identify Tyr147, Tyr229, and Trp105 as oxidative agents, and find Trp377 to be a long-lived reaction intermediate. The results are compared to recent experimental findings. PMID:27182684
Physics at the biomolecular interface fundamentals for molecular targeted therapy
Fernández, Ariel
2016-01-01
This book focuses primarily on the role of interfacial forces in understanding biological phenomena at the molecular scale. By providing a suitable statistical mechanical apparatus to handle the biomolecular interface, the book becomes uniquely positioned to address core problems in molecular biophysics. It highlights the importance of interfacial tension in delineating a solution to the protein folding problem, in unravelling the physico-chemical basis of enzyme catalysis and protein associations, and in rationally designing molecular targeted therapies. Thus grounded in fundamental science, the book develops a powerful technological platform for drug discovery, while it is set to inspire scientists at any level in their careers determined to address the major challenges in molecular biophysics. The acknowledgment of how exquisitely the structure and dynamics of proteins and their aqueous environment are related attests to the overdue recognition that biomolecular phenomena cannot be effectively understood w...
Biomolecular structure refinement using the GROMOS simulation software
Schmid, Nathan; Allison, Jane R.; Dolenc, Jozica; Eichenberger, Andreas P.; Kunz, Anna-Pitschna E.; Gunsteren, Wilfred F. van, E-mail: wfvgn@igc.phys.chem.ethz.ch [Swiss Federal Institute of Technology ETH, Laboratory of Physical Chemistry (Switzerland)
2011-11-15
For the understanding of cellular processes the molecular structure of biomolecules has to be accurately determined. Initial models can be significantly improved by structure refinement techniques. Here, we present the refinement methods and analysis techniques implemented in the GROMOS software for biomolecular simulation. The methodology and some implementation details of the computation of NMR NOE data, {sup 3}J-couplings and residual dipolar couplings, X-ray scattering intensities from crystals and solutions and neutron scattering intensities used in GROMOS is described and refinement strategies and concepts are discussed using example applications. The GROMOS software allows structure refinement combining different types of experimental data with different types of restraining functions, while using a variety of methods to enhance conformational searching and sampling and the thermodynamically calibrated GROMOS force field for biomolecular simulation.
Wang, Ling; Stumm, Boris; Helms, Volkhard
2010-03-01
Biomolecular association and dissociation reactions take place on complicated interaction free energy landscapes that are still very hard to characterize computationally. For large enough distances, though, it often suffices to consider the six relative translational and rotational degrees of freedom of the two particles treated as rigid bodies. Here, we computed the six-dimensional free energy surface of a dimer of water-soluble alpha-helices by scanning these six degrees of freedom in about one million grid points. In each point, the relative free energy difference was computed as the sum of the polar and nonpolar solvation free energies of the helix dimer and of the intermolecular coulombic interaction energy. The Dijkstra graph algorithm was then applied to search for the lowest cost dissociation pathways based on a weighted, directed graph, where the vertices represent the grid points, the edges connect the grid points and their neighbors, and the weights are the reaction costs between adjacent pairs of grid points. As an example, the configuration of the bound state was chosen as the source node, and the eight corners of the translational cube were chosen as the destination nodes. With the strong electrostatic interaction of the two helices giving rise to a clearly funnel-shaped energy landscape, the eight lowest-energy cost pathways coming from different orientations converge into a well-defined pathway for association. We believe that the methodology presented here will prove useful for identifying low-energy association and dissociation pathways in future studies of complicated free energy landscapes for biomolecular interaction. PMID:19603501
Quantifying Modularity in the Evolution of Biomolecular Systems
2004-01-01
Functional modules are considered the primary building blocks of biomolecular systems. Here we study to what extent functional modules behave cohesively across genomes:That is, are functional modules also evolutionary modules? We probe this question by analyzing for a large collection of functional modules the phyletic patterns of their genes across 110 genomes. The majority of functional modules display limited evolutionary modularity. This result confirms certain comparative genome analyses...
The biomolecular and ultrastructural basis of epidermolysis bullosa:
Ciolan, Maria; Olariu, Liviu; Solovan, Caius
2005-01-01
Transmission electron microscopy, immunoelectron microscopy, immunofluorescence and antigenic mapping have improved our understanding of the dermo-epidermal junction. We have reviewed some ultrastructural and biomolecular aspects related to the dermo-epidermal junction. In part, they are implicated in the pathogenesis of a group of hereditary disorders characterized by skin fragility, collectively known as epidermolysis bullosa (EB). These disorders could benefit in the near future from a gen...
Biomolecular Detection employing the Interferometric Reflectance Imaging Sensor (IRIS)
Carlos A Lopez; George G Daaboul; Ahn, Sunmin; Reddington, Alexander P.; Monroe, Margo R.; Zhang, Xirui; Irani, Rostem J.; Yu, Chunxiao; Genco, Caroline A.; Cretich, Marina; Chiari, Marcella; Goldberg, Bennett B.; Connor, John H.; Ünlü, M. Selim
2011-01-01
The sensitive measurement of biomolecular interactions has use in many fields and industries such as basic biology and microbiology, environmental/agricultural/biodefense monitoring, nanobiotechnology, and more. For diagnostic applications, monitoring (detecting) the presence, absence, or abnormal expression of targeted proteomic or genomic biomarkers found in patient samples can be used to determine treatment approaches or therapy efficacy. In the research arena, information on molecular aff...
A mechanical Turing machine: blueprint for a biomolecular computer
Shapiro, Ehud
2012-01-01
We describe a working mechanical device that embodies the theoretical computing machine of Alan Turing, and as such is a universal programmable computer. The device operates on three-dimensional building blocks by applying mechanical analogues of polymer elongation, cleavage and ligation, movement along a polymer, and control by molecular recognition unleashing allosteric conformational changes. Logically, the device is not more complicated than biomolecular machines of the living cell, and a...
Dynamic Presentation of Immobilized Ligands Regulated through Biomolecular Recognition
Liu, Bo; Liu, Yang; Riesberg, Jeremiah J.; Shen, Wei
2010-01-01
To mimic the dynamic regulation of signaling ligands immobilized on extracellular matrices or on the surfaces of neighboring cells for guidance of cell behavior and fate selection, we have harnessed biomolecular recognition in combination with polymer engineering to create dynamic surfaces on which the accessibility of immobilized ligands to cell surface receptors can be reversibly interconverted under physiological conditions. The cell-adhesive RGD peptide is chosen as a model ligand. RGD is...
Lorenz, Sönke; Scheffler, Matthias; Gross, Axel
2006-03-01
A neural network (NN) approach is proposed for the representation of six-dimensional ab initio potential-energy surfaces (PES) for the dissociation of a diatomic molecule at surfaces. We report tests of NN representations that are fitted to six-dimensional analytical PESs for H2 dissociation on the clean and the sulfur covered Pd(100) surfaces. For the present study we use high-dimensional analytical PESs as the basis for the NN training, as this enables us to investigate the influence of phase space sampling on adsorption rates in great detail. We note, however, that these analytical PESs were obtained from detailed density functional theory calculations. When information about the PES is collected only from a few high-symmetric adsorption sites, we find that the obtained adsorption probabilities are not reliable. Thus, intermediate configurations need to be considered as well. However, it is not necessary to map out complete elbow plots above nonsymmetric sites. Our study suggests that only a few additional energies need to be considered in the region of activated systems where the molecular bond breaks. With this understanding, the required number of NN training energies for obtaining a high-quality PES that provides a reliable description of the dissociation and adsorption dynamics is orders of magnitude smaller than the number of total-energy calculations needed in traditional ab initio on the fly molecular dynamics. Our analysis also demonstrates the importance of a reliable, high-dimensional PES to describe reaction rates for dissociative adsorption of molecules at surfaces.
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.
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.
Highlights: • States with identical steady dissipation rate are distinguished. • Variation of the entropy production rate with the reaction rate is studied. • Enzyme catalysis under chemiostatic condition is taken as an exactly-solvable test case. • A connection between the total entropy production with the enzyme efficiency is established. • The treatment is generalized to a more complex kinetic network. - Abstract: A non-equilibrium steady state is characterized by a non-zero steady dissipation rate. Chemical reaction systems under suitable conditions may generate such states. We propose here a method that is able to distinguish states with identical values of the steady dissipation rate. This necessitates a study of the variation of the entropy production rate with the experimentally observable reaction rate in regions close to the steady states. As an exactly-solvable test case, we choose the problem of enzyme catalysis. Link of the total entropy production with the enzyme efficiency is also established, offering a desirable connection with the inherent irreversibility of the process. The chief outcomes are finally noted in a more general reaction network with numerical demonstrations
Theoretical models for the emergence of biomolecular homochirality
Walker, Sara Imari
Little is known about the emergence of life from nonliving precursors. A key missing-piece is the origin of homochirality: nearly all life is characterized by exclusively dextrorotary sugars and levorotary amino acids. The research presented in this thesis addresses the challenge of uncovering mechanisms for chiral symmetry breaking in a prebiotic environment and implications for the origin of life on Earth. Expanding on a well-known model for chiral selection through polymerization, and modeling the spatiotemporal dynamics starting from near-racemic initial conditions, it is demonstrated that the net chirality of molecular building blocks grows with the longest polymer in the reaction network (of length N) with critical behavior for the onset of chiral asymmetry determined by the value of N. This surprising result indicates that significant chiral asymmetry occurs only for systems which permit growth of long polymers. Expanding on this work, the effects of environmental disturbances on the evolution of chirality in prebiotic reaction-diffusion networks are studied via the implementation of a stochastic spatiotemporal Langevin equation. The results show that environmental interactions can have significant impact on the evolution of prebiotic chirality: the history of prebiotic chirality is therefore interwoven with the Earths early environmental history in a mechanism we call punctuated chirality. This result establishes that the onset of homochirality is not an isolated phenomenon: chiral selection must occur in tandem with the transition from chemistry to biology, otherwise the prebiotic soup is unstable to environmental events. Addressing the challenge of understanding the role of chirality in the transition from non-life to life, the diffusive slowdown of reaction networks induced, for example, through tidal cycles or evaporating pools, is modeled. The results of this study demonstrate that such diffusive slowdown leads to the stabilization of homochiral
Resonance Reaction in Diffusion-Influenced Bimolecular Reactions
Kolb, Jakob J; Dzubiella, Joachim
2016-01-01
We investigate the influence of a stochastically fluctuating step-barrier potential on bimolecular reaction rates by exact analytical theory and stochastic simulations. We demonstrate that the system exhibits a new resonant reaction behavior with rate enhancement if an appropriately defined fluctuation decay length is of the order of the system size. Importantly, we find that in the proximity of resonance the standard reciprocal additivity law for diffusion and surface reaction rates is violated due to the dynamical coupling of multiple kinetic processes. Together, these findings may have important repercussions on the correct interpretation of various kinetic reaction problems in complex systems, as, e.g., in biomolecular association or catalysis.
Communication: Resonance reaction in diffusion-influenced bimolecular reactions
Kolb, Jakob J.; Angioletti-Uberti, Stefano; Dzubiella, Joachim
2016-02-01
We investigate the influence of a stochastically fluctuating step-barrier potential on bimolecular reaction rates by exact analytical theory and stochastic simulations. We demonstrate that the system exhibits a new "resonant reaction" behavior with rate enhancement if an appropriately defined fluctuation decay length is of the order of the system size. Importantly, we find that in the proximity of resonance, the standard reciprocal additivity law for diffusion and surface reaction rates is violated due to the dynamical coupling of multiple kinetic processes. Together, these findings may have important repercussions on the correct interpretation of various kinetic reaction problems in complex systems, as, e.g., in biomolecular association or catalysis.
Scanning probe and optical tweezer investigations of biomolecular interactions
Rigby-Singleton, Shellie
2002-07-01
A complex array of intermolecular forces controls the interactions between and within biological molecules. The desire to empirically explore the fundamental forces has led to the development of several biophysical techniques. Of these, the atomic force microscope (AFM) and the optical tweezers have been employed throughout this thesis to monitor the intermolecular forces involved in biomolecular interactions. The AFM is a well-established force sensing technique capable of measuring biomolecular interactions at a single molecule level. However, its versatility has not been extrapolated to the investigation of a drug-enzyme complex. The energy landscape for the force induced dissociation of the DHFR-methotrexate complex was studied. Revealing an energy barrier to dissociation located {approx}0.3 nm from the bound state. Unfortunately, the AFM has a limited range of accessible loading rates and in order to profile the complete energy landscape alternative force sensing instrumentation should be considered, for example the BFP and optical tweezers. Thus, this thesis outlines the development and construction an optical trap capable of measuring intermolecular forces between biomolecules at the single molecule level. To demonstrate the force sensing abilities of the optical set up, proof of principle measurements were performed which investigate the interactions between proteins and polymer surfaces subjected to varying degrees of argon plasma treatment. Complementary data was gained from measurements performed independently by the AFM. Changes in polymer resistance to proteins as a response to changes in polymer surface chemistry were detected utilising both AFM and optical tweezers measurements. Finally, the AFM and optical tweezers were employed as ultrasensitive biosensors. Single molecule investigations of the antibody-antigen interaction between the cardiac troponin I marker and its complementary antibody, reveals the impact therapeutic concentrations of heparin
Plyasunov, S
2005-01-01
This paper is concerned with classes of models of stochastic reaction dynamics with time-scales separation. We demonstrate that the existence of the time-scale separation naturally leads to the application of the averaging principle and elimination of degrees of freedom via the renormalization of transition rates of slow reactions. The method suggested in this work is more general than other approaches presented previously: it is not limited to a particular type of stochastic processes and can be applied to different types of processes describing fast dynamics, and also provides crossover to the case when separation of time scales is not well pronounced. We derive a family of exact fluctuation-dissipation relations which establish the connection between effective rates and the statistics of the reaction events in fast reaction channels. An illustration of the technique is provided. Examples show that renormalized transition rates exhibit in general non-exponential relaxation behavior with a broad range of pos...
Delay-induced Turing-like waves for one-species reaction-diffusion model on a network
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.
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)
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...... 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 ...
MPBEC, a Matlab Program for Biomolecular Electrostatic Calculations
Vergara-Perez, Sandra; Marucho, Marcelo
2016-01-01
One of the most used and efficient approaches to compute electrostatic properties of biological systems is to numerically solve the Poisson-Boltzmann (PB) equation. There are several software packages available that solve the PB equation for molecules in aqueous electrolyte solutions. Most of these software packages are useful for scientists with specialized training and expertise in computational biophysics. However, the user is usually required to manually take several important choices, depending on the complexity of the biological system, to successfully obtain the numerical solution of the PB equation. This may become an obstacle for researchers, experimentalists, even students with no special training in computational methodologies. Aiming to overcome this limitation, in this article we present MPBEC, a free, cross-platform, open-source software that provides non-experts in the field an easy and efficient way to perform biomolecular electrostatic calculations on single processor computers. MPBEC is a Matlab script based on the Adaptative Poisson-Boltzmann Solver, one of the most popular approaches used to solve the PB equation. MPBEC does not require any user programming, text editing or extensive statistical skills, and comes with detailed user-guide documentation. As a unique feature, MPBEC includes a useful graphical user interface (GUI) application which helps and guides users to configure and setup the optimal parameters and approximations to successfully perform the required biomolecular electrostatic calculations. The GUI also incorporates visualization tools to facilitate users pre- and post-analysis of structural and electrical properties of biomolecules.
Biomolecular interaction analysis for carbon nanotubes and for biocompatibility prediction.
Chen, Xiaoping; Fang, Jinzhang; Cheng, Yun; Zheng, Jianhui; Zhang, Jingjing; Chen, Tao; Ruan, Benfang Helen
2016-07-15
The interactions between carbon nanotubes (CNTs) and biologics have been commonly studied by various microscopy and spectroscopy methods. We tried biomolecular interaction analysis to measure the kinetic interactions between proteins and CNTs. The analysis demonstrated that wheat germ agglutinin (WGA) and other proteins have high affinity toward carboxylated CNT (f-MWCNT) but essentially no binding to normal CNT (p-MWCNT). The binding of f-MWCNT-protein showed dose dependence, and the observed kinetic constants were in the range of 10(-9) to 10(-11) M with very small off-rates (10(-3) to 10(-7) s(-1)), indicating a relatively tight and stable f-MWCNT-protein complex formation. Interestingly in hemolysis assay, p-MWCNT showed good biocompatibility, f-MWCNT caused 30% hemolysis, but WGA-coated f-MWCNT did not show hemolysis. Furthermore, the f-MWCNT-WGA complex demonstrated enhanced cytotoxicity toward cancer cells, perhaps through the glycoproteins expressed on the cells' surface. Taken together, biomolecular interaction analysis is a precise method that might be useful in evaluating the binding affinity of biologics to CNTs and in predicting biological actions. PMID:27108187
Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks
Liao, S.; Vejchodský, Tomáš; Erban, R.
2015-01-01
Roč. 12, č. 108 (2015), s. 20150233. ISSN 1742-5689 EU Projects: European Commission(XE) 328008 - STOCHDETBIOMODEL Institutional support : RVO:67985840 Keywords : gene regulatory networks * stochastic modelling * parametric analysis Subject RIV: BA - General Mathematics Impact factor: 3.917, year: 2014 http://rsif.royalsocietypublishing.org/ content /12/108/20150233
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