Characterizing multistationarity regimes in biochemical reaction networks.
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Irene Otero-Muras
Full Text Available Switch like responses appear as common strategies in the regulation of cellular systems. Here we present a method to characterize bistable regimes in biochemical reaction networks that can be of use to both direct and reverse engineering of biological switches. In the design of a synthetic biological switch, it is important to study the capability for bistability of the underlying biochemical network structure. Chemical Reaction Network Theory (CRNT may help at this level to decide whether a given network has the capacity for multiple positive equilibria, based on their structural properties. However, in order to build a working switch, we also need to ensure that the bistability property is robust, by studying the conditions leading to the existence of two different steady states. In the reverse engineering of biological switches, knowledge collected about the bistable regimes of the underlying potential model structures can contribute at the model identification stage to a drastic reduction of the feasible region in the parameter space of search. In this work, we make use and extend previous results of the CRNT, aiming not only to discriminate whether a biochemical reaction network can exhibit multiple steady states, but also to determine the regions within the whole space of parameters capable of producing multistationarity. To that purpose we present and justify a condition on the parameters of biochemical networks for the appearance of multistationarity, and propose an efficient and reliable computational method to check its satisfaction through the parameter space.
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.
SABRE: A Tool for Stochastic Analysis of Biochemical Reaction Networks
Didier, Frederic; Mateescu, Maria; Wolf, Verena
2010-01-01
The importance of stochasticity within biological systems has been shown repeatedly during the last years and has raised the need for efficient stochastic tools. We present SABRE, a tool for stochastic analysis of biochemical reaction networks. SABRE implements fast adaptive uniformization (FAU), a direct numerical approximation algorithm for computing transient solutions of biochemical reaction networks. Biochemical reactions networks represent biological systems studied at a molecular level and these reactions can be modeled as transitions of a Markov chain. SABRE accepts as input the formalism of guarded commands, which it interprets either as continuous-time or as discrete-time Markov chains. Besides operating in a stochastic mode, SABRE may also perform a deterministic analysis by directly computing a mean-field approximation of the system under study. We illustrate the different functionalities of SABRE by means of biological case studies.
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.
Programming the dynamics of biochemical reaction networks.
Simmel, Friedrich C
2013-01-22
The development of complex self-organizing molecular systems for future nanotechnology requires not only robust formation of molecular structures by self-assembly but also precise control over their temporal dynamics. As an exquisite example of such control, in this issue of ACS Nano, Fujii and Rondelez demonstrate a particularly compact realization of a molecular "predator-prey" ecosystem consisting of only three DNA species and three enzymes. The system displays pronounced oscillatory dynamics, in good agreement with the predictions of a simple theoretical model. Moreover, its considerable modularity also allows for ecological studies of competition and cooperation within molecular networks.
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.
Model-order reduction of biochemical reaction networks
Rao, Shodhan; Schaft, Arjan van der; Eunen, Karen van; Bakker, Barbara M.; Jayawardhana, Bayu
2013-01-01
In this paper we propose a model-order reduction method for chemical reaction networks governed by general enzyme kinetics, including the mass-action and Michaelis-Menten kinetics. The model-order reduction method is based on the Kron reduction of the weighted Laplacian matrix which describes the gr
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Marchetti, Luca, E-mail: marchetti@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy); University of Trento, Department of Mathematics (Italy); Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research – University of Trento Centre for Computational and Systems Biology (COSBI), Piazza Manifattura, 1, 38068 Rovereto (Italy)
2016-07-15
This paper introduces HRSSA (Hybrid Rejection-based Stochastic Simulation Algorithm), a new efficient hybrid stochastic simulation algorithm for spatially homogeneous biochemical reaction networks. HRSSA is built on top of RSSA, an exact stochastic simulation algorithm which relies on propensity bounds to select next reaction firings and to reduce the average number of reaction propensity updates needed during the simulation. HRSSA exploits the computational advantage of propensity bounds to manage time-varying transition propensities and to apply dynamic partitioning of reactions, which constitute the two most significant bottlenecks of hybrid simulation. A comprehensive set of simulation benchmarks is provided for evaluating performance and accuracy of HRSSA against other state of the art algorithms.
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.
2013-01-01
Background Stochastic modeling and simulation provide powerful predictive methods for the intrinsic understanding of fundamental mechanisms in complex biochemical networks. Typically, such mathematical models involve networks of coupled jump stochastic processes with a large number of parameters that need to be suitably calibrated against experimental data. In this direction, the parameter sensitivity analysis of reaction networks is an essential mathematical and computational tool, yielding information regarding the robustness and the identifiability of model parameters. However, existing sensitivity analysis approaches such as variants of the finite difference method can have an overwhelming computational cost in models with a high-dimensional parameter space. Results We develop a sensitivity analysis methodology suitable for complex stochastic reaction networks with a large number of parameters. The proposed approach is based on Information Theory methods and relies on the quantification of information loss due to parameter perturbations between time-series distributions. For this reason, we need to work on path-space, i.e., the set consisting of all stochastic trajectories, hence the proposed approach is referred to as “pathwise”. The pathwise sensitivity analysis method is realized by employing the rigorously-derived Relative Entropy Rate, which is directly computable from the propensity functions. A key aspect of the method is that an associated pathwise Fisher Information Matrix (FIM) is defined, which in turn constitutes a gradient-free approach to quantifying parameter sensitivities. The structure of the FIM turns out to be block-diagonal, revealing hidden parameter dependencies and sensitivities in reaction networks. Conclusions As a gradient-free method, the proposed sensitivity analysis provides a significant advantage when dealing with complex stochastic systems with a large number of parameters. In addition, the knowledge of the structure of the
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.
A moment-convergence method for stochastic analysis of biochemical reaction networks.
Zhang, Jiajun; Nie, Qing; Zhou, Tianshou
2016-05-21
Traditional moment-closure methods need to assume that high-order cumulants of a probability distribution approximate to zero. However, this strong assumption is not satisfied for many biochemical reaction networks. Here, we introduce convergent moments (defined in mathematics as the coefficients in the Taylor expansion of the probability-generating function at some point) to overcome this drawback of the moment-closure methods. As such, we develop a new analysis method for stochastic chemical kinetics. This method provides an accurate approximation for the master probability equation (MPE). In particular, the connection between low-order convergent moments and rate constants can be more easily derived in terms of explicit and analytical forms, allowing insights that would be difficult to obtain through direct simulation or manipulation of the MPE. In addition, it provides an accurate and efficient way to compute steady-state or transient probability distribution, avoiding the algorithmic difficulty associated with stiffness of the MPE due to large differences in sizes of rate constants. Applications of the method to several systems reveal nontrivial stochastic mechanisms of gene expression dynamics, e.g., intrinsic fluctuations can induce transient bimodality and amplify transient signals, and slow switching between promoter states can increase fluctuations in spatially heterogeneous signals. The overall approach has broad applications in modeling, analysis, and computation of complex biochemical networks with intrinsic noise.
Ruess, Jakob
2015-12-28
Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space.
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.
Kügler, Philipp; Yang, Wei
2014-06-01
Model building of biochemical reaction networks typically involves experiments in which changes in the behavior due to natural or experimental perturbations are observed. Computational models of reaction networks are also used in a systems biology approach to study how transitions from a healthy to a diseased state result from changes in genetic or environmental conditions. In this paper we consider the nonlinear inverse problem of inferring information about the Jacobian of a Langevin type network model from covariance data of steady state concentrations associated to two different experimental conditions. Under idealized assumptions on the Langevin fluctuation matrices we prove that relative alterations in the network Jacobian can be uniquely identified when comparing the two data sets. Based on this result and the premise that alteration is locally confined to separable parts due to network modularity we suggest a computational approach using hybrid stochastic-deterministic optimization for the detection of perturbations in the network Jacobian using the sparsity promoting effect of [Formula: see text]-penalization. Our approach is illustrated by means of published metabolomic and signaling reaction networks.
Translated chemical reaction networks.
Johnston, Matthew D
2014-05-01
Many biochemical and industrial applications involve complicated networks of simultaneously occurring chemical reactions. Under the assumption of mass action kinetics, the dynamics of these chemical reaction networks are governed by systems of polynomial ordinary differential equations. The steady states of these mass action systems have been analyzed via a variety of techniques, including stoichiometric network analysis, deficiency theory, and algebraic techniques (e.g., Gröbner bases). In this paper, we present a novel method for characterizing the steady states of mass action systems. Our method explicitly links a network's capacity to permit a particular class of steady states, called toric steady states, to topological properties of a generalized network called a translated chemical reaction network. These networks share their reaction vectors with their source network but are permitted to have different complex stoichiometries and different network topologies. We apply the results to examples drawn from the biochemical literature.
Dynamic analysis of biochemical network using complex network method
Directory of Open Access Journals (Sweden)
Wang Shuqiang
2015-01-01
Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.
Model reduction and parameter estimation of non-linear dynamical biochemical reaction networks.
Sun, Xiaodian; Medvedovic, Mario
2016-02-01
Parameter estimation for high dimension complex dynamic system is a hot topic. However, the current statistical model and inference approach is known as a large p small n problem. How to reduce the dimension of the dynamic model and improve the accuracy of estimation is more important. To address this question, the authors take some known parameters and structure of system as priori knowledge and incorporate it into dynamic model. At the same time, they decompose the whole dynamic model into subset network modules, based on different modules, and then they apply different estimation approaches. This technique is called Rao-Blackwellised particle filters decomposition methods. To evaluate the performance of this method, the authors apply it to synthetic data generated from repressilator model and experimental data of the JAK-STAT pathway, but this method can be easily extended to large-scale cases.
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St Laurent Georges
2010-03-01
Full Text Available Abstract Background Signal transduction networks represent the information processing systems that dictate which dynamical regimes of biochemical activity can be accessible to a cell under certain circumstances. One of the major concerns in molecular systems biology is centered on the elucidation of the robustness properties and information processing capabilities of signal transduction networks. Achieving this goal requires the establishment of causal relations between the design principle of biochemical reaction systems and their emergent dynamical behaviors. Methods In this study, efforts were focused in the construction of a relatively well informed, deterministic, non-linear dynamic model, accounting for reaction mechanisms grounded on standard mass action and Hill saturation kinetics, of the canonical reaction topology underlying Toll-like receptor 4 (TLR4-mediated signaling events. This signaling mechanism has been shown to be deployed in macrophages during a relatively short time window in response to lypopolysaccharyde (LPS stimulation, which leads to a rapidly mounted innate immune response. An extensive computational exploration of the biochemical reaction space inhabited by this signal transduction network was performed via local and global perturbation strategies. Importantly, a broad spectrum of biologically plausible dynamical regimes accessible to the network in widely scattered regions of parameter space was reconstructed computationally. Additionally, experimentally reported transcriptional readouts of target pro-inflammatory genes, which are actively modulated by the network in response to LPS stimulation, were also simulated. This was done with the main goal of carrying out an unbiased statistical assessment of the intrinsic robustness properties of this canonical reaction topology. Results Our simulation results provide convincing numerical evidence supporting the idea that a canonical reaction mechanism of the TLR4
Associative learning in biochemical networks
Ghandi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel
2007-01-01
We develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. In our model, two types of replicating molecules, denoted A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted GA and GB, respectively. It is also assu...
Explorations into Chemical Reactions and Biochemical Pathways.
Gasteiger, Johann
2016-12-01
A brief overview of the work in the research group of the present author on extracting knowledge from chemical reaction data is presented. Methods have been developed to calculate physicochemical effects at the reaction site. It is shown that these physicochemical effects can quite favourably be used to derive equations for the calculation of data on gas phase reactions and on reactions in solution such as aqueous acidity of alcohols or carboxylic acids or the hydrolysis of amides. Furthermore, it is shown that these physicochemical effects are quite effective for assigning reactions into reaction classes that correspond to chemical knowledge. Biochemical reactions constitute a particularly interesting and challenging task for increasing our understanding of living species. The BioPath.Database is a rich source of information on biochemical reactions and has been used for a variety of applications of chemical, biological, or medicinal interests. Thus, it was shown that biochemical reactions can be assigned by the physicochemical effects into classes that correspond to the classification of enzymes by the EC numbers. Furthermore, 3D models of reaction intermediates can be used for searching for novel enzyme inhibitors. It was shown in a combined application of chemoinformatics and bioinformatics that essential pathways of diseases can be uncovered. Furthermore, a study showed that bacterial flavor-forming pathways can be discovered.
Vector Encoding in Biochemical Networks
Potter, Garrett; Sun, Bo
Encoding of environmental cues via biochemical signaling pathways is of vital importance in the transmission of information for cells in a network. The current literature assumes a single cell state is used to encode information, however, recent research suggests the optimal strategy utilizes a vector of cell states sampled at various time points. To elucidate the optimal sampling strategy for vector encoding, we take an information theoretic approach and determine the mutual information of the calcium signaling dynamics obtained from fibroblast cells perturbed with different concentrations of ATP. Specifically, we analyze the sampling strategies under the cases of fixed and non-fixed vector dimension as well as the efficiency of these strategies. Our results show that sampling with greater frequency is optimal in the case of non-fixed vector dimension but that, in general, a lower sampling frequency is best from both a fixed vector dimension and efficiency standpoint. Further, we find the use of a simple modified Ornstein-Uhlenbeck process as a model qualitatively captures many of our experimental results suggesting that sampling in biochemical networks is based on a few basic components.
Associative learning in biochemical networks.
Gandhi, Nikhil; Ashkenasy, Gonen; Tannenbaum, Emmanuel
2007-11-07
It has been recently suggested that there are likely generic features characterizing the emergence of systems constructed from the self-organization of self-replicating agents acting under one or more selection pressures. Therefore, structures and behaviors at one length scale may be used to infer analogous structures and behaviors at other length scales. Motivated by this suggestion, we seek to characterize various "animate" behaviors in biochemical networks, and the influence that these behaviors have on genomic evolution. Specifically, in this paper, we develop a simple, chemostat-based model illustrating how a process analogous to associative learning can occur in a biochemical network. Associative learning is a form of learning whereby a system "learns" to associate two stimuli with one another. Associative learning, also known as conditioning, is believed to be a powerful learning process at work in the brain (associative learning is essentially "learning by analogy"). In our model, two types of replicating molecules, denoted as A and B, are present in some initial concentration in the chemostat. Molecules A and B are stimulated to replicate by some growth factors, denoted as G(A) and G(B), respectively. It is also assumed that A and B can covalently link, and that the conjugated molecule can be stimulated by either the G(A) or G(B) growth factors (and can be degraded). We show that, if the chemostat is stimulated by both growth factors for a certain time, followed by a time gap during which the chemostat is not stimulated at all, and if the chemostat is then stimulated again by only one of the growth factors, then there will be a transient increase in the number of molecules activated by the other growth factor. Therefore, the chemostat bears the imprint of earlier, simultaneous stimulation with both growth factors, which is indicative of associative learning. It is interesting to note that the dynamics of our model is consistent with certain aspects of
Identification of biochemical network modules based on shortest retroactive distances.
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Gautham Vivek Sridharan
2011-11-01
Full Text Available Modularity analysis offers a route to better understand the organization of cellular biochemical networks as well as to derive practically useful, simplified models of these complex systems. While there is general agreement regarding the qualitative properties of a biochemical module, there is no clear consensus on the quantitative criteria that may be used to systematically derive these modules. In this work, we investigate cyclical interactions as the defining characteristic of a biochemical module. We utilize a round trip distance metric, termed Shortest Retroactive Distance (ShReD, to characterize the retroactive connectivity between any two reactions in a biochemical network and to group together network components that mutually influence each other. We evaluate the metric on two types of networks that feature feedback interactions: (i epidermal growth factor receptor (EGFR signaling and (ii liver metabolism supporting drug transformation. For both networks, the ShReD partitions found hierarchically arranged modules that confirm biological intuition. In addition, the partitions also revealed modules that are less intuitive. In particular, ShReD-based partition of the metabolic network identified a 'redox' module that couples reactions of glucose, pyruvate, lipid and drug metabolism through shared production and consumption of NADPH. Our results suggest that retroactive interactions arising from feedback loops and metabolic cycles significantly contribute to the modularity of biochemical networks. For metabolic networks, cofactors play an important role as allosteric effectors that mediate the retroactive interactions.
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.
Elimination of intermediate species in multiscale stochastic reaction networks
DEFF Research Database (Denmark)
Cappelletti, Daniele; Wiuf, Carsten
2016-01-01
We study networks of biochemical reactions modelled by continuoustime Markov processes. Such networks typically contain many molecular species and reactions and are hard to study analytically as well as by simulation. Particularly, we are interested in reaction networks with intermediate species ...
Inferring biochemical reaction pathways: the case of the gemcitabine pharmacokinetics
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Lecca Paola
2012-05-01
Full Text Available Abstract Background The representation of a biochemical system as a network is the precursor of any mathematical model of the processes driving the dynamics of that system. Pharmacokinetics uses mathematical models to describe the interactions between drug, and drug metabolites and targets and through the simulation of these models predicts drug levels and/or dynamic behaviors of drug entities in the body. Therefore, the development of computational techniques for inferring the interaction network of the drug entities and its kinetic parameters from observational data is raising great interest in the scientific community of pharmacologists. In fact, the network inference is a set of mathematical procedures deducing the structure of a model from the experimental data associated to the nodes of the network of interactions. In this paper, we deal with the inference of a pharmacokinetic network from the concentrations of the drug and its metabolites observed at discrete time points. Results The method of network inference presented in this paper is inspired by the theory of time-lagged correlation inference with regard to the deduction of the interaction network, and on a maximum likelihood approach with regard to the estimation of the kinetic parameters of the network. Both network inference and parameter estimation have been designed specifically to identify systems of biotransformations, at the biochemical level, from noisy time-resolved experimental data. We use our inference method to deduce the metabolic pathway of the gemcitabine. The inputs to our inference algorithm are the experimental time series of the concentration of gemcitabine and its metabolites. The output is the set of reactions of the metabolic network of the gemcitabine. Conclusions Time-lagged correlation based inference pairs up to a probabilistic model of parameter inference from metabolites time series allows the identification of the microscopic pharmacokinetics and
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...
Balanced biochemical reactions: a new approach to unify chemical and biochemical thermodynamics.
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Antonio Sabatini
Full Text Available A novel procedure is presented which, by balancing elements and electric charge of biochemical reactions which occur at constant pH and pMg, allows assessing the thermodynamics properties of reaction Δ(rG'⁰, Δ(rH'⁰, Δ(rS'⁰ and the change in binding of hydrogen and magnesium ions of these reactions. This procedure of general applicability avoids the complex calculations required by the use of the Legendre transformed thermodynamic properties of formation Δ(fG'⁰, Δ(fH'⁰ and Δ(fS'⁰ hitherto considered an obligatory prerequisite to deal with the thermodynamics of biochemical reactions. As a consequence, the term "conditional" is proposed in substitution of "Legendre transformed" to indicate these thermodynamics properties. It is also shown that the thermodynamic potential G is fully adequate to give a criterion of spontaneous chemical change for all biochemical reactions and then that the use of the Legendre transformed G' is unnecessary. The procedure proposed can be applied to any biochemical reaction, making possible to re-unify the two worlds of chemical and biochemical thermodynamics, which so far have been treated separately.
BNDB – The Biochemical Network Database
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Kaufmann Michael
2007-10-01
Full Text Available Abstract Background Technological advances in high-throughput techniques and efficient data acquisition methods have resulted in a massive amount of life science data. The data is stored in numerous databases that have been established over the last decades and are essential resources for scientists nowadays. However, the diversity of the databases and the underlying data models make it difficult to combine this information for solving complex problems in systems biology. Currently, researchers typically have to browse several, often highly focused, databases to obtain the required information. Hence, there is a pressing need for more efficient systems for integrating, analyzing, and interpreting these data. The standardization and virtual consolidation of the databases is a major challenge resulting in a unified access to a variety of data sources. Description We present the Biochemical Network Database (BNDB, a powerful relational database platform, allowing a complete semantic integration of an extensive collection of external databases. BNDB is built upon a comprehensive and extensible object model called BioCore, which is powerful enough to model most known biochemical processes and at the same time easily extensible to be adapted to new biological concepts. Besides a web interface for the search and curation of the data, a Java-based viewer (BiNA provides a powerful platform-independent visualization and navigation of the data. BiNA uses sophisticated graph layout algorithms for an interactive visualization and navigation of BNDB. Conclusion BNDB allows a simple, unified access to a variety of external data sources. Its tight integration with the biochemical network library BN++ offers the possibility for import, integration, analysis, and visualization of the data. BNDB is freely accessible at http://www.bndb.org.
Thermodynamics of random reaction networks.
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Jakob Fischer
Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.
Accurate atom-mapping computation for biochemical reactions.
Latendresse, Mario; Malerich, Jeremiah P; Travers, Mike; Karp, Peter D
2012-11-26
The complete atom mapping of a chemical reaction is a bijection of the reactant atoms to the product atoms that specifies the terminus of each reactant atom. Atom mapping of biochemical reactions is useful for many applications of systems biology, in particular for metabolic engineering where synthesizing new biochemical pathways has to take into account for the number of carbon atoms from a source compound that are conserved in the synthesis of a target compound. Rapid, accurate computation of the atom mapping(s) of a biochemical reaction remains elusive despite significant work on this topic. In particular, past researchers did not validate the accuracy of mapping algorithms. We introduce a new method for computing atom mappings called the minimum weighted edit-distance (MWED) metric. The metric is based on bond propensity to react and computes biochemically valid atom mappings for a large percentage of biochemical reactions. MWED models can be formulated efficiently as Mixed-Integer Linear Programs (MILPs). We have demonstrated this approach on 7501 reactions of the MetaCyc database for which 87% of the models could be solved in less than 10 s. For 2.1% of the reactions, we found multiple optimal atom mappings. We show that the error rate is 0.9% (22 reactions) by comparing these atom mappings to 2446 atom mappings of the manually curated Kyoto Encyclopedia of Genes and Genomes (KEGG) RPAIR database. To our knowledge, our computational atom-mapping approach is the most accurate and among the fastest published to date. The atom-mapping data will be available in the MetaCyc database later in 2012; the atom-mapping software will be available within the Pathway Tools software later in 2012.
Concordant chemical reaction networks and the Species-Reaction Graph.
Shinar, Guy; Feinberg, Martin
2013-01-01
In a recent paper it was shown that, for chemical reaction networks possessing a subtle structural property called concordance, dynamical behavior of a very circumscribed (and largely stable) kind is enforced, so long as the kinetics lies within the very broad and natural weakly monotonic class. In particular, multiple equilibria are precluded, as are degenerate positive equilibria. Moreover, under certain circumstances, also related to concordance, all real eigenvalues associated with a positive equilibrium are negative. Although concordance of a reaction network can be decided by readily available computational means, we show here that, when a nondegenerate network's Species-Reaction Graph satisfies certain mild conditions, concordance and its dynamical consequences are ensured. These conditions are weaker than earlier ones invoked to establish kinetic system injectivity, which, in turn, is just one ramification of network concordance. Because the Species-Reaction Graph resembles pathway depictions often drawn by biochemists, results here expand the possibility of inferring significant dynamical information directly from standard biochemical reaction diagrams.
Plant Glutathione Biosynthesis: Diversity in Biochemical Regulation and Reaction Products
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Ashley eGalant
2011-09-01
Full Text Available In plants, exposure to temperature extremes, heavy metal-contaminated soils, drought, air pollutants, and pathogens results in the generation of reactive oxygen species that alter the intracellular redox environment, which in turn influences signaling pathways and cell fate. As part of their response to these stresses, plants produce glutathione. Glutathione acts as an antioxidant by quenching reactive oxygen species, and is involved in the ascorbate-glutathione cycle that eliminates damaging peroxides. Plants also use glutathione for the detoxification of xenobiotics, herbicides, air pollutants (sulfur dioxide and ozone, and toxic heavy metals. Two enzymes catalyze glutathione synthesis: glutamate-cysteine ligase (GCL, and glutathione synthetase (GS. Glutathione is a ubiquitous protective compound in plants, but the structural and functional details of the proteins that synthesize it, as well as the potential biochemical mechanisms of their regulation, have only begun to be explored. As discussed here, the core reactions of glutathione synthesis are conserved across various organisms, but plants have diversified both the regulatory mechanisms that control its synthesis and the range of products derived from this pathway. Understanding the molecular basis of glutathione biosynthesis and its regulation will expand our knowledge of this component in the plant stress response network.
Plant glutathione biosynthesis: diversity in biochemical regulation and reaction products.
Galant, Ashley; Preuss, Mary L; Cameron, Jeffrey C; Jez, Joseph M
2011-01-01
In plants, exposure to temperature extremes, heavy metal-contaminated soils, drought, air pollutants, and pathogens results in the generation of reactive oxygen species that alter the intracellular redox environment, which in turn influences signaling pathways and cell fate. As part of their response to these stresses, plants produce glutathione. Glutathione acts as an anti-oxidant by quenching reactive oxygen species, and is involved in the ascorbate-glutathione cycle that eliminates damaging peroxides. Plants also use glutathione for the detoxification of xenobiotics, herbicides, air pollutants (sulfur dioxide and ozone), and toxic heavy metals. Two enzymes catalyze glutathione synthesis: glutamate-cysteine ligase, and glutathione synthetase. Glutathione is a ubiquitous protective compound in plants, but the structural and functional details of the proteins that synthesize it, as well as the potential biochemical mechanisms of their regulation, have only begun to be explored. As discussed here, the core reactions of glutathione synthesis are conserved across various organisms, but plants have diversified both the regulatory mechanisms that control its synthesis and the range of products derived from this pathway. Understanding the molecular basis of glutathione biosynthesis and its regulation will expand our knowledge of this component in the plant stress response network.
Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems
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Goutsias John
2010-11-01
Full Text Available Abstract Background Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable. Results We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html. Conclusions Our approach provides an attractive statistical methodology for
Coarse-graining stochastic biochemical networks: adiabaticity and fast simulations
Energy Technology Data Exchange (ETDEWEB)
Nemenman, Ilya [Los Alamos National Laboratory; Sinitsyn, Nikolai [Los Alamos National Laboratory; Hengartner, Nick [Los Alamos National Laboratory
2008-01-01
We propose a universal approach for analysis and fast simulations of stiff stochastic biochemical kinetics networks, which rests on elimination of fast chemical species without a loss of information about mesoscoplc, non-Poissonian fluctuations of the slow ones. Our approach, which is similar to the Born-Oppenhelmer approximation in quantum mechanics, follows from the stochastic path Integral representation of the cumulant generating function of reaction events. In applications with a small number of chemIcal reactions, It produces analytical expressions for cumulants of chemical fluxes between the slow variables. This allows for a low-dimensional, Interpretable representation and can be used for coarse-grained numerical simulation schemes with a small computational complexity and yet high accuracy. As an example, we derive the coarse-grained description for a chain of biochemical reactions, and show that the coarse-grained and the microscopic simulations are in an agreement, but the coarse-gralned simulations are three orders of magnitude faster.
Complete integrability of information processing by biochemical reactions
Agliari, Elena; Barra, Adriano; Dello Schiavo, Lorenzo; Moro, Antonio
2016-11-01
Statistical mechanics provides an effective framework to investigate information processing in biochemical reactions. Within such framework far-reaching analogies are established among (anti-) cooperative collective behaviors in chemical kinetics, (anti-)ferromagnetic spin models in statistical mechanics and operational amplifiers/flip-flops in cybernetics. The underlying modeling - based on spin systems - has been proved to be accurate for a wide class of systems matching classical (e.g. Michaelis-Menten, Hill, Adair) scenarios in the infinite-size approximation. However, the current research in biochemical information processing has been focusing on systems involving a relatively small number of units, where this approximation is no longer valid. Here we show that the whole statistical mechanical description of reaction kinetics can be re-formulated via a mechanical analogy - based on completely integrable hydrodynamic-type systems of PDEs - which provides explicit finite-size solutions, matching recently investigated phenomena (e.g. noise-induced cooperativity, stochastic bi-stability, quorum sensing). The resulting picture, successfully tested against a broad spectrum of data, constitutes a neat rationale for a numerically effective and theoretically consistent description of collective behaviors in biochemical reactions.
Modelling biochemical reaction systems by stochastic differential equations with reflection.
Niu, Yuanling; Burrage, Kevin; Chen, Luonan
2016-05-07
In this paper, we gave a new framework for modelling and simulating biochemical reaction systems by stochastic differential equations with reflection not in a heuristic way but in a mathematical way. The model is computationally efficient compared with the discrete-state Markov chain approach, and it ensures that both analytic and numerical solutions remain in a biologically plausible region. Specifically, our model mathematically ensures that species numbers lie in the domain D, which is a physical constraint for biochemical reactions, in contrast to the previous models. The domain D is actually obtained according to the structure of the corresponding chemical Langevin equations, i.e., the boundary is inherent in the biochemical reaction system. A variant of projection method was employed to solve the reflected stochastic differential equation model, and it includes three simple steps, i.e., Euler-Maruyama method was applied to the equations first, and then check whether or not the point lies within the domain D, and if not perform an orthogonal projection. It is found that the projection onto the closure D¯ is the solution to a convex quadratic programming problem. Thus, existing methods for the convex quadratic programming problem can be employed for the orthogonal projection map. Numerical tests on several important problems in biological systems confirmed the efficiency and accuracy of this approach.
Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm
Energy Technology Data Exchange (ETDEWEB)
Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research - University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento, Trento (Italy)
2015-08-07
We address the problem of simulating biochemical reaction networks with time-dependent rates and propose a new algorithm based on our rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)]. The computation for selecting next reaction firings by our time-dependent RSSA (tRSSA) is computationally efficient. Furthermore, the generated trajectory is exact by exploiting the rejection-based mechanism. We benchmark tRSSA on different biological systems with varying forms of reaction rates to demonstrate its applicability and efficiency. We reveal that for nontrivial cases, the selection of reaction firings in existing algorithms introduces approximations because the integration of reaction rates is very computationally demanding and simplifying assumptions are introduced. The selection of the next reaction firing by our approach is easier while preserving the exactness.
Dynamic biochemical reaction process analysis and pathway modification predictions.
Conejeros, R; Vassiliadis, V S
2000-05-05
Recently, the area of model predictive modification of biochemical pathways has received attention with the aim to increase the productivity of microbial systems. In this study, we present a generalization of previous work, where, using a sensitivity study over the fermentation as a dynamic system, the optimal selection of reaction steps for modification (amplification or attenuation) is determined. The influence of metabolites in the activity of enzymes has also been considered (through activation or inhibition). We further introduce a new concept in the dynamic modeling of biochemical reaction systems including a generalized continuous superstructure in which two artificial multiplicative terms are included to account for: (a) enzyme overexpression or underexpression (attenuation or amplification) for the whole enzyme pool; and (b) modification of the apparent order of a kinetic expression with respect to the concentration of a metabolite or any subset of metabolites participating in the pathway. This new formulation allows the prediction of the sensitivity of the pathway performance index (objective function) with respect to the concentration of the enzyme, as well as the interaction of the enzyme with other metabolites. Using this framework, a case study for the production of penicillin V is analyzed, obtaining the most sensitive reaction steps (or bottlenecks) and the most significant regulations of the system, due to the effect of concentration of intracellular metabolites on the activity of each enzyme.
Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)
2001-01-01
Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.
On Projection-Based Model Reduction of Biochemical Networks Part I: The Deterministic Case
Sootla, Aivar; Anderson, James
2014-01-01
This paper addresses the problem of model reduction for dynamical system models that describe biochemical reaction networks. Inherent in such models are properties such as stability, positivity and network structure. Ideally these properties should be preserved by model reduction procedures, although traditional projection based approaches struggle to do this. We propose a projection based model reduction algorithm which uses generalised block diagonal Gramians to preserve structure and posit...
Modular verification of chemical reaction network encodings via serializability analysis.
Lakin, Matthew R; Stefanovic, Darko; Phillips, Andrew
2016-06-13
Chemical reaction networks are a powerful means of specifying the intended behaviour of synthetic biochemical systems. A high-level formal specification, expressed as a chemical reaction network, may be compiled into a lower-level encoding, which can be directly implemented in wet chemistry and may itself be expressed as a chemical reaction network. Here we present conditions under which a lower-level encoding correctly emulates the sequential dynamics of a high-level chemical reaction network. We require that encodings are transactional, such that their execution is divided by a "commit reaction" that irreversibly separates the reactant-consuming phase of the encoding from the product-generating phase. We also impose restrictions on the sharing of species between reaction encodings, based on a notion of "extra tolerance", which defines species that may be shared between encodings without enabling unwanted reactions. Our notion of correctness is serializability of interleaved reaction encodings, and if all reaction encodings satisfy our correctness properties then we can infer that the global dynamics of the system are correct. This allows us to infer correctness of any system constructed using verified encodings. As an example, we show how this approach may be used to verify two- and four-domain DNA strand displacement encodings of chemical reaction networks, and we generalize our result to the limit where the populations of helper species are unlimited.
LucidDraw: Efficiently visualizing complex biochemical networks within MATLAB
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Shi Guiyang
2010-01-01
Full Text Available Abstract Background Biochemical networks play an essential role in systems biology. Rapidly growing network data and versatile research activities call for convenient visualization tools to aid intuitively perceiving abstract structures of networks and gaining insights into the functional implications of networks. There are various kinds of network visualization software, but they are usually not adequate for visual analysis of complex biological networks mainly because of the two reasons: 1 most existing drawing methods suitable for biochemical networks have high computation loads and can hardly achieve near real-time visualization; 2 available network visualization tools are designed for working in certain network modeling platforms, so they are not convenient for general analyses due to lack of broader range of readily accessible numerical utilities. Results We present LucidDraw as a visual analysis tool, which features (a speed: typical biological networks with several hundreds of nodes can be drawn in a few seconds through a new layout algorithm; (b ease of use: working within MATLAB makes it convenient to manipulate and analyze the network data using a broad spectrum of sophisticated numerical functions; (c flexibility: layout styles and incorporation of other available information about functional modules can be controlled by users with little effort, and the output drawings are interactively modifiable. Conclusions Equipped with a new grid layout algorithm proposed here, LucidDraw serves as an auxiliary network analysis tool capable of visualizing complex biological networks in near real-time with controllable layout styles and drawing details. The framework of the algorithm enables easy incorporation of extra biological information, if available, to influence the output layouts with predefined node grouping features.
Chemical Reaction Networks for Computing Polynomials.
Salehi, Sayed Ahmad; Parhi, Keshab K; Riedel, Marc D
2017-01-20
Chemical reaction networks (CRNs) provide a fundamental model in the study of molecular systems. Widely used as formalism for the analysis of chemical and biochemical systems, CRNs have received renewed attention as a model for molecular computation. This paper demonstrates that, with a new encoding, CRNs can compute any set of polynomial functions subject only to the limitation that these functions must map the unit interval to itself. These polynomials can be expressed as linear combinations of Bernstein basis polynomials with positive coefficients less than or equal to 1. In the proposed encoding approach, each variable is represented using two molecular types: a type-0 and a type-1. The value is the ratio of the concentration of type-1 molecules to the sum of the concentrations of type-0 and type-1 molecules. The proposed encoding naturally exploits the expansion of a power-form polynomial into a Bernstein polynomial. Molecular encoders for converting any input in a standard representation to the fractional representation as well as decoders for converting the computed output from the fractional to a standard representation are presented. The method is illustrated first for generic CRNs; then chemical reactions designed for an example are mapped to DNA strand-displacement reactions.
Autocatalytic, bistable, oscillatory networks of biologically relevant organic reactions
Semenov, Sergey N.; Kraft, Lewis J.; Ainla, Alar; Zhao, Mengxia; Baghbanzadeh, Mostafa; Campbell, Victoria E.; Kang, Kyungtae; Fox, Jerome M.; Whitesides, George M.
2016-09-01
Networks of organic chemical reactions are important in life and probably played a central part in its origin. Network dynamics regulate cell division, circadian rhythms, nerve impulses and chemotaxis, and guide the development of organisms. Although out-of-equilibrium networks of chemical reactions have the potential to display emergent network dynamics such as spontaneous pattern formation, bistability and periodic oscillations, the principles that enable networks of organic reactions to develop complex behaviours are incompletely understood. Here we describe a network of biologically relevant organic reactions (amide formation, thiolate-thioester exchange, thiolate-disulfide interchange and conjugate addition) that displays bistability and oscillations in the concentrations of organic thiols and amides. Oscillations arise from the interaction between three subcomponents of the network: an autocatalytic cycle that generates thiols and amides from thioesters and dialkyl disulfides; a trigger that controls autocatalytic growth; and inhibitory processes that remove activating thiol species that are produced during the autocatalytic cycle. In contrast to previous studies that have demonstrated oscillations and bistability using highly evolved biomolecules (enzymes and DNA) or inorganic molecules of questionable biochemical relevance (for example, those used in Belousov-Zhabotinskii-type reactions), the organic molecules we use are relevant to metabolism and similar to those that might have existed on the early Earth. By using small organic molecules to build a network of organic reactions with autocatalytic, bistable and oscillatory behaviour, we identify principles that explain the ways in which dynamic networks relevant to life could have developed. Modifications of this network will clarify the influence of molecular structure on the dynamics of reaction networks, and may enable the design of biomimetic networks and of synthetic self-regulating and evolving
Biochemical reaction engineering and process development in anaerobic wastewater treatment.
Aivasidis, Alexander; Diamantis, Vasileios
2005-01-01
Developments in production technology have frequently resulted in the concentrated local accumulation of highly organic-laden wastewaters. Anaerobic wastewater treatment, in industrial applications, constitutes an advanced method of synthesis by which inexpensive substrates are converted into valuable disproportionate products. A critical discussion of certain fundamental principles of biochemical reaction engineering relevant to the anaerobic mode of operation is made here, with special emphasis on the roles of thermodynamics, kinetics, mass and heat transfer, reactor design, biomass retention and recycling. The applications of the anaerobic processes are discussed, introducing the principles of an upflow anaerobic sludge bed reactor and a fixed-bed loop reactor. The merits of staging reactor systems are presented using selected examples based on two decades of research in the field of anaerobic fermentation and wastewater treatment at the Forschungszentrum Julich (Julich Research Center, Germany). Wastewater treatment is an industrial process associated with one of the largest levels of mass throughput known, and for this reason it provides a major impetus to further developments in bioprocess technology in general.
Information processing by biochemical networks: a dynamic approach.
Bowsher, Clive G
2011-02-06
Understanding how information is encoded and transferred by biochemical networks is of fundamental importance in cellular and systems biology. This requires analysis of the relationships between the stochastic trajectories of the constituent molecular (or submolecular) species that comprise the network. We describe how to identify conditional independences between the trajectories or time courses of groups of species. These are robust network properties that provide important insight into how information is processed. An entire network can then be decomposed exactly into modules on informational grounds. In the context of signalling networks with multiple inputs, the approach identifies the routes and species involved in sequential information processing between input and output modules. An algorithm is developed which allows automated identification of decompositions for large networks and visualization using a tree that encodes the conditional independences. Only stoichiometric information is used and neither simulations nor knowledge of rate parameters are required. A bespoke version of the algorithm for signalling networks identifies the routes of sequential encoding between inputs and outputs, visualized as paths in the tree. Application to the toll-like receptor signalling network reveals that inputs can be informative in ways unanticipated by steady-state analyses, that the information processing structure is not well described as a bow tie, and that encoding for the interferon response is unusually sparse compared with other outputs of this innate immune system.
Molecular codes in biological and chemical reaction networks.
Görlich, Dennis; Dittrich, Peter
2013-01-01
Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio-) chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries), biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades), an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.
Thin-Film Transistor-Based Biosensors for Determining Stoichiometry of Biochemical Reactions
Wang, Yi-Wen; Chen, Ting-Yang; Yang, Tsung-Han; Chang, Cheng-Chung; Yang, Tsung-Lin; Lo, Yu-Hwa
2016-01-01
The enzyme kinetic in a biochemical reaction is critical to scientific research and drug discovery but can hardly be determined experimentally from enzyme assays. In this work, a charge-current transducer (a transistor) is proposed to evaluate the status of biochemical reaction by monitoring the electrical charge changes. Using the malate-aspartate shuttle as an example, a thin-film transistor (TFT)-based biosensor with an extended gold pad is demonstrated to detect the biochemical reaction between NADH and NAD+. The drain current change indicates the status of chemical equilibrium and stoichiometry. PMID:28033412
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.
A study for multiple steady states of biochemical reactions under substrate and product inhibition.
Chien
2000-08-01
This paper combines Sturm's method with the tangent analysis method to solve a biochemical reaction involving multiplicity. This method can easily derive the necessary conditions for multiplicity. In addition, we find a starting bifurcation point for multiplicity which cannot be obtained by the tangent method alone. Moreover, a start-up strategy is suggested to obtain a high conversion and unique steady state in four selected kinetic models of biochemical reactions, with inhibition.
A Networks Approach to Modeling Enzymatic Reactions.
Imhof, P
2016-01-01
Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes.
Efficient Parallel Statistical Model Checking of Biochemical Networks
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Paolo Ballarini
2009-12-01
Full Text Available We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture.
Switching Dynamics in Reaction Networks Induced by Molecular Discreteness
Togashi, Y; Kaneko, Kunihiko; Togashi, Yuichi
2006-01-01
To study the fluctuations and dynamics in chemical reaction processes, stochastic differential equations based on the rate equation involving chemical concentrations are often adopted. When the number of molecules is very small, however, the discreteness in the number of molecules cannot be neglected since the number of molecules must be an integer. This discreteness can be important in biochemical reactions, where the total number of molecules is not significantly larger than the number of chemical species. To elucidate the effects of such discreteness, we study autocatalytic reaction systems comprising several chemical species through stochastic particle simulations. The generation of novel states is observed; it is caused by the extinction of some molecular species due to the discreteness in their number. We demonstrate that the reaction dynamics are switched by a single molecule, which leads to the reconstruction of the acting network structure. We also show the strong dependence of the chemical concentra...
Fast Evaluation of Fluctuations in Biochemical Networks With the Linear Noise Approximation
Elf, Johan; Ehrenberg, Måns
2003-01-01
Biochemical networks in single cells can display large fluctuations in molecule numbers, making mesoscopic approaches necessary for correct system descriptions. We present a general method that allows rapid characterization of the stochastic properties of intracellular networks. The starting point is a macroscopic description that identifies the system's elementary reactions in terms of rate laws and stoichiometries. From this formulation follows directly the stationary solution of the linear noise approximation (LNA) of the Master equation for all the components in the network. The method complements bifurcation studies of the system's parameter dependence by providing estimates of sizes, correlations, and time scales of stochastic fluctuations. We describe how the LNA can give precise system descriptions also near macroscopic instabilities by suitable variable changes and elimination of fast variables. PMID:14597656
Atoms of multistationarity in chemical reaction networks
Joshi, Badal
2011-01-01
Chemical reaction networks taken with mass-action kinetics are dynamical systems that arise in chemical engineering and systems biology. Deciding whether a chemical reaction network admits multiple positive steady states is to determine existence of multiple positive solutions to a system of polynomials with unknown coefficients. In this work, we consider the question of whether the minimal (in a precise sense) networks, which we propose to call `atoms of multistationarity,' characterize the entire set of multistationary networks. We show that if a subnetwork admits multiple nondegenerate positive steady states, then these steady states can be extended to establish multistationarity of a larger network, provided that the two networks share the same stoichiometric subspace. Our result provides the mathematical foundation for a technique used by Siegal-Gaskins et al. of establishing bistability by way of `network ancestry.' Here, our main application is for enumerating small multistationary continuous-flow stir...
Limits for Stochastic Reaction Networks
DEFF Research Database (Denmark)
Cappelletti, Daniele
at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...... of the stochastic reaction systems. Specically, we build a theory for stochastic reaction systems that is parallel to the deciency zero theory for deterministic systems, which dates back to the 70s. A deciency theory for stochastic reaction systems was missing, and few results connecting deciency and stochastic....... Such species, in the deterministic modelling regime, assume always the same value at any positive steady state. In the stochastic setting, we prove that, if the initial condition is a point in the basin of attraction of a positive steady state of the corresponding deterministic model and tends to innity...
Multilayer Network Analysis of Nuclear Reactions
Zhu, Liang; Ma, Yu-Gang; Chen, Qu; Han, Ding-Ding
2016-08-01
The nuclear reaction network is usually studied via precise calculation of differential equation sets, and much research interest has been focused on the characteristics of nuclides, such as half-life and size limit. In this paper, however, we adopt the methods from both multilayer and reaction networks, and obtain a distinctive view by mapping all the nuclear reactions in JINA REACLIB database into a directed network with 4 layers: neutron, proton, 4He and the remainder. The layer names correspond to reaction types decided by the currency particles consumed. This combined approach reveals that, in the remainder layer, the β-stability has high correlation with node degree difference and overlapping coefficient. Moreover, when reaction rates are considered as node strength, we find that, at lower temperatures, nuclide half-life scales reciprocally with its out-strength. The connection between physical properties and topological characteristics may help to explore the boundary of the nuclide chart.
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.
Directory of Open Access Journals (Sweden)
Qian Hong
2008-05-01
Full Text Available Abstract Background: Several approaches, including metabolic control analysis (MCA, flux balance analysis (FBA, correlation metric construction (CMC, and biochemical circuit theory (BCT, have been developed for the quantitative analysis of complex biochemical networks. Here, we present a comprehensive theory of linear analysis for nonequilibrium steady-state (NESS biochemical reaction networks that unites these disparate approaches in a common mathematical framework and thermodynamic basis. Results: In this theory a number of relationships between key matrices are introduced: the matrix A obtained in the standard, linear-dynamic-stability analysis of the steady-state can be decomposed as A = SRT where R and S are directly related to the elasticity-coefficient matrix for the fluxes and chemical potentials in MCA, respectively; the control-coefficients for the fluxes and chemical potentials can be written in terms of RT BS and ST BS respectively where matrix B is the inverse of A; the matrix S is precisely the stoichiometric matrix in FBA; and the matrix eAt plays a central role in CMC. Conclusion: One key finding that emerges from this analysis is that the well-known summation theorems in MCA take different forms depending on whether metabolic steady-state is maintained by flux injection or concentration clamping. We demonstrate that if rate-limiting steps exist in a biochemical pathway, they are the steps with smallest biochemical conductances and largest flux control-coefficients. We hypothesize that biochemical networks for cellular signaling have a different strategy for minimizing energy waste and being efficient than do biochemical networks for biosynthesis. We also discuss the intimate relationship between MCA and biochemical systems analysis (BSA.
Modeling isotopomer distributions in biochemical networks using isotopomer mapping matrices
DEFF Research Database (Denmark)
Schmidt, Karsten; Carlsen, Morten; Nielsen, Jens Bredal
1997-01-01
must be estimated by nonlinear least squares analysis, in which experimental labeling data is compared with simulated steady state isotopomer distributions. Hence, mathematical models are required to compute the steady state isotopomer distribution as a function of a given set of steady state fluxes......Within the last decades NMR spectroscopy has undergone tremendous development and has become a powerful analytical tool for the investigation of intracellular flux distributions in biochemical networks using C-13-labeled substrates. Not only are the experiments much easier to conduct than...... of the isotopomer distribution in metabolite pools can be obtained. The isotopomer distribution is the maximum amount of information that in theory can be obtained from C-13-tracer studies. The wealth of information contained in NMR spectra frequently leads to overdetermined algebraic systems. Consequently, fluxes...
Algebraic Statistical Model for Biochemical Network Dynamics Inference.
Linder, Daniel F; Rempala, Grzegorz A
2013-12-01
With modern molecular quantification methods, like, for instance, high throughput sequencing, biologists may perform multiple complex experiments and collect longitudinal data on RNA and DNA concentrations. Such data may be then used to infer cellular level interactions between the molecular entities of interest. One method which formalizes such inference is the stoichiometric algebraic statistical model (SASM) of [2] which allows to analyze the so-called conic (or single source) networks. Despite its intuitive appeal, up until now the SASM has been only heuristically studied on few simple examples. The current paper provides a more formal mathematical treatment of the SASM, expanding the original model to a wider class of reaction systems decomposable into multiple conic subnetworks. In particular, it is proved here that on such networks the SASM enjoys the so-called sparsistency property, that is, it asymptotically (with the number of observed network trajectories) discards the false interactions by setting their reaction rates to zero. For illustration, we apply the extended SASM to in silico data from a generic decomposable network as well as to biological data from an experimental search for a possible transcription factor for the heat shock protein 70 (Hsp70) in the zebrafish retina.
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.
Linear systems approach to analysis of complex dynamic behaviours in biochemical networks.
Schmidt, H; Jacobsen, E W
2004-06-01
Central functions in the cell are often linked to complex dynamic behaviours, such as sustained oscillations and multistability, in a biochemical reaction network. Determination of the specific mechanisms underlying such behaviours is important, e.g. to determine sensitivity, robustness, and modelling requirements of given cell functions. In this work we adopt a systems approach to the analysis of complex behaviours in intracellular reaction networks, described by ordinary differential equations with known kinetic parameters. We propose to decompose the overall system into a number of low complexity subsystems, and consider the importance of interactions between these in generating specific behaviours. Rather than analysing the network in a state corresponding to the complex non-linear behaviour, we move the system to the underlying unstable steady state, and focus on the mechanisms causing destabilisation of this steady state. This is motivated by the fact that all complex behaviours in unforced systems can be traced to destabilisation (bifurcation) of some steady state, and hence enables us to use tools from linear system theory to qualitatively analyse the sources of given network behaviours. One important objective of the present study is to see how far one can come with a relatively simple approach to the analysis of highly complex biochemical networks. The proposed method is demonstrated by application to a model of mitotic control in Xenopus frog eggs, and to a model of circadian oscillations in Drosophila. In both examples we are able to identify the subsystems, and the related interactions, which are instrumental in generating the observed complex non-linear behaviours.
New Possibilities for Magnetic Control of Chemical and Biochemical Reactions.
Buchachenko, Anatoly; Lawler, Ronald G
2017-02-20
Chemistry is controlled by Coulomb energy; magnetic energy is lower by many orders of magnitude and may be confidently ignored in the energy balance of chemical reactions. The situation becomes less clear, however, when reaction rates are considered. In this case, magnetic perturbations of nearly degenerate energy surface crossings may produce observable, and sometimes even dramatic, effects on reactions rates, product yields, and spectroscopic transitions. A case in point that has been studied for nearly five decades is electron spin-selective chemistry via the intermediacy of radical pairs. Magnetic fields, external (permanent or oscillating) and the internal magnetic fields of magnetic nuclei, have been shown to overcome electron spin selection rules for pairs of reactive paramagnetic intermediates, catalyzing or inhibiting chemical reaction pathways. The accelerating effects of magnetic stimulation may therefore be considered to be magnetic catalysis. This type of catalysis is most commonly observed for reactions of a relatively long-lived radical pair containing two weakly interacting electron spins formed by dissociation of molecules or by electron transfer. The pair may exist in singlet (total electron spin is zero) or triplet (total spin is unity) spin states. In virtually all cases, only the singlet state yields stable reaction products. Magnetic interactions with nuclear spins or applied fields may therefore affect the reactivity of radical pairs by changing the angular momentum of the pairs. Magnetic catalysis, first detected via its effect on spin state populations in nuclear and electron spin resonance, has been shown to function in a great variety of well-characterized reactions of organic free radicals. Considerably less well studied are examples suggesting that the basic mechanism may also explain magnetic effects that stimulate ATP synthesis, eliminating ATP deficiency in cardiac diseases, control cell proliferation, killing cancer cells, and
Trade-Offs in Delayed Information Transmission in Biochemical Networks
Mancini, F.; Marsili, M.; Walczak, A. M.
2016-03-01
In order to transmit biochemical signals, biological regulatory systems dissipate energy with concomitant entropy production. Additionally, signaling often takes place in challenging environmental conditions. In a simple model regulatory circuit given by an input and a delayed output, we explore the trade-offs between information transmission and the system's energetic efficiency. We determine the maximally informative network, given a fixed amount of entropy production and a delayed response, exploring both the case with and without feedback. We find that feedback allows the circuit to overcome energy constraints and transmit close to the maximum available information even in the dissipationless limit. Negative feedback loops, characteristic of shock responses, are optimal at high dissipation. Close to equilibrium positive feedback loops, known for their stability, become more informative. Asking how the signaling network should be constructed to best function in the worst possible environment, rather than an optimally tuned one or in steady state, we discover that at large dissipation the same universal motif is optimal in all of these conditions.
Thermodynamically based constraints for rate coefficients of large biochemical networks.
Vlad, Marcel O; Ross, John
2009-01-01
Wegscheider cyclicity conditions are relationships among the rate coefficients of a complex reaction network, which ensure the compatibility of kinetic equations with the conditions for thermodynamic equilibrium. The detailed balance at equilibrium, that is the equilibration of forward and backward rates for each elementary reaction, leads to compatibility between the conditions of kinetic and thermodynamic equilibrium. Therefore, Wegscheider cyclicity conditions can be derived by eliminating the equilibrium concentrations from the conditions of detailed balance. We develop matrix algebra tools needed to carry out this elimination, reexamine an old derivation of the general form of Wegscheider cyclicity condition, and develop new derivations which lead to more compact and easier-to-use formulas. We derive scaling laws for the nonequilibrium rates of a complex reaction network, which include Wegscheider conditions as a particular case. The scaling laws for the rates are used for clarifying the kinetic and thermodynamic meaning of Wegscheider cyclicity conditions. Finally, we discuss different ways of using Wegscheider cyclicity conditions for kinetic computations in systems biology.
Temperature Control System for Biochemical Reactions in Microchip-Based Devices
Institute of Scientific and Technical Information of China (English)
荆高山; 张坚; 朱小山; 冯继宏; 谭智敏; 刘理天; 程京
2001-01-01
A silicon-glass chip based microreactor has been designed and fabricated for biochemical reactions such as polymerase chain reactions (PCR). The chip based microreactor has integrated resistive heating elements. The computer-controlled temperature control system is highly reliable with precise temperature control, excellent temperature uniformity, and rapid heating and cooling capabilities. The development of the microreaction system is an important step towards the construction of a lab-on-a-chip system.
Radical-ion-pair reactions are the biochemical equivalent of the optical double slit experiment
Kominis, I. K.
2010-01-01
Radical-ion-pair reactions were recently shown to represent a rich biophysical laboratory for the application of quantum measurement theory methods and concepts. We here show that radical-ion-pair reactions essentially form a non-linear biochemical double slit interferometer. Quantum coherence effects are visible when "which-path" information is limited, and the incoherent limit is approached when measurement-induced decoherence sets in. Based on this analogy with the optical double slit expe...
Cumulative signal transmission in nonlinear reaction-diffusion networks.
Directory of Open Access Journals (Sweden)
Diego A Oyarzún
Full Text Available Quantifying signal transmission in biochemical systems is key to uncover the mechanisms that cells use to control their responses to environmental stimuli. In this work we use the time-integral of chemical species as a measure of a network's ability to cumulatively transmit signals encoded in spatiotemporal concentrations. We identify a class of nonlinear reaction-diffusion networks in which the time-integrals of some species can be computed analytically. The derived time-integrals do not require knowledge of the solution of the reaction-diffusion equation, and we provide a simple graphical test to check if a given network belongs to the proposed class. The formulae for the time-integrals reveal how the kinetic parameters shape signal transmission in a network under spatiotemporal stimuli. We use these to show that a canonical complex-formation mechanism behaves as a spatial low-pass filter, the bandwidth of which is inversely proportional to the diffusion length of the ligand.
Vitalis, K M
2016-01-01
Radical-ion pairs and their reactions have triggered the study of quantum effects in biological systems. This is because they exhibit a number of effects best understood within quantum information science, and at the same time are central in understanding the avian magnetic compass and the spin transport dynamics in photosynthetic reaction centers. Here we address radical-pair reactions from the perspective of quantum metrology. Since the coherent spin motion of radical-pairs is effected by an external magnetic field, these spin-dependent reactions essentially realize a biochemical magnetometer. Using the quantum Fisher information, we find the fundamental quantum limits to the magnetic sensitivity of radical-pair magnetometers. We then explore how well the usual measurement scheme considered in radical-pair reactions, the measurement of reaction yields, approaches the fundamental limits. In doing so, we find the optimal hyperfine interaction Hamiltonian that leads to the best magnetic sensitivity as obtained...
Institute of Scientific and Technical Information of China (English)
田允; 黄继云; 王锐; 陶蓉蓉; 卢应梅; 廖美华; 陆楠楠; 李静; 芦博; 韩峰
2012-01-01
Autism is a highly heritable neurodevelopmental condition characterized by impaired social interaction and communication. However, the role of synaptic dysfunction during development of autism remains unclear. In the present study, we address the alterations of biochemical signaling in hippocampal network following induction of the autism in experimental animals. Here, the an- imal disease model and DNA array being used to investigate the differences in transcriptome or- ganization between autistic and normal brain by gene co--expression network analysis.
Hamiltonian perspective on compartmental reaction-diffusion networks
Seslija, Marko; van der Schaft, Arjan; Scherpen, Jacquelien M. A.
2014-01-01
Inspired by the recent developments in modeling and analysis of reaction networks, we provide a geometric formulation of the reversible reaction networks under the influence of diffusion. Using the graph knowledge of the underlying reaction network, the obtained reaction diffusion system is a distri
Directory of Open Access Journals (Sweden)
Thomas Philipp
2012-05-01
Full Text Available Abstract Background It is well known that the deterministic dynamics of biochemical reaction networks can be more easily studied if timescale separation conditions are invoked (the quasi-steady-state assumption. In this case the deterministic dynamics of a large network of elementary reactions are well described by the dynamics of a smaller network of effective reactions. Each of the latter represents a group of elementary reactions in the large network and has associated with it an effective macroscopic rate law. A popular method to achieve model reduction in the presence of intrinsic noise consists of using the effective macroscopic rate laws to heuristically deduce effective probabilities for the effective reactions which then enables simulation via the stochastic simulation algorithm (SSA. The validity of this heuristic SSA method is a priori doubtful because the reaction probabilities for the SSA have only been rigorously derived from microscopic physics arguments for elementary reactions. Results We here obtain, by rigorous means and in closed-form, a reduced linear Langevin equation description of the stochastic dynamics of monostable biochemical networks in conditions characterized by small intrinsic noise and timescale separation. The slow-scale linear noise approximation (ssLNA, as the new method is called, is used to calculate the intrinsic noise statistics of enzyme and gene networks. The results agree very well with SSA simulations of the non-reduced network of elementary reactions. In contrast the conventional heuristic SSA is shown to overestimate the size of noise for Michaelis-Menten kinetics, considerably under-estimate the size of noise for Hill-type kinetics and in some cases even miss the prediction of noise-induced oscillations. Conclusions A new general method, the ssLNA, is derived and shown to correctly describe the statistics of intrinsic noise about the macroscopic concentrations under timescale separation conditions
Combinatorics of reaction-network posets.
Klein, Douglas J; Ivanciuc, Teodora; Ryzhov, Anton; Ivanciuc, Ovidiu
2008-11-01
Reaction networks are viewed as derived from ordinary molecular structures related in reactant-product pairs so as to manifest a chemical super-structure. Such super-structures then are candidates for applications in a general combinatoric chemistry. Notable additional characterization of a reaction super-structure occurs when such reaction graphs are directed, as for example when there is progressive substitution (or addition) on a fixed molecular skeleton. Such a set of partially ordered entities is in mathematics termed a poset, which further manifests a number of special properties, as then might be utilized in different applications. Focus on the overall "super-structural" poset goes beyond ordinary molecular structure in attending to how a structure fits into a (reaction) network, and thereby brings an extra "dimension" to conventional stereochemical theory. The possibility that different molecular properties vary smoothly along chains of interconnections in such a super-structure is a natural assumption for a novel approach to molecular property and bioactivity correlations. Different manners to interpolate/extrapolate on a poset network yield quantitative super-structure/activity relationships (QSSARs), with some numerical fits, e.g., for properties of polychlorinated biphenyls (PCBs) seemingly being quite reasonable. There seems to be promise for combinatoric posetic ideas.
Trans-Omics: How To Reconstruct Biochemical Networks Across Multiple 'Omic' Layers.
Yugi, Katsuyuki; Kubota, Hiroyuki; Hatano, Atsushi; Kuroda, Shinya
2016-04-01
We propose 'trans-omic' analysis for reconstructing global biochemical networks across multiple omic layers by use of both multi-omic measurements and computational data integration. We introduce technologies for connecting multi-omic data based on prior knowledge of biochemical interactions and characterize a biochemical trans-omic network by concepts of a static and dynamic nature. We introduce case studies of metabolism-centric trans-omic studies to show how to reconstruct a biochemical trans-omic network by connecting multi-omic data and how to analyze it in terms of the static and dynamic nature. We propose a trans-ome-wide association study (trans-OWAS) connecting phenotypes with trans-omic networks that reflect both genetic and environmental factors, which can characterize several complex lifestyle diseases as breakdowns in the trans-omic system.
Tong, Xinming; Yang, Fan
2014-02-01
Hydrogels have been widely used as artificial cell niche to mimic extracellular matrix with tunable properties. However, changing biochemical cues in hydrogels developed-to-date would often induce simultaneous changes in mechanical properties, which do not support mechanistic studies on stem cell-niche interactions. Here we report the development of a PEG-based interpenetrating network (IPN), which is composed of two polymer networks that can independently and simultaneously crosslink to form hydrogels in a cell-friendly manner. The resulting IPN hydrogel allows independently tunable biochemical and mechanical properties, as well as stable and more homogeneous presentation of biochemical ligands in 3D than currently available methods. We demonstrate the potential of our IPN platform for elucidating stem cell-niche interactions by modulating osteogenic differentiation of human adipose-derived stem cells. The versatility of such IPN hydrogels is further demonstrated using three distinct and widely used polymers to form the mechanical network while keeping the biochemical network constant.
A stronger necessary condition for the multistationarity of chemical reaction networks.
Soliman, Sylvain
2013-11-01
Biochemical reaction networks grow bigger and bigger, fed by the high-throughput data provided by biologists and bred in open repositories of models allowing merging and evolution. Nevertheless, since the available data is still very far from permitting the identification of the increasing number of kinetic parameters of such models, the necessity of structural analyses for describing the dynamics of chemical networks appears stronger every day. Using the structural information, notably from the stoichiometric matrix, of a biochemical reaction system, we state a more strict version of the famous Thomas' necessary condition for multistationarity. In particular, the obvious cases where Thomas' condition was trivially satisfied, mutual inhibition due to a multimolecular reaction and mutual activation due to a reversible reaction, can now easily be ruled out. This more strict condition shall not be seen as some version of Thomas' circuit functionality for the continuous case but rather as related and complementary to the whole domain of the structural analysis of (bio)chemical reaction systems, as pioneered by the chemical reaction network theory.
Insights into the organization of biochemical regulatory networks using graph theory analyses.
Ma'ayan, Avi
2009-02-27
Graph theory has been a valuable mathematical modeling tool to gain insights into the topological organization of biochemical networks. There are two types of insights that may be obtained by graph theory analyses. The first provides an overview of the global organization of biochemical networks; the second uses prior knowledge to place results from multivariate experiments, such as microarray data sets, in the context of known pathways and networks to infer regulation. Using graph analyses, biochemical networks are found to be scale-free and small-world, indicating that these networks contain hubs, which are proteins that interact with many other molecules. These hubs may interact with many different types of proteins at the same time and location or at different times and locations, resulting in diverse biological responses. Groups of components in networks are organized in recurring patterns termed network motifs such as feedback and feed-forward loops. Graph analysis revealed that negative feedback loops are less common and are present mostly in proximity to the membrane, whereas positive feedback loops are highly nested in an architecture that promotes dynamical stability. Cell signaling networks have multiple pathways from some input receptors and few from others. Such topology is reminiscent of a classification system. Signaling networks display a bow-tie structure indicative of funneling information from extracellular signals and then dispatching information from a few specific central intracellular signaling nexuses. These insights show that graph theory is a valuable tool for gaining an understanding of global regulatory features of biochemical networks.
SBMLsqueezer 2: context-sensitive creation of kinetic equations in biochemical networks
DEFF Research Database (Denmark)
Draeger, Andreas; Zielinski, Daniel C.; Keller, Roland;
2015-01-01
Background: The size and complexity of published biochemical network reconstructions are steadily increasing, expanding the potential scale of derived computational models. However, the construction of large biochemical network models is a laborious and error-prone task. Automated methods have...... desired. Conclusions: The described approach fills a heretofore absent niche in workflows for large-scale biochemical kinetic model construction. In several applications the algorithm has already been demonstrated to be useful and scalable. SBMLsqueezer is platform independent and can be used as a stand...
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
Bellesia, Giovanni
2015-01-01
We investigate, via Brownian dynamics simulations, the reaction dynamics of a simple, non-linear chemical network (the Willamowski-Rossler network) under spatial confinement and crowding conditions. Our results show that the presence of inert crowders has a non-nontrivial effect on the dynamics of the network and, consequently, that effective modeling efforts aiming at a general understanding of the behavior of biochemical networks in vivo should be stochastic in nature and based on an explicit representation of both spatial confinement and macromolecular crowding.
Mathematics of small stochastic reaction networks: a boundary layer theory for eigenstate analysis.
Mjolsness, Eric; Prasad, Upendra
2013-03-14
We study and analyze the stochastic dynamics of a reversible bimolecular reaction A + B ↔ C called the "trivalent reaction." This reaction is of a fundamental nature and is part of many biochemical reaction networks. The stochastic dynamics is given by the stochastic master equation, which is difficult to solve except when the equilibrium state solution is desired. We present a novel way of finding the eigenstates of this system of difference-differential equations, using perturbation analysis of ordinary differential equations arising from approximation of the difference equations. The time evolution of the state probabilities can then be expressed in terms of the eigenvalues and the eigenvectors.
Johnston, Matthew D; Pantea, Casian; Donnell, Pete
2016-01-01
We introduce a mixed-integer linear programming (MILP) framework capable of determining whether a chemical reaction network possesses the property of being endotactic or strongly endotactic. The network property of being strongly endotactic is known to lead to persistence and permanence of chemical species under genetic kinetic assumptions, while the same result is conjectured but as yet unproved for general endotactic networks. The algorithms we present are the first capable of verifying endotacticity of chemical reaction networks for systems with greater than two constituent species. We implement the algorithms in the open-source online package CoNtRol and apply them to a large sample of networks from the European Bioinformatics Institute's BioModels Database. We use strong endotacticity to establish for the first time the permanence of a well-studied circadian clock mechanism.
A new computational method to split large biochemical networks into coherent subnets
Directory of Open Access Journals (Sweden)
Verwoerd Wynand S
2011-02-01
Full Text Available Abstract Background Compared to more general networks, biochemical networks have some special features: while generally sparse, there are a small number of highly connected metabolite nodes; and metabolite nodes can also be divided into two classes: internal nodes with associated mass balance constraints and external ones without. Based on these features, reclassifying selected internal nodes (separators to external ones can be used to divide a large complex metabolic network into simpler subnetworks. Selection of separators based on node connectivity is commonly used but affords little detailed control and tends to produce excessive fragmentation. The method proposed here (Netsplitter allows the user to control separator selection. It combines local connection degree partitioning with global connectivity derived from random walks on the network, to produce a more even distribution of subnetwork sizes. Partitioning is performed progressively and the interactive visual matrix presentation used allows the user considerable control over the process, while incorporating special strategies to maintain the network integrity and minimise the information loss due to partitioning. Results Partitioning of a genome scale network of 1348 metabolites and 1468 reactions for Arabidopsis thaliana encapsulates 66% of the network into 10 medium sized subnets. Applied to the flavonoid subnetwork extracted in this way, it is shown that Netsplitter separates this naturally into four subnets with recognisable functionality, namely synthesis of lignin precursors, flavonoids, coumarin and benzenoids. A quantitative quality measure called efficacy is constructed and shows that the new method gives improved partitioning for several metabolic networks, including bacterial, plant and mammal species. Conclusions For the examples studied the Netsplitter method is a considerable improvement on the performance of connection degree partitioning, giving a better balance of
Global parameter identification of stochastic reaction networks from single trajectories.
Müller, Christian L; Ramaswamy, Rajesh; Sbalzarini, Ivo F
2012-01-01
We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy (FCS) provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell-cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation (GaA), and efficient exact stochastic simulation algorithms (SSA) that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.
Nikel, Pablo I; Chavarría, Max; Danchin, Antoine; de Lorenzo, Víctor
2016-10-01
The soil bacterium Pseudomonas putida is endowed with a central carbon metabolic network capable of fulfilling high demands of reducing power. This situation arises from a unique metabolic architecture that encompasses the partial recycling of triose phosphates to hexose phosphates-the so-called EDEMP cycle. In this article, the value of P. putida as a bacterial chassis of choice for contemporary, industrially-oriented metabolic engineering is addressed. The biochemical properties that make this bacterium adequate for hosting biotransformations involving redox reactions as well as toxic compounds and intermediates are discussed. Finally, novel developments and open questions in the continuous quest for an optimal microbial cell factory are presented at the light of current and future needs in the area of biocatalysis.
van Roekel, Hendrik W H; Rosier, Bas J H M; Meijer, Lenny H H; Hilbers, Peter A J; Markvoort, Albert J; Huck, Wilhelm T S; de Greef, Tom F A
2015-11-07
Living cells are able to produce a wide variety of biological responses when subjected to biochemical stimuli. It has become apparent that these biological responses are regulated by complex chemical reaction networks (CRNs). Unravelling the function of these circuits is a key topic of both systems biology and synthetic biology. Recent progress at the interface of chemistry and biology together with the realisation that current experimental tools are insufficient to quantitatively understand the molecular logic of pathways inside living cells has triggered renewed interest in the bottom-up development of CRNs. This builds upon earlier work of physical chemists who extensively studied inorganic CRNs and showed how a system of chemical reactions can give rise to complex spatiotemporal responses such as oscillations and pattern formation. Using purified biochemical components, in vitro synthetic biologists have started to engineer simplified model systems with the goal of mimicking biological responses of intracellular circuits. Emulation and reconstruction of system-level properties of intracellular networks using simplified circuits are able to reveal key design principles and molecular programs that underlie the biological function of interest. In this Tutorial Review, we present an accessible overview of this emerging field starting with key studies on inorganic CRNs followed by a discussion of recent work involving purified biochemical components. Finally, we review recent work showing the versatility of programmable biochemical reaction networks (BRNs) in analytical and diagnostic applications.
Erickson, Keesha; Chatterjee, Anushree
2014-03-01
The use of in silico methods has become standard practice to correlate the structure of a biochemical network to the expression of a desired phenotype. Flux balance analysis (FBA) is one of the most prevalent techniques for modeling metabolism. FBA models have been successfully applied to obtain growth predictions, theoretical product yields from heterologous pathways, and genome engineering targets. We take inspiration from high-throughput recombineering techniques, which show that combinatorial exploration can reveal optimal mutants, and apply the advantages of computational techniques to analyze these combinations. We introduce Constrictor, an in silico tool for FBA that allows gene mutations to be analyzed in a combinatorial fashion, by applying simulated constraints accounting for regulation of gene expression. We apply this algorithm to study ethylene production in E. coli through the addition of the heterologous ethylene-forming enzyme from P. syringae. Targeting individual reactions as well as sets of reactions results in theoretical ethylene yields that are as much 65% greater than yields calculated using typical FBA. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants & select gene expression levels.
Hahnel, G.B.; Gould, R.W.
1982-01-01
Incubation temperatures of 11°, 18° and 28° did not substantially affect biochemical reactions of either virulent or avirulent forms of Aeromonas salmonicida subspecies salmonicida. The only change observed, amygdalin fermentation, was positive at 11° and 18° but negative at 28°C. Several isolates utilized sucrose, a characteristic not normally recognized for A. salmonicida subspecies salmonicida.Antimicrobial susceptibility screening indicated resistance to novobiocin increased at the higher incubation temperatures. Standardized drug sensitivity testing procedures and precise zone diameter interpretive standards for bacterial fish pathogens are needed.
A Geometric Method for Model Reduction of Biochemical Networks with Polynomial Rate Functions.
Samal, Satya Swarup; Grigoriev, Dima; Fröhlich, Holger; Weber, Andreas; Radulescu, Ovidiu
2015-12-01
Model reduction of biochemical networks relies on the knowledge of slow and fast variables. We provide a geometric method, based on the Newton polytope, to identify slow variables of a biochemical network with polynomial rate functions. The gist of the method is the notion of tropical equilibration that provides approximate descriptions of slow invariant manifolds. Compared to extant numerical algorithms such as the intrinsic low-dimensional manifold method, our approach is symbolic and utilizes orders of magnitude instead of precise values of the model parameters. Application of this method to a large collection of biochemical network models supports the idea that the number of dynamical variables in minimal models of cell physiology can be small, in spite of the large number of molecular regulatory actors.
Proton mediated control of biochemical reactions with bioelectronic pH modulation
Deng, Yingxin; Miyake, Takeo; Keene, Scott; Josberger, Erik E.; Rolandi, Marco
2016-04-01
In Nature, protons (H+) can mediate metabolic process through enzymatic reactions. Examples include glucose oxidation with glucose dehydrogenase to regulate blood glucose level, alcohol dissolution into carboxylic acid through alcohol dehydrogenase, and voltage-regulated H+ channels activating bioluminescence in firefly and jellyfish. Artificial devices that control H+ currents and H+ concentration (pH) are able to actively influence biochemical processes. Here, we demonstrate a biotransducer that monitors and actively regulates pH-responsive enzymatic reactions by monitoring and controlling the flow of H+ between PdHx contacts and solution. The present transducer records bistable pH modulation from an “enzymatic flip-flop” circuit that comprises glucose dehydrogenase and alcohol dehydrogenase. The transducer also controls bioluminescence from firefly luciferase by affecting solution pH.
Piecewise linear and Boolean models of chemical reaction networks.
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-12-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions ([Formula: see text]). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent [Formula: see text] is large. However, while the case of small constant [Formula: see text] appears in practice, it is not well understood. We provide a mathematical analysis of this limit and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed-form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator.
Piecewise linear and Boolean models of chemical reaction networks
Veliz-Cuba, Alan; Kumar, Ajit; Josić, Krešimir
2014-01-01
Models of biochemical networks are frequently complex and high-dimensional. Reduction methods that preserve important dynamical properties are therefore essential for their study. Interactions in biochemical networks are frequently modeled using Hill functions (xn/(Jn + xn)). Reduced ODEs and Boolean approximations of such model networks have been studied extensively when the exponent n is large. However, while the case of small constant J appears in practice, it is not well understood. We provide a mathematical analysis of this limit, and show that a reduction to a set of piecewise linear ODEs and Boolean networks can be mathematically justified. The piecewise linear systems have closed form solutions that closely track those of the fully nonlinear model. The simpler, Boolean network can be used to study the qualitative behavior of the original system. We justify the reduction using geometric singular perturbation theory and compact convergence, and illustrate the results in network models of a toggle switch and an oscillator. PMID:25412739
Structural simplification of chemical reaction networks in partial steady states.
Madelaine, Guillaume; Lhoussaine, Cédric; Niehren, Joachim; Tonello, Elisa
2016-11-01
We study the structural simplification of chemical reaction networks with partial steady state semantics assuming that the concentrations of some but not all species are constant. We present a simplification rule that can eliminate intermediate species that are in partial steady state, while preserving the dynamics of all other species. Our simplification rule can be applied to general reaction networks with some but few restrictions on the possible kinetic laws. We can also simplify reaction networks subject to conservation laws. We prove that our simplification rule is correct when applied to a module of a reaction network, as long as the partial steady state is assumed with respect to the complete network. Michaelis-Menten's simplification rule for enzymatic reactions falls out as a special case. We have implemented an algorithm that applies our simplification rules repeatedly and applied it to reaction networks from systems biology.
On the rejection-based algorithm for simulation and analysis of large-scale reaction networks
Energy Technology Data Exchange (ETDEWEB)
Thanh, Vo Hong, E-mail: vo@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento, Trento (Italy); Priami, Corrado, E-mail: priami@cosbi.eu [The Microsoft Research-University of Trento Centre for Computational and Systems Biology, Piazza Manifattura 1, Rovereto 38068 (Italy); Department of Mathematics, University of Trento, Trento (Italy)
2015-06-28
Stochastic simulation for in silico studies of large biochemical networks requires a great amount of computational time. We recently proposed a new exact simulation algorithm, called the rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)], to improve simulation performance by postponing and collapsing as much as possible the propensity updates. In this paper, we analyze the performance of this algorithm in detail, and improve it for simulating large-scale biochemical reaction networks. We also present a new algorithm, called simultaneous RSSA (SRSSA), which generates many independent trajectories simultaneously for the analysis of the biochemical behavior. SRSSA improves simulation performance by utilizing a single data structure across simulations to select reaction firings and forming trajectories. The memory requirement for building and storing the data structure is thus independent of the number of trajectories. The updating of the data structure when needed is performed collectively in a single operation across the simulations. The trajectories generated by SRSSA are exact and independent of each other by exploiting the rejection-based mechanism. We test our new improvement on real biological systems with a wide range of reaction networks to demonstrate its applicability and efficiency.
SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks
Directory of Open Access Journals (Sweden)
Schröder Adrian
2008-04-01
Full Text Available Abstract Background The development of complex biochemical models has been facilitated through the standardization of machine-readable representations like SBML (Systems Biology Markup Language. This effort is accompanied by the ongoing development of the human-readable diagrammatic representation SBGN (Systems Biology Graphical Notation. The graphical SBML editor CellDesigner allows direct translation of SBGN into SBML, and vice versa. For the assignment of kinetic rate laws, however, this process is not straightforward, as it often requires manual assembly and specific knowledge of kinetic equations. Results SBMLsqueezer facilitates exactly this modeling step via automated equation generation, overcoming the highly error-prone and cumbersome process of manually assigning kinetic equations. For each reaction the kinetic equation is derived from the stoichiometry, the participating species (e.g., proteins, mRNA or simple molecules as well as the regulatory relations (activation, inhibition or other modulations of the SBGN diagram. Such information allows distinctions between, for example, translation, phosphorylation or state transitions. The types of kinetics considered are numerous, for instance generalized mass-action, Hill, convenience and several Michaelis-Menten-based kinetics, each including activation and inhibition. These kinetics allow SBMLsqueezer to cover metabolic, gene regulatory, signal transduction and mixed networks. Whenever multiple kinetics are applicable to one reaction, parameter settings allow for user-defined specifications. After invoking SBMLsqueezer, the kinetic formulas are generated and assigned to the model, which can then be simulated in CellDesigner or with external ODE solvers. Furthermore, the equations can be exported to SBML, LaTeX or plain text format. Conclusion SBMLsqueezer considers the annotation of all participating reactants, products and regulators when generating rate laws for reactions. Thus, for
Breitling, Rainer; Gilbert, David; Heiner, Monika; Orton, Richard
2008-01-01
Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted
Dramatic reduction of dimensionality in large biochemical networks owing to strong pair correlations
Dworkin, Michael; Mukherjee, Sayak; Jayaprakash, Ciriyam; Das, Jayajit
2012-01-01
Large multi-dimensionality of high-throughput datasets pertaining to cell signalling and gene regulation renders it difficult to extract mechanisms underlying the complex kinetics involving various biochemical compounds (e.g. proteins and lipids). Data-driven models often circumvent this difficulty by using pair correlations of the protein expression levels to produce a small number (fewer than 10) of principal components, each a linear combination of the concentrations, to successfully model how cells respond to different stimuli. However, it is not understood if this reduction is specific to a particular biological system or to nature of the stimuli used in these experiments. We study temporal changes in pair correlations, described by the covariance matrix, between concentrations of different molecular species that evolve following deterministic mass-action kinetics in large biologically relevant reaction networks and show that this dramatic reduction of dimensions (from hundreds to less than five) arises from the strong correlations between different species at any time and is insensitive to the form of the nonlinear interactions, network architecture, and to a wide range of values of rate constants and concentrations. We relate temporal changes in the eigenvalue spectrum of the covariance matrix to low-dimensional, local changes in directions of the system trajectory embedded in much larger dimensions using elementary differential geometry. We illustrate how to extract biologically relevant insights such as identifying significant timescales and groups of correlated chemical species from our analysis. Our work provides for the first time, to our knowledge, a theoretical underpinning for the successful experimental analysis and points to a way to extract mechanisms from large-scale high-throughput datasets. PMID:22378749
Directory of Open Access Journals (Sweden)
Chang Yu-Te
2008-11-01
Full Text Available Abstract Background Gene networks in nanoscale are of nonlinear stochastic process. Time delays are common and substantial in these biochemical processes due to gene transcription, translation, posttranslation protein modification and diffusion. Molecular noises in gene networks come from intrinsic fluctuations, transmitted noise from upstream genes, and the global noise affecting all genes. Knowledge of molecular noise filtering and biochemical process delay compensation in gene networks is crucial to understand the signal processing in gene networks and the design of noise-tolerant and delay-robust gene circuits for synthetic biology. Results A nonlinear stochastic dynamic model with multiple time delays is proposed for describing a gene network under process delays, intrinsic molecular fluctuations, and extrinsic molecular noises. Then, the stochastic biochemical processing scheme of gene regulatory networks for attenuating these molecular noises and compensating process delays is investigated from the nonlinear signal processing perspective. In order to improve the robust stability for delay toleration and noise filtering, a robust gene circuit for nonlinear stochastic time-delay gene networks is engineered based on the nonlinear robust H∞ stochastic filtering scheme. Further, in order to avoid solving these complicated noise-tolerant and delay-robust design problems, based on Takagi-Sugeno (T-S fuzzy time-delay model and linear matrix inequalities (LMIs technique, a systematic gene circuit design method is proposed to simplify the design procedure. Conclusion The proposed gene circuit design method has much potential for application to systems biology, synthetic biology and drug design when a gene regulatory network has to be designed for improving its robust stability and filtering ability of disease-perturbed gene network or when a synthetic gene network needs to perform robustly under process delays and molecular noises.
Mélykúti, Bence; Burrage, Kevin; Zygalakis, Konstantinos C
2010-04-28
The Chemical Langevin Equation (CLE), which is a stochastic differential equation driven by a multidimensional Wiener process, acts as a bridge between the discrete stochastic simulation algorithm and the deterministic reaction rate equation when simulating (bio)chemical kinetics. The CLE model is valid in the regime where molecular populations are abundant enough to assume their concentrations change continuously, but stochastic fluctuations still play a major role. The contribution of this work is that we observe and explore that the CLE is not a single equation, but a parametric family of equations, all of which give the same finite-dimensional distribution of the variables. On the theoretical side, we prove that as many Wiener processes are sufficient to formulate the CLE as there are independent variables in the equation, which is just the rank of the stoichiometric matrix. On the practical side, we show that in the case where there are m(1) pairs of reversible reactions and m(2) irreversible reactions there is another, simple formulation of the CLE with only m(1) + m(2) Wiener processes, whereas the standard approach uses 2(m(1) + m(2)). We demonstrate that there are considerable computational savings when using this latter formulation. Such transformations of the CLE do not cause a loss of accuracy and are therefore distinct from model reduction techniques. We illustrate our findings by considering alternative formulations of the CLE for a human ether a-go-go related gene ion channel model and the Goldbeter-Koshland switch.
Efficient Characterization of Parametric Uncertainty of Complex (Biochemical Networks.
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Claudia Schillings
2015-08-01
Full Text Available Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
DEFF Research Database (Denmark)
Nielsen, Jens Bredal
1997-01-01
Metabolic control analysis is a powerful technique for the evaluation of flux control within biochemical pathways. Its foundation is the elasticity coefficients and the flux control coefficients (FCCs). On the basis of a thermokinetic description of reaction rates it is here shown...... affinity. This parameter can often be determined from experiments in vitro. The methodology is applicable only to the analysis of simple two-step pathways, but in many cases larger pathways can be lumped into two overall conversions. In cases where this cannot be done it is necessary to apply an extension...... be much more widely applied, although it was originally based on linearized kinetics. The methodology of determining elasticity coefficients directly from pool levels is illustrated with an analysis of the first two steps of the biosynthetic pathway of penicillin. The results compare well with previous...
The Kirkwood-Buff theory and the effect of cosolvents on biochemical reactions.
Shimizu, Seishi; Boon, Chandra L
2004-11-08
Cosolvents added to aqueous solutions of biomolecules profoundly affect protein stability, as well as biochemical equilibria. Some cosolvents, such as urea and guanidine hydrochloride, denature proteins, whereas others, such as osmolytes and crowders, stabilize the native structures of proteins. The way cosolvents interact with biomolecules is crucial information required to understand the cosolvent effect at a molecular level. We present a statistical mechanical framework based upon Kirkwood-Buff theory, which enables one to extract this picture from experimental data. The combination of two experimental results, namely, the cosolvent-induced equilibrium shift and the partial molar volume change upon the reaction, supplimented by the structural change, is shown to yield the number of water and cosolvent molecules bound or released during a reaction. Previously, denaturation experiments (e.g., m-value analysis) were analyzed by empirical and stoichiometric solvent-binding models, while the effects of osmolytes and crowders were analyzed by the approximate molecular crowding approach for low cosolvent concentration. Here we synthesize these previous approaches in a rigorous statistical mechanical treatment, which is applicable at any cosolvent concentration. The usefulness and accuracy of previous approaches was also evaluated.
Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki
2014-09-01
Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.
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 the
Safe design of cooled tubular reactors for exothermic multiple reactions: Multiple-reaction networks
Westerink, E.J.; Westerterp, K.R.
1988-01-01
The model of the pseudo-homogeneous, one-dimensional cooled tubular reactor is applied to a multiple-reaction network. It is demonstrated for a network which consists of two parallel and two consecutive reactions. Three criteria are developed to obtain an integral yield which does not deviate more t
Mélykúti, Bence
2010-01-01
The Chemical Langevin Equation (CLE), which is a stochastic differential equation driven by a multidimensional Wiener process, acts as a bridge between the discrete stochastic simulation algorithm and the deterministic reaction rate equation when simulating (bio)chemical kinetics. The CLE model is valid in the regime where molecular populations are abundant enough to assume their concentrations change continuously, but stochastic fluctuations still play a major role. The contribution of this work is that we observe and explore that the CLE is not a single equation, but a parametric family of equations, all of which give the same finite-dimensional distribution of the variables. On the theoretical side, we prove that as many Wiener processes are sufficient to formulate the CLE as there are independent variables in the equation, which is just the rank of the stoichiometric matrix. On the practical side, we show that in the case where there are m1 pairs of reversible reactions and m2 irreversible reactions there is another, simple formulation of the CLE with only m1 + m2 Wiener processes, whereas the standard approach uses 2 m1 + m2. We demonstrate that there are considerable computational savings when using this latter formulation. Such transformations of the CLE do not cause a loss of accuracy and are therefore distinct from model reduction techniques. We illustrate our findings by considering alternative formulations of the CLE for a human ether a-go-go related gene ion channel model and the Goldbeter-Koshland switch. © 2010 American Institute of Physics.
Nonlinear Biochemical Signal Processing via Noise Propagation
Kim, Kyung Hyuk; Qian, Hong; Sauro, Herbert M.
2013-01-01
Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cell...
On the Complexity of Reconstructing Chemical Reaction Networks
DEFF Research Database (Denmark)
Fagerberg, Rolf; Flamm, Christoph; Merkle, Daniel
2013-01-01
The analysis of the structure of chemical reaction networks is crucial for a better understanding of chemical processes. Such networks are well described as hypergraphs. However, due to the available methods, analyses regarding network properties are typically made on standard graphs derived from...... the full hypergraph description, e.g. on the so-called species and reaction graphs. However, a reconstruction of the underlying hypergraph from these graphs is not necessarily unique. In this paper, we address the problem of reconstructing a hypergraph from its species and reaction graph and show NP...
van Riel, Natal A W
2006-12-01
Systems biology applies quantitative, mechanistic modelling to study genetic networks, signal transduction pathways and metabolic networks. Mathematical models of biochemical networks can look very different. An important reason is that the purpose and application of a model are essential for the selection of the best mathematical framework. Fundamental aspects of selecting an appropriate modelling framework and a strategy for model building are discussed. Concepts and methods from system and control theory provide a sound basis for the further development of improved and dedicated computational tools for systems biology. Identification of the network components and rate constants that are most critical to the output behaviour of the system is one of the major problems raised in systems biology. Current approaches and methods of parameter sensitivity analysis and parameter estimation are reviewed. It is shown how these methods can be applied in the design of model-based experiments which iteratively yield models that are decreasingly wrong and increasingly gain predictive power.
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.
2012-01-01
This paper describes details of an automatic matrix decomposition approach for a reaction-based stream water quality model. The method yields a set of equilibrium equations, a set of kinetic-variable transport equations involving kinetic reactions only, and a set of component transport equations involving no reactions. Partial decomposition of the system of water quality constituent transport equations is performed via Gauss-Jordan column reduction of the reaction network by pivoting on equil...
Research on Nuclear Reaction Network Equation for Fission Product Nuclides
Institute of Scientific and Technical Information of China (English)
无
2011-01-01
Nuclear Reaction Network Equation calculation system for fission product nuclides was developed. With the system, the number of the fission product nuclides at different time can be calculated in the different neutron field intensity and neutron energy spectra
Stochastic Generator of Chemical Structure. 3. Reaction Network Generation
Energy Technology Data Exchange (ETDEWEB)
FAULON,JEAN-LOUP; SAULT,ALLEN G.
2000-07-15
A new method to generate chemical reaction network is proposed. The particularity of the method is that network generation and mechanism reduction are performed simultaneously using sampling techniques. Our method is tested for hydrocarbon thermal cracking. Results and theoretical arguments demonstrate that our method scales in polynomial time while other deterministic network generator scale in exponential time. This finding offers the possibility to investigate complex reacting systems such as those studied in petroleum refining and combustion.
Jao, Tun; Schröter, Manuel; Chen, Chao-Long; Cheng, Yu-Fan; Lo, Chun-Yi Zac; Chou, Kun-Hsien; Patel, Ameera X; Lin, Wei-Che; Lin, Ching-Po; Bullmore, Edward T
2015-11-15
Functional properties of the brain may be associated with changes in complex brain networks. However, little is known about how properties of large-scale functional brain networks may be altered stepwise in patients with disturbance of consciousness, e.g., an encephalopathy. We used resting-state fMRI data on patients suffering from various degrees of hepatic encephalopathy (HE) to explore how topological and spatial network properties of functional brain networks changed at different cognitive and consciousness states. Severity of HE was measured clinically and by neuropsychological tests. Fifty-eight non-alcoholic liver cirrhosis patients and 62 normal controls were studied. Patients were subdivided into liver cirrhosis with no outstanding HE (NoHE, n=23), minimal HE with cognitive impairment only detectable by neuropsychological tests (MHE, n=28), and clinically overt HE (OHE, n=7). From the earliest stage, the NoHE, functional brain networks were progressively more random, less clustered, and less modular. Since the intermediate stage (MHE), increased ammonia level was accompanied by concomitant exponential decay of mean connectivity strength, especially in the primary cortical areas and midline brain structures. Finally, at the OHE stage, there were radical reorganization of the topological centrality-i.e., the relative importance-of the hubs and reorientation of functional connections between nodes. In summary, this study illustrated progressively greater abnormalities in functional brain network organization in patients with clinical and biochemical evidence of more severe hepatic encephalopathy. The early-than-expected brain network dysfunction in cirrhotic patients suggests that brain functional connectivity and network analysis may provide useful and complementary biomarkers for more aggressive and earlier intervention of hepatic encephalopathy. Moreover, the stepwise deterioration of functional brain networks in HE patients may suggest that hierarchical
A computational framework for the automated construction of glycosylation reaction networks.
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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
Neural networks for the prediction organic chemistry reactions
Wei, Jennifer N.; Duvenaud, David; Aspuru-Guzik, Alán
2016-01-01
Reaction prediction remains one of the major challenges for organic chemistry, and is a pre-requisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reag...
Jebrail, Mais J; Renzi, Ronald F; Sinha, Anupama; Van De Vreugde, Jim; Gondhalekar, Carmen; Ambriz, Cesar; Meagher, Robert J; Branda, Steven S
2015-01-07
Digital microfluidics (DMF) is a powerful technique for sample preparation and analysis for a broad range of biological and chemical applications. In many cases, it is desirable to carry out DMF on an open surface, such that the matrix surrounding the droplets is ambient air. However, the utility of the air-matrix DMF format has been severely limited by problems with droplet evaporation, especially when the droplet-based biochemical reactions require high temperatures for long periods of time. We present a simple solution for managing evaporation in air-matrix DMF: just-in-time replenishment of the reaction volume using droplets of solvent. We demonstrate that this solution enables DMF-mediated execution of several different biochemical reactions (RNA fragmentation, first-strand cDNA synthesis, and PCR) over a range of temperatures (4-95 °C) and incubation times (up to 1 h or more) without use of oil, humidifying chambers, or off-chip heating modules. Reaction volumes and temperatures were maintained roughly constant over the course of each experiment, such that the reaction kinetics and products generated by the air-matrix DMF device were comparable to those of conventional benchscale reactions. This simple yet effective solution for evaporation management is an important advance in developing air-matrix DMF for a wide variety of new, high-impact applications, particularly in the biomedical sciences.
Kotera, Masaaki; Nishimura, Yosuke; Nakagawa, Zen-ichi; Muto, Ai; Moriya, Yuki; Okamoto, Shinobu; Kawashima, Shuichi; Katayama, Toshiaki; Tokimatsu, Toshiaki; Kanehisa, Minoru; Goto, Susumu
2014-12-01
Genomics is faced with the issue of many partially annotated putative enzyme-encoding genes for which activities have not yet been verified, while metabolomics is faced with the issue of many putative enzyme reactions for which full equations have not been verified. Knowledge of enzymes has been collected by IUBMB, and has been made public as the Enzyme List. To date, however, the terminology of the Enzyme List has not been assessed comprehensively by bioinformatics studies. Instead, most of the bioinformatics studies simply use the identifiers of the enzymes, i.e. the Enzyme Commission (EC) numbers. We investigated the actual usage of terminology throughout the Enzyme List, and demonstrated that the partial characteristics of reactions cannot be retrieved by simply using EC numbers. Thus, we developed a novel ontology, named PIERO, for annotating biochemical transformations as follows. First, the terminology describing enzymatic reactions was retrieved from the Enzyme List, and was grouped into those related to overall reactions and biochemical transformations. Consequently, these terms were mapped onto the actual transformations taken from enzymatic reaction equations. This ontology was linked to Gene Ontology (GO) and EC numbers, allowing the extraction of common partial reaction characteristics from given sets of orthologous genes and the elucidation of possible enzymes from the given transformations. Further future development of the PIERO ontology should enhance the Enzyme List to promote the integration of genomics and metabolomics.
Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach
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Oliveira Rui
2010-09-01
Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.
A microfluidic platform for controlled biochemical stimulation of twin neuronal networks.
Biffi, Emilia; Piraino, Francesco; Pedrocchi, Alessandra; Fiore, Gianfranco B; Ferrigno, Giancarlo; Redaelli, Alberto; Menegon, Andrea; Rasponi, Marco
2012-06-01
Spatially and temporally resolved delivery of soluble factors is a key feature for pharmacological applications. In this framework, microfluidics coupled to multisite electrophysiology offers great advantages in neuropharmacology and toxicology. In this work, a microfluidic device for biochemical stimulation of neuronal networks was developed. A micro-chamber for cell culturing, previously developed and tested for long term neuronal growth by our group, was provided with a thin wall, which partially divided the cell culture region in two sub-compartments. The device was reversibly coupled to a flat micro electrode array and used to culture primary neurons in the same microenvironment. We demonstrated that the two fluidically connected compartments were able to originate two parallel neuronal networks with similar electrophysiological activity but functionally independent. Furthermore, the device allowed to connect the outlet port to a syringe pump and to transform the static culture chamber in a perfused one. At 14 days invitro, sub-networks were independently stimulated with a test molecule, tetrodotoxin, a neurotoxin known to block action potentials, by means of continuous delivery. Electrical activity recordings proved the ability of the device configuration to selectively stimulate each neuronal network individually. The proposed microfluidic approach represents an innovative methodology to perform biological, pharmacological, and electrophysiological experiments on neuronal networks. Indeed, it allows for controlled delivery of substances to cells, and it overcomes the limitations due to standard drug stimulation techniques. Finally, the twin network configuration reduces biological variability, which has important outcomes on pharmacological and drug screening.
Harrison, Peter J; Bugg, Timothy D H
2014-02-15
Carotenoid cleavage dioxygenases (CCDs) are a large family of non-heme iron (II) dependent enzymes. CCDs catalyse the selective oxidative cleavage of carotenoids to produce apocarotenoids. Apocarotenoid derived molecules form important signalling molecules in plants in the form of abscisic acid and strigolactone and in mammals in the form of retinal. Very little is known biochemically about the CCDs and only a handful of CCDs have been biochemically characterised. Mechanistically, debate surrounds whether CCDs utilise a mono or dioxygenase mechanism. Here, we review the biochemical roles of CCDs, discuss the mechanisms by which CCD cleavage is proposed to occur, and discuss recent reports of selective CCD enzyme inhibitors.
Scalable rule-based modelling of allosteric proteins and biochemical networks.
Directory of Open Access Journals (Sweden)
Julien F Ollivier
Full Text Available Much of the complexity of biochemical networks comes from the information-processing abilities of allosteric proteins, be they receptors, ion-channels, signalling molecules or transcription factors. An allosteric protein can be uniquely regulated by each combination of input molecules that it binds. This "regulatory complexity" causes a combinatorial increase in the number of parameters required to fit experimental data as the number of protein interactions increases. It therefore challenges the creation, updating, and re-use of biochemical models. Here, we propose a rule-based modelling framework that exploits the intrinsic modularity of protein structure to address regulatory complexity. Rather than treating proteins as "black boxes", we model their hierarchical structure and, as conformational changes, internal dynamics. By modelling the regulation of allosteric proteins through these conformational changes, we often decrease the number of parameters required to fit data, and so reduce over-fitting and improve the predictive power of a model. Our method is thermodynamically grounded, imposes detailed balance, and also includes molecular cross-talk and the background activity of enzymes. We use our Allosteric Network Compiler to examine how allostery can facilitate macromolecular assembly and how competitive ligands can change the observed cooperativity of an allosteric protein. We also develop a parsimonious model of G protein-coupled receptors that explains functional selectivity and can predict the rank order of potency of agonists acting through a receptor. Our methodology should provide a basis for scalable, modular and executable modelling of biochemical networks in systems and synthetic biology.
Uncertainty quantification for quantum chemical models of complex reaction networks.
Proppe, Jonny; Husch, Tamara; Simm, Gregor N; Reiher, Markus
2016-12-22
For the quantitative understanding of complex chemical reaction mechanisms, it is, in general, necessary to accurately determine the corresponding free energy surface and to solve the resulting continuous-time reaction rate equations for a continuous state space. For a general (complex) reaction network, it is computationally hard to fulfill these two requirements. However, it is possible to approximately address these challenges in a physically consistent way. On the one hand, it may be sufficient to consider approximate free energies if a reliable uncertainty measure can be provided. On the other hand, a highly resolved time evolution may not be necessary to still determine quantitative fluxes in a reaction network if one is interested in specific time scales. In this paper, we present discrete-time kinetic simulations in discrete state space taking free energy uncertainties into account. The method builds upon thermo-chemical data obtained from electronic structure calculations in a condensed-phase model. Our kinetic approach supports the analysis of general reaction networks spanning multiple time scales, which is here demonstrated for the example of the formose reaction. An important application of our approach is the detection of regions in a reaction network which require further investigation, given the uncertainties introduced by both approximate electronic structure methods and kinetic models. Such cases can then be studied in greater detail with more sophisticated first-principles calculations and kinetic simulations.
2008-02-01
investigate the simulation of a biomolecular reaction network with BNS, a simple model of a generic self-assembling catalytic ligation reaction in a...Amino Acid Pools Nucleotide Triphosphate Pools Nucleotide Monophosphate Pools Ligation Reaction 1551 517 7 RESULTS Simulation of exemplar...and reaction r8 is the catalytic ligation reaction . In figures 5(B) through 5(F), both the time-averaged event rate for a single simulation run
Mochizuki, Atsushi; Fiedler, Bernold
2015-02-21
In biological cells, chemical reaction pathways lead to complex network systems like metabolic networks. One experimental approach to the dynamics of such systems examines their "sensitivity": each enzyme mediating a reaction in the system is increased/decreased or knocked out separately, and the responses in the concentrations of chemicals or their fluxes are observed. In this study, we present a mathematical method, named structural sensitivity analysis, to determine the sensitivity of reaction systems from information on the network alone. We investigate how the sensitivity responses of chemicals in a reaction network depend on the structure of the network, and on the position of the perturbed reaction in the network. We establish and prove some general rules which relate the sensitivity response to the structure of the underlying network. We describe a hierarchical pattern in the flux response which is governed by branchings in the network. We apply our method to several hypothetical and real life chemical reaction networks, including the metabolic network of the Escherichia coli TCA cycle.
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.
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.
Directory of Open Access Journals (Sweden)
Fan Zhang
2012-01-01
Full Text Available This paper describes details of an automatic matrix decomposition approach for a reaction-based stream water quality model. The method yields a set of equilibrium equations, a set of kinetic-variable transport equations involving kinetic reactions only, and a set of component transport equations involving no reactions. Partial decomposition of the system of water quality constituent transport equations is performed via Gauss-Jordan column reduction of the reaction network by pivoting on equilibrium reactions to decouple equilibrium and kinetic reactions. This approach minimizes the number of partial differential advective-dispersive transport equations and enables robust numerical integration. Complete matrix decomposition by further pivoting on linearly independent kinetic reactions allows some rate equations to be formulated individually and explicitly enforces conservation of component species when component transport equations are solved. The methodology is demonstrated for a case study involving eutrophication reactions in the Des Moines River in Iowa, USA and for two hypothetical examples to illustrate the ability of the model to simulate sediment and chemical transport with both mobile and immobile water phases and with complex reaction networks involving both kinetic and equilibrium reactions.
Li, X Y; Yang, G W; Zheng, D S; Guo, W S; Hung, W N N
2015-01-01
Genetic regulatory networks are the key to understanding biochemical systems. One condition of the genetic regulatory network under different living environments can be modeled as a synchronous Boolean network. The attractors of these Boolean networks will help biologists to identify determinant and stable factors. Existing methods identify attractors based on a random initial state or the entire state simultaneously. They cannot identify the fixed length attractors directly. The complexity of including time increases exponentially with respect to the attractor number and length of attractors. This study used the bounded model checking to quickly locate fixed length attractors. Based on the SAT solver, we propose a new algorithm for efficiently computing the fixed length attractors, which is more suitable for large Boolean networks and numerous attractors' networks. After comparison using the tool BooleNet, empirical experiments involving biochemical systems demonstrated the feasibility and efficiency of our approach.
Efficient parameter sensitivity computation for spatially extended reaction networks
Lester, C.; Yates, C. A.; Baker, R. E.
2017-01-01
Reaction-diffusion models are widely used to study spatially extended chemical reaction systems. In order to understand how the dynamics of a reaction-diffusion model are affected by changes in its input parameters, efficient methods for computing parametric sensitivities are required. In this work, we focus on the stochastic models of spatially extended chemical reaction systems that involve partitioning the computational domain into voxels. Parametric sensitivities are often calculated using Monte Carlo techniques that are typically computationally expensive; however, variance reduction techniques can decrease the number of Monte Carlo simulations required. By exploiting the characteristic dynamics of spatially extended reaction networks, we are able to adapt existing finite difference schemes to robustly estimate parametric sensitivities in a spatially extended network. We show that algorithmic performance depends on the dynamics of the given network and the choice of summary statistics. We then describe a hybrid technique that dynamically chooses the most appropriate simulation method for the network of interest. Our method is tested for functionality and accuracy in a range of different scenarios.
Ye, Jianxiong; Feng, Enmin; Wang, Lei; Xiu, Zhilong; Sun, Yaqin
Glycerol bioconversion to 1,3-propanediol (1,3-PD) by Klebsiella pneumoniae (K. pneumoniae) can be characterized by an intricate network of interactions among biochemical fluxes, metabolic compounds, key enzymes and genetic regulatory. To date, there still exist some uncertain factors in this complex network because of the limitation in bio-techniques, especially in measuring techniques for intracellular substances. In this paper, among these uncertain factors, we aim to infer the transport mechanisms of glycerol and 1,3-PD across the cell membrane, which have received intensive interest in recent years. On the basis of different inferences of the transport mechanisms, we reconstruct various metabolic networks correspondingly and subsequently develop their dynamical systems (S-systems). To determine the most reasonable metabolic network from all possible ones, we establish a quantitative definition of biological robustness and undertake parameter identification and robustness analysis for each system. Numerical results show that it is most possible that both glycerol and 1,3-PD pass the cell membrane by active transport and passive diffusion.
Directory of Open Access Journals (Sweden)
August Elias
2010-04-01
Full Text Available Abstract Background The success of molecular systems biology hinges on the ability to use computational models to design predictive experiments, and ultimately unravel underlying biological mechanisms. A problem commonly encountered in the computational modelling of biological networks is that alternative, structurally different models of similar complexity fit a set of experimental data equally well. In this case, more than one molecular mechanism can explain available data. In order to rule out the incorrect mechanisms, one needs to invalidate incorrect models. At this point, new experiments maximizing the difference between the measured values of alternative models should be proposed and conducted. Such experiments should be optimally designed to produce data that are most likely to invalidate incorrect model structures. Results In this paper we develop methodologies for the optimal design of experiments with the aim of discriminating between different mathematical models of the same biological system. The first approach determines the 'best' initial condition that maximizes the L2 (energy distance between the outputs of the rival models. In the second approach, we maximize the L2-distance of the outputs by designing the optimal external stimulus (input profile of unit L2-norm. Our third method uses optimized structural changes (corresponding, for example, to parameter value changes reflecting gene knock-outs to achieve the same goal. The numerical implementation of each method is considered in an example, signal processing in starving Dictyostelium amœbæ. Conclusions Model-based design of experiments improves both the reliability and the efficiency of biochemical network model discrimination. This opens the way to model invalidation, which can be used to perfect our understanding of biochemical networks. Our general problem formulation together with the three proposed experiment design methods give the practitioner new tools for a systems
Giirsoy, Gamze; Terebus, Anna; Cao, Youfang; Liang, Jie; Gursoy, Gamze; Terebus, Anna; Youfang Cao; Jie Liang; Gursoy, Gamze; Cao, Youfang; Terebus, Anna; Liang, Jie
2016-08-01
Stochasticity plays important roles in regulation of biochemical reaction networks when the copy numbers of molecular species are small. Studies based on Stochastic Simulation Algorithm (SSA) has shown that a basic reaction system can display stochastic focusing (SF) by increasing the sensitivity of the network as a result of the signal noise. Although SSA has been widely used to study stochastic networks, it is ineffective in examining rare events and this becomes a significant issue when the tails of probability distributions are relevant as is the case of SF. Here we use the ACME method to solve the exact solution of the discrete Chemical Master Equations and to study a network where SF was reported. We showed that the level of SF depends on the degree of the fluctuations of signal molecule. We discovered that signaling noise under certain conditions in the same reaction network can lead to a decrease in the system sensitivities, thus the network can experience stochastic defocusing. These results highlight the fundamental role of stochasticity in biological reaction networks and the need for exact computation of probability landscape of the molecules in the system.
Neural Networks for the Prediction of Organic Chemistry Reactions
2016-01-01
Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, “learn” from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook. PMID:27800555
Neural Networks for the Prediction of Organic Chemistry Reactions.
Wei, Jennifer N; Duvenaud, David; Aspuru-Guzik, Alán
2016-10-26
Reaction prediction remains one of the major challenges for organic chemistry and is a prerequisite for efficient synthetic planning. It is desirable to develop algorithms that, like humans, "learn" from being exposed to examples of the application of the rules of organic chemistry. We explore the use of neural networks for predicting reaction types, using a new reaction fingerprinting method. We combine this predictor with SMARTS transformations to build a system which, given a set of reagents and reactants, predicts the likely products. We test this method on problems from a popular organic chemistry textbook.
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.
Complex Chemical Reaction Networks from Heuristics-Aided Quantum Chemistry.
Rappoport, Dmitrij; Galvin, Cooper J; Zubarev, Dmitry Yu; Aspuru-Guzik, Alán
2014-03-11
While structures and reactivities of many small molecules can be computed efficiently and accurately using quantum chemical methods, heuristic approaches remain essential for modeling complex structures and large-scale chemical systems. Here, we present a heuristics-aided quantum chemical methodology applicable to complex chemical reaction networks such as those arising in cell metabolism and prebiotic chemistry. Chemical heuristics offer an expedient way of traversing high-dimensional reactive potential energy surfaces and are combined here with quantum chemical structure optimizations, which yield the structures and energies of the reaction intermediates and products. Application of heuristics-aided quantum chemical methodology to the formose reaction reproduces the experimentally observed reaction products, major reaction pathways, and autocatalytic cycles.
Chemical and genomic evolution of enzyme-catalyzed reaction networks.
Kanehisa, Minoru
2013-09-02
There is a tendency that a unit of enzyme genes in an operon-like structure in the prokaryotic genome encodes enzymes that catalyze a series of consecutive reactions in a metabolic pathway. Our recent analysis shows that this and other genomic units correspond to chemical units reflecting chemical logic of organic reactions. From all known metabolic pathways in the KEGG database we identified chemical units, called reaction modules, as the conserved sequences of chemical structure transformation patterns of small molecules. The extracted patterns suggest co-evolution of genomic units and chemical units. While the core of the metabolic network may have evolved with mechanisms involving individual enzymes and reactions, its extension may have been driven by modular units of enzymes and reactions.
LENUS (Irish Health Repository)
Casey, Fergal
2011-08-22
Abstract Background Gene and protein interactions are commonly represented as networks, with the genes or proteins comprising the nodes and the relationship between them as edges. Motifs, or small local configurations of edges and nodes that arise repeatedly, can be used to simplify the interpretation of networks. Results We examined triplet motifs in a network of quantitative epistatic genetic relationships, and found a non-random distribution of particular motif classes. Individual motif classes were found to be associated with different functional properties, suggestive of an underlying biological significance. These associations were apparent not only for motif classes, but for individual positions within the motifs. As expected, NNN (all negative) motifs were strongly associated with previously reported genetic (i.e. synthetic lethal) interactions, while PPP (all positive) motifs were associated with protein complexes. The two other motif classes (NNP: a positive interaction spanned by two negative interactions, and NPP: a negative spanned by two positives) showed very distinct functional associations, with physical interactions dominating for the former but alternative enrichments, typical of biochemical pathways, dominating for the latter. Conclusion We present a model showing how NNP motifs can be used to recognize supportive relationships between protein complexes, while NPP motifs often identify opposing or regulatory behaviour between a gene and an associated pathway. The ability to use motifs to point toward underlying biological organizational themes is likely to be increasingly important as more extensive epistasis mapping projects in higher organisms begin.
Biological and biochemical methane reactions. Annual report, March 1987-February 1988
Energy Technology Data Exchange (ETDEWEB)
Dalton, H.; Willse, A.R.G.; Pienkos, P.T.; Stirling, D.I.
1988-05-01
This project is aimed at determining those features of the enzyme methane monooxygenase that contribute to its catalytic conversion of methane to methanol. Biological and biochemical efforts identified the essential nature of the bi-metal center at the active site of the enzyme, solubilized its more active copper-dependent form, and found and screened many new methanotrophs that function in widely different chemical and physical environments.
Vitalis, K. M.; Kominis, I. K.
2016-01-01
Radical-ion pairs and their reactions have triggered the study of quantum effects in biological systems. This is because they exhibit a number of effects best understood within quantum information science, and at the same time are central in understanding the avian magnetic compass and the spin transport dynamics in photosynthetic reaction centers. Here we address radical-pair reactions from the perspective of quantum metrology. Since the coherent spin motion of radical-pairs is effected by a...
Wu, Qianqian; Tian, Tianhai
2016-08-24
To deal with the growing scale of molecular systems, sophisticated modelling techniques have been designed in recent years to reduce the complexity of mathematical models. Among them, a widely used approach is delayed reaction for simplifying multistep reactions. However, recent research results suggest that a delayed reaction with constant time delay is unable to describe multistep reactions accurately. To address this issue, we propose a novel approach using state-dependent time delay to approximate multistep reactions. We first use stochastic simulations to calculate time delay arising from multistep reactions exactly. Then we design algorithms to calculate time delay based on system dynamics precisely. To demonstrate the power of proposed method, two processes of mRNA degradation are used to investigate the function of time delay in determining system dynamics. In addition, a multistep pathway of metabolic synthesis is used to explore the potential of the proposed method to simplify multistep reactions with nonlinear reaction rates. Simulation results suggest that the state-dependent time delay is a promising and accurate approach to reduce model complexity and decrease the number of unknown parameters in the models.
Directory of Open Access Journals (Sweden)
David eGomez
2015-06-01
Full Text Available Molecular crowding is ubiquitous within cells and affects many biological processes including protein-protein binding, enzyme activities and gene regulation. Here we revisit some generic effects of crowding using a combination of lattice simulations and reaction-diffusion simulations with the program ReaDDy. Specifically, we implement three reactions, simple binding, a diffusion-limited reaction and a reaction with Michaelis-Menten kinetics. Histograms of binding and unbinding times provide a detailed picture how crowding affects these reactions and how the separate effects of crowding on binding equilibrium and on diffusion act together. In addition, we discuss how crowding affects processes related to gene expression such as RNA polymerase-promoter binding and translation elongation.
Golovin, Yu. I.; Klyachko, N. L.; Golovin, D. Yu.; Efremova, M. V.; Samodurov, A. A.; Sokolski-Papkov, M.; Kabanov, A. V.
2013-03-01
A new approach to the control of biochemical reactions in magnetic nanosuspensions exposed to a low-frequency (nonheating) magnetic field, which has a nanomechanical effect on macro-molecules chemically linked to magnetic nanoparticles (MNPs), is described. Experimental verification of this approach showed that a magnetic field with an intensity of from 15 to 220 kA/m and a frequency of 50 Hz affected the kinetics of a chemical reaction in an aqueous solution containing suspended MNPs of magnetite (FeO · Fe2O3) and chymotrypsin molecules linked to them through polymer bridges. The field dependence of the effect is shown. The effect is interpreted within the framework of a nanomechanical model taking into account the deformations, conformational change, and destruction of weak bonds in the enzyme macromolecule under the action of the forces applied to it during the orientation of MNPs in the field.
Bellesia, Giovanni; Bales, Benjamin B
2016-10-01
We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.
Bellesia, Giovanni; Bales, Benjamin B.
2016-10-01
We investigate, via Brownian dynamics simulations, the reaction dynamics of a generic, nonlinear chemical network under spatial confinement and crowding conditions. In detail, the Willamowski-Rossler chemical reaction system has been "extended" and considered as a prototype reaction-diffusion system. Our results are potentially relevant to a number of open problems in biophysics and biochemistry, such as the synthesis of primitive cellular units (protocells) and the definition of their role in the chemical origin of life and the characterization of vesicle-mediated drug delivery processes. More generally, the computational approach presented in this work makes the case for the use of spatial stochastic simulation methods for the study of biochemical networks in vivo where the "well-mixed" approximation is invalid and both thermal and intrinsic fluctuations linked to the possible presence of molecular species in low number copies cannot be averaged out.
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.
Constructing and visualizing chemical reaction networks from pi-calculus models
M. John; H.-J. Schulz; H. Schumann; A. M. Uhrmacher; Andrea Unger
2013-01-01
International audience; The pi-calculus, in particular its stochastic version the stochastic pi-calculus, is a common modeling formalism to concisely describe the chemical reactions occurring in biochemical systems. However, it remains largely unexplored how to transform a biochemical model expressed in the stochastic pi-calculus back into a set of meaningful reactions. To this end, we present a two step approach of first translating model states to reaction sets and then visualizing sequence...
Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks
Energy Technology Data Exchange (ETDEWEB)
Ziaul Huque
2007-08-31
This is the final technical report for the project titled 'Mathematically Reduced Chemical Reaction Mechanism Using Neural Networks'. The aim of the project was to develop an efficient chemistry model for combustion simulations. The reduced chemistry model was developed mathematically without the need of having extensive knowledge of the chemistry involved. To aid in the development of the model, Neural Networks (NN) was used via a new network topology known as Non-linear Principal Components Analysis (NPCA). A commonly used Multilayer Perceptron Neural Network (MLP-NN) was modified to implement NPCA-NN. The training rate of NPCA-NN was improved with the GEneralized Regression Neural Network (GRNN) based on kernel smoothing techniques. Kernel smoothing provides a simple way of finding structure in data set without the imposition of a parametric model. The trajectory data of the reaction mechanism was generated based on the optimization techniques of genetic algorithm (GA). The NPCA-NN algorithm was then used for the reduction of Dimethyl Ether (DME) mechanism. DME is a recently discovered fuel made from natural gas, (and other feedstock such as coal, biomass, and urban wastes) which can be used in compression ignition engines as a substitute for diesel. An in-house two-dimensional Computational Fluid Dynamics (CFD) code was developed based on Meshfree technique and time marching solution algorithm. The project also provided valuable research experience to two graduate students.
Directory of Open Access Journals (Sweden)
Ayse Yarali
Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.
Directory of Open Access Journals (Sweden)
Kentaro Inoue
Full Text Available BACKGROUND: For visualizing large-scale biochemical network maps, it is important to calculate the coordinates of molecular nodes quickly and to enhance the understanding or traceability of them. The grid layout is effective in drawing compact, orderly, balanced network maps with node label spaces, but existing grid layout algorithms often require a high computational cost because they have to consider complicated positional constraints through the entire optimization process. RESULTS: We propose a hybrid grid layout algorithm that consists of a non-grid, fast layout (preprocessor algorithm and an approximate pattern matching algorithm that distributes the resultant preprocessed nodes on square grid points. To demonstrate the feasibility of the hybrid layout algorithm, it is characterized in terms of the calculation time, numbers of edge-edge and node-edge crossings, relative edge lengths, and F-measures. The proposed algorithm achieves outstanding performances compared with other existing grid layouts. CONCLUSIONS: Use of an approximate pattern matching algorithm quickly redistributes the laid-out nodes by fast, non-grid algorithms on the square grid points, while preserving the topological relationships among the nodes. The proposed algorithm is a novel use of the pattern matching, thereby providing a breakthrough for grid layout. This application program can be freely downloaded from http://www.cadlive.jp/hybridlayout/hybridlayout.html.
Directory of Open Access Journals (Sweden)
Urszula Małolepsza
2013-12-01
Full Text Available The reactions of strawberry plants to infection with B. cinerea and treatment with salicylic acid has been studied. Infection of leaves with B. cinerea resulted in early increases in active oxygen species generation, superoxide dismutase and peroxidase activities and phenolic compounds content. Some increases of the above reactions were noticed in plants treated with salicylic acid but not in the plants treated with SA and then later infected with B. cinerea.
Reachability bounds for chemical reaction networks and strand displacement systems.
Condon, Anne; Kirkpatrick, Bonnie; Maňuch, Ján
2014-01-01
Chemical reaction networks (CRNs) and DNA strand displacement systems (DSDs) are widely-studied and useful models of molecular programming. However, in order for some DSDs in the literature to behave in an expected manner, the initial number of copies of some reagents is required to be fixed. In this paper we show that, when multiple copies of all initial molecules are present, general types of CRNs and DSDs fail to work correctly if the length of the shortest sequence of reactions needed to produce any given molecule exceeds a threshold that grows polynomially with attributes of the system.
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...
Hun Yeon, Ju; Chan, Karen Y. T.; Wong, Ting-Chia; Chan, Kelvin; Sutherland, Michael R.; Ismagilov, Rustem F.; Pryzdial, Edward L. G.; Kastrup, Christian J.
2015-05-01
Developing bio-compatible smart materials that assemble in response to environmental cues requires strategies that can discriminate multiple specific stimuli in a complex milieu. Synthetic materials have yet to achieve this level of sensitivity, which would emulate the highly evolved and tailored reaction networks of complex biological systems. Here we show that the output of a naturally occurring network can be replaced with a synthetic material. Exploiting the blood coagulation system as an exquisite biological sensor, the fibrin clot end-product was replaced with a synthetic material under the biological control of a precisely regulated cross-linking enzyme. The functions of the coagulation network remained intact when the material was incorporated. Clot-like polymerization was induced in indirect response to distinct small molecules, phospholipids, enzymes, cells, viruses, an inorganic solid, a polyphenol, a polysaccharide, and a membrane protein. This strategy demonstrates for the first time that an existing stimulus-responsive biological network can be used to control the formation of a synthetic material by diverse classes of physiological triggers.
Noise transmission and delay-induced stochasticoscillations in biochemical network motifs
Institute of Scientific and Technical Information of China (English)
Liu Sheng-Jun; Wang Qi; Liu Bo; Yan Shi-Wei; Fumihiko Sakata
2011-01-01
With the aid of stochastic delayed-feedback differential equations,we derive an analytic expression for the power spectra of reacting molecules included in a generic biological network motif that is incorporated with a feedback mechanism and time delays in gene regulation.We systematically analyse the effects of time delays,the feedback mechanism,and biological stochasticity on the power spectra.It has been clarified that the time delays together with the feedback mechanism can induce stochastic oscillations at the molecular level and invalidate the noise addition rule for a modular description of the noise propagator.Delay-induced stochastic resonance can be expected,which is related to the stability loss of the reaction systems and Hopf bifurcation occurring for solutions of the corresponding deterministic reaction equations.Through the analysis of the power spectrum,a new approach is proposed to estimate the oscillation period.
Maschke, C; Rupp, T; Hecht, K
2000-03-01
For every faculty of perception there is, according to the degree of irritation, a biochemical or psychobiological activation. This is also true for the perception of sound or noise. Initially, these processes allow for the adjustment of the organism to a changed situation (eustress). Prolonged effects of stressors may ultimately lead to regulatory disturbances and induce pathological processes (distress). The pathogenetic concept that psychobiological stresses (e.g. noise) may be connected with the well-known risk factors of cardiovascular diseases, through excitation of the central nervous system, is based on the known stress models. The central connective factors are the activation hormones of the adrenal gland, also referred to as stress hormones. From blood and urine parameters recorded in epidemiological and experimental studies under the influence of acute or chronic noise, a simplified model of the pathogenetic mechanism has been developed. Fundamental conditions for future assessing the "stress hormones" have been derived, by means of which premorbid conditions can be determined on a population or group basis.
A new method for studying caffeine's antioxygenic property: Peroxidase-Oxidase biochemical reaction
Institute of Scientific and Technical Information of China (English)
WANG Jun; CAI Ruxiu; LIN Zhixin; LIU Zhihong
2003-01-01
The effect of Caffeine on Peroxidase-Oxidase (PO) reaction was studied systematically in this paper. We proved that the valley of PO oscillation is the best phase angle which was used to research antioxygenic property by the Analyte Pulse Perturbation Technique (APP), based on investigating the mechanism. Area integral calculus was proposed to use in quantitative analysis for the first time. There is a good linear relationship (R = 0.9950) between the ratio of amplitude changes of PO oscillation and the concentration of caffeine in the range 4.61×10-7 mol/L-1.84×10-5 mol/L. A new method for analysis by PO oscillation was set up. We also investigated two-dimensional projections and Fourier spectrum of nonlinear complicate system--PO reaction which was perturbed by caffeine, in order to provide a theoretical basis for studying effects of kinds of antioxidants on life system.
Wong, Kin-Yiu; Xu, Yuqing; Xu, Liang
2015-11-01
Enzymatic reactions are integral components in many biological functions and malfunctions. The iconic structure of each reaction path for elucidating the reaction mechanism in details is the molecular structure of the rate-limiting transition state (RLTS). But RLTS is very hard to get caught or to get visualized by experimentalists. In spite of the lack of explicit molecular structure of the RLTS in experiment, we still can trace out the RLTS unique "fingerprints" by measuring the isotope effects on the reaction rate. This set of "fingerprints" is considered as a most direct probe of RLTS. By contrast, for computer simulations, oftentimes molecular structures of a number of TS can be precisely visualized on computer screen, however, theoreticians are not sure which TS is the actual rate-limiting one. As a result, this is an excellent stage setting for a perfect "marriage" between experiment and theory for determining the structure of RLTS, along with the reaction mechanism, i.e., experimentalists are responsible for "fingerprinting", whereas theoreticians are responsible for providing candidates that match the "fingerprints". In this Review, the origin of isotope effects on a chemical reaction is discussed from the perspectives of classical and quantum worlds, respectively (e.g., the origins of the inverse kinetic isotope effects and all the equilibrium isotope effects are purely from quantum). The conventional Bigeleisen equation for isotope effect calculations, as well as its refined version in the framework of Feynman's path integral and Kleinert's variational perturbation (KP) theory for systematically incorporating anharmonicity and (non-parabolic) quantum tunneling, are also presented. In addition, the outstanding interplay between theory and experiment for successfully deducing the RLTS structures and the reaction mechanisms is demonstrated by applications on biochemical reactions, namely models of bacterial squalene-to-hopene polycyclization and RNA 2'-O
Patterns of Stochastic Behavior in Dynamically Unstable High-Dimensional Biochemical Networks
Directory of Open Access Journals (Sweden)
Simon Rosenfeld
2009-01-01
Full Text Available The question of dynamical stability and stochastic behavior of large biochemical networks is discussed. It is argued that stringent conditions of asymptotic stability have very little chance to materialize in a multidimensional system described by the differential equations of chemical kinetics. The reason is that the criteria of asymptotic stability (Routh- Hurwitz, Lyapunov criteria, Feinberg’s Deficiency Zero theorem would impose the limitations of very high algebraic order on the kinetic rates and stoichiometric coefficients, and there are no natural laws that would guarantee their unconditional validity. Highly nonlinear, dynamically unstable systems, however, are not necessarily doomed to collapse, as a simple Jacobian analysis would suggest. It is possible that their dynamics may assume the form of pseudo-random fluctuations quite similar to a shot noise, and, therefore, their behavior may be described in terms of Langevin and Fokker-Plank equations. We have shown by simulation that the resulting pseudo-stochastic processes obey the heavy-tailed Generalized Pareto Distribution with temporal sequence of pulses forming the set of constituent-specific Poisson processes. Being applied to intracellular dynamics, these properties are naturally associated with burstiness, a well documented phenomenon in the biology of gene expression.
Patterns of stochastic behavior in dynamically unstable high-dimensional biochemical networks.
Rosenfeld, Simon
2009-01-29
The question of dynamical stability and stochastic behavior of large biochemical networks is discussed. It is argued that stringent conditions of asymptotic stability have very little chance to materialize in a multidimensional system described by the differential equations of chemical kinetics. The reason is that the criteria of asymptotic stability (Routh-Hurwitz, Lyapunov criteria, Feinberg's Deficiency Zero theorem) would impose the limitations of very high algebraic order on the kinetic rates and stoichiometric coefficients, and there are no natural laws that would guarantee their unconditional validity. Highly nonlinear, dynamically unstable systems, however, are not necessarily doomed to collapse, as a simple Jacobian analysis would suggest. It is possible that their dynamics may assume the form of pseudo-random fluctuations quite similar to a shot noise, and, therefore, their behavior may be described in terms of Langevin and Fokker-Plank equations. We have shown by simulation that the resulting pseudo-stochastic processes obey the heavy-tailed Generalized Pareto Distribution with temporal sequence of pulses forming the set of constituent-specific Poisson processes. Being applied to intracellular dynamics, these properties are naturally associated with burstiness, a well documented phenomenon in the biology of gene expression.
Combining Genomics, Metabolome Analysis, and Biochemical Modelling to Understand Metabolic Networks
Directory of Open Access Journals (Sweden)
Oliver Fiehn
2006-04-01
Full Text Available Now that complete genome sequences are available for a variety of organisms, the elucidation of gene functions involved in metabolism necessarily includes a better understanding of cellular responses upon mutations on all levels of gene products, mRNA, proteins, and metabolites. Such progress is essential since the observable properties of organisms Ã¢Â€Â“ the phenotypes Ã¢Â€Â“ are produced by the genotype in juxtaposition with the environment. Whereas much has been done to make mRNA and protein profiling possible, considerably less effort has been put into profiling the end products of gene expression, metabolites. To date, analytical approaches have been aimed primarily at the accurate quantification of a number of pre-defined target metabolites, or at producing fingerprints of metabolic changes without individually determining metabolite identities. Neither of these approaches allows the formation of an in-depth understanding of the biochemical behaviour within metabolic networks. Yet, by carefully choosing protocols for sample preparation and analytical techniques, a number of chemically different classes of compounds can be quantified simultaneously to enable such understanding. In this review, the terms describing various metabolite-oriented approaches are given, and the differences among these approaches are outlined. Metabolite target analysis, metabolite profiling, metabolomics, and metabolic fingerprinting are considered. For each approach, a number of examples are given, and potential applications are discussed.
Directory of Open Access Journals (Sweden)
N. O. Khromykh
2015-07-01
Full Text Available Morphometrical indexes, and spectrophotometrically measured protein and glutathione (GSH, GSSG contents and activity of peroxidase (POD, EC 1.11.1.7, glutathione-reductase (GR, EC 1.6.4.2 and glutathione S-transferase (GST, EС 2.5.1.18 were examined in Hordeum vulgare L. seedlings after 0.01 and 0.1 mg/l AgNPs treatment during 24 h. We tested the hypothesis that the action of nanoparticles has a stressful effect on the physiological and biochemical processes of seedlings. Growth of roots was inhibited and fresh weight decreased by 29% and 21% under low and high concentrations respectively. Conversely, leaf growth was intensified, and leaf length (16% and 18% and fresh weight (35% and 44% increased at low and high concentrations respectively. POD activity in roots increased by 26% and 7%, and decreased in leaves to 57% and 81% of control at low and high concentrations respectively. GSH content changed insignificantly, but GSSG content increased in roots (2 and 2.5-fold and in leaves (13% and 30% at both AgNPs concentrations. GSH/GSSG-ratio decreased in roots (1.9 and 2.6-fold and in leaves (1.1 and 1.3-fold at low and high concentrations respectively. GR activity decreased at a concentration of 0.01 mg/l (7% in roots and 17% in leaves respectively and increased at 0.1 mg/l (52% in roots and 6% in leaves. GST activity increased in leaves (52% and 78% at low and high concentrations but decreased by 17% in roots under high concentration of nanosilver. Thus, the action of AgNPs on barley seedlings had a dose-dependent and organ-specific character. The various directions of changes in growth, metabolic processes and activity of antioxidant defense systems appear to be a stress response of barley seedlings to the impact of AgNPs, which underlines the necessity of detailed study of plant intracellular processes exposed to the action of nanomaterial.
Bianconi, Fortunato; Baldelli, Elisa; Luovini, Vienna; Petricoin, Emanuel F.; Crinò, Lucio; Valigi, Paolo
2015-01-01
Background The study of cancer therapy is a key issue in the field of oncology research and the development of target therapies is one of the main problems currently under investigation. This is particularly relevant in different types of tumor where traditional chemotherapy approaches often fail, such as lung cancer. Results We started from the general definition of robustness introduced by Kitano and applied it to the analysis of dynamical biochemical networks, proposing a new algorithm bas...
Sorribas, Albert; Hernández-Bermejo, Benito; Vilaprinyo, Ester; Alves, Rui
2007-08-01
Cooperative and saturable systems are common in molecular biology. Nevertheless, common canonical formalisms for kinetic modeling that are theoretically well justified do not have a saturable form. Modeling and fitting data from saturable systems are widely done using Hill-like equations. In practice, there is no theoretical justification for the generalized use of these equations, other than their ability to fit experimental data. Thus it is important to find a canonical formalism that is (a) theoretically well supported, (b) has a saturable functional form, and (c) can be justifiably applicable to any biochemical network. Here we derive such a formalism using Taylor approximations in a special transformation space defined by power-inverses and logarithms of power-inverses. This formalism is generalized for processes with n-variables, leading to a useful mathematical representation for molecular biology: the Saturable and Cooperative Formalism (SC formalism). This formalism provides an appropriate representation that can be used for modeling processes with cooperativity and saturation. We also show that the Hill equation can be seen as a special case within this formalism. Parameter estimation for the SC formalism requires information that is also necessary to build Power-Law models, Metabolic Control Analysis descriptions or (log)linear and Lin-log models. In addition, the saturation fraction of the relevant processes at the operating point needs to be considered. The practical use of the SC formalism for modeling is illustrated with a few examples. Similar models are built using different formalisms and compared to emphasize advantages and limitations of the different approaches.
ConvAn: a convergence analyzing tool for optimization of biochemical networks.
Kostromins, Andrejs; Mozga, Ivars; Stalidzans, Egils
2012-01-01
Dynamic models of biochemical networks usually are described as a system of nonlinear differential equations. In case of optimization of models for purpose of parameter estimation or design of new properties mainly numerical methods are used. That causes problems of optimization predictability as most of numerical optimization methods have stochastic properties and the convergence of the objective function to the global optimum is hardly predictable. Determination of suitable optimization method and necessary duration of optimization becomes critical in case of evaluation of high number of combinations of adjustable parameters or in case of large dynamic models. This task is complex due to variety of optimization methods, software tools and nonlinearity features of models in different parameter spaces. A software tool ConvAn is developed to analyze statistical properties of convergence dynamics for optimization runs with particular optimization method, model, software tool, set of optimization method parameters and number of adjustable parameters of the model. The convergence curves can be normalized automatically to enable comparison of different methods and models in the same scale. By the help of the biochemistry adapted graphical user interface of ConvAn it is possible to compare different optimization methods in terms of ability to find the global optima or values close to that as well as the necessary computational time to reach them. It is possible to estimate the optimization performance for different number of adjustable parameters. The functionality of ConvAn enables statistical assessment of necessary optimization time depending on the necessary optimization accuracy. Optimization methods, which are not suitable for a particular optimization task, can be rejected if they have poor repeatability or convergence properties. The software ConvAn is freely available on www.biosystems.lv/convan.
COEL: A Cloud-based Reaction Network Simulator
Directory of Open Access Journals (Sweden)
Peter eBanda
2016-04-01
Full Text Available Chemical Reaction Networks (CRNs are a formalism to describe the macroscopic behavior of chemical systems. We introduce COEL, a web- and cloud-based CRN simulation framework that does not require a local installation, runs simulations on a large computational grid, provides reliable database storage, and offers a visually pleasing and intuitive user interface. We present an overview of the underlying software, the technologies, and the main architectural approaches employed. Some of COEL's key features include ODE-based simulations of CRNs and multicompartment reaction networks with rich interaction options, a built-in plotting engine, automatic DNA-strand displacement transformation and visualization, SBML/Octave/Matlab export, and a built-in genetic-algorithm-based optimization toolbox for rate constants.COEL is an open-source project hosted on GitHub (http://dx.doi.org/10.5281/zenodo.46544, which allows interested research groups to deploy it on their own sever. Regular users can simply use the web instance at no cost at http://coel-sim.org. The framework is ideally suited for a collaborative use in both research and education.
A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks
Moraes, Alvaro
2016-07-07
In this work, we present a novel multilevel Monte Carlo method for kinetic simulation of stochastic reaction networks characterized by having simultaneously fast and slow reaction channels. To produce efficient simulations, our method adaptively classifies the reactions channels into fast and slow channels. To this end, we first introduce a state-dependent quantity named level of activity of a reaction channel. Then, we propose a low-cost heuristic that allows us to adaptively split the set of reaction channels into two subsets characterized by either a high or a low level of activity. Based on a time-splitting technique, the increments associated with high-activity channels are simulated using the tau-leap method, while those associated with low-activity channels are simulated using an exact method. This path simulation technique is amenable for coupled path generation and a corresponding multilevel Monte Carlo algorithm. To estimate expected values of observables of the system at a prescribed final time, our method bounds the global computational error to be below a prescribed tolerance, TOL, within a given confidence level. This goal is achieved with a computational complexity of order O(TOL-2), the same as with a pathwise-exact method, but with a smaller constant. We also present a novel low-cost control variate technique based on the stochastic time change representation by Kurtz, showing its performance on a numerical example. We present two numerical examples extracted from the literature that show how the reaction-splitting method obtains substantial gains with respect to the standard stochastic simulation algorithm and the multilevel Monte Carlo approach by Anderson and Higham. © 2016 Society for Industrial and Applied Mathematics.
Markov chain aggregation and its applications to combinatorial reaction networks.
Ganguly, Arnab; Petrov, Tatjana; Koeppl, Heinz
2014-09-01
We consider a continuous-time Markov chain (CTMC) whose state space is partitioned into aggregates, and each aggregate is assigned a probability measure. A sufficient condition for defining a CTMC over the aggregates is presented as a variant of weak lumpability, which also characterizes that the measure over the original process can be recovered from that of the aggregated one. We show how the applicability of de-aggregation depends on the initial distribution. The application section is devoted to illustrate how the developed theory aids in reducing CTMC models of biochemical systems particularly in connection to protein-protein interactions. We assume that the model is written by a biologist in form of site-graph-rewrite rules. Site-graph-rewrite rules compactly express that, often, only a local context of a protein (instead of a full molecular species) needs to be in a certain configuration in order to trigger a reaction event. This observation leads to suitable aggregate Markov chains with smaller state spaces, thereby providing sufficient reduction in computational complexity. This is further exemplified in two case studies: simple unbounded polymerization and early EGFR/insulin crosstalk.
Teodorowicz, M.; Fiedorowicz, E.; Kostyra, H.; Wichers, H.J.; Kostyra, E.
2013-01-01
Purpose The purpose of this study was to determine the influence of Maillard reaction (MR, glycation) on biochemical and biological properties of the major peanut allergen Ara h 1. Methods Three different time/temperature conditions of treatment were applied (37, 60, and 145 °C). The extent of MR wa
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A new analytical method for the determination of urea-urease system based on biochemical reaction heat induced laser beam deflection is presented in this paper. With the method, the Michaelis constant (Km) of urease and apparent inhibition constant (Ki) of some metal ion inhibitors were measured respectively. This method was also used for the quantitative determination of metal ions with satisfactory result.
Multiplexing oscillatory biochemical signals.
de Ronde, Wiet; ten Wolde, Pieter Rein
2014-04-01
In recent years it has been increasingly recognized that biochemical signals are not necessarily constant in time and that the temporal dynamics of a signal can be the information carrier. Moreover, it is now well established that the protein signaling network of living cells has a bow-tie structure and that components are often shared between different signaling pathways. Here we show by mathematical modeling that living cells can multiplex a constant and an oscillatory signal: they can transmit these two signals simultaneously through a common signaling pathway, and yet respond to them specifically and reliably. We find that information transmission is reduced not only by noise arising from the intrinsic stochasticity of biochemical reactions, but also by crosstalk between the different channels. Yet, under biologically relevant conditions more than 2 bits of information can be transmitted per channel, even when the two signals are transmitted simultaneously. These observations suggest that oscillatory signals are ideal for multiplexing signals.
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
Privman, Vladimir; Arugula, Mary A; Halámek, Jan; Pita, Marcos; Katz, Evgeny
2009-04-16
We develop an approach aimed at optimizing the parameters of a network of biochemical logic gates for reduction of the "analog" noise buildup. Experiments for three coupled enzymatic AND gates are reported, illustrating our procedure. Specifically, starch, one of the controlled network inputs, is converted to maltose by beta-amylase. With the use of phosphate (another controlled input), maltose phosphorylase then produces glucose. Finally, nicotinamide adenine dinucleotide (NAD(+)), the third controlled input, is reduced under the action of glucose dehydrogenase to yield the optically detected signal. Network functioning is analyzed by varying selective inputs and fitting standardized few-parameters "response-surface" functions assumed for each gate. This allows a certain probe of the individual gate quality, but primarily yields information on the relative contribution of the gates to noise amplification. The derived information is then used to modify our experimental system to put it in a regime of a less noisy operation.
Exact probability distributions of selected species in stochastic chemical reaction networks.
López-Caamal, Fernando; Marquez-Lago, Tatiana T
2014-09-01
Chemical reactions are discrete, stochastic events. As such, the species' molecular numbers can be described by an associated master equation. However, handling such an equation may become difficult due to the large size of reaction networks. A commonly used approach to forecast the behaviour of reaction networks is to perform computational simulations of such systems and analyse their outcome statistically. This approach, however, might require high computational costs to provide accurate results. In this paper we opt for an analytical approach to obtain the time-dependent solution of the Chemical Master Equation for selected species in a general reaction network. When the reaction networks are composed exclusively of zeroth and first-order reactions, this analytical approach significantly alleviates the computational burden required by simulation-based methods. By building upon these analytical solutions, we analyse a general monomolecular reaction network with an arbitrary number of species to obtain the exact marginal probability distribution for selected species. Additionally, we study two particular topologies of monomolecular reaction networks, namely (i) an unbranched chain of monomolecular reactions with and without synthesis and degradation reactions and (ii) a circular chain of monomolecular reactions. We illustrate our methodology and alternative ways to use it for non-linear systems by analysing a protein autoactivation mechanism. Later, we compare the computational load required for the implementation of our results and a pure computational approach to analyse an unbranched chain of monomolecular reactions. Finally, we study calcium ions gates in the sarco/endoplasmic reticulum mediated by ryanodine receptors.
Energy Technology Data Exchange (ETDEWEB)
Phelps, Michael E.
2009-09-01
Radiotracer techniques are used in environmental sciences, geology, biology and medicine. Radiotracers with Positron Emission Tomography (PET) provided biological examinations of ~3 million patients 2008. Despite the success of positron labeled tracers in many sciences, there is limited access in an affordable and convenient manner to develop and use new tracers. Integrated microfluidic chips are a new technology well matched to the concentrations of tracers. Our goal is to develop microfluidic chips and new synthesis approaches to enable wide dissemination of diverse types of tracers at low cost, and to produce new generations of radiochemists for which there are many unfilled jobs. The program objectives are to: 1. Develop an integrated microfluidic platform technology for synthesizing and 18F-labeling diverse arrays of different classes of molecules. 2. Incorporate microfluidic chips into small PC controlled devices (“Synthesizer”) with a platform interfaced to PC for electronic and fluid input/out control. 3. Establish a de-centralized model with Synthesizers for discovering and producing molecular imaging probes, only requiring delivery of inexpensive [18F]fluoride ion from commercial PET radiopharmacies vs the centralized approach of cyclotron facilities synthesizing and shipping a few different types of 18F-probes. 4. Develop a position sensitive avalanche photo diode (PSAPD) camera for beta particles embedded in a microfluidic chip for imaging and measuring transport and biochemical reaction rates to valid new 18F-labeled probes in an array of cell cultures. These objectives are met within a research and educational program integrating radio-chemistry, synthetic chemistry, biochemistry, engineering and biology in the Crump Institute for Molecular Imaging. The Radiochemistry Training Program exposes PhD and post doctoral students to molecular imaging in vitro in cells and microorganisms in microfluidic chips and in vivo with PET, from new technologies
Energy Technology Data Exchange (ETDEWEB)
Phelps, Michael E. [Univ. of California, Los Angeles, CA (United States)
2009-09-01
Radiotracer techniques are used in environmental sciences, geology, biology and medicine. Radiotracers with Positron Emission Tomography (PET) provided biological examinations of ~3 million patients 2008. Despite the success of positron labeled tracers in many sciences, there is limited access in an affordable and convenient manner to develop and use new tracers. Integrated microfluidic chips are a new technology well matched to the concentrations of tracers. Our goal is to develop microfluidic chips and new synthesis approaches to enable wide dissemination of diverse types of tracers at low cost, and to produce new generations of radiochemists for which there are many unfilled jobs. The program objectives are to: 1. Develop an integrated microfluidic platform technology for synthesizing and 18F-labeling diverse arrays of different classes of molecules. 2. Incorporate microfluidic chips into small PC controlled devices (“Synthesizer”) with a platform interfaced to PC for electronic and fluid input/out control. 3. Establish a de-centralized model with Synthesizers for discovering and producing molecular imaging probes, only requiring delivery of inexpensive [18F]fluoride ion from commercial PET radiopharmacies vs the centralized approach of cyclotron facilities synthesizing and shipping a few different types of 18F-probes. 4. Develop a position sensitive avalanche photo diode (PSAPD) camera for beta particles embedded in a microfluidic chip for imaging and measuring transport and biochemical reaction rates to valid new 18F-labeled probes in an array of cell cultures. These objectives are met within a research and educational program integrating radio-chemistry, synthetic chemistry, biochemistry, engineering and biology in the Crump Institute for Molecular Imaging. The Radiochemistry Training Program exposes PhD and post doctoral students to molecular imaging in vitro in cells and microorganisms in microfluidic chips and in vivo with PET, from new technologies
Stochastic Simulation and Analysis of Biomolecular Reaction Networks
2009-01-01
a discrete stochastic system, a hypothetical model of a generic two gene, self- assembling catalytic ligation reaction in a cell-free tran- scription...ligation reactions and the tRNA charging reactions terminate. Third, the first metabolic ligation reaction terminated when Sub_1 was depleted at about...2900 sec and subsequently, the second metabolic ligation reaction would have terminated when all of Prod_A formed by the first ligation reaction was
Defect reaction network in Si-doped InAs. Numerical predictions.
Energy Technology Data Exchange (ETDEWEB)
Schultz, Peter A. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2015-05-01
This Report characterizes the defects in the def ect reaction network in silicon - doped, n - type InAs predicted with first principles density functional theory. The reaction network is deduced by following exothermic defect reactions starting with the initially mobile interstitial defects reacting with common displacement damage defects in Si - doped InAs , until culminating in immobile reaction p roducts. The defect reactions and reaction energies are tabulated, along with the properties of all the silicon - related defects in the reaction network. This Report serves to extend the results for the properties of intrinsic defects in bulk InAs as colla ted in SAND 2013 - 2477 : Simple intrinsic defects in InAs : Numerical predictions to include Si - containing simple defects likely to be present in a radiation - induced defect reaction sequence . This page intentionally left blank
Stochastic analysis of Chemical Reaction Networks using Linear Noise Approximation.
Cardelli, Luca; Kwiatkowska, Marta; Laurenti, Luca
2016-11-01
Stochastic evolution of Chemical Reactions Networks (CRNs) over time is usually analyzed through solving the Chemical Master Equation (CME) or performing extensive simulations. Analysing stochasticity is often needed, particularly when some molecules occur in low numbers. Unfortunately, both approaches become infeasible if the system is complex and/or it cannot be ensured that initial populations are small. We develop a probabilistic logic for CRNs that enables stochastic analysis of the evolution of populations of molecular species. We present an approximate model checking algorithm based on the Linear Noise Approximation (LNA) of the CME, whose computational complexity is independent of the population size of each species and polynomial in the number of different species. The algorithm requires the solution of first order polynomial differential equations. We prove that our approach is valid for any CRN close enough to the thermodynamical limit. However, we show on four case studies that it can still provide good approximation even for low molecule counts. Our approach enables rigorous analysis of CRNs that are not analyzable by solving the CME, but are far from the deterministic limit. Moreover, it can be used for a fast approximate stochastic characterization of a CRN.
A Design Tool Utilizing Stoichiometric Structure for the Analysis of Biochemical Reaction Networks
1990-05-20
Valine Pathway (adapted from Demain, 1966 and Lehninger , 1975) Key: 5. 1, Embden-Meyerhof pathway; 5.2, pyruvate dehydrogenase complex; 5.14...physiological systems, FASEB, 4fl, 2490-2493, (1987). Lehninger , A. L., Biochmista, 2nd edition, Worth Publishers, New York, 1975. Mavrovouniotis, M. L., Computer
van der Schaft, Arjan; Jayawardhana, Bayu
2011-01-01
Motivated by recent progress on the interplay between graph theory, dynamics, and systems theory, we revisit the analysis of chemical reaction networks described by mass action kinetics. For reaction networks possessing a thermodynamic equilibrium we derive a compact formulation exhibiting at the same time the structure of the complex graph and the stoichiometry of the network, and which admits a direct thermodynamical interpretation. This formulation allows us to easily characterize the set of equilibria and their stability properties. Furthermore, we develop a framework for interconnection of chemical reaction networks. Finally we discuss how the established framework leads to a new approach for model reduction.
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.
Directory of Open Access Journals (Sweden)
Ketki D Verkhedkar
Full Text Available BACKGROUND: Several types of networks, such as transcriptional, metabolic or protein-protein interaction networks of various organisms have been constructed, that have provided a variety of insights into metabolism and regulation. Here, we seek to exploit the reaction-based networks of three organisms for comparative genomics. We use concepts from spectral graph theory to systematically determine how differences in basic metabolism of organisms are reflected at the systems level and in the overall topological structures of their metabolic networks. METHODOLOGY/PRINCIPAL FINDINGS: Metabolome-based reaction networks of Mycobacterium tuberculosis, Mycobacterium leprae and Escherichia coli have been constructed based on the KEGG LIGAND database, followed by graph spectral analysis of the network to identify hubs as well as the sub-clustering of reactions. The shortest and alternate paths in the reaction networks have also been examined. Sub-cluster profiling demonstrates that reactions of the mycolic acid pathway in mycobacteria form a tightly connected sub-cluster. Identification of hubs reveals reactions involving glutamate to be central to mycobacterial metabolism, and pyruvate to be at the centre of the E. coli metabolome. The analysis of shortest paths between reactions has revealed several paths that are shorter than well established pathways. CONCLUSIONS: We conclude that severe downsizing of the leprae genome has not significantly altered the global structure of its reaction network but has reduced the total number of alternate paths between its reactions while keeping the shortest paths between them intact. The hubs in the mycobacterial networks that are absent in the human metabolome can be explored as potential drug targets. This work demonstrates the usefulness of constructing metabolome based networks of organisms and the feasibility of their analyses through graph spectral methods. The insights obtained from such studies provide a
An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks
Directory of Open Access Journals (Sweden)
Michael Gormley
2011-01-01
Full Text Available Identification of regulatory molecules in signaling pathways is critical for understanding cellular behavior. Given the complexity of the transcriptional gene network, the relationship between molecular expression and phenotype is difficult to determine using reductionist experimental methods. Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. Integrating gene expression data with phenotypic data in transcriptional network models enables systematic identification of critical molecules in a biological network. We developed an approach based on fuzzy logic to model cell budding in Saccharomyces cerevisiae using time series expression microarray data of the cell cycle. Cell budding is a phenotype of viable cells undergoing division. Predicted interactions between gene expression and phenotype reflected known biological relationships. Dynamic simulation analysis reproduced the behavior of the yeast cell cycle and accurately identified genes and interactions which are essential for cell viability.
Ganscha, Stefan; Claassen, Manfred
2016-01-01
Stochastic chemical reaction networks constitute a model class to quantitatively describe dynamics and cell-to-cell variability in biological systems. The topology of these networks typically is only partially characterized due to experimental limitations. Current approaches for refining network topology are based on the explicit enumeration of alternative topologies and are therefore restricted to small problem instances with almost complete knowledge. We propose the reactionet lasso, a computational procedure that derives a stepwise sparse regression approach on the basis of the Chemical Master Equation, enabling large-scale structure learning for reaction networks by implicitly accounting for billions of topology variants. We have assessed the structure learning capabilities of the reactionet lasso on synthetic data for the complete TRAIL induced apoptosis signaling cascade comprising 70 reactions. We find that the reactionet lasso is able to efficiently recover the structure of these reaction systems, ab initio, with high sensitivity and specificity. With only lasso is able to recover 45% of all true reactions ab initio among > 6000 possible reactions and over 102000 network topologies. In conjunction with information rich single cell technologies such as single cell RNA sequencing or mass cytometry, the reactionet lasso will enable large-scale structure learning, particularly in areas with partial network structure knowledge, such as cancer biology, and thereby enable the detection of pathological alterations of reaction networks. We provide software to allow for wide applicability of the reactionet lasso. PMID:27923064
Szaleniec, Maciej; Witko, Małgorzata; Tadeusiewicz, Ryszard; Goclon, Jakub
2006-03-01
Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure-activity relationship (QSAR) approaches.
Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.
2017-03-01
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations.
Ulissi, Zachary W.; Medford, Andrew J.; Bligaard, Thomas; Nørskov, Jens K.
2017-01-01
Surface reaction networks involving hydrocarbons exhibit enormous complexity with thousands of species and reactions for all but the very simplest of chemistries. We present a framework for optimization under uncertainty for heterogeneous catalysis reaction networks using surrogate models that are trained on the fly. The surrogate model is constructed by teaching a Gaussian process adsorption energies based on group additivity fingerprints, combined with transition-state scaling relations and a simple classifier for determining the rate-limiting step. The surrogate model is iteratively used to predict the most important reaction step to be calculated explicitly with computationally demanding electronic structure theory. Applying these methods to the reaction of syngas on rhodium(111), we identify the most likely reaction mechanism. Propagating uncertainty throughout this process yields the likelihood that the final mechanism is complete given measurements on only a subset of the entire network and uncertainty in the underlying density functional theory calculations. PMID:28262694
Graphical reduction of reaction networks by linear elimination of species
DEFF Research Database (Denmark)
Saez Cornellana, Meritxell; Wiuf, Carsten Henrik; Feliu, Elisenda
2016-01-01
of the network and its kinetics. We conclude by comparing our approach to an older similar approach by Temkin and co-workers. Finally, we apply the procedure to biological examples such as substrate mechanisms, post-translational modification systems and networks with intermediates (transient) steps....
Lattice based Kinetic Monte Carlo Simulations of a complex chemical reaction network
Danielson, Thomas; Savara, Aditya; Hin, Celine
Lattice Kinetic Monte Carlo (KMC) simulations offer a powerful alternative to using ordinary differential equations for the simulation of complex chemical reaction networks. Lattice KMC provides the ability to account for local spatial configurations of species in the reaction network, resulting in a more detailed description of the reaction pathway. In KMC simulations with a large number of reactions, the range of transition probabilities can span many orders of magnitude, creating subsets of processes that occur more frequently or more rarely. Consequently, processes that have a high probability of occurring may be selected repeatedly without actually progressing the system (i.e. the forward and reverse process for the same reaction). In order to avoid the repeated occurrence of fast frivolous processes, it is necessary to throttle the transition probabilities in such a way that avoids altering the overall selectivity. Likewise, as the reaction progresses, new frequently occurring species and reactions may be introduced, making a dynamic throttling algorithm a necessity. We present a dynamic steady-state detection scheme with the goal of accurately throttling rate constants in order to optimize the KMC run time without compromising the selectivity of the reaction network. The algorithm has been applied to a large catalytic chemical reaction network, specifically that of methanol oxidative dehydrogenation, as well as additional pathways on CeO2(111) resulting in formaldehyde, CO, methanol, CO2, H2 and H2O as gas products.
Gan, Qintao; Lv, Tianshi; Fu, Zhenhua
2016-04-01
In this paper, the synchronization problem for a class of generalized neural networks with time-varying delays and reaction-diffusion terms is investigated concerning Neumann boundary conditions in terms of p-norm. The proposed generalized neural networks model includes reaction-diffusion local field neural networks and reaction-diffusion static neural networks as its special cases. By establishing a new inequality, some simple and useful conditions are obtained analytically to guarantee the global exponential synchronization of the addressed neural networks under the periodically intermittent control. According to the theoretical results, the influences of diffusion coefficients, diffusion space, and control rate on synchronization are analyzed. Finally, the feasibility and effectiveness of the proposed methods are shown by simulation examples, and by choosing different diffusion coefficients, diffusion spaces, and control rates, different controlled synchronization states can be obtained.
Nobuto, Kyosuke; Mizui, Yasutaka; Miyagi, Shigeyuki; Sakai, Osamu; Murakami, Tomoyuki
2016-09-01
We visualize complicated chemical reaction systems in weakly-ionized plasmas by analysing network structure for chemical processes, and calculate some indexes by assuming interspecies relationships to be a network to clarify them. With the current social evolution, the mean size of general data which we can use in computers grows huge, and significance of the data analysis increases. The methods of the network analysis which we focus on in this study do not depend on a specific analysis target, but the field where it has been already applied is still limited. In this study, we analyse chemical reaction systems in plasmas for configuring the network structure. We visualize them by expressing a reaction system in a specific plasma by a directed graph and examine the indexes and the relations with the characteristic of the species in the reaction system. For example, in the methane plasma network, the centrality index reveals importance of CH3 in an influential position of species in the reaction. In addition, silane and atmospheric pressure plasmas can be also visualized in reaction networks, suggesting other characteristics in the centrality indexes.
Reaction Networks For Interstellar Chemical Modelling: Improvements and Challenges
Wakelam, V; Herbst, E; Troe, J; Geppert, W; Linnartz, H; Oberg, K; Roueff, E; Agundez, M; Pernot, P; Cuppen, H M; Loison, J C; Talbi, D
2010-01-01
We survey the current situation regarding chemical modelling of the synthesis of molecules in the interstellar medium. The present state of knowledge concerning the rate coefficients and their uncertainties for the major gas-phase processes -- ion-neutral reactions, neutral-neutral reactions, radiative association, and dissociative recombination -- is reviewed. Emphasis is placed on those reactions that have been identified, by sensitivity analyses, as 'crucial' in determining the predicted abundances of the species observed in the interstellar medium. These sensitivity analyses have been carried out for gas-phase models of three representative, molecule-rich, astronomical sources: the cold dense molecular clouds TMC-1 and L134N, and the expanding circumstellar envelope IRC +10216. Our review has led to the proposal of new values and uncertainties for the rate coefficients of many of the key reactions. The impact of these new data on the predicted abundances in TMC-1 and L134N is reported. Interstellar dust p...
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
Nonlinear biochemical signal processing via noise propagation.
Kim, Kyung Hyuk; Qian, Hong; Sauro, Herbert M
2013-10-14
Single-cell studies often show significant phenotypic variability due to the stochastic nature of intra-cellular biochemical reactions. When the numbers of molecules, e.g., transcription factors and regulatory enzymes, are in low abundance, fluctuations in biochemical activities become significant and such "noise" can propagate through regulatory cascades in terms of biochemical reaction networks. Here we develop an intuitive, yet fully quantitative method for analyzing how noise affects cellular phenotypes based on identifying a system's nonlinearities and noise propagations. We observe that such noise can simultaneously enhance sensitivities in one behavioral region while reducing sensitivities in another. Employing this novel phenomenon we designed three biochemical signal processing modules: (a) A gene regulatory network that acts as a concentration detector with both enhanced amplitude and sensitivity. (b) A non-cooperative positive feedback system, with a graded dose-response in the deterministic case, that serves as a bistable switch due to noise-induced ultra-sensitivity. (c) A noise-induced linear amplifier for gene regulation that requires no feedback. The methods developed in the present work allow one to understand and engineer nonlinear biochemical signal processors based on fluctuation-induced phenotypes.
Self-organized criticality of a catalytic reaction network under flow.
Awazu, Akinori; Kaneko, Kunihiko
2009-07-01
Self-organized critical behavior in a catalytic reaction network system induced by smallness in the molecule number is reported. The system under a flow of chemicals is shown to undergo a transition from a stationary to an intermittent reaction phase when the flow rate is decreased. In the intermittent reaction phase, two temporal regimes with active and halted reactions alternate. The number frequency of reaction events at each active regime and its duration time are shown to obey a universal power law with the exponents 4/3 and 3/2, respectively, independently of the parameters and network structure. These power laws are explained by a one-dimensional random-walk representation of the number of catalytically active chemicals. Possible relevance of the result to reaction dynamics in artificial and biological cells is briefly discussed.
Efficient Characterization of Parametric Uncertainty of Complex (Bio)chemical Networks.
Schillings, Claudia; Sunnåker, Mikael; Stelling, Jörg; Schwab, Christoph
2015-08-01
Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
Separation of time-scales and model reduction for stochastic reaction networks
Kang, Hye-Won
2010-01-01
A stochastic model for a chemical reaction network is embedded in a one-parameter family of models with species numbers and rate constants scaled by powers of the parameter. A systematic approach is developed for determining appropriate choices of the exponents that can be applied to large complex networks. When the scaling implies subnetworks have different time-scales, the subnetworks can be approximated separately providing insight into the behavior of the full network through the analysis of these lower dimensional approximations.
[The psychoimmunological network og panic disorders, agoraphobia and allergic reactions].
Schmidt-Traub, S
1995-02-01
While treating panic and agoraphobia patients with behaviour therapy, a high frequency of allergic reaction of the IgE-mediated type I was observed. Panic disorder, agoraphobia, allergic disorder, and vasomotor reactions are briefly discussed in the framework of psycho-endocrino-immunological research. A pilot study had shown a high correlation between panic disorder with and without agoraphobia and allergic reaction. A controlled study was then planned to test the hypothesized psychoimmunological relationship. 100 allergic patients, 79 panic/agoraphobic patients, and 66 controls underwent psychodiagnostic and allergic screening. 70% of the anxiety patients responded to test allergens with IgE-mediated type-I immediate reactions in comparison to 28% of the control persons. Another 15% of the panic patients reacted to nickle compound with type-IV delayed skin reactions (7% of the controls). Conversely, 10% of the allergic patients suffered from panic disorder (45% had experienced panic attacks) in contrast to 2% of the controls (24% of these reported panic attacks). The relative risk for allergic patients to develop panic disorder with and without agoraphobia is obviously five times as high as for controls. With this assumption of a psychoimmunological preparedness in mind, a behavioural medical diagnostic and therapeutic concept seems more adequate in coping both with panic/agoraphobia and allergic disorder.
The Activity Reaction Core and Plasticity of Metabolic Networks.
Directory of Open Access Journals (Sweden)
2005-12-01
Full Text Available Understanding the system-level adaptive changes taking place in an organism in response to variations in the environment is a key issue of contemporary biology. Current modeling approaches, such as constraint-based flux-balance analysis, have proved highly successful in analyzing the capabilities of cellular metabolism, including its capacity to predict deletion phenotypes, the ability to calculate the relative flux values of metabolic reactions, and the capability to identify properties of optimal growth states. Here, we use flux-balance analysis to thoroughly assess the activity of Escherichia coli, Helicobacter pylori, and Saccharomyces cerevisiae metabolism in 30,000 diverse simulated environments. We identify a set of metabolic reactions forming a connected metabolic core that carry non-zero fluxes under all growth conditions, and whose flux variations are highly correlated. Furthermore, we find that the enzymes catalyzing the core reactions display a considerably higher fraction of phenotypic essentiality and evolutionary conservation than those catalyzing noncore reactions. Cellular metabolism is characterized by a large number of species-specific conditionally active reactions organized around an evolutionary conserved, but always active, metabolic core. Finally, we find that most current antibiotics interfering with bacterial metabolism target the core enzymes, indicating that our findings may have important implications for antimicrobial drug-target discovery.
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
Discovering missing reactions of metabolic networks by using gene co-expression data
Hosseini, Zhaleh; Marashi, Sayed-Amir
2017-02-01
Flux coupling analysis is a computational method which is able to explain co-expression of metabolic genes by analyzing the topological structure of a metabolic network. It has been suggested that if genes in two seemingly fully-coupled reactions are not highly co-expressed, then these two reactions are not fully coupled in reality, and hence, there is a gap or missing reaction in the network. Here, we present GAUGE as a novel approach for gap filling of metabolic networks, which is a two-step algorithm based on a mixed integer linear programming formulation. In GAUGE, the discrepancies between experimental co-expression data and predicted flux coupling relations is minimized by adding a minimum number of reactions to the network. We show that GAUGE is able to predict missing reactions of E. coli metabolism that are not detectable by other popular gap filling approaches. We propose that our algorithm may be used as a complementary strategy for the gap filling problem of metabolic networks. Since GAUGE relies only on gene expression data, it can be potentially useful for exploring missing reactions in the metabolism of non-model organisms, which are often poorly characterized, cannot grow in the laboratory, and lack genetic tools for generating knockouts.
Integration of metabolome data with metabolic networks reveals reporter reactions
DEFF Research Database (Denmark)
Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen;
2006-01-01
Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...
Amason, Charlee; Dreyfuss, Alison; Launey, Kristina; Draayer, Jerry
2017-01-01
We use the ab initio (first-principle) symmetry-adapted no-core shell model (SA-NCSM) to calculate reaction rates of significance to type I X-ray burst nucleosynthesis. We consider the 18O(p,γ)19F reaction, which may influence the production of fluorine, as well as the 16O(α,γ)20Ne reaction, which is key to understanding the production of heavier elements in the universe. Results are compared to those obtained in the no-core sympletic shell model (NCSpM) with a schematic interaction. We discuss how these reaction rates affect the relevant elemental abundances. We thank the NSF for supporting this work through the REU Site in Physics & Astronomy (NSF grant #1560212) at Louisiana State University. This work was also supported by the U.S. NSF (OCI-0904874, ACI -1516338) and the U.S. DOE (DE-SC0005248).
Fuhrer, Tobias; Sauer, Uwe
2009-04-01
To sustain growth, the catabolic formation of the redox equivalent NADPH must be balanced with the anabolic demand. The mechanisms that ensure such network-wide balancing, however, are presently not understood. Based on 13C-detected intracellular fluxes, metabolite concentrations, and cofactor specificities for all relevant central metabolic enzymes, we have quantified catabolic NADPH production in Agrobacterium tumefaciens, Bacillus subtilis, Escherichia coli, Paracoccus versutus, Pseudomonas fluorescens, Rhodobacter sphaeroides, Sinorhizobium meliloti, and Zymomonas mobilis. For six species, the estimated NADPH production from glucose catabolism exceeded the requirements for biomass synthesis. Exceptions were P. fluorescens, with balanced rates, and E. coli, with insufficient catabolic production, in which about one-third of the NADPH is supplied via the membrane-bound transhydrogenase PntAB. P. versutus and B. subtilis were the only species that appear to rely on transhydrogenases for balancing NADPH overproduction during growth on glucose. In the other four species, the main but not exclusive redox-balancing mechanism appears to be the dual cofactor specificities of several catabolic enzymes and/or the existence of isoenzymes with distinct cofactor specificities, in particular glucose 6-phosphate dehydrogenase. An unexpected key finding for all species, except E. coli and B. subtilis, was the lack of cofactor specificity in the oxidative pentose phosphate pathway, which contrasts with the textbook view of the pentose phosphate pathway dehydrogenases as being NADP+ dependent.
Dondi, Daniele; Merli, Daniele; Albini, Angelo; Zeffiro, Alberto; Serpone, Nick
2012-05-01
When a chemical system is submitted to high energy sources (UV, ionizing radiation, plasma sparks, etc.), as is expected to be the case of prebiotic chemistry studies, a plethora of reactive intermediates could form. If oxygen is present in excess, carbon dioxide and water are the major products. More interesting is the case of reducing conditions where synthetic pathways are also possible. This article examines the theoretical modeling of such systems with random-generated chemical networks. Four types of random-generated chemical networks were considered that originated from a combination of two connection topologies (viz., Poisson and scale-free) with reversible and irreversible chemical reactions. The results were analyzed taking into account the number of the most abundant products required for reaching 50% of the total number of moles of compounds at equilibrium, as this may be related to an actual problem of complex mixture analysis. The model accounts for multi-component reaction systems with no a priori knowledge of reacting species and the intermediates involved if system components are sufficiently interconnected. The approach taken is relevant to an earlier study on reactions that may have occurred in prebiotic systems where only a few compounds were detected. A validation of the model was attained on the basis of results of UVC and radiolytic reactions of prebiotic mixtures of low molecular weight compounds likely present on the primeval Earth.
Xie, L; Wang, D I
1996-12-05
A detailed reaction network of mammalian cell metabolism contains hundreds of enzymatic reactions. By grouping serial reactions into single overall reactions and separating overlapped pathways into independent reactions, the total number of reactions of the network is significantly reduced. This strategy of manipulating the reaction network avoids the manipulations of a large number of reactions otherwise needed to determine the reaction extents. A stoichiometric material balance model is developed based on the stoichiometry of the simplified reaction network. Closures of material balances on glucose and each of the 20 amino acids are achieved using experimental data from three controlled fed-batch and one-batch hybridoma cultures. Results show that the critical role of essential amino acids, except glutamine, is to provide precursors for protein synthesis. The catabolism of some of the essential amino acids, particularly isoleucine and leucine, is observed when an excess amount of these amino acids is available in the culture medium. It was found that the reduction of glutamine utilization (for reducing ammonia production) is accompanied by an increase in the uptake of nonessential amino acids (NAAs) from the culture medium. This suggests that NAAs are necessary even though they are not essential for cell growth. A glutamine balance shows that less than 20% of the glutamine nitrogen is utilized for essential roles, such as protein and nucleotide syntheses. A relatively constant percentage (about 45%) of the glutamine nitrogen is utilized for NAA biosynthesis, despite the fact that the absolute amount varies among the four experiments. As to the carbon skeleton of glutamine, a significant portion enters the tricarboxylic acid (TCA) cycle. A material balance on glucose shows that most of the glucose (81%) is converted into lactate when glucose is in excess. On the other hand, when glucose is limited, lactate production is considerably reduced, while a major portion
Brivio, Monica; Fokkens, Roel H.; Verboom, Willem; Reinhoudt, David N.; Tas, Niels R.; Goedbloed, Martijn; Berg, van den Albert
2002-01-01
A continuous flow micro total analysis system (μ-TAS) consisting of an on-chip microfluidic device connected to a matrix assisted laser desorption ionization [MALDI] time-of-flight [TOF] mass spectrometer (MS) as an analytical screening system is presented. Reaction microchannels and inlet/outlet re
The US nuclear reaction data network. Summary of the first meeting, March 13 & 14 1996
Energy Technology Data Exchange (ETDEWEB)
NONE
1996-03-01
The first meeting of the US Nuclear Reaction Data Network (USNRDN) was held at the Colorado School of Mines, March 13-14, 1996 chaired by F. Edward Cecil. The Agenda of the meeting is attached. The Network, its mission, products and services; related nuclear data and data networks, members, and organization are described in Attachment 1. The following progress reports from the members of the USNRDN were distributed prior to the meeting and are given as Attachment 2. (1) Measurements and Development of Analytic Techniques for Basic Nuclear Physics and Nuclear Applications; (2) Nuclear Reaction Data Activities at the National Nuclear Data Center; (3) Studies of nuclear reactions at very low energies; (4) Nuclear Reaction Data Activities, Nuclear Data Group; (5) Progress in Neutron Physics at Los Alamos - Experiments; (6) Nuclear Reaction Data Activities in Group T2; (7) Progress Report for the US Nuclear Reaction Data Network Meeting; (8) Nuclear Astrophysics Research Group (ORNL); (9) Progress Report from Ohio University; (10) Exciton Model Phenomenology; and (11) Progress Report for Coordination Meeting USNRDN.
Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.
Directory of Open Access Journals (Sweden)
Andrea Ciliberto
2007-03-01
Full Text Available In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C is much less than the free substrate concentration (S0. However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1 it unveils the modular structure of the enzymatic reactions, (2 it suggests a simple algorithm to formulate correct kinetic equations, and (3 contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively.
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.
Automatic Verification of Biochemical Network Using Model Checking Method%基于模型校核的生化网络自动辨别方法
Institute of Scientific and Technical Information of China (English)
Jinkyung Kim; Younghee Lee; Il Moon
2008-01-01
This study focuses on automatic searching and verifying methods for the reachability, transition logics and hierarchical structure in all possible paths of biological processes using model checking. The automatic search and verification for alternative paths within complex and large networks in biological process can provide a consid-erable amount of solutions, which is difficult to handle manually. Model checking is an automatic method for veri-fying if a circuit or a condition, expressed as a concurrent transition system, satisfies a set of properties expressed ina temporal logic, such as computational tree logic (CTL). This article represents that model checking is feasible in biochemical network verification and it shows certain advantages over simulation for querying and searching of special behavioral properties in biochemical processes.
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...
Approaches to Chemical and Biochemical Information and Signal Processing
Privman, Vladimir
2012-02-01
We outline models and approaches for error control required to prevent buildup of noise when ``gates'' and other ``network elements'' based on (bio)chemical reaction processes are utilized to realize stable, scalable networks for information and signal processing. We also survey challenges and possible future research. [4pt] [1] Control of Noise in Chemical and Biochemical Information Processing, V. Privman, Israel J. Chem. 51, 118-131 (2010).[0pt] [2] Biochemical Filter with Sigmoidal Response: Increasing the Complexity of Biomolecular Logic, V. Privman, J. Halamek, M. A. Arugula, D. Melnikov, V. Bocharova and E. Katz, J. Phys. Chem. B 114, 14103-14109 (2010).[0pt] [3] Towards Biosensing Strategies Based on Biochemical Logic Systems, E. Katz, V. Privman and J. Wang, in: Proc. Conf. ICQNM 2010 (IEEE Comp. Soc. Conf. Publ. Serv., Los Alamitos, California, 2010), pages 1-9.
Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks
DEFF Research Database (Denmark)
Anderson, David F; Craciun, Gheorghe; Gopalkrishnan, Manoj;
2015-01-01
We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potent......We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non......-equilibrium potential of the stationary distribution of stochastically modeled complex balanced systems. We extend this result to general birth-death models and demonstrate via example that similar scaling limits can yield Lyapunov functions even for models that are not complex or detailed balanced, and may even have...
Directory of Open Access Journals (Sweden)
Julio Michael Stern
2014-03-01
Full Text Available This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks
Navarro Jimenez, M.
2016-12-26
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol’s decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks.
Navarro Jimenez, M; Le Maître, O P; Knio, O M
2016-12-28
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Global sensitivity analysis in stochastic simulators of uncertain reaction networks
Navarro Jimenez, M.; Le Maître, O. P.; Knio, O. M.
2016-12-01
Stochastic models of chemical systems are often subjected to uncertainties in kinetic parameters in addition to the inherent random nature of their dynamics. Uncertainty quantification in such systems is generally achieved by means of sensitivity analyses in which one characterizes the variability with the uncertain kinetic parameters of the first statistical moments of model predictions. In this work, we propose an original global sensitivity analysis method where the parametric and inherent variability sources are both treated through Sobol's decomposition of the variance into contributions from arbitrary subset of uncertain parameters and stochastic reaction channels. The conceptual development only assumes that the inherent and parametric sources are independent, and considers the Poisson processes in the random-time-change representation of the state dynamics as the fundamental objects governing the inherent stochasticity. A sampling algorithm is proposed to perform the global sensitivity analysis, and to estimate the partial variances and sensitivity indices characterizing the importance of the various sources of variability and their interactions. The birth-death and Schlögl models are used to illustrate both the implementation of the algorithm and the richness of the proposed analysis method. The output of the proposed sensitivity analysis is also contrasted with a local derivative-based sensitivity analysis method classically used for this type of systems.
Self-Organized Stationary Patterns in Networks of Bistable Chemical Reactions.
Kouvaris, Nikos E; Sebek, Michael; Mikhailov, Alexander S; Kiss, István Z
2016-10-10
Experiments with networks of discrete reactive bistable electrochemical elements organized in regular and nonregular tree networks are presented to confirm an alternative to the Turing mechanism for the formation of self-organized stationary patterns. The results show that the pattern formation can be described by the identification of domains that can be activated individually or in combinations. The method also enabled the localization of chemical reactions to network substructures and the identification of critical sites whose activation results in complete activation of the system. Although the experiments were performed with a specific nickel electrodissolution system, they reproduced all the salient dynamic behavior of a general network model with a single nonlinearity parameter. Thus, the considered pattern-formation mechanism is very robust, and similar behavior can be expected in other natural or engineered networked systems that exhibit, at least locally, a treelike structure.
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...
Variable elimination in chemical reaction networks with mass-action kinetics
DEFF Research Database (Denmark)
Feliu, Elisenda; Wiuf, C.
2012-01-01
We consider chemical reaction networks taken with mass-action kinetics. The steady states of such a system are solutions to a system of polynomial equations. Even for small systems the task of finding the solutions is daunting. We develop an algebraic framework and procedure for linear elimination...
Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks.
Liao, Shuohao; Vejchodský, Tomáš; Erban, Radek
2015-07-06
Stochastic modelling of gene regulatory networks provides an indispensable tool for understanding how random events at the molecular level influence cellular functions. A common challenge of stochastic models is to calibrate a large number of model parameters against the experimental data. Another difficulty is to study how the behaviour of a stochastic model depends on its parameters, i.e. whether a change in model parameters can lead to a significant qualitative change in model behaviour (bifurcation). In this paper, tensor-structured parametric analysis (TPA) is developed to address these computational challenges. It is based on recently proposed low-parametric tensor-structured representations of classical matrices and vectors. This approach enables simultaneous computation of the model properties for all parameter values within a parameter space. The TPA is illustrated by studying the parameter estimation, robustness, sensitivity and bifurcation structure in stochastic models of biochemical networks. A Matlab implementation of the TPA is available at http://www.stobifan.org.
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.
Directory of Open Access Journals (Sweden)
Janmenjoy Nayak
2015-09-01
Full Text Available In this paper, a Chemical Reaction Optimization (CRO based higher order neural network with a single hidden layer called Pi–Sigma Neural Network (PSNN has been proposed for data classification which maintains fast learning capability and avoids the exponential increase of number of weights and processing units. CRO is a recent metaheuristic optimization algorithm inspired by chemical reactions, free from intricate operator and parameter settings such as other algorithms and loosely couples chemical reactions with optimization. The performance of the proposed CRO-PSNN has been tested with various benchmark datasets from UCI machine learning repository and compared with the resulting performance of PSNN, GA-PSNN, PSO-PSNN. The methods have been implemented in MATLAB and the accuracy measures have been tested by using the ANOVA statistical tool. Experimental results show that the proposed method is fast, steady and reliable and provides better classification accuracy than others.
Morshed, Monjur; Ingalls, Brian; Ilie, Silvana
2017-01-01
Sensitivity analysis characterizes the dependence of a model's behaviour on system parameters. It is a critical tool in the formulation, characterization, and verification of models of biochemical reaction networks, for which confident estimates of parameter values are often lacking. In this paper, we propose a novel method for sensitivity analysis of discrete stochastic models of biochemical reaction systems whose dynamics occur over a range of timescales. This method combines finite-difference approximations and adaptive tau-leaping strategies to efficiently estimate parametric sensitivities for stiff stochastic biochemical kinetics models, with negligible loss in accuracy compared with previously published approaches. We analyze several models of interest to illustrate the advantages of our method.
Zhou, G; Liu, P; He, J; Dong, M; Yang, X; Hou, B; Von Deneen, K M; Qin, W; Tian, J
2012-01-27
Both anatomical and functional brain network studies have drawn great attention recently. Previous studies have suggested the significant impacts of brain network topology on cognitive function. However, the relationship between non-task-related resting-state functional brain network topology and overall efficiency of sensorimotor processing has not been well identified. In the present study, we investigated the relationship between non-task-related resting-state functional brain network topology and reaction time (RT) in a Go/Nogo task using an electroencephalogram (EEG). After estimating the functional connectivity between each pair of electrodes, graph analysis was applied to characterize the network topology. Two fundamental measures, clustering coefficient (functional segregation) and characteristic path length (functional integration), as well as "small-world-ness" (the ratio between the clustering coefficient and characteristic path length) were calculated in five frequency bands. Then, the correlations between the network measures and RT were evaluated in each band separately. The present results showed that increased overall functional connectivity in alpha and gamma frequency bands was correlated with a longer RT. Furthermore, shorter RT was correlated with a shorter characteristic path length in the gamma band. This result suggested that human RTs were likely to be related to the efficiency of the brain integrating information across distributed brain regions. The results also showed that a longer RT was related to an increased gamma clustering coefficient and decreased small-world-ness. These results provided further evidence of the association between the resting-state functional brain network and cognitive function.
Accurate High-Temperature Reaction Networks for Alternative Fuels: Butanol Isomers
Energy Technology Data Exchange (ETDEWEB)
Van Geem, K. M.; Pyl, S. P.; Marin, G. B.; Harper, M. R.; Green, W. H.
2010-11-03
Oxygenated hydrocarbons, particularly alcohol compounds, are being studied extensively as alternatives and additives to conventional fuels due to their propensity of decreasing soot formation and improving the octane number of gasoline. However, oxygenated fuels also increase the production of toxic byproducts, such as formaldehyde. To gain a better understanding of the oxygenated functional group’s influence on combustion properties—e.g., ignition delay at temperatures above the negative temperature coefficient regime, and the rate of benzene production, which is the common precursor to soot formation—a detailed pressure-dependent reaction network for n-butanol, sec-butanol, and tert-butanol consisting of 281 species and 3608 reactions is presented. The reaction network is validated against shock tube ignition delays and doped methane flame concentration profiles reported previously in the literature, in addition to newly acquired pyrolysis data. Good agreement between simulated and experimental data is achieved in all cases. Flux and sensitivity analyses for each set of experiments have been performed, and high-pressure-limit reaction rate coefficients for important pathways, e.g., the dehydration reactions of the butanol isomers, have been computed using statistical mechanics and quantum chemistry. The different alcohol decomposition pathways, i.e., the pathways from primary, secondary, and tertiary alcohols, are discussed. Furthermore, comparisons between ethanol and n-butanol, two primary alcohols, are presented, as they relate to ignition delay.
生化网络的随机Petri网建模与分析%Modeling and analyzing biochemical networks using stochastic Petri nets
Institute of Scientific and Technical Information of China (English)
丁德武; 李文泽
2012-01-01
细胞的行为是随机性的,学习细胞中的随机性有助于理解细胞的组织,设计和进化.建立、确认和分析随机的生化网络模型是当前计算系统生物学领域的一个重要研究主题.当前,标准的Petri网模型已经成为生化网络模拟和定性分析的有力工具.尝试使用随机Petri网对生化网络进行建模与分析,简单描述了随机Petri网理论对标准Petri网的扩充,通过对二聚作用和肌动蛋白这两个典型例子的建模与演化模拟,介绍、论证了随机Petri网理论的新应用.%Cellular behavior is stochastic, and studying stochastic in the cell can help to understand the organization, design and evolution of the cell. Establishment, identification and analysis of stochastic biochemical network models is an important research topic in the field of computational systems biology. At present, the standard Petri net model has become a powerful tool for biochemical network modeling and qualitative analysis. This paper attempts to apply stochastic Petri nets to modeling and analyzing biochemical network, it briefly describes the expansion of the stochastic Petri net theory to standard Petri nets, then by modeling and evolution simulating two classic examples (dimerization and actin), it demonstrates new applications of stochastic Petri nets theory.
Lyapunov Functions, Stationary Distributions, and Non-equilibrium Potential for Reaction Networks.
Anderson, David F; Craciun, Gheorghe; Gopalkrishnan, Manoj; Wiuf, Carsten
2015-09-01
We consider the relationship between stationary distributions for stochastic models of reaction systems and Lyapunov functions for their deterministic counterparts. Specifically, we derive the well-known Lyapunov function of reaction network theory as a scaling limit of the non-equilibrium potential of the stationary distribution of stochastically modeled complex balanced systems. We extend this result to general birth-death models and demonstrate via example that similar scaling limits can yield Lyapunov functions even for models that are not complex or detailed balanced, and may even have multiple equilibria.
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.
Doubly Periodic Traveling Waves in a Cellular Neural Network with Linear Reaction
Directory of Open Access Journals (Sweden)
Lin JianJhong
2009-01-01
Full Text Available Szekeley observed that the dynamic pattern of the locomotion of salamanders can be explained by periodic vector sequences generated by logical neural networks. Such sequences can mathematically be described by "doubly periodic traveling waves" and therefore it is of interest to propose dynamic models that may produce such waves. One such dynamic network model is built here based on reaction-diffusion principles and a complete discussion is given for the existence of doubly periodic waves as outputs. Since there are 2 parameters in our model and 4 a priori unknown parameters involved in our search of solutions, our results are nontrivial. The reaction term in our model is a linear function and hence our results can also be interpreted as existence criteria for solutions of a nontrivial linear problem depending on 6 parameters.
INFLUENCE OF NOISE AND DELAY ON REACTION-DIFFUSION RECURRENT NEURAL NETWORKS
Institute of Scientific and Technical Information of China (English)
Li Wu
2006-01-01
In this paper, the influence of the noise and delay upon the stability property of reaction-diffusion recurrent neural networks (RNNs) with the time-varying delay is discussed. The new and easily verifiable conditions to guarantee the mean value exponential stability of an equilibrium solution are derived. The rate of exponential convergence can be estimated by means of a simple computation based on these criteria.
Directory of Open Access Journals (Sweden)
Chuangxia Huang
2011-01-01
Full Text Available Stability of reaction-diffusion recurrent neural networks (RNNs with continuously distributed delays and stochastic influence are considered. Some new sufficient conditions to guarantee the almost sure exponential stability and mean square exponential stability of an equilibrium solution are obtained, respectively. Lyapunov's functional method, M-matrix properties, some inequality technique, and nonnegative semimartingale convergence theorem are used in our approach. The obtained conclusions improve some published results.
Dynamics of small autocatalytic reaction network; 2, replication, mutation and catalysis
Stadler, P F; Först, C J; Schuster, P; Stadler, Peter F; Schnabl, Wolfgang; Forst, Christian V; Schuster, Peter; Biotechnology, Molecuar
1994-01-01
Mutation is introduced into autocatalytic reaction networks. Examples of low dimensional dynamical systems --- n = 2, 3 and 4 --- are discussed and complete qualitative analysis is presented. Error thresholds known from simple replication-mutation kinetics with frequency independent replication rates occur here as well. Instead of cooperative transitions or higher order phase transistions the thresholds appear here as supercritical or subcritical bifurcations being analogous to first order phase transitions.
Variable elimination in post-translational modification reaction networks with mass-action kinetics
DEFF Research Database (Denmark)
Feliu, Elisenda; Wiuf, Carsten
2013-01-01
We define a subclass of chemical reaction networks called post-translational modification systems. Important biological examples of such systems include MAPK cascades and two-component systems which are well-studied experimentally as well as theoretically. The steady states of such a system are s...... of the species graph and provide conservation laws. A criterion for when a (maximal) set of independent conservation laws can be derived from cuts is given....
A reaction-diffusion model of ROS-induced ROS release in a mitochondrial network.
Directory of Open Access Journals (Sweden)
Lufang Zhou
2010-01-01
Full Text Available Loss of mitochondrial function is a fundamental determinant of cell injury and death. In heart cells under metabolic stress, we have previously described how the abrupt collapse or oscillation of the mitochondrial energy state is synchronized across the mitochondrial network by local interactions dependent upon reactive oxygen species (ROS. Here, we develop a mathematical model of ROS-induced ROS release (RIRR based on reaction-diffusion (RD-RIRR in one- and two-dimensional mitochondrial networks. The nodes of the RD-RIRR network are comprised of models of individual mitochondria that include a mechanism of ROS-dependent oscillation based on the interplay between ROS production, transport, and scavenging; and incorporating the tricarboxylic acid (TCA cycle, oxidative phosphorylation, and Ca(2+ handling. Local mitochondrial interaction is mediated by superoxide (O2.- diffusion and the O2.(--dependent activation of an inner membrane anion channel (IMAC. In a 2D network composed of 500 mitochondria, model simulations reveal DeltaPsi(m depolarization waves similar to those observed when isolated guinea pig cardiomyocytes are subjected to a localized laser-flash or antioxidant depletion. The sensitivity of the propagation rate of the depolarization wave to O(2.- diffusion, production, and scavenging in the reaction-diffusion model is similar to that observed experimentally. In addition, we present novel experimental evidence, obtained in permeabilized cardiomyocytes, confirming that DeltaPsi(m depolarization is mediated specifically by O2.-. The present work demonstrates that the observed emergent macroscopic properties of the mitochondrial network can be reproduced in a reaction-diffusion model of RIRR. Moreover, the findings have uncovered a novel aspect of the synchronization mechanism, which is that clusters of mitochondria that are oscillating can entrain mitochondria that would otherwise display stable dynamics. The work identifies the
Directory of Open Access Journals (Sweden)
Parizad Babaei
2014-01-01
Full Text Available To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.
Tanaka, Manabu; Fujita, Remi; Nishide, Hiroyuki
2009-01-01
The novel gold nanoparticle, which was stabilized with pi-conjugated molecules bearing functional groups at the terminals, was prepared via conventional procedure by using 5-bromo-2,2'-bithiophene-5'-thiol as a stabilizer. The gold nanoparticle (ca. 3 nm-diameter) showed good dispersion stability in various organic solvents, and its electrochemical and spectroscopic study revealed peculiar properties originated in the pi-conjugated molecular stabilizer, bithiophene derivative. The Pd-catalyzed coupling reaction on the gold nanoparticle was first achieved by using the gold nanoparticle bearing bromo groups at the particle surface and the model boronic acid molecule, 5-formyl-2-thiopheneboronic acid, to yield the terthiophene derivatives on the gold nanoparticle. The 1H-NMR, UV, and TGA analysis supported the progress of the coupling reaction on the gold nanoparticle. This Pd-catalyzed coupling reaction was applied with the borate-terminated polythiophene to form polythiophene/gold nanoparticle alternate network film. The electron microscopic images supported the formation of the network structure. The high electric conductivity on the network film suggested that the conductive characteristic of the film originated from that of the pi-conjugated polythiophene backbone connected with the gold nanoparticle.
Henri, Christopher V.; Fernàndez-Garcia, Daniel
2015-12-01
The interplay between the spatial variability of the aquifer hydraulic properties, mass transfer due to sub-grid heterogeneity and chemical reactions often complicates reactive transport simulations. It is well documented that hydro-biochemical properties are ubiquitously heterogeneous and that diffusion and slow advection at the sub-grid scale typically leads to the conceptualization of an aquifer as a multi-porosity system. Within this context, chemical reactions taking place in mobile/immobile water regions can be substantially different between each other. This paper presents a particle-based method that can efficiently simulate heterogeneity, network reactions and multi-rate mass transfer. The approach is based on the development of transition probabilities that describe the likelihood that particles belonging to a given species and mobile/immobile domain at a given time will be transformed into another species and mobile/immobile domain afterwards. The joint effect of mass transfer and sequential degradation is shown to be non-trivial. A characteristic rebound of degradation products can be observed. This late rebound of concentrations is not driven by any change in the flow regime (e.g., pumping ceases in the pump-and-treat remediation strategy) but due to the natural interplay between mass transfer and chemical reactions. To illustrate that the method can simultaneously represent mass transfer, spatially varying properties and network reactions without numerical problems, we have simulated the degradation of tetrachloroethylene (PCE) in a three-dimensional fully heterogeneous aquifer subjected to rate-limited mass transfer. Two types of degradation modes were considered to compare the effect of an active biofilm with that of clay pods present in the aquifer. Results of the two scenarios display significantly differences. Biofilms that promote the degradation of compounds in an immobile region are shown to significantly enhance degradation, rapidly producing
A multi-time-scale analysis of chemical reaction networks: II. Stochastic systems.
Kan, Xingye; Lee, Chang Hyeong; Othmer, Hans G
2016-11-01
We consider stochastic descriptions of chemical reaction networks in which there are both fast and slow reactions, and for which the time scales are widely separated. We develop a computational algorithm that produces the generator of the full chemical master equation for arbitrary systems, and show how to obtain a reduced equation that governs the evolution on the slow time scale. This is done by applying a state space decomposition to the full equation that leads to the reduced dynamics in terms of certain projections and the invariant distributions of the fast system. The rates or propensities of the reduced system are shown to be the rates of the slow reactions conditioned on the expectations of fast steps. We also show that the generator of the reduced system is a Markov generator, and we present an efficient stochastic simulation algorithm for the slow time scale dynamics. We illustrate the numerical accuracy of the approximation by simulating several examples. Graph-theoretic techniques are used throughout to describe the structure of the reaction network and the state-space transitions accessible under the dynamics.
Spreading of infection in a two species reaction-diffusion process in networks.
Korosoglou, Paschalis; Kittas, Aristotelis; Argyrakis, Panos
2010-12-01
We study the dynamics of the infection of a two mobile species reaction from a single infected agent in a population of healthy agents. Historically, the main focus for infection propagation has been through spreading phenomena, where a random location of the system is initially infected and then propagates by successfully infecting its neighbor sites. Here both the infected and healthy agents are mobile, performing classical random walks. This may be a more realistic picture to such epidemiological models, such as the spread of a virus in communication networks of routers, where data travel in packets, the communication time of stations in ad hoc mobile networks, information spreading (such as rumor spreading) in social networks, etc. We monitor the density of healthy particles ρ(t), which we find in all cases to be an exponential function in the long-time limit in two-dimensional and three-dimensional lattices and Erdős-Rényi (ER) and scale-free (SF) networks. We also investigate the scaling of the crossover time t(c) from short- to long-time exponential behavior, which we find to be a power law in lattices and ER networks. This crossover is shown to be absent in SF networks, where we reveal the role of the connectivity of the network in the infection process. We compare this behavior to ER networks and lattices and highlight the significance of various connectivity patterns, as well as the important differences of this process in the various underlying geometries, revealing a more complex behavior of ρ(t).
Aikawa, M.; Arnould, M.; Goriely, S.; Jorissen, A.; Takahashi, K.
2005-10-01
Nuclear reaction rates are quantities of fundamental importance in astrophysics. Substantial efforts have been devoted in the last decades to measuring or calculating them. This paper presents a detailed description of the Brussels nuclear reaction rate library BRUSLIB and of the nuclear network generator NETGEN. BRUSLIB is made of two parts. The first one contains the 1999 NACRE compilation based on experimental data for 86 reactions with (mainly) stable targets up to Si. BRUSLIB provides an electronic link to the published, as well as to a large body of unpublished, NACRE data containing adopted rates, as well as lower and upper limits. The second part of BRUSLIB concerns nuclear reaction rate predictions to complement the experimentally-based rates. An electronic access is provided to tables of rates calculated within a statistical Hauser-Feshbach approximation, which limits the reliability of the rates to reactions producing compound nuclei with a high enough level density. These calculations make use of global and coherent microscopic nuclear models for the quantities entering the rate calculations. The use of such models makes the BRUSLIB rate library unique. A description of the Nuclear Network Generator NETGEN that complements the BRUSLIB package is also presented. NETGEN is a tool to generate nuclear reaction rates for temperature grids specified by the user. The information it provides can be used for a large variety of applications, including Big Bang nucleosynthesis, the energy generation and nucleosynthesis associated with the non-explosive and explosive hydrogen to silicon burning stages, or the synthesis of the heavy nuclides through the s-, α- and r-, rp- or p-processes.
Cao, Youfang; Liang, Jie
2013-07-14
Critical events that occur rarely in biological processes are of great importance, but are challenging to study using Monte Carlo simulation. By introducing biases to reaction selection and reaction rates, weighted stochastic simulation algorithms based on importance sampling allow rare events to be sampled more effectively. However, existing methods do not address the important issue of barrier crossing, which often arises from multistable networks and systems with complex probability landscape. In addition, the proliferation of parameters and the associated computing cost pose significant problems. Here we introduce a general theoretical framework for obtaining optimized biases in sampling individual reactions for estimating probabilities of rare events. We further describe a practical algorithm called adaptively biased sequential importance sampling (ABSIS) method for efficient probability estimation. By adopting a look-ahead strategy and by enumerating short paths from the current state, we estimate the reaction-specific and state-specific forward and backward moving probabilities of the system, which are then used to bias reaction selections. The ABSIS algorithm can automatically detect barrier-crossing regions, and can adjust bias adaptively at different steps of the sampling process, with bias determined by the outcome of exhaustively generated short paths. In addition, there are only two bias parameters to be determined, regardless of the number of the reactions and the complexity of the network. We have applied the ABSIS method to four biochemical networks: the birth-death process, the reversible isomerization, the bistable Schlögl model, and the enzymatic futile cycle model. For comparison, we have also applied the finite buffer discrete chemical master equation (dCME) method recently developed to obtain exact numerical solutions of the underlying discrete chemical master equations of these problems. This allows us to assess sampling results objectively
FERN – a Java framework for stochastic simulation and evaluation of reaction networks
Directory of Open Access Journals (Sweden)
Zimmer Ralf
2008-08-01
Full Text Available Abstract Background Stochastic simulation can be used to illustrate the development of biological systems over time and the stochastic nature of these processes. Currently available programs for stochastic simulation, however, are limited in that they either a do not provide the most efficient simulation algorithms and are difficult to extend, b cannot be easily integrated into other applications or c do not allow to monitor and intervene during the simulation process in an easy and intuitive way. Thus, in order to use stochastic simulation in innovative high-level modeling and analysis approaches more flexible tools are necessary. Results In this article, we present FERN (Framework for Evaluation of Reaction Networks, a Java framework for the efficient simulation of chemical reaction networks. FERN is subdivided into three layers for network representation, simulation and visualization of the simulation results each of which can be easily extended. It provides efficient and accurate state-of-the-art stochastic simulation algorithms for well-mixed chemical systems and a powerful observer system, which makes it possible to track and control the simulation progress on every level. To illustrate how FERN can be easily integrated into other systems biology applications, plugins to Cytoscape and CellDesigner are included. These plugins make it possible to run simulations and to observe the simulation progress in a reaction network in real-time from within the Cytoscape or CellDesigner environment. Conclusion FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward
Brogioli, Doriano
2013-02-07
I recently reported some examples of mass-action equations that have a continuous manifold of marginally stable stationary states [Brogioli, D., 2010. Marginally stable chemical systems as precursors of life. Phys. Rev. Lett. 105, 058102; Brogioli, D., 2011. Marginal stability in chemical systems and its relevance in the origin of life. Phys. Rev. E 84, 031931]. The corresponding chemical reaction networks show nonclassical effects, i.e. a violation of the mass-action equations, under the effect of the concentration fluctuations: the chemical system drifts along the marginally stable states. I proposed that this effect is potentially involved in abiogenesis. In the present paper, I analyze the mathematical properties of mass-action equations of marginally stable chemical reaction networks. The marginal stability implies that the mass-action equations obey some conservation law; I show that the mathematical properties of the conserved quantity characterize the motion along the marginally stable stationary state manifold, i.e. they allow to predict if the fluctuations give rise to a random walk or a drift under the effect of concentration fluctuations. Moreover, I show that the presence of the drift along the manifold of marginally stable stationary-states is a critical property, i.e. at least one of the reaction constants must be fine tuned in order to obtain the drift.
Simulation and fitting of complex reaction network TPR: The key is the objective function
Savara, Aditya
2016-11-01
A method has been developed for finding improved fits during simulation and fitting of data from complex reaction network temperature programmed reactions (CRN-TPR). It was found that simulation and fitting of CRN-TPR presents additional challenges relative to simulation and fitting of simpler TPR systems. The method used here can enable checking the plausibility of proposed chemical mechanisms and kinetic models. The most important finding was that when choosing an objective function, use of an objective function that is based on integrated production provides more utility in finding improved fits when compared to an objective function based on the rate of production. The response surface produced by using the integrated production is monotonic, suppresses effects from experimental noise, requires fewer points to capture the response behavior, and can be simulated numerically with smaller errors. For CRN-TPR, there is increased importance (relative to simple reaction network TPR) in resolving of peaks prior to fitting, as well as from weighting of experimental data points. Using an implicit ordinary differential equation solver was found to be inadequate for simulating CRN-TPR. The method employed here was capable of attaining improved fits in simulation and fitting of CRN-TPR when starting with a postulated mechanism and physically realistic initial guesses for the kinetic parameters.
An Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning
Parete-Koon, Suzanne; Hix, W.; Thielemann, F.
2008-03-01
The nuclei of the "iron peak" are formed in massive stars shortly before core collapse and during their supernova outbursts as well as during thermonuclear supernovae. Complete and incomplete silicon burning during these events are responsible for the production of a wide range of nuclei with atomic mass numbers from 28 to 64. Because of the large number of nuclei involved, accurate modeling of silicon burning is computationally expensive. However, examination of the physics of silicon burning has revealed that the nuclear evolution is dominated by large groups of nuclei in mutual equilibrium. We present an improvement on our hybrid equilibrium-network scheme which takes advantage of this quasi-equilibrium in order to reduce the number of independent variables calculated. Because the size and membership of these groups vary as the temperature, density and electron faction change, achieving maximal efficiency requires dynamic adjustment of group number and membership. Toward this end, we are implementing a scheme beginning with a single QSE (NSE) group at appropriately high temperature, then progressing through 2, 3 and 4 group stages (with successively more independent variables) as temperature declines. This combination allows accurate prediction of the nuclear abundance evolution, deleptonization and energy generation at a further reduced computational cost when compared to a conventional nuclear reaction network or our previous 3 fixed group QSE-reduced network. During silicon burning, the resultant QSE-reduced network is up to 20 times faster than the full network it replaces without significant loss of accuracy. These reductions in computational cost and the number of species evolved make QSE-reduced networks well suited for inclusion within hydrodynamic simulations, particularly in multi-dimensional applications. This work has been supported by the National Science Foundation, by the Department of Energy's Scientic Discovery through Advanced Computing
Energy Technology Data Exchange (ETDEWEB)
Steele, W.V.; Chirico, R.D.; Knipmeyer, S.E.; Nguyen, A.
1990-12-01
Catalytic hydrodenitrogenation (HDN) is a key step in upgrading processes for conversion of heavy petroleum, shale oil, tar sands, and the products of the liquefaction of coal to economically viable products. This research program provides accurate experimental thermochemical and thermophysical properties for key organic nitrogen-containing compounds present in the range of alternative feedstocks, and applies the experimental information to thermodynamic analyses of key HDN reaction networks. This report is the first in a series that will lead to an analysis of a three-ring HDN system; the carbazole/hydrogen reaction network. 2-Aminobiphenyl is the initial intermediate in the HDN pathway for carbazole, which consumes the least hydrogen possible. Measurements leading to the calculation of the ideal-gas thermodynamic properties for 2-aminobiphenyl are reported. Experimental methods included combustion calorimetry, adiabatic heat-capacity calorimetry, comparative ebulliometry, inclined-piston gauge manometry, and differential-scanning calorimetry (d.s.c). Entropies, enthalpies, and Gibbs energies of formation were derived for the ideal gas for selected temperatures between 298.15 K and 820 K. The critical temperature and critical density were determined for 2-aminobiphenyl with the d.s.c., and the critical pressure was derived. The Gibbs energies of formation are used in thermodynamic calculations to compare the feasibility of the initial hydrogenolysis step in the carbazole/H{sub 2} network with that of its hydrocarbon and oxygen-containing analogous; i.e., fluorene/H{sub 2} and dibenzofuran/H{sub 2}. Results of the thermodynamic calculations are compared with those of batch-reaction studies reported in the literature. 57 refs., 8 figs., 18 tabs.
Liang, Xiao; Wang, Linshan; Wang, Yangfan; Wang, Ruili
2016-09-01
In this paper, we focus on the long time behavior of the mild solution to delayed reaction-diffusion Hopfield neural networks (DRDHNNs) driven by infinite dimensional Wiener processes. We analyze the existence, uniqueness, and stability of this system under the local Lipschitz function by constructing an appropriate Lyapunov-Krasovskii function and utilizing the semigroup theory. Some easy-to-test criteria affecting the well-posedness and stability of the networks, such as infinite dimensional noise and diffusion effect, are obtained. The criteria can be used as theoretic guidance to stabilize DRDHNNs in practical applications when infinite dimensional noise is taken into consideration. Meanwhile, considering the fact that the standard Brownian motion is a special case of infinite dimensional Wiener process, we undertake an analysis of the local Lipschitz condition, which has a wider range than the global Lipschitz condition. Two samples are given to examine the availability of the results in this paper. Simulations are also given using the MATLAB.
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.
Neural Network Control of CSTR for Reversible Reaction Using Reverence Model Approach
Directory of Open Access Journals (Sweden)
Duncan ALOKO
2007-01-01
Full Text Available In this work, non-linear control of CSTR for reversible reaction is carried out using Neural Network as design tool. The Model Reverence approach in used to design ANN controller. The idea is to have a control system that will be able to achieve improvement in the level of conversion and to be able to track set point change and reject load disturbance. We use PID control scheme as benchmark to study the performance of the controller. The comparison shows that ANN controller out perform PID in the extreme range of non-linearity.This paper represents a preliminary effort to design a simplified neutral network control scheme for a class of non-linear process. Future works will involve further investigation of the effectiveness of thin approach for the real industrial chemical process
A reaction-diffusion-based coding rate control mechanism for camera sensor networks.
Yamamoto, Hiroshi; Hyodo, Katsuya; Wakamiya, Naoki; Murata, Masayuki
2010-01-01
A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.
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.
Ozasa, Kazunari; Aono, Masashi; Maeda, Mizuo; Hara, Masahiko
In order to develop an adaptive computing system, we investigate microscopic optical feedback to a group of microbes (Euglena gracilis in this study) with a neural network algorithm, expecting that the unique characteristics of microbes, especially their strategies to survive/adapt against unfavorable environmental stimuli, will explicitly determine the temporal evolution of the microbe-based feedback system. The photophobic reactions of Euglena are extracted from experiments, and built in the Monte-Carlo simulation of a microbe-based neurocomputing. The simulation revealed a good performance of Euglena-based neurocomputing. Dynamic transition among the solutions is discussed from the viewpoint of feedback instability.
Wolf, Elizabeth Skubak; Anderson, David F
2012-12-14
We present an efficient finite difference method for the approximation of second derivatives, with respect to system parameters, of expectations for a class of discrete stochastic chemical reaction networks. The method uses a coupling of the perturbed processes that yields a much lower variance than existing methods, thereby drastically lowering the computational complexity required to solve a given problem. Further, the method is simple to implement and will also prove useful in any setting in which continuous time Markov chains are used to model dynamics, such as population processes. We expect the new method to be useful in the context of optimization algorithms that require knowledge of the Hessian.
DEFF Research Database (Denmark)
Frauzem, Rebecca; Kongpanna, Pichayapan; Roh, Kosan
. With the information of sources and reactions, a tree of reaction paths is formed and investigated. This forms a superstructure of CO2 utilization to a variety of products. Each of the paths in the network involves CO2 and a co-reactant, such as hydrogen, which may also be captured from process purge streams...
Surface reaction network of CO oxidation on CeO2/Au(110) inverse model catalysts.
Ding, Liangbing; Xiong, Feng; Jin, Yuekang; Wang, Zhengming; Sun, Guanghui; Huang, Weixin
2016-11-30
CeO2/Au(110) inverse model catalysts were prepared and their activity toward the adsorption and co-adsorption of O2, CO, CO2 and water was studied by means of X-ray photoelectron spectroscopy, low energy electron diffraction, thermal desorption spectra and temperature-programmed reaction spectra. The Au surface of CeO2/Au(110) inverse model catalysts molecularly adsorbs CO, CO2 and water, and the polycrystalline CeO2 surface of CeO2/Au(110) inverse model catalysts molecularly adsorbs O2, and molecularly and reactively adsorbs CO, CO2 and water. By controllably preparing co-adsorbed surface species on CeO2/Au(110) inverse model catalysts, we successfully identified various surface reaction pathways of CO oxidation to produce CO2 with different barriers both on the CeO2 surface and at the Au-CeO2 interface, including CO oxidation by various oxygen species, and water/hydroxyl group-involved CO oxidation. These results establish a surface reaction network of CO oxidation catalyzed by Au/CeO2 catalysts, greatly advancing the fundamental understandings of catalytic CO oxidation reactions.
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Waddington Michael
2006-03-01
Full Text Available Abstract Background Enterobacter sakazakii is an emergent pathogen associated with ingestion of infant formula and accurate identification is important in both industrial and clinical settings. Bacterial species can be difficult to accurately characterise from complex biochemical datasets and computer algorithms can potentially simplify the process. Results Artificial Neural Networks were applied to biochemical and 16S rDNA data derived from 282 strains of Enterobacteriaceae, including 189 E. sakazakii isolates, in order to identify key characteristics which could improve the identification of E. sakazakii. The models developed resulted in a predictive performance for blind (validation data of 99.3 % correct discrimination between E. sakazakii and closely related species for both phenotypic and genotypic data. Three main regions of the partial rDNA sequence were found to be key in discriminating the species. Comparison between E. sakazakii and other strains also constitutively positive for expression of the enzyme α-glucosidase resulted in a predictive performance of 98.7 % for 16S rDNA sequence data and 100% for phenotypic data. Conclusion The computationally based methods developed here show a remarkable ability in reducing data dimensionality and complexity, in order to eliminate noise from the system in order to facilitate the speed and reliability of a potential strain identification system. Furthermore, the approaches described are also able to provide valuable information regarding the population structure and distribution of individual species thus providing the foundations for novel assays and diagnostic tests for rapid identification of pathogens.
DEFF Research Database (Denmark)
Rohatgi, Neha; Nielsen, Tine Kragh; Bjørn, Sara Petersen
2014-01-01
and strict specificity towards gluconate out of 122 substrates tested. In order to evaluate the metabolic impact of gluconate in humans we modeled gluconate metabolism using steady state metabolic network analysis. The results indicate that significant metabolic flux changes in anabolic pathways linked......The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role...... to the hexose monophosphate shunt (HMS) are induced through a small increase in gluconate concentration. We argue that the enzyme takes part in a context specific carbon flux route into the HMS that, in humans, remains incompletely explored. Apart from the biochemical description of human gluconokinase...
On the Ionisation Fraction in Protoplanetary Disks I: Comparing Different Reaction Networks
Ilgner, M; Ilgner, Martin; Richard P. Nelson
2005-01-01
We calculate the ionisation fraction in protostellar disk models using a number of different chemical reaction networks, including gas-phase and gas-grain reaction schemes. The disk models we consider are conventional alpha-disks, which include viscous heating and radiative cooling. The primary source of ionisation is assumed to be X-ray irradiation from the central star. We consider a number of gas-phase chemical networks. In general we find that the simple models predict higher fractional ionisation levels and more extensive active zones than the more complex models. When heavy metal atoms are included the simple models predict that the disk is magnetically active throughout. The complex models predict that extensive regions of the disk remain magnetically uncoupled even with a fractional abundance of magnesium of 10(-8). The addition of submicron sized grains with a concentration of 10(-12) causes the size of the dead zone to increase dramatically for all kinetic models considered. We find that the simple ...
Ohya, Tatsuo; Araki, Hiroshi; Sueyoshi, Masuo
2008-08-01
We examined the usefulness of PCR-based restriction fragment length polymorphism (PCR-RFLP) and species-specific PCR combined with a newly devised rapid biochemical test using microplates for identifying weakly beta-hemolytic intestinal spirochetes (WBHIS) isolated from pigs. WBHIS strains showing atypical biochemical characteristics were decisively identified at the species level by PCR-RFLP and species-specific PCR. Identification of WBHIS at the species level in routine diagnostic work will certainly contribute to clarifying the pathogenicity of WBHIS.
Das, Biswajit; Banerjee, Kinshuk; Gangopadhyay, Gautam
2013-12-28
In this work, we develop an approach to nonequilibrium thermodynamics of an open chemical reaction network in terms of the elementary reaction propensities. The method is akin to the microscopic formulation of the dissipation function in terms of the Kullback-Leibler distance of phase space trajectories in Hamiltonian system. The formalism is applied to a single oligomeric enzyme kinetics at chemiostatic condition that leads the reaction system to a nonequilibrium steady state, characterized by a positive total entropy production rate. Analytical expressions are derived, relating the individual reaction contributions towards the total entropy production rate with experimentally measurable reaction velocity. Taking a real case of Escherichia coli β-galactosidase enzyme obeying Michaelis-Menten kinetics, we thoroughly analyze the temporal as well as the steady state behavior of various thermodynamic quantities for each elementary reaction. This gives a useful insight in the relative magnitudes of various energy terms and the dissipated heat to sustain a steady state of the reaction system operating far-from-equilibrium. It is also observed that, the reaction is entropy-driven at low substrate concentration and becomes energy-driven as the substrate concentration rises.
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.
Bayer, Christian
2016-02-20
© 2016 Taylor & Francis Group, LLC. ABSTRACT: In this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the expectation-maximization algorithm to the phase I output. By selecting a set of overdispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.
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Ignat Drozdov
Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional
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.
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Yihui Liu
2015-02-01
Full Text Available Adverse drug reaction (ADR is widely concerned for public health issue. ADRs are one of most common causes to withdraw some drugs from market. Prescription event monitoring (PEM is an important approach to detect the adverse drug reactions. The main problem to deal with this method is how to automatically extract the medical events or side effects from high-throughput medical events, which are collected from day to day clinical practice. In this study we propose a novel concept of feature matrix to detect the ADRs. Feature matrix, which is extracted from big medical data from The Health Improvement Network (THIN database, is created to characterize the medical events for the patients who take drugs. Feature matrix builds the foundation for the irregular and big medical data. Then feature selection methods are performed on feature matrix to detect the significant features. Finally the ADRs can be located based on the significant features. The experiments are carried out on three drugs: Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug are detected and better performance is achieved compared to other computerized methods. The detected ADRs are based on computerized methods, further investigation is needed.
Halász, Adám M; Lai, Hong-Jian; McCabe Pryor, Meghan; Radhakrishnan, Krishnan; Edwards, Jeremy S
2013-01-01
True steady states are a rare occurrence in living organisms, yet their knowledge is essential for quasi-steady-state approximations, multistability analysis, and other important tools in the investigation of chemical reaction networks (CRN) used to describe molecular processes on the cellular level. Here, we present an approach that can provide closed form steady-state solutions to complex systems, resulting from CRN with binary reactions and mass-action rate laws. We map the nonlinear algebraic problem of finding steady states onto a linear problem in a higher-dimensional space. We show that the linearized version of the steady-state equations obeys the linear conservation laws of the original CRN. We identify two classes of problems for which complete, minimally parameterized solutions may be obtained using only the machinery of linear systems and a judicious choice of the variables used as free parameters. We exemplify our method, providing explicit formulae, on CRN describing signal initiation of two important types of RTK receptor-ligand systems, VEGF and EGF-ErbB1.
Lo, Chien-Chi; Bonner, Carol A; Xie, Gary; D'Souza, Mark; Jensen, Roy A
2009-12-01
Aspartokinase (Ask) exists within a variable network that supports the synthesis of 9 amino acids and a number of other important metabolites. Lysine, isoleucine, aromatic amino acids, and dipicolinate may arise from the ASK network or from alternative pathways. Ask proteins were subjected to cohesion group analysis, a methodology that sorts a given protein assemblage into groups in which evolutionary continuity is assured. Two subhomology divisions, ASK(alpha) and ASK(beta), have been recognized. The ASK(alpha) subhomology division is the most ancient, being widely distributed throughout the Archaea and Eukarya and in some Bacteria. Within an indel region of about 75 amino acids near the N terminus, ASK(beta) sequences differ from ASK(alpha) sequences by the possession of a proposed ancient deletion. ASK(beta) sequences are present in most Bacteria and usually exhibit an in-frame internal translational start site that can generate a small Ask subunit that is identical to the C-terminal portion of the larger subunit of a heterodimeric unit. Particularly novel are ask genes embedded in gene contexts that imply specialization for ectoine (osmotic agent) or aromatic amino acids. The cohesion group approach is well suited for the easy recognition of relatively recent lateral gene transfer (LGT) events, and many examples of these are described. Given the current density of genome representation for Proteobacteria, it is possible to reconstruct more ancient landmark LGT events. Thus, a plausible scenario in which the three well-studied and iconic Ask homologs of Escherichia coli are not within the vertical genealogy of Gammaproteobacteria, but rather originated via LGT from a Bacteroidetes donor, is supported.
Computer-assisted design for scaling up systems based on DNA reaction networks
Aubert, Nathanaël; Mosca, Clément; Fujii, Teruo; Hagiya, Masami; Rondelez, Yannick
2014-01-01
In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the
Computer-assisted design for scaling up systems based on DNA reaction networks.
Aubert, Nathanaël; Mosca, Clément; Fujii, Teruo; Hagiya, Masami; Rondelez, Yannick
2014-04-06
In the past few years, there have been many exciting advances in the field of molecular programming, reaching a point where implementation of non-trivial systems, such as neural networks or switchable bistable networks, is a reality. Such systems require nonlinearity, be it through signal amplification, digitalization or the generation of autonomous dynamics such as oscillations. The biochemistry of DNA systems provides such mechanisms, but assembling them in a constructive manner is still a difficult and sometimes counterintuitive process. Moreover, realistic prediction of the actual evolution of concentrations over time requires a number of side reactions, such as leaks, cross-talks or competitive interactions, to be taken into account. In this case, the design of a system targeting a given function takes much trial and error before the correct architecture can be found. To speed up this process, we have created DNA Artificial Circuits Computer-Assisted Design (DACCAD), a computer-assisted design software that supports the construction of systems for the DNA toolbox. DACCAD is ultimately aimed to design actual in vitro implementations, which is made possible by building on the experimental knowledge available on the DNA toolbox. We illustrate its effectiveness by designing various systems, from Montagne et al.'s Oligator or Padirac et al.'s bistable system to new and complex networks, including a two-bit counter or a frequency divider as well as an example of very large system encoding the game Mastermind. In the process, we highlight a variety of behaviours, such as enzymatic saturation and load effect, which would be hard to handle or even predict with a simpler model. We also show that those mechanisms, while generally seen as detrimental, can be used in a positive way, as functional part of a design. Additionally, the number of parameters included in these simulations can be large, especially in the case of complex systems. For this reason, we included the
Directory of Open Access Journals (Sweden)
Wadén Johan
2009-10-01
Full Text Available Background Cardiovascular disease is the main cause of premature death in patients with type 1 diabetes. Patients with diabetic kidney disease have an increased risk of heart attack or stroke. Accurate knowledge of the complex inter-dependencies between the risk factors is critical for pinpointing the best targets for research and treatment. Therefore, the aim of this study was to describe the association patterns between clinical and biochemical features of diabetic complications. Methods Medical records and serum and urine samples of 4,197 patients with type 1 diabetes were collected from health care centers in Finland. At baseline, the mean diabetes duration was 22 years, 52% were male, 23% had kidney disease (urine albumin excretion over 300 mg/24 h or end-stage renal disease and 8% had a history of macrovascular events. All-cause mortality was evaluated after an average of 6.5 years of follow-up (25,714 patient years. The dataset comprised 28 clinical and 25 biochemical variables that were regarded as the nodes of a network to assess their mutual relationships. Results The networks contained cliques that were densely inter-connected (r > 0.6, including cliques for high-density lipoprotein (HDL markers, for triglycerides and cholesterol, for urinary excretion and for indices of body mass. The links between the cliques showed biologically relevant interactions: an inverse relationship between HDL cholesterol and the triglyceride clique (r P -16, a connection between triglycerides and body mass via C-reactive protein (r > 0.3, P -16 and intermediate-density cholesterol as the connector between lipoprotein metabolism and albuminuria (r > 0.3, P -16. Aging and macrovascular disease were linked to death via working ability and retinopathy. Diabetic kidney disease, serum creatinine and potassium, retinopathy and blood pressure were inter-connected. Blood pressure correlations indicated accelerated vascular aging in individuals with kidney disease
Ou, Yu-Yen; Chen, Shu-An; Chang, Yun-Min; Velmurugan, Devadasan; Fukui, Kazuhiko; Michael Gromiha, M
2013-09-01
Efflux proteins are membrane proteins, which are involved in the transportation of multidrugs. The annotation of efflux proteins in genomic sequences would aid to understand the function. Although the percentage of membrane proteins in genomes is estimated to be 25-30%, there is no information about the content of efflux proteins. For annotating such class of proteins it is necessary to develop a reliable method to identify efflux proteins from amino acid sequence information. In this work, we have developed a method based on radial basis function networks using position specific scoring matrices (PSSM) and amino acid properties. We noticed that the C-terminal domain of efflux proteins contain vital information for discrimination. Our method showed an accuracy of 78 and 92% in discriminating efflux proteins from transporters and membrane proteins, respectively using fivefold cross-validation. We utilized our method for annotating the genomes E. coli and P. aeruginosa and it predicted 8.7 and 9.2% of proteins as efflux proteins in these genomes, respectively. The predicted efflux proteins have been compared with available experimental data and we observed a very good agreement between them. Further, we developed a web server for classifying efflux proteins and it is freely available at http://rbf.bioinfo.tw/∼sachen/EFFLUXpredict/Efflux-RBF.php. We suggest that our method could be an effective tool for annotating efflux proteins in genomic sequences.
Ou, Yu-Yen; Chen, Shu-An; Gromiha, M Michael
2010-05-15
Transporters are proteins that are involved in the movement of ions or molecules across biological membranes. Transporters are generally classified into channels/pores, electrochemical transporters, and active transporters. Discriminating the specific class of transporters and their subfamilies are essential tasks in computational biology for the advancement of structural and functional genomics. We have systematically analyzed the amino acid composition, residue pair preference and amino acid properties in six different families of transporters. Utilizing the information, we have developed a radial basis function (RBF) network method based on profiles obtained with position specific scoring matrices for discriminating transporters belonging to three different classes and six families. Our method showed a fivefold cross validation accuracy of 76%, 73%, and 69% for discriminating transporters and nontransporters, three different classes and six different families of transporters, respectively. Further, the method was tested with independent datasets, which showed similar level of accuracy. A web server has been developed for discriminating transporters based on three classes and six families, and it is available at http://rbf.bioinfo.tw/ approximately sachen/tcrbf.html. We suggest that our method could be effectively used to identify transporters and discriminating them into different classes and families.
Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network
DEFF Research Database (Denmark)
Förster, Jochen; Famili, I.; Fu, P.;
2003-01-01
The metabolic network in the yeast Saccharomyces cerevisiae was reconstructed using currently available genomic, biochemical, and physiological information. The metabolic reactions were compartmentalized between the cytosol and the mitochondria, and transport steps between the compartments...... containing 1175 metabolic reactions and 584 metabolites. The number of gene functions included in the reconstructed network corresponds to similar to16% of all characterized ORFs in S. cerevisiae. Using the reconstructed network, the metabolic capabilities of S. cerevisiae were calculated and compared...
Lu, Wei; Tamura, Takeyuki; Song, Jiangning; Akutsu, Tatsuya
2014-01-01
In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."
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Wei Lu
Full Text Available In this paper, we consider the Minimum Reaction Insertion (MRI problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at "http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html."
Song, Jiangning; Akutsu, Tatsuya
2014-01-01
In this paper, we consider the Minimum Reaction Insertion (MRI) problem for finding the minimum number of additional reactions from a reference metabolic network to a host metabolic network so that a target compound becomes producible in the revised host metabolic network in a Boolean model. Although a similar problem for larger networks is solvable in a flux balance analysis (FBA)-based model, the solution of the FBA-based model tends to include more reactions than that of the Boolean model. However, solving MRI using the Boolean model is computationally more expensive than using the FBA-based model since the Boolean model needs more integer variables. Therefore, in this study, to solve MRI for larger networks in the Boolean model, we have developed an efficient Integer Programming formalization method in which the number of integer variables is reduced by the notion of feedback vertex set and minimal valid assignment. As a result of computer experiments conducted using the data of metabolic networks of E. coli and reference networks downloaded from the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we have found that the developed method can appropriately solve MRI in the Boolean model and is applicable to large scale-networks for which an exhaustive search does not work. We have also compared the developed method with the existing connectivity-based methods and FBA-based methods, and show the difference between the solutions of our method and the existing methods. A theoretical analysis of MRI is also conducted, and the NP-completeness of MRI is proved in the Boolean model. Our developed software is available at “http://sunflower.kuicr.kyoto-u.ac.jp/~rogi/minRect/minRect.html.” PMID:24651476
Directory of Open Access Journals (Sweden)
Chuangxia Huang
2009-01-01
Full Text Available This paper is concerned with pth moment exponential stability of stochastic reaction-diffusion Cohen-Grossberg neural networks with time-varying delays. With the help of Lyapunov method, stochastic analysis, and inequality techniques, a set of new suffcient conditions on pth moment exponential stability for the considered system is presented. The proposed results generalized and improved some earlier publications.
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
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
Cholesterol photo-oxidation: A chemical reaction network for kinetic modeling.
Barnaba, Carlo; Rodríguez-Estrada, Maria Teresa; Lercker, Giovanni; García, Hugo Sergio; Medina-Meza, Ilce Gabriela
2016-12-01
In this work we studied the effect of polyunsaturated fatty acids (PUFAs) methyl esters on cholesterol photo-induced oxidation. The oxidative routes were modeled with a chemical reaction network (CRN), which represents the first application of CRN to the oxidative degradation of a food-related lipid matrix. Docosahexaenoic acid (DHA, T-I), eicosapentaenoic acid (EPA, T-II) and a mixture of both (T-III) were added to cholesterol using hematoporphyrin as sensitizer, and were exposed to a fluorescent lamp for 48h. High amounts of Type I cholesterol oxidation products (COPs) were recovered (epimers 7α- and 7β-OH, 7-keto and 25-OH), as well as 5β,6β-epoxy. Fitting the experimental data with the CRN allowed characterizing the associated kinetics. DHA and EPA exerted different effects on the oxidative process. DHA showed a protective effect to 7-hydroxy derivatives, whereas EPA enhanced side-chain oxidation and 7β-OH kinetic rates. The mixture of PUFAs increased the kinetic rates several fold, particularly for 25-OH. With respect to the control, the formation of β-epoxy was reduced, suggesting potential inhibition in the presence of PUFAs.
Maginnis, P. A.; West, M.; Dullerud, G. E.
2016-10-01
We propose an algorithm to accelerate Monte Carlo simulation for a broad class of stochastic processes. Specifically, the class of countable-state, discrete-time Markov chains driven by additive Poisson noise, or lattice discrete-time Markov chains. In particular, this class includes simulation of reaction networks via the tau-leaping algorithm. To produce the speedup, we simulate pairs of fair-draw trajectories that are negatively correlated. Thus, when averaged, these paths produce an unbiased Monte Carlo estimator that has reduced variance and, therefore, reduced error. Numerical results for three example systems included in this work demonstrate two to four orders of magnitude reduction of mean-square error. The numerical examples were chosen to illustrate different application areas and levels of system complexity. The areas are: gene expression (affine state-dependent rates), aerosol particle coagulation with emission and human immunodeficiency virus infection (both with nonlinear state-dependent rates). Our algorithm views the system dynamics as a "black-box", i.e., we only require control of pseudorandom number generator inputs. As a result, typical codes can be retrofitted with our algorithm using only minor changes. We prove several analytical results. Among these, we characterize the relationship of covariances between paths in the general nonlinear state-dependent intensity rates case, and we prove variance reduction of mean estimators in the special case of affine intensity rates.
Tanaka, T; Katayama, H; Shirakata, A; Takahasi, H
1988-09-01
The adverse reactions to contrast media have been investigated by several authors but the exact mechanisms have not yet been established. To study whether kinin-releasing systems are involved in these adverse reactions, we determined total plasma kininogen levels at intervals up to 30 minutes after the intravenous injections of contrast media in dogs. Injections of iohexol, iopamidol, and iothalamate decreased total plasma kininogen levels. This effect increased with increasing dose of the media and suggests that they activated the kinin-releasing systems in the plasma.
Stochastic bifurcation, slow fluctuations, and bistability as an origin of biochemical complexity.
Qian, Hong; Shi, Pei-Zhe; Xing, Jianhua
2009-06-28
We present a simple, unifying theory for stochastic biochemical systems with multiple time-scale dynamics that exhibit noise-induced bistability in an open-chemical environment, while the corresponding macroscopic reaction is unistable. Nonlinear stochastic biochemical systems like these are fundamentally different from classical systems in equilibrium or near-equilibrium steady state whose fluctuations are unimodal following Einstein-Onsager-Lax-Keizer theory. We show that noise-induced bistability in general arises from slow fluctuations, and a pitchfork bifurcation occurs as the rate of fluctuations decreases. Since an equilibrium distribution, due to detailed balance, has to be independent of changes in time-scale, the bifurcation is necessarily a driven phenomenon. As examples, we analyze three biochemical networks of currently interest: self-regulating gene, stochastic binary decision, and phosphorylation-dephosphorylation cycle with fluctuating kinase. The implications of bistability to biochemical complexity are discussed.
Goldstein, Lee Hilary; Berlin, Maya; Saliba, Walid; Elias, Mazen; Berkovitch, Matitiyahu
2013-11-01
Adverse drug reactions (ADR) are underreported by doctors despite numerous efforts. We aimed to determine if establishing an "ADR reporting doctor's network" within a hospital would increase the quantity of ADRs reported by hospital doctors. One hundred hospital doctors joined the network. Email reminders were sent to network members during the 1 year study period, conveying information about ADRs reported, amusingly and pleasantly reminding them to report ADRs in minimal detail, by phone, email, text message or mail to the Clinical Pharmacology Unit, who would further complete the report. A total of 114 ADRs were reported during the study period in comparison to 48, 26, and 17 in the previous 3 years (2008, 2009, 2010, respectively). In the 3 years prior, doctors reported 41.7% of the reported ADRs whereas in the study period, doctors reported 74.3% of ADRs (P reports. Ninety seven percent of doctors' reports were of ADR network members. Thirty-four (34%) network members reported an ADR during the study period and 31 of the 34 reporters had never reported ADRs before becoming network members. Establishing an ADR network of doctors substantially increases ADR reporting amongst its members.
Directory of Open Access Journals (Sweden)
Qiankun Song
2007-06-01
Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.
Directory of Open Access Journals (Sweden)
Cao Jinde
2007-01-01
Full Text Available Impulsive bidirectional associative memory neural network model with time-varying delays and reaction-diffusion terms is considered. Several sufficient conditions ensuring the existence, uniqueness, and global exponential stability of equilibrium point for the addressed neural network are derived by M-matrix theory, analytic methods, and inequality techniques. Moreover, the exponential convergence rate index is estimated, which depends on the system parameters. The obtained results in this paper are less restrictive than previously known criteria. Two examples are given to show the effectiveness of the obtained results.
Fan, Xiaofei; Zhang, Xian; Wu, Ligang; Shi, Michael
2016-04-11
This paper is concerned with the finite-time stability problem of the delayed genetic regulatory networks (GRNs) with reaction-diffusion terms under Dirichlet boundary conditions. By constructing a Lyapunov-Krasovskii functional including quad- slope integrations, we establish delay-dependent finite-time stabil- ity criteria by employing the Wirtinger-type integral inequality, Gronwall inequality, convex technique, and reciprocally convex technique. In addition, the obtained criteria are also reaction- diffusion-dependent. Finally, a numerical example is provided to illustrate the effectiveness of the theoretical results.
LiHe$^+$ in the early Universe: a full assessment of its reaction network and final abundances
Bovino, Stefano; Galli, Daniele; Tacconi, Mario; Gianturco, Francesco A
2012-01-01
We present the results of quantum calculations based on entirely ab initio methods for a variety of molecular processes and chemical reactions involving the LiHe$^+$ ionic polar molecule. With the aid of these calculations we derive accurate reaction rates and fitting expressions valid over a range of gas temperatures representative of the typical conditions of the pregalactic gas. With the help of a full chemical network, we then compute the evolution of the abundance of LiHe$^+$ as function of redshift in the early Universe. Finally, we compare the relative abundance of LiHe$^+$ with that of other polar cations formed in the same redshift interval.
Biochemical Hypermedia: Galactose Metabolism.
Directory of Open Access Journals (Sweden)
J.K. Sugai
2013-05-01
Full Text Available Introduction: Animations of biochemical processes and virtual laboratory environments lead to true molecular simulations. The use of interactive software’s in education can improve cognitive capacity, better learning and, mainly, it makes information acquisition easier. Material and Methods: This work presents the development of a biochemical hypermedia to understanding of the galactose metabolism. It was developed with the help of concept maps, ISIS Draw, ADOBE Photoshop and FLASH MX Program. Results and Discussion: A step by step animation process shows the enzymatic reactions of galactose conversion to glucose-1-phosphate (to glycogen synthesis, glucose-6-phosphate (glycolysis intermediary, UDP-galactose (substrate to mucopolysaccharides synthesis and collagen’s glycosylation. There are navigation guide that allow scrolling the mouse over the names of the components of enzymatic reactions of via the metabolism of galactose. Thus, explanatory text box, chemical structures and animation of the actions of enzymes appear to navigator. Upon completion of the module, the user’s response to the proposed exercise can be checked immediately through text box with interactive content of the answer. Conclusion: This hypermedia was presented for undergraduate students (UFSC who revealed that it was extremely effective in promoting the understanding of the theme.
Directory of Open Access Journals (Sweden)
Saucerman Jeffrey J
2010-11-01
Full Text Available Abstract Background New approaches are needed for large-scale predictive modeling of cellular signaling networks. While mass action and enzyme kinetic approaches require extensive biochemical data, current logic-based approaches are used primarily for qualitative predictions and have lacked direct quantitative comparison with biochemical models. Results We developed a logic-based differential equation modeling approach for cell signaling networks based on normalized Hill activation/inhibition functions controlled by logical AND and OR operators to characterize signaling crosstalk. Using this approach, we modeled the cardiac β1-adrenergic signaling network, including 36 reactions and 25 species. Direct comparison of this model to an extensively characterized and validated biochemical model of the same network revealed that the new model gave reasonably accurate predictions of key network properties, even with default parameters. Normalized Hill functions improved quantitative predictions of global functional relationships compared with prior logic-based approaches. Comprehensive sensitivity analysis revealed the significant role of PKA negative feedback on upstream signaling and the importance of phosphodiesterases as key negative regulators of the network. The model was then extended to incorporate recently identified protein interaction data involving integrin-mediated mechanotransduction. Conclusions The normalized-Hill differential equation modeling approach allows quantitative prediction of network functional relationships and dynamics, even in systems with limited biochemical data.
DEFF Research Database (Denmark)
Rottach, Dana R.; Curro, John G.; Budzien, Joanne;
2007-01-01
The effects of sequential cross-linking and scission of polymer networks formed in two states of strain are investigated using molecular dynamics simulations. Two-stage networks are studied in which a network formed in the unstrained state (stage 1) undergoes additional cross-linking in a uniaxia......The effects of sequential cross-linking and scission of polymer networks formed in two states of strain are investigated using molecular dynamics simulations. Two-stage networks are studied in which a network formed in the unstrained state (stage 1) undergoes additional cross...... good agreement with the predictions of Flory and Fricker. It was found that the fractional stress reduction upon removal of the first-stage cross-links could be accurately calculated from the slip tube model of Rubinstein and Panyukov modified to use the theoretical transfer functions of Fricker. ...
Characterizing and prototyping genetic networks with cell-free transcription-translation reactions.
Takahashi, Melissa K; Hayes, Clarmyra A; Chappell, James; Sun, Zachary Z; Murray, Richard M; Noireaux, Vincent; Lucks, Julius B
2015-09-15
A central goal of synthetic biology is to engineer cellular behavior by engineering synthetic gene networks for a variety of biotechnology and medical applications. The process of engineering gene networks often involves an iterative 'design-build-test' cycle, whereby the parts and connections that make up the network are built, characterized and varied until the desired network function is reached. Many advances have been made in the design and build portions of this cycle. However, the slow process of in vivo characterization of network function often limits the timescale of the testing step. Cell-free transcription-translation (TX-TL) systems offer a simple and fast alternative to performing these characterizations in cells. Here we provide an overview of a cell-free TX-TL system that utilizes the native Escherichia coli TX-TL machinery, thereby allowing a large repertoire of parts and networks to be characterized. As a way to demonstrate the utility of cell-free TX-TL, we illustrate the characterization of two genetic networks: an RNA transcriptional cascade and a protein regulated incoherent feed-forward loop. We also provide guidelines for designing TX-TL experiments to characterize new genetic networks. We end with a discussion of current and emerging applications of cell free systems.
Networking with noise at the molecular, cellular, and population level
Vilar, Jose
2002-03-01
The intrinsic stochastic nature of biochemical reactions affects enzymatic and transcriptional networks at different levels. Yet, cells are able to function effectively and consistently amidst such random fluctuations. I will discuss some molecular mechanisms that are able to reduce the intrinsic noise of chemical reactions, how suitable designs can make networks resistant to noise, and what strategies can be used by populations to achieve precise functions.
Perez-Garcia, Octavio; Chandran, Kartik; Villas-Boas, Silas G; Singhal, Naresh
2016-05-01
Over the coming decades nitrous oxide (N2O) is expected to become a dominant greenhouse gas and atmospheric ozone depleting substance. In wastewater treatment systems, N2O is majorly produced by nitrifying microbes through biochemical reduction of nitrite (NO2(-)) and nitric oxide (NO). However it is unknown if the amount of N2O formed is affected by alternative NO redox reactions catalyzed by oxidative nitrite oxidoreductase (NirK), cytochromes (i.e., P460 [CytP460] and 554 [Cyt554 ]) and flavohemoglobins (Hmp) in ammonia- and nitrite-oxidizing bacteria (AOB and NOB, respectively). In this study, a mathematical model is developed to assess how N2O formation is affected by such alternative nitrogen redox transformations. The developed multispecies metabolic network model captures the nitrogen respiratory pathways inferred from genomes of eight AOB and NOB species. The performance of model variants, obtained as different combinations of active NO redox reactions, was assessed against nine experimental datasets for nitrifying cultures producing N2O at different concentration of electron donor and acceptor. Model predicted metabolic fluxes show that only variants that included NO oxidation to NO2(-) by CytP460 and Hmp in AOB gave statistically similar estimates to observed production rates of N2O, NO, NO2(-) and nitrate (NO3(-)), together with fractions of AOB and NOB species in biomass. Simulations showed that NO oxidation to NO2(-) decreased N2O formation by 60% without changing culture's NO2(-) production rate. Model variants including NO reduction to N2O by Cyt554 and cNor in NOB did not improve the accuracy of experimental datasets estimates, suggesting null N2O production by NOB during nitrification. Finally, the analysis shows that in nitrifying cultures transitioning from dissolved oxygen levels above 3.8 ± 0.38 to <1.5 ± 0.8 mg/L, NOB cells can oxidize the NO produced by AOB through reactions catalyzed by oxidative NirK.
Rational design of functional and tunable oscillating enzymatic networks
Semenov, Sergey N.; Wong, Albert S. Y.; van der Made, R. Martijn; Postma, Sjoerd G. J.; Groen, Joost; van Roekel, Hendrik W. H.; de Greef, Tom F. A.; Huck, Wilhelm T. S.
2015-02-01
Life is sustained by complex systems operating far from equilibrium and consisting of a multitude of enzymatic reaction networks. The operating principles of biology's regulatory networks are known, but the in vitro assembly of out-of-equilibrium enzymatic reaction networks has proved challenging, limiting the development of synthetic systems showing autonomous behaviour. Here, we present a strategy for the rational design of programmable functional reaction networks that exhibit dynamic behaviour. We demonstrate that a network built around autoactivation and delayed negative feedback of the enzyme trypsin is capable of producing sustained oscillating concentrations of active trypsin for over 65 h. Other functions, such as amplification, analog-to-digital conversion and periodic control over equilibrium systems, are obtained by linking multiple network modules in microfluidic flow reactors. The methodology developed here provides a general framework to construct dissipative, tunable and robust (bio)chemical reaction networks.
Modeling dual-scale epidemic dynamics on complex networks with reaction diffusion processes
Institute of Scientific and Technical Information of China (English)
Xiao-gang JIN; Yong MIN
2014-01-01
The frequent outbreak of severe foodborne diseases (e.g., haemolytic uraemic syndrome and Listeriosis) in 2011 warns of a potential threat that world trade could spread fatal pathogens (e.g., enterohemorrhagic Escherichia coli). The epidemic potential from trade involves both intra-proliferation and inter-diffusion. Here, we present a worldwide vegetable trade network and a stochastic computational model to simulate global trade-mediated epidemics by considering the weighted nodes and edges of the network and the dual-scale dynamics of epidemics. We address two basic issues of network structural impact in global epi-demic patterns:(1) in contrast to the prediction of heterogeneous network models, the broad variability of node degree and edge weights of the vegetable trade network do not determine the threshold of global epidemics;(2) a‘penetration effect’, by which community structures do not restrict propagation at the global scale, quickly facilitates bridging the edges between communities, and leads to synchronized diffusion throughout the entire network. We have also defined an appropriate metric that combines dual-scale behavior and enables quantification of the critical role of bridging edges in disease diffusion from widespread trading. The unusual structure mechanisms of the trade network model may be useful in producing strategies for adaptive immunity and reducing international trade frictions.
Farazdaghi, Hadi
2011-02-01
Photosynthesis is the origin of oxygenic life on the planet, and its models are the core of all models of plant biology, agriculture, environmental quality and global climate change. A theory is presented here, based on single process biochemical reactions of Rubisco, recognizing that: In the light, Rubisco activase helps separate Rubisco from the stored ribulose-1,5-bisphosphate (RuBP), activates Rubisco with carbamylation and addition of Mg²(+), and then produces two products, in two steps: (Step 1) Reaction of Rubisco with RuBP produces a Rubisco-enediol complex, which is the carboxylase-oxygenase enzyme (Enco) and (Step 2) Enco captures CO₂ and/or O₂ and produces intermediate products leading to production and release of 3-phosphoglycerate (PGA) and Rubisco. PGA interactively controls (1) the carboxylation-oxygenation, (2) electron transport, and (3) triosephosphate pathway of the Calvin-Benson cycle that leads to the release of glucose and regeneration of RuBP. Initially, the total enzyme participates in the two steps of the reaction transitionally and its rate follows Michaelis-Menten kinetics. But, for a continuous steady state, Rubisco must be divided into two concurrently active segments for the two steps. This causes a deviation of the steady state from the transitional rate. Kinetic models are developed that integrate the transitional and the steady state reactions. They are tested and successfully validated with verifiable experimental data. The single-process theory is compared to the widely used two-process theory of Farquhar et al. (1980. Planta 149, 78-90), which assumes that the carboxylation rate is either Rubisco-limited at low CO₂ levels such as CO₂ compensation point, or RuBP regeneration-limited at high CO₂. Since the photosynthesis rate cannot increase beyond the two-process theory's Rubisco limit at the CO₂ compensation point, net photosynthesis cannot increase above zero in daylight, and since there is always respiration at
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...
Pattern formation in a two-component reaction-diffusion system with delayed processes on a network
Petit, Julien; Asllani, Malbor; Fanelli, Duccio; Lauwens, Ben; Carletti, Timoteo
2016-11-01
Reaction-diffusion systems with time-delay defined on complex networks have been studied in the framework of the emergence of Turing instabilities. The use of the Lambert W-function allowed us to get explicit analytic conditions for the onset of patterns as a function of the main involved parameters, the time-delay, the network topology and the diffusion coefficients. Depending on these parameters, the analysis predicts whether the system will evolve towards a stationary Turing pattern or rather to a wave pattern associated to a Hopf bifurcation. The possible outcomes of the linear analysis overcome the respective limitations of the single-species case with delay, and that of the classical activator-inhibitor variant without delay. Numerical results gained from the Mimura-Murray model support the theoretical approach.
DEFF Research Database (Denmark)
Müller, Stefan; Feliu, Elisenda; Regensburger, Georg;
2016-01-01
We give necessary and sufficient conditions in terms of sign vectors for the injectivity of families of polynomials maps with arbitrary real exponents defined on the positive orthant. Our work relates and extends existing injectivity conditions expressed in terms of Jacobian matrices and determin...... and determinants. In the context of chemical reaction networks with power-law kinetics, our results can be used to preclude as well as to guarantee multiple positive steady states. In the context of real algebraic geometry, our results reveal the first ...
Moya, A A
2016-02-01
The Gerischer impedance, i.e., the diffusion-reaction impedance of an ionic species in semi-infinite space, has been modelled by means of a novel simple equivalent ladder electric circuit constituted by a finite number of resistors and capacitors, which corresponds to the Cauer structure obtained from development into continued fractions. The Nyquist plots of the impedance of the ladder network or Cauer circuit and the deviation with respect to the Gerischer impedance have been originally analysed as a function of the number of circuit elements. From the Cauer equivalent circuit, a new and simple expression modelling the Gerischer impedance at the limit of the lowest frequencies has been derived.
Tan, Qiang; Du, Chunyu; Sun, Yongrong; Du, Lei; Yin, Geping; Gao, Yunzhi
2015-08-01
A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique interconnected one-dimensional core-sheath structure is revealed to facilitate immobilization of the metal catalysts, leading to the improved durability. This core-sheath nanowire network opens up a new strategy for catalyst performance optimization for next-generation fuel cells.A novel palladium-doped ceria and carbon core-sheath nanowire network (Pd-CeO2@C CSNWN) is synthesized by a template-free and surfactant-free solvothermal process, followed by high temperature carbonization. This hierarchical network serves as a new class of catalyst support to enhance the activity and durability of noble metal catalysts for alcohol oxidation reactions. Its supported Pd nanoparticles, Pd/(Pd-CeO2@C CSNWN), exhibit >9 fold increase in activity toward the ethanol oxidation over the state-of-the-art Pd/C catalyst, which is the highest among the reported Pd systems. Moreover, stability tests show a virtually unchanged activity after 1000 cycles. The high activity is mainly attributed to the superior oxygen-species releasing capability of Pd-doped CeO2 nanowires by accelerating the removal of the poisoning intermediate. The unique
Preclusion of switch behavior in reaction networks with mass-action kinetics
DEFF Research Database (Denmark)
Feliu, Elisenda; Wiuf, C.
2012-01-01
consists of components of the species formation rate function and a maximal set of independent conservation laws. The determinant of the function is a polynomial in the species concentrations and the rate constants (linear in the latter) and its coefficients are fully determined. The criterion also......We study networks taken with mass-action kinetics and provide a Jacobian criterion that applies to an arbitrary network to preclude the existence of multiple positive steady states within any stoichiometric class for any choice of rate constants. We are concerned with the characterization...
Welsch, Ralph; Manthe, Uwe
2015-02-01
Initial state-selected reaction probabilities of the H + CH4 → H2 + CH3 reaction are calculated in full and reduced dimensionality on a recent neural network potential [X. Xu, J. Chen, and D. H. Zhang, Chin. J. Chem. Phys. 27, 373 (2014)]. The quantum dynamics calculation employs the quantum transition state concept and the multi-layer multi-configurational time-dependent Hartree approach and rigorously studies the reaction for vanishing total angular momentum (J = 0). The calculations investigate the accuracy of the neutral network potential and study the effect resulting from a reduced-dimensional treatment. Very good agreement is found between the present results obtained on the neural network potential and previous results obtained on a Shepard interpolated potential energy surface. The reduced-dimensional calculations only consider motion in eight degrees of freedom and retain the C3v symmetry of the methyl fragment. Considering reaction starting from the vibrational ground state of methane, the reaction probabilities calculated in reduced dimensionality are moderately shifted in energy compared to the full-dimensional ones but otherwise agree rather well. Similar agreement is also found if reaction probabilities averaged over similar types of vibrational excitation of the methane reactant are considered. In contrast, significant differences between reduced and full-dimensional results are found for reaction probabilities starting specifically from symmetric stretching, asymmetric (f2-symmetric) stretching, or e-symmetric bending excited states of methane.
Mlynarczyk, Paul J.; Pullen, Robert H.; Abel, Steven M.
2016-01-01
Positive feedback is a common feature in signal transduction networks and can lead to phenomena such as bistability and signal propagation by domain growth. Physical features of the cellular environment, such as spatial confinement and the mobility of proteins, play important but inadequately understood roles in shaping the behavior of signaling networks. Here, we use stochastic, spatially resolved kinetic Monte Carlo simulations to explore a positive feedback network as a function of system size, system shape, and mobility of molecules. We show that these physical properties can markedly alter characteristics of bistability and stochastic switching when compared with well-mixed simulations. Notably, systems of equal volume but different shapes can exhibit qualitatively different behaviors under otherwise identical conditions. We show that stochastic switching to a state maintained by positive feedback occurs by cluster formation and growth. Additionally, the frequency at which switching occurs depends nontrivially on the diffusion coefficient, which can promote or suppress switching relative to the well-mixed limit. Taken together, the results provide a framework for understanding how confinement and protein mobility influence emergent features of the positive feedback network by modulating molecular concentrations, diffusion-influenced rate parameters, and spatiotemporal correlations between molecules.
Effects of macromolecular crowding on genetic networks.
Morelli, Marco J; Allen, Rosalind J; Wolde, Pieter Rein ten
2011-12-21
The intracellular environment is crowded with proteins, DNA, and other macromolecules. Under physiological conditions, macromolecular crowding can alter both molecular diffusion and the equilibria of bimolecular reactions and therefore is likely to have a significant effect on the function of biochemical networks. We propose a simple way to model the effects of macromolecular crowding on biochemical networks via an appropriate scaling of bimolecular association and dissociation rates. We use this approach, in combination with kinetic Monte Carlo simulations, to analyze the effects of crowding on a constitutively expressed gene, a repressed gene, and a model for the bacteriophage λ genetic switch, in the presence and absence of nonspecific binding of transcription factors to genomic DNA. Our results show that the effects of crowding are mainly caused by the shift of association-dissociation equilibria rather than the slowing down of protein diffusion, and that macromolecular crowding can have relevant and counterintuitive effects on biochemical network performance.
DEFF Research Database (Denmark)
Baldi, P.; Blanke, Mogens; Castaldi, P.
2015-01-01
This paper suggests a novel diagnosis scheme for detection, isolation and estimation of faults affecting satellite reaction wheels. Both spin rate measurements and actuation torque defects are dealt with. The proposed system consists of a fault detection and isolation module composed by a bank...
High-resolution mapping of bifurcations in nonlinear biochemical circuits
Genot, A. J.; Baccouche, A.; Sieskind, R.; Aubert-Kato, N.; Bredeche, N.; Bartolo, J. F.; Taly, V.; Fujii, T.; Rondelez, Y.
2016-08-01
Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.
Statistical physics approaches to subnetwork dynamics in biochemical systems
Bravi, Barbara
2016-01-01
We apply a Gaussian variational approximation to model reduction in large biochemical networks of unary and binary reactions. We focus on a small subset of variables (subnetwork) of interest, e.g. because they are accessible experimentally, embedded in a larger network (bulk). The key goal is to write dynamical equations reduced to the subnetwork but still retaining the effects of the bulk. As a result, the subnetwork-reduced dynamics contains a memory term and an extrinsic noise term with non-trivial temporal correlations. We first derive expressions for this memory and noise in the linearized (Gaussian) dynamics and then use a perturbative power expansion to obtain first order nonlinear corrections. For the case of vanishing intrinsic noise, our description is explicitly shown to be equivalent to projection methods up to quadratic terms, but it is applicable also in the presence of stochastic fluctuations in the original dynamics. An example from the Epidermal Growth Factor Receptor (EGFR) signalling pathwa...
High-resolution mapping of bifurcations in nonlinear biochemical circuits.
Genot, A J; Baccouche, A; Sieskind, R; Aubert-Kato, N; Bredeche, N; Bartolo, J F; Taly, V; Fujii, T; Rondelez, Y
2016-08-01
Analog molecular circuits can exploit the nonlinear nature of biochemical reaction networks to compute low-precision outputs with fewer resources than digital circuits. This analog computation is similar to that employed by gene-regulation networks. Although digital systems have a tractable link between structure and function, the nonlinear and continuous nature of analog circuits yields an intricate functional landscape, which makes their design counter-intuitive, their characterization laborious and their analysis delicate. Here, using droplet-based microfluidics, we map with high resolution and dimensionality the bifurcation diagrams of two synthetic, out-of-equilibrium and nonlinear programs: a bistable DNA switch and a predator-prey DNA oscillator. The diagrams delineate where function is optimal, dynamics bifurcates and models fail. Inverse problem solving on these large-scale data sets indicates interference from enzymatic coupling. Additionally, data mining exposes the presence of rare, stochastically bursting oscillators near deterministic bifurcations.
Xu, Chao; Yu, Le; Zhu, Chaoyuan; Yu, Jianguo
2015-10-22
6SA-CASSCF(10, 10) /6-31G (d, p) and MRCI/cc-pVDZ methods were performed to probe photoisomerization reaction mechanisms of o-nitrophenol. Two low-lying singlet electronic states (S0 and S1) and two low-lying triplet electronic states (T1 and T2) were found to weave an intersystem crossing network in which a dominant stepwise photoisomerization provides a very efficient reaction pathway; the reaction takes place in the wide region of crossing seam-surface woven by S1 and T1 states first, followed by T1 and S0 states. Both intersystem crossing regions show strong spin-orbital coupling in the order of 40 wavenumbers. All nitro and aci-nitro isomers and transition states on four electronic potential energy surfaces are calculated along with analysis of both dominant and subdominant relaxation pathways, especially weak spin-orbital coupling (∼10 wavenumbers) between T2 and S1 states and effective conical intersection between T2 and T1 states opening a new relaxation pathway S1 → T2→ T1.
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S. Rafajlović
2009-06-01
Full Text Available The goal of this study was to test the applicability of accelerometer as the sensor for assessment of the walking. We present here the comparison of gait phases detected from the data recorded by force sensing resistors mounted in the shoe insoles, non-processed acceleration and processed acceleration perpendicular to the direction of the foot. The gait phases in all three cases were detected by means of a neural network. The output from the neural network was the gait phase, while the inputs were data from the sensors. The results show that the errors were in the ranges: 30 ms (2.7% – force sensors; 150 ms (13.6% – nonprocessed acceleration, and 120 ms (11% – processed acceleration data. This result suggests that it is possible to use the accelerometer as the gait phase detector, however, with the knowledge that the gait phases are time shifted for about 100 ms with respect the neural network predicted times.
A computational model for the identification of biochemical pathways in the krebs cycle.
Oliveira, Joseph S; Bailey, Colin G; Jones-Oliveira, Janet B; Dixon, David A; Gull, Dean W; Chandler, Mary L
2003-01-01
We have applied an algorithmic methodology which provably decomposes any complex network into a complete family of principal subcircuits to study the minimal circuits that describe the Krebs cycle. Every operational behavior that the network is capable of exhibiting can be represented by some combination of these principal subcircuits and this computational decomposition is linearly efficient. We have developed a computational model that can be applied to biochemical reaction systems which accurately renders pathways of such reactions via directed hypergraphs (Petri nets). We have applied the model to the citric acid cycle (Krebs cycle). The Krebs cycle, which oxidizes the acetyl group of acetyl CoA to CO(2) and reduces NAD and FAD to NADH and FADH(2), is a complex interacting set of nine subreaction networks. The Krebs cycle was selected because of its familiarity to the biological community and because it exhibits enough complexity to be interesting in order to introduce this novel analytic approach. This study validates the algorithmic methodology for the identification of significant biochemical signaling subcircuits, based solely upon the mathematical model and not upon prior biological knowledge. The utility of the algebraic-combinatorial model for identifying the complete set of biochemical subcircuits as a data set is demonstrated for this important metabolic process.
Energy Technology Data Exchange (ETDEWEB)
Bonnet, J.; Bounor-Legare, V.; Alcouffe, P. [Universite de Lyon, 69003 Lyon (France); Universite de Lyon 1, CNRS UMR5223, Ingenierie des Materiaux Polymeres, 15 Boulevard Latarjet, F-69622 Villeurbanne (France); Cassagnau, P., E-mail: philippe.cassagnau@univ-lyon1.fr [Universite de Lyon, 69003 Lyon (France); Universite de Lyon 1, CNRS UMR5223, Ingenierie des Materiaux Polymeres, 15 Boulevard Latarjet, F-69622 Villeurbanne (France)
2012-10-15
A new and original method based on carbonyl hydrosilylation was developed to prepare ethylene-vinyl acetate (EVA)/polysiloxane polymer blends. This focused on the addition of hydrogenosilane groups (SiH) from polysiloxane to the carbonyl groups of EVA. The influence of the nature of the polysiloxane on blend properties was investigated by rheology and scanning electron microscopy. Mixing of a low viscosity polysiloxane with a high viscosity EVA matrix produced a two-phase morphology. The occurrence of the hydrosilylation reaction at the EVA/polysiloxane interface promoted a homogenisation of the blend depending on the molar ratio SiH/vinyl acetate groups, [SiH]/[VA], and the viscosity ratio of the blend. Two distinct behaviours were observed. The formation of a crosslinked network under shear was obtained for a low viscosity ratio between polysiloxane and EVA ({lambda}{sub polysiloxane/EVA} = 4.0 Multiplication-Sign 10{sup -6}) with a high concentration of SiH groups ([SiH]/[VA] = 0.5), while the formation of a compatibilised blend was observed for high molar mass polysiloxanes (Mn > 15,000 g mol{sup -1}) with a low concentration of SiH ([SiH]/[VA] < 4.0 Multiplication-Sign 10{sup -3}). -- Highlights: Black-Right-Pointing-Pointer Carbonyl hydrosilylation reaction was found to enhance EVA/polysiloxane immiscible blends. Black-Right-Pointing-Pointer EVA crosslinking was obtained with a low molar mass polysiloxane. Black-Right-Pointing-Pointer EVA compatibilisation was obtained with a high molar mass polysiloxane. Black-Right-Pointing-Pointer Shear rate was found to improve the hydrosilylation reaction at the interface. Black-Right-Pointing-Pointer A two-phase morphology of the blends was observed after reaction with fine polysiloxane nodules.
Allison, Thomas C
2016-03-03
Rate constants for reactions of chemical compounds with hydroxyl radical are a key quantity used in evaluating the global warming potential of a substance. Experimental determination of these rate constants is essential, but it can also be difficult and time-consuming to produce. High-level quantum chemistry predictions of the rate constant can suffer from the same issues. Therefore, it is valuable to devise estimation schemes that can give reasonable results on a variety of chemical compounds. In this article, the construction and training of an artificial neural network (ANN) for the prediction of rate constants at 298 K for reactions of hydroxyl radical with a diverse set of molecules is described. Input to the ANN consists of counts of the chemical bonds and bends present in the target molecule. The ANN is trained using 792 (•)OH reaction rate constants taken from the NIST Chemical Kinetics Database. The mean unsigned percent error (MUPE) for the training set is 12%, and the MUPE of the testing set is 51%. It is shown that the present methodology yields rate constants of reasonable accuracy for a diverse set of inputs. The results are compared to high-quality literature values and to another estimation scheme. This ANN methodology is expected to be of use in a wide range of applications for which (•)OH reaction rate constants are required. The model uses only information that can be gathered from a 2D representation of the molecule, making the present approach particularly appealing, especially for screening applications.
Hamann, Heiko; Schmickl, Thomas; Crailsheim, Karl
2010-01-01
The semi-automatic or automatic synthesis of robot controller software is both desirable and challenging. Synthesis of rather simple behaviors such as collision avoidance by applying artificial evolution has been shown multiple times. However, the difficulty of this synthesis increases heavily with increasing complexity of the task that should be performed by the robot. We try to tackle this problem of complexity with Artificial Homeostatic Hormone Systems (AHHS), which provide both intrinsic, homeostatic processes and (transient) intrinsic, variant behavior. By using AHHS the need for pre-defined controller topologies or information about the field of application is minimized. We investigate how the principle design of the controller and the hormone network size affects the overall performance of the artificial evolution (i.e., evolvability). This is done by comparing two variants of AHHS that show different effects when mutated. We evolve a controller for a robot built from five autonomous, cooperating modu...
Qin, Chao-Zhong; Hassanizadeh, S. Majid; Ebigbo, Anozie
2016-11-01
The engineering of microbially induced calcium carbonate precipitation (MICP) has attracted much attention in a number of applications, such as sealing of CO2 leakage pathways, soil stabilization, and subsurface remediation of radionuclides and toxic metals. The goal of this work is to gain insight into pore-scale processes of MICP and scale dependence of biogeochemical reaction rates. This will help us develop efficient field-scale MICP models. In this work, we have developed a comprehensive pore-network model for MICP, with geochemical speciation calculated by the open-source PHREEQC module. A numerical pseudo-3-D micromodel as the computational domain was generated by a novel pore-network generation method. We modeled a three-stage process in the engineering of MICP including the growth of biofilm, the injection of calcium-rich medium, and the precipitation of calcium carbonate. A number of test cases were conducted to illustrate how calcite precipitation was influenced by different operating conditions. In addition, we studied the possibility of reducing the computational effort by simplifying geochemical calculations. Finally, the effect of mass transfer limitation of possible carbonate ions in a pore element on calcite precipitation was explored.
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Zixiang Xu
Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.
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Gallo Giuseppe
2012-11-01
Full Text Available Abstract Background A bacterial strain previously isolated from pyrite mine drainage and named BAS-10 was tentatively identified as Klebsiella oxytoca. Unlikely other enterobacteria, BAS-10 is able to grow on Fe(III-citrate as sole carbon and energy source, yielding acetic acid and CO2 coupled with Fe(III reduction to Fe(II and showing unusual physiological characteristics. In fact, under this growth condition, BAS-10 produces an exopolysaccharide (EPS having a high rhamnose content and metal-binding properties, whose biotechnological applications were proven as very relevant. Results Further phylogenetic analysis, based on 16S rDNA sequence, definitively confirmed that BAS-10 belongs to K. oxytoca species. In order to rationalize the biochemical peculiarities of this unusual enterobacteriun, combined 2D-Differential Gel Electrophoresis (2D-DIGE analysis and mass spectrometry procedures were used to investigate its proteomic changes: i under aerobic or anaerobic cultivation with Fe(III-citrate as sole carbon source; ii under anaerobic cultivations using Na(I-citrate or Fe(III-citrate as sole carbon source. Combining data from these differential studies peculiar levels of outer membrane proteins, key regulatory factors of carbon and nitrogen metabolism and enzymes involved in TCA cycle and sugar biosynthesis or required for citrate fermentation and stress response during anaerobic growth on Fe(III-citrate were revealed. The protein differential regulation seems to ensure efficient cell growth coupled with EPS production by adapting metabolic and biochemical processes in order to face iron toxicity and to optimize energy production. Conclusion Differential proteomics provided insights on the molecular mechanisms necessary for anaeorobic utilization of Fe(III-citrate in a biotechnologically promising enterobacteriun, also revealing genes that can be targeted for the rational design of high-yielding EPS producer strains.
Model building and model checking for biochemical processes.
Antoniotti, Marco; Policriti, Alberto; Ugel, Nadia; Mishra, Bud
2003-01-01
A central claim of computational systems biology is that, by drawing on mathematical approaches developed in the context of dynamic systems, kinetic analysis, computational theory and logic, it is possible to create powerful simulation, analysis, and reasoning tools for working biologists to decipher existing data, devise new experiments, and ultimately to understand functional properties of genomes, proteomes, cells, organs, and organisms. In this article, a novel computational tool is described that achieves many of the goals of this new discipline. The novelty of this system involves an automaton-based semantics of the temporal evolution of complex biochemical reactions starting from the representation given as a set of differential equations. The related tools also provide ability to qualitatively reason about the systems using a propositional temporal logic that can express an ordered sequence of events succinctly and unambiguously. The implementation of mathematical and computational models in the Simpathica and XSSYS systems is described briefly. Several example applications of these systems to cellular and biochemical processes are presented: the two most prominent are Leibler et al.'s repressilator (an artificial synthesized oscillatory network), and Curto- Voit-Sorribas-Cascante's purine metabolism reaction model.
Cotter, Simon L.
2016-10-01
Efficient analysis and simulation of multiscale stochastic systems of chemical kinetics is an ongoing area for research, and is the source of many theoretical and computational challenges. In this paper, we present a significant improvement to the constrained approach, which is a method for computing effective dynamics of slowly changing quantities in these systems, but which does not rely on the quasi-steady-state assumption (QSSA). The QSSA can cause errors in the estimation of effective dynamics for systems where the difference in timescales between the "fast" and "slow" variables is not so pronounced. This new application of the constrained approach allows us to compute the effective generator of the slow variables, without the need for expensive stochastic simulations. This is achieved by finding the null space of the generator of the constrained system. For complex systems where this is not possible, or where the constrained subsystem is itself multiscale, the constrained approach can then be applied iteratively. This results in breaking the problem down into finding the solutions to many small eigenvalue problems, which can be efficiently solved using standard methods. Since this methodology does not rely on the quasi steady-state assumption, the effective dynamics that are approximated are highly accurate, and in the case of systems with only monomolecular reactions, are exact. We will demonstrate this with some numerics, and also use the effective generators to sample paths of the slow variables which are conditioned on their endpoints, a task which would be computationally intractable for the generator of the full system.
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Urassa Willy
2009-08-01
Full Text Available Abstract Background Reference values for hematological and biochemical assays in pregnant women and in newborn infants are based primarily on Caucasian populations. Normative data are limited for populations in sub-Saharan Africa, especially comparing women with and without HIV infection, and comparing infants with and without HIV infection or HIV exposure. Methods We determined HIV status and selected hematological and biochemical measurements in women at 20–24 weeks and at 36 weeks gestation, and in infants at birth and 4–6 weeks of age. All were recruited within a randomized clinical trial of antibiotics to prevent chorioamnionitis-associated mother-to-child transmission of HIV (HPTN024. We report nearly complete laboratory data on 2,292 HIV-infected and 367 HIV-uninfected pregnant African women who were representative of the public clinics from which the women were recruited. Nearly all the HIV-infected mothers received nevirapine prophylaxis at the time of labor, as did their infants after birth (always within 72 hours of birth, but typically within just a few hours at the four study sites in Malawi (2 sites, Tanzania, and Zambia. Results HIV-infected pregnant women had lower red blood cell counts, hemoglobin, hematocrit, and white blood cell counts than HIV-uninfected women. Platelet and monocyte counts were higher among HIV-infected women at both time points. At the 4–6-week visit, HIV-infected infants had lower hemoglobin, hematocrit and white blood cell counts than uninfected infants. Platelet counts were lower in HIV-infected infants than HIV-uninfected infants, both at birth and at 4–6 weeks of age. At 4–6 weeks, HIV-infected infants had higher alanine aminotransferase measures than uninfected infants. Conclusion Normative data in pregnant African women and their newborn infants are needed to guide the large-scale HIV care and treatment programs being scaled up throughout the continent. These laboratory measures will help
Wind-driven gas networks and star formation in galaxies: reaction-advection hydrodynamic simulations
Chappell, David; Scalo, John
2001-07-01
The effects of wind-driven star formation feedback on the spatio-temporal organization of stars and gas in galaxies is studied using two-dimensional intermediate-representational quasi-hydrodynamical simulations. The model retains only a reduced subset of the physics, including mass and momentum conservation, fully non-linear fluid advection, inelastic macroscopic interactions, threshold star formation, and momentum forcing by winds from young star clusters on the surrounding gas. Expanding shells of swept-up gas evolve through the action of fluid advection to form a `turbulent' network of interacting shell fragments which have the overall appearance of a web of filaments (in two dimensions). A new star cluster is formed whenever the column density through a filament exceeds a critical threshold based on the gravitational instability criterion for an expanding shell, which then generates a new expanding shell after some time delay. A filament-finding algorithm is developed to locate the potential sites of new star formation. The major result is the dominance of multiple interactions between advectively distorted shells in controlling the gas and star morphology, gas velocity distribution and mass spectrum of high mass density peaks, and the global star formation history. The gas morphology strongly resembles the model envisioned by Norman & Silk, and observations of gas in the Large Magellanic Cloud (LMC)Q1 and local molecular clouds. The dependence of the frequency distribution of present-to-past average global star formation rate on a number of parameters is investigated. Bursts of star formation only occur when the time-averaged star formation rate per unit area is low, or the system is small. Percolation does not play a role. The broad distribution observed in late-type galaxies can be understood as a result of either small size or small metallicity, resulting in larger shell column densities required for gravitational instability. The star formation rate
Robustness analysis of stochastic biochemical systems.
Ceska, Milan; Safránek, David; Dražan, Sven; Brim, Luboš
2014-01-01
We propose a new framework for rigorous robustness analysis of stochastic biochemical systems that is based on probabilistic model checking techniques. We adapt the general definition of robustness introduced by Kitano to the class of stochastic systems modelled as continuous time Markov Chains in order to extensively analyse and compare robustness of biological models with uncertain parameters. The framework utilises novel computational methods that enable to effectively evaluate the robustness of models with respect to quantitative temporal properties and parameters such as reaction rate constants and initial conditions. We have applied the framework to gene regulation as an example of a central biological mechanism where intrinsic and extrinsic stochasticity plays crucial role due to low numbers of DNA and RNA molecules. Using our methods we have obtained a comprehensive and precise analysis of stochastic dynamics under parameter uncertainty. Furthermore, we apply our framework to compare several variants of two-component signalling networks from the perspective of robustness with respect to intrinsic noise caused by low populations of signalling components. We have successfully extended previous studies performed on deterministic models (ODE) and showed that stochasticity may significantly affect obtained predictions. Our case studies demonstrate that the framework can provide deeper insight into the role of key parameters in maintaining the system functionality and thus it significantly contributes to formal methods in computational systems biology.
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M. Poor Arab Moghadam
2015-12-01
Full Text Available Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher - and its computational costs were lower - than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving
Poor Arab Moghadam, M.; Pahlavani, P.
2015-12-01
Car following models as well-known moving objects trajectory problems have been used for more than half a century in all traffic simulation software for describing driving behaviour in traffic flows. However, previous empirical studies and modeling about car following behavior had some important limitations. One of the main and clear defects of the introduced models was the very large number of parameters that made their calibration very time-consuming and costly. Also, any change in these parameters, even slight ones, severely disrupted the output. In this study, an artificial neural network approximator was used to introduce a trajectory model for vehicle movements. In this regard, the Levenberg-Marquardt back propagation function and the hyperbolic tangent sigmoid function were employed as the training and the transfer functions, respectively. One of the important aspects in identifying driver behavior is the reaction time. This parameter shows the period between the time the driver recognizes a stimulus and the time a suitable response is shown to that stimulus. In this paper, the actual data on car following from the NGSIM project was used to determine the performance of the proposed model. This dataset was used for the purpose of expanding behavioral algorithm in micro simulation. Sixty percent of the data was entered into the designed artificial neural network approximator as the training data, twenty percent as the testing data, and twenty percent as the evaluation data. A statistical and a micro simulation method were employed to show the accuracy of the proposed model. Moreover, the two popular Gipps and Helly models were implemented. Finally, it was shown that the accuracy of the proposed model was much higher - and its computational costs were lower - than those of other models when calibration operations were not performed on these models. Therefore, the proposed model can be used for displaying and predicting trajectories of moving objects being
eQuilibrator--the biochemical thermodynamics calculator.
Flamholz, Avi; Noor, Elad; Bar-Even, Arren; Milo, Ron
2012-01-01
The laws of thermodynamics constrain the action of biochemical systems. However, thermodynamic data on biochemical compounds can be difficult to find and is cumbersome to perform calculations with manually. Even simple thermodynamic questions like 'how much Gibbs energy is released by ATP hydrolysis at pH 5?' are complicated excessively by the search for accurate data. To address this problem, eQuilibrator couples a comprehensive and accurate database of thermodynamic properties of biochemical compounds and reactions with a simple and powerful online search and calculation interface. The web interface to eQuilibrator (http://equilibrator.weizmann.ac.il) enables easy calculation of Gibbs energies of compounds and reactions given arbitrary pH, ionic strength and metabolite concentrations. The eQuilibrator code is open-source and all thermodynamic source data are freely downloadable in standard formats. Here we describe the database characteristics and implementation and demonstrate its use.
Duardo-Sánchez, Aliuska; Munteanu, Cristian R; Riera-Fernández, Pablo; López-Díaz, Antonio; Pazos, Alejandro; González-Díaz, Humberto
2014-01-27
The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein interactions, or metabolic networks. However, the applications are not restricted to the world of molecules and can be extended to the study of macroscopic nonliving systems, organisms, or even legal or social networks. On the other hand, the development of the field of Artificial Intelligence has led to the formulation of computational algorithms whose design is based on the structure and functioning of networks of biological neurons. These algorithms, called Artificial Neural Networks (ANNs), can be useful for the study of complex networks, since the numerical parameters that encode information of the network (for example centralities/node descriptors) can be used as inputs for the ANNs. The Wiener index (W) is a graph invariant widely used in chemoinformatics to quantify the molecular structure of drugs and to study complex networks. In this work, we explore for the first time the possibility of using Markov chains to calculate analogues of node distance numbers/W to describe complex networks from the point of view of their nodes. These parameters are called Markov-Wiener node descriptors of order k(th) (W(k)). Please, note that these descriptors are not related to Markov-Wiener stochastic processes. Here, we calculated the W(k)(i) values for a very high number of nodes (>100,000) in more than 100 different complex networks using the software MI-NODES. These networks were grouped according to the field of application. Molecular networks include the Metabolic Reaction Networks (MRNs) of 40 different organisms. In addition, we analyzed other biological and legal and social networks. These include the Interaction Web Database Biological Networks (IWDBNs), with 75 food webs or ecological systems and the Spanish Financial Law Network (SFLN). The calculated W
Chevalier, Michael W.; El-Samad, Hana
2014-12-01
Noise and stochasticity are fundamental to biology and derive from the very nature of biochemical reactions where thermal motion of molecules translates into randomness in the sequence and timing of reactions. This randomness leads to cell-to-cell variability even in clonal populations. Stochastic biochemical networks have been traditionally modeled as continuous-time discrete-state Markov processes whose probability density functions evolve according to a chemical master equation (CME). In diffusion reaction systems on membranes, the Markov formalism, which assumes constant reaction propensities is not directly appropriate. This is because the instantaneous propensity for a diffusion reaction to occur depends on the creation times of the molecules involved. In this work, we develop a chemical master equation for systems of this type. While this new CME is computationally intractable, we make rational dimensional reductions to form an approximate equation, whose moments are also derived and are shown to yield efficient, accurate results. This new framework forms a more general approach than the Markov CME and expands upon the realm of possible stochastic biochemical systems that can be efficiently modeled.
Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido
2011-01-28
Stochastic effects from correlated noise non-trivially modulate the kinetics of non-linear chemical reaction networks. This is especially important in systems where reactions are confined to small volumes and reactants are delivered in bursts. We characterise how the two noise sources confinement and burst modulate the relaxation kinetics of a non-linear reaction network around a non-equilibrium steady state. We find that the lifetimes of species change with burst input and confinement. Confinement increases the lifetimes of all species that are involved in any non-linear reaction as a reactant. Burst monotonically increases or decreases lifetimes. Competition between burst-induced and confinement-induced modulation may hence lead to a non-monotonic modulation. We quantify lifetime as the integral of the time autocorrelation function (ACF) of concentration fluctuations around a non-equilibrium steady state of the reaction network. Furthermore, we look at the first and second derivatives of the ACF, each of which is affected in opposite ways by burst and confinement. This allows discriminating between these two noise sources. We analytically derive the ACF from the linear Fokker-Planck approximation of the chemical master equation in order to establish a baseline for the burst-induced modulation at low confinement. Effects of higher confinement are then studied using a partial-propensity stochastic simulation algorithm. The results presented here may help understand the mechanisms that deviate stochastic kinetics from its deterministic counterpart. In addition, they may be instrumental when using fluorescence-lifetime imaging microscopy (FLIM) or fluorescence-correlation spectroscopy (FCS) to measure confinement and burst in systems with known reaction rates, or, alternatively, to correct for the effects of confinement and burst when experimentally measuring reaction rates.
Mitochondrial network energetics in the heart.
Aon, Miguel A; Cortassa, Sonia
2012-01-01
At the core of eukaryotic aerobic life, mitochondrial function like 'hubs' in the web of energetic and redox processes in cells. In the heart, these networks-extending beyond the complex connectivity of biochemical circuit diagrams and apparent morphology-exhibit collective dynamics spanning several spatiotemporal levels of organization, from the cell, to the tissue, and the organ. The network function of mitochondria, i.e., mitochondrial network energetics, represents an advantageous behavior. Its coordinated action, under normal physiology, provides robustness despite failure in a few nodes, and improves energy supply toward a swiftly changing demand. Extensive diffuse loops, encompassing mitochondrial-cytoplasmic reaction/transport networks, control and regulate energy supply and demand in the heart. Under severe energy crises, the network behavior of mitochondria and associated glycolytic and other metabolic networks collapse, thereby triggering fatal arrhythmias.
Walker, Joan Marie
Nanoparticles of gold and iron oxide not only possess remarkable optical and magnetic properties, respectively, but are also capable of influencing their local environment with an astounding degree of precision. Using nanoparticles to direct the reactivity of organic molecules near their surface provides a unique method of spatial and temporal control. Enediynes represent an exceptional class of compounds that are thermally reactive to produce a diradical intermediate via Bergman cycloaromatization. While natural product enediynes are famously cytotoxic, a rich chemistry of synthetic enediynes has developed utilizing creative means to control this reactivity through structure, electronics, metal chelation, and external triggering mechanisms. In a heretofore unexplored arena for Bergman cyclization, we have investigated the reactivity of enediynes in connection with inorganic nanoparticles in which the physical properties of the nanomaterial are directly excited to thermally promote aromatization. As the first example of this methodology, gold nanoparticles conjugated with (Z)-octa-4-en-2,6-diyne-1,8-dithiol were excited with 514 nm laser irradiation. The formation of aromatic and polymeric products was confirmed through Raman spectroscopy and electron microscopy. Water soluble analogues Au-PEG-EDDA and Fe3O4-PEG-EDDA (EDDA = (Z)-octa-4-en-2,6-diyne-1,8-diamine) show similar reactivity under laser irradiation or alternating magnetic field excitation, respectively. Furthermore, we have used these functionalized nanoparticles to attack proteinaceous substrates including fibrin and extracellular matrix proteins, capitalizing on the ability of diradicals to disrupt peptidic bonds. By delivering a locally high payload of reactive molecules and thermal energy to the large biopolymer, network restructuring and collapse is achieved. As a synthetic extension towards multifunctional nanoparticles, noble metal seed-decorated iron oxides have also been prepared and assessed for
Efficient, sparse biological network determination
Directory of Open Access Journals (Sweden)
Papachristodoulou Antonis
2009-02-01
Full Text Available Abstract Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational and uncertainties in the data are taken into account. It also produces a network structure that can
Ping, Jianfeng; Wang, Yixian; Lu, Qipeng; Chen, Bo; Chen, Junze; Huang, Ying; Ma, Qinglang; Tan, Chaoliang; Yang, Jian; Cao, Xiehong; Wang, Zhijuan; Wu, Jian; Ying, Yibin; Zhang, Hua
2016-09-01
A non-noble metal based 3D porous electrocatalyst is prepared by self-assembly of the liquid-exfoliated single-layer CoAl-layered double hydroxide nanosheets (CoAl-NSs) onto 3D graphene network, which exhibits higher catalytic activity and better stability for electrochemical oxygen evolution reaction compared to the commercial IrO2 nanoparticle-based 3D porous electrocatalyst.
Ouroboros - Playing A Biochemical
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D. T. Rodrigues
2014-08-01
Full Text Available Ouroboros: Playing A Biochemical RODRIGUES,D.T.1,2;GAYER, M.C.1,2; ESCOTO, D.F.1; DENARDIN, E.L.G.2, ROEHRS, R.1,2 1Interdisciplinary Research Group on Teaching Practice, Graduate Program in Biochemistry, Unipampa, RS, Brazil 2Laboratory of Physicochemical Studies and Natural Products, Post Graduate Program in Biochemistry, Unipampa, RS, Brazil Introduction: Currently, teachers seek different alternatives to enhance the teaching-learning process. Innovative teaching methodologies are increasingly common tools in educational routine. The use of games, electronic or conventional, is an effective tool to assist in learning and also to raise the social interaction between students. Objective: In this sense our work aims to evaluate the card game and "Ouroboros" board as a teaching and learning tool in biochemistry for a graduating class in Natural Sciences. Materials and methods: The class gathered 22 students of BSc in Natural Sciences. Each letter contained a question across the board that was drawn to a group to answer within the allotted time. The questions related concepts of metabolism, organic and inorganic chemical reactions, bioenergetics, etc.. Before the game application, students underwent a pre-test with four issues involving the content that was being developed. Soon after, the game was applied. Then again questions were asked. Data analysis was performed from the ratio of the number of correct pre-test and post-test answers. Results and discussion: In the pre-test 18.1% of the students knew all issues, 18.1% got 3 correct answers, 40.9% answered only 2 questions correctly and 22.7% did not hit any. In post-test 45.4% answered all the questions right, 31.8% got 3 questions and 22.7% got 2 correct answers. The results show a significant improvement of the students about the field of content taught through the game. Conclusion: Generally, traditional approaches of chemistry and biochemistry are abstract and complex. Thus, through games
Biochemical abnormalities in Pearson syndrome.
Crippa, Beatrice Letizia; Leon, Eyby; Calhoun, Amy; Lowichik, Amy; Pasquali, Marzia; Longo, Nicola
2015-03-01
Pearson marrow-pancreas syndrome is a multisystem mitochondrial disorder characterized by bone marrow failure and pancreatic insufficiency. Children who survive the severe bone marrow dysfunction in childhood develop Kearns-Sayre syndrome later in life. Here we report on four new cases with this condition and define their biochemical abnormalities. Three out of four patients presented with failure to thrive, with most of them having normal development and head size. All patients had evidence of bone marrow involvement that spontaneously improved in three out of four patients. Unique findings in our patients were acute pancreatitis (one out of four), renal Fanconi syndrome (present in all patients, but symptomatic only in one), and an unusual organic aciduria with 3-hydroxyisobutyric aciduria in one patient. Biochemical analysis indicated low levels of plasma citrulline and arginine, despite low-normal ammonia levels. Regression analysis indicated a significant correlation between each intermediate of the urea cycle and the next, except between ornithine and citrulline. This suggested that the reaction catalyzed by ornithine transcarbamylase (that converts ornithine to citrulline) might not be very efficient in patients with Pearson syndrome. In view of low-normal ammonia levels, we hypothesize that ammonia and carbamylphosphate could be diverted from the urea cycle to the synthesis of nucleotides in patients with Pearson syndrome and possibly other mitochondrial disorders.
Measures of Biochemical Sociology
Snell, Joel; Marsh, Mitchell
2008-01-01
In a previous article, the authors introduced a new sub field in sociology that we labeled "biochemical sociology." We introduced the definition of a sociology that encompasses sociological measures, psychological measures, and biological indicators Snell & Marsh (2003). In this article, we want to demonstrate a research strategy that would assess…
Biochemical Education in Brazil.
Vella, F.
1988-01-01
Described are discussions held concerning the problems of biochemical education in Brazil at a meeting of the Sociedade Brazileira de Bioquimica in April 1988. Also discussed are other visits that were made to universities in Brazil. Three major recommendations to improve the state of biochemistry education in Brazil are presented. (CW)
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Bushell Michael E
2011-05-01
Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a
Forward design of a complex enzyme cascade reaction
Hold, Christoph; Billerbeck, Sonja; Panke, Sven
2016-01-01
Enzymatic reaction networks are unique in that one can operate a large number of reactions under the same set of conditions concomitantly in one pot, but the nonlinear kinetics of the enzymes and the resulting system complexity have so far defeated rational design processes for the construction of such complex cascade reactions. Here we demonstrate the forward design of an in vitro 10-membered system using enzymes from highly regulated biological processes such as glycolysis. For this, we adapt the characterization of the biochemical system to the needs of classical engineering systems theory: we combine online mass spectrometry and continuous system operation to apply standard system theory input functions and to use the detailed dynamic system responses to parameterize a model of sufficient quality for forward design. This allows the facile optimization of a 10-enzyme cascade reaction for fine chemical production purposes. PMID:27677244
Orth, Jeffrey D; Fleming, R M T; Palsson, Bernhard Ø
2010-09-01
Biochemical network reconstructions have become popular tools in systems biology. Metabolicnetwork reconstructions are biochemically, genetically, and genomically (BiGG) structured databases of biochemical reactions and metabolites. They contain information such as exact reaction stoichiometry, reaction reversibility, and the relationships between genes, proteins, and reactions. Network reconstructions have been used extensively to study the phenotypic behavior of wild-type and mutant stains under a variety of conditions, linking genotypes with phenotypes. Such phenotypic simulations have allowed for the prediction of growth after genetic manipulations, prediction of growth phenotypes after adaptive evolution, and prediction of essential genes. Additionally, because network reconstructions are organism specific, they can be used to understand differences between organisms of species in a functional context.There are different types of reconstructions representing various types of biological networks (metabolic, regulatory, transcription/translation). This chapter serves as an introduction to metabolic and regulatory network reconstructions and models and gives a complete description of the core Escherichia coli metabolic model. This model can be analyzed in any computational format (such as MATLAB or Mathematica) based on the information given in this chapter. The core E. coli model is a small-scale model that can be used for educational purposes. It is meant to be used by senior undergraduate and first-year graduate students learning about constraint-based modeling and systems biology. This model has enough reactions and pathways to enable interesting and insightful calculations, but it is also simple enough that the results of such calculations can be understoodeasily.
In situ (α-Al2O3+ZrB2)/Al composites with network distribution fabricated by reaction hot pressing
Institute of Scientific and Technical Information of China (English)
El Oualid Mokhnache; Gui-song Wang; Lin Geng; Kaveendran Balasubramaniam; Abdelkhalek Henniche; Noureddine Ramdani
2015-01-01
In situ (α-Al2O3+ZrB2)/Al composites with network distribution were fabricated using low-energy ball milling and reaction hot pressing. Differential thermal analysis (DTA) was used to study the reaction mechanisms in the Al–ZrO2–B system. X-ray diffraction (XRD) and scanning electron microscopy (SEM) in conjunction with energy-dispersive X-ray spectroscopy (EDX) were used to investigate the composite phases, morphology, and microstructure of the composites. The effect of matrix network size on the microstructure and mechani-cal properties was investigated.The results show that the optimum sintering parameters to complete reactions in the Al–ZrO2–B system are 850℃ and 60 min.In situ-synthesizedα-Al2O3 and ZrB2 particles are dispersed uniformly around Al particles, forming a network micro-structure; the diameters of theα-Al2O3 and ZrB2 particles are approximately 1–3μm. When the size of Al powder increases from 60–110μm to 150–300μm, the overall surface contact between Al powders and reactants decreases, thereby increasing the local volume fraction of re-inforcements from 12% to 21%. This increase of the local volume leads to a significant increase in microhardness of thein situ (α-Al2O3–ZrB2)/Al composites from Hv 163 to Hv 251.
GKIN: a tool for drawing genetic networks
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Jonathan Arnold
2012-03-01
Full Text Available We present GKIN, a simulator and a comprehensive graphical interface where one can draw the model specification of reactions between hypothesized molecular participants in a gene regulatory and biochemical reaction network (or genetic network for short. The solver is written in C++ in a nearly platform independentmanner to simulate large ensembles of models, which can run on PCs, Macintoshes, and UNIX machines, and its graphical user interface is written in Java which can run as a standalone or WebStart application. The drawing capability for rendering a network significantly enhances the ease of use of other reaction network simulators, such as KINSOLVER (Aleman-Meza et al., 2009 and enforces a correct semantic specification of the network. In a usability study with novice users, drawing the network with GKIN was preferred and faster in comparison with entry with a dialog-box guided interface in COPASI (Hoops, et al., 2006 with no difference in error rates between GKIN and COPASI in specifying the network. GKIN is freely available at http://faculty.cs.wit.edu/~ldeligia/PROJECTS/GKIN/.
Gan, Qintao
2017-01-01
In this paper, the exponential synchronization problem of generalized reaction-diffusion neural networks with mixed time-varying delays is investigated concerning Dirichlet boundary conditions in terms of p-norm. Under the framework of the Lyapunov stability method, stochastic theory, and mathematical analysis, some novel synchronization criteria are derived, and an aperiodically intermittent control strategy is proposed simultaneously. Moreover, the effects of diffusion coefficients, diffusion space, and stochastic perturbations on the synchronization process are explicitly expressed under the obtained conditions. Finally, some numerical simulations are performed to illustrate the feasibility of the proposed control strategy and show different synchronization dynamics under a periodically/aperiodically intermittent control.
Energy Technology Data Exchange (ETDEWEB)
Choi, Yong S.; Singh, Rahul; Zhang, Jing; Balasubramanian, Ganesh; Sturgeon, Matthew R.; Katahira, Rui; Chupka, Gina; Beckham, Gregg T.; Shanks, Brent H.
2016-01-01
Although lignin is one of the main components of biomass, its pyrolysis chemistry is not well understood due to complex heterogeneity. To gain insights into this chemistry, the pyrolysis of seven lignin model compounds (five ..beta..-O-4 and two ..alpha..-O-4 linked molecules) was investigated in a micropyrolyzer connected to GC-MS/FID. According to quantitative product mole balance for the reaction networks, concerted retro-ene fragmentation and homolytic dissociation were strongly suggested as the initial reaction step for ..beta..-O-4 compounds and ..alpha..-O-4 compounds, respectively. The difference in reaction pathway between compounds with different linkages was believed to result from thermodynamics of the radical initiation. The rate constants for the different reaction pathways were predicted from ab initio density functional theory calculations and pre-exponential literature values. The computational findings were consistent with the experiment results, further supporting the different pyrolysis mechanisms for the ..beta..-ether linked and ..alpha..-ether linked compounds. A combination of the two pathways from the dimeric model compounds was able to describe qualitatively the pyrolysis of a trimeric lignin model compound containing both ..beta..-O-4 and ..alpha..-O-4 linkages.
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Ildefonso M De la Fuente
Full Text Available BACKGROUND: The experimental observations and numerical studies with dissipative metabolic networks have shown that cellular enzymatic activity self-organizes spontaneously leading to the emergence of a Systemic Metabolic Structure in the cell, characterized by a set of different enzymatic reactions always locked into active states (metabolic core while the rest of the catalytic processes are only intermittently active. This global metabolic structure was verified for Escherichia coli, Helicobacter pylori and Saccharomyces cerevisiae, and it seems to be a common key feature to all cellular organisms. In concordance with these observations, the cell can be considered a complex metabolic network which mainly integrates a large ensemble of self-organized multienzymatic complexes interconnected by substrate fluxes and regulatory signals, where multiple autonomous oscillatory and quasi-stationary catalytic patterns simultaneously emerge. The network adjusts the internal metabolic activities to the external change by means of flux plasticity and structural plasticity. METHODOLOGY/PRINCIPAL FINDINGS: In order to research the systemic mechanisms involved in the regulation of the cellular enzymatic activity we have studied different catalytic activities of a dissipative metabolic network under different external stimuli. The emergent biochemical data have been analysed using statistical mechanic tools, studying some macroscopic properties such as the global information and the energy of the system. We have also obtained an equivalent Hopfield network using a Boltzmann machine. Our main result shows that the dissipative metabolic network can behave as an attractor metabolic network. CONCLUSIONS/SIGNIFICANCE: We have found that the systemic enzymatic activities are governed by attractors with capacity to store functional metabolic patterns which can be correctly recovered from specific input stimuli. The network attractors regulate the catalytic patterns
Szczepanski, Caroline R; Stansbury, Jeffrey W
2014-10-05
A mechanism for polymerization shrinkage and stress reduction was developed for heterogeneous networks formed via ambient, photo-initiated polymerization-induced phase separation (PIPS). The material system used consists of a bulk homopolymer matrix of triethylene glycol dimethacrylate (TEGDMA) modified with one of three non-reactive, linear prepolymers (poly-methyl, ethyl and butyl methacrylate). At higher prepolymer loading levels (10-20 wt%) an enhanced reduction in both shrinkage and polymerization stress is observed. The onset of gelation in these materials is delayed to a higher degree of methacrylate conversion (~15-25%), providing more time for phase structure evolution by thermodynamically driven monomer diffusion between immiscible phases prior to network macro-gelation. The resulting phase structure was probed by introducing a fluorescently tagged prepolymer into the matrix. The phase structure evolves from a dispersion of prepolymer at low loading levels to a fully co-continuous heterogeneous network at higher loadings. The bulk modulus in phase separated networks is equivalent or greater than that of poly(TEGDMA), despite a reduced polymerization rate and cross-link density in the prepolymer-rich domains.
Hervás, C; Toledo, R; Silva, M
2001-01-01
The suitability of pruned computational neural networks (CNNs) for resolving nonlinear multicomponent systems involving synergistic effects by use of oscillating chemical reaction-based methods implemented using the analyte pulse perturbation technique is demonstrated. The CNN input data used for this purpose are estimates provided by the Levenberg-Marquardt method in the form of a three-parameter Gaussian curve associated with the singular profile obtained when the oscillating system is perturbed by an analyte mixture. The performance of the proposed method was assessed by applying it to the resolution of mixtures of pyrogallol and gallic acid based on their perturbating effect on a classical oscillating chemical system, viz. the Belousov-Zhabotinskyi reaction. A straightforward network topology (3:3:2, with 18 connections after pruning) allowed the resolution of mixtures of the two analytes in concentration ratios from 1:7 to 6:2 with a standard error of prediction for the testing set of 4.01 and 8.98% for pyrogallol and gallic acid, respectively. The reduced dimensions of the selected CNN architecture allowed a mathematical transformation of the input vector into the output one that can be easily implemented via software. Finally, the suitability of response surface analysis as an alternative to CNNs was also tested. The results were poor (relative errors were high), which confirms that properly selected pruned CNNs are effective tools for solving the analytical problem addressed in this work.
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P. Bala Anki Reddy
2016-09-01
Full Text Available In this paper, the prediction of the magnetohydrodynamic boundary layer slip flow over a permeable stretched cylinder with chemical reaction is investigated by using some mathematical techniques, namely Runge–Kutta fourth order method along with shooting technique and artificial neural network (ANN. A numerical method is implemented to approximate the flow of heat and mass transfer characteristics as a function of some input parameters, explicitly the curvature parameter, magnetic parameter, permeability parameter, velocity slip, Grashof number, solutal Grashof number, Prandtl number, temperature exponent, Schmidt number, concentration exponent and chemical reaction parameter. The non-linear partial differential equations of the governing flow are converted into a system of highly non-linear ordinary differential equations by using the suitable similarity transformations, which are then solved numerically by a Runge–Kutta fourth order along with shooting technique and then ANN is applied to them. The Back Propagation Neural Network is applied for forecasting the desired outputs. The reported numerical values and the ANN values are in good agreement than those published works on various special cases. According to the findings of this study, the ANN approach is reliable, effective and easily applicable for simulating heat and mass transfer flow over a stretched cylinder.
A Theory of Decomposition of Complex Chemical Networks using the Hill Functions
Chikayama, Eisuke
2014-01-01
The design and synthesis of complex and large mimicked biochemical networks de novo is an unsolved problem in synthetic biology. To address this limitation without resorting to ad hoc computations and experiments, a predictive mathematical theory is required to reduce these complex chemical networks into natural physico-chemical expressions. Here we provide a mathematical theory that offers a physico-chemical expression for a large chemical network that is almost arbitrarily both nonlinear and complex. Unexpectedly, the theory demonstrates that such networks can be decomposed into reactions based solely on the Hill equation, a simple chemical logic gate. This theory, analogous to implemented electrical logic gates or functional algorithms in a computer, is proposed for implementing regulated sequences of functional chemical reactions, such as mimicked genes, transcriptional regulation, signal transduction, protein interaction, and metabolic networks, into an artificial designed chemical network.
McLean, James; Sutton, Robert; Post, John
2003-10-01
One useful method of acoustic impedance measurement involves the measurement of the electrical impedance ``looking into'' the electrical port of a reciprocal electroacoustic transducer. This reaction on the source method greatly facilitates the measurement of acoustic impedance by borrowing highly refined techniques to measure electrical impedance. It is also well suited for in situ acoustic impedance measurements. In order to accurately determine acoustic impedance from the measured electrical impedance, the characteristics of the transducer must be accurately known, i.e., the characteristics of the transducer must be ``removed'' completely from the data. The measurement of acoustic impedance via the measurement of the reaction on the source is analogous to modern microwave measurements made with an automatic vector network analyzer. The action of the analyzer is described as de-embedding the desired data (such as acoustic impedance) from the raw data. Such measurements are fundamentally substitution measurements in that the transducer's characteristics are determined by measuring a set of reference standards. The reaction on the source method is extended to take advantage of improvements in microwave measurement techniques which allow calibration via imperfect standard loads. This removes one of the principal weaknesses of the method in that the requirement of high-quality reference standards is relaxed.
On the Reaction Diffusion Master Equation in the Microscopic Limit
Hellander, Stefan; Petzold, Linda
2011-01-01
Stochastic modeling of reaction-diffusion kinetics has emerged as a powerful theoretical tool in the study of biochemical reaction networks. Two frequently employed models are the particle-tracking Smoluchowski framework and the on-lattice Reaction-Diffusion Master Equation (RDME) framework. As the mesh size goes from coarse to fine, the RDME initially becomes more accurate. However, recent developments have shown that it will become increasingly inaccurate compared to the Smoluchowski model as the lattice spacing becomes very fine. In this paper we give a new, general and simple argument for why the RDME breaks down. Our analysis reveals a hard limit on the voxel size for which no local RDME can agree with the Smoluchowski model.
Reaction-diffusion master equation in the microscopic limit
Hellander, Stefan; Hellander, Andreas; Petzold, Linda
2012-04-01
Stochastic modeling of reaction-diffusion kinetics has emerged as a powerful theoretical tool in the study of biochemical reaction networks. Two frequently employed models are the particle-tracking Smoluchowski framework and the on-lattice reaction-diffusion master equation (RDME) framework. As the mesh size goes from coarse to fine, the RDME initially becomes more accurate. However, recent developments have shown that it will become increasingly inaccurate compared to the Smoluchowski model as the lattice spacing becomes very fine. Here we give a general and simple argument for why the RDME breaks down. Our analysis reveals a hard limit on the voxel size for which no local RDME can agree with the Smoluchowski model and lets us quantify this limit in two and three dimensions. In this light we review and discuss recent work in which the RDME has been modified in different ways in order to better agree with the microscale model for very small voxel sizes.
Gon Ryu, Sam; Wan Lee, Hae
2015-01-01
The nerve agent, O-ethyl S-[2-(diisopropylamino)ethyl] methylphosphonothioate (VX) must be promptly eliminated following its release into the environment because it is extremely toxic, can cause death within a few minutes after exposure, acts through direct skin contact as well as inhalation, and persists in the environment for several weeks after release. A mixture of hydrogen peroxide vapor and ammonia gas was examined as a decontaminant for the removal of VX on solid surfaces at ambient temperature, and the reaction products were analyzed by gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectrometry (NMR). All the VX on glass wool filter disks was found to be eliminated after 2 h of exposure to the decontaminant mixtures, and the primary decomposition product was determined to be non-toxic ethyl methylphosphonic acid (EMPA); no toxic S-[2-(diisopropylamino)ethyl] methylphosphonothioic acid (EA-2192), which is usually produced in traditional basic hydrolysis systems, was found to be formed. However, other by-products, such as toxic O-ethyl S-vinyl methylphosphonothioate and (2-diisopropylaminoethyl) vinyl disulfide, were detected up to 150 min of exposure to the decontaminant mixture; these by-products disappeared after 3 h. The two detected vinyl byproducts were identified first in this study with the decontamination system of liquid VX on solid surfaces using a mixture of hydrogen peroxide vapor and ammonia gas. The detailed decontamination reaction networks of VX on solid surfaces produced by the mixture of hydrogen peroxide vapor and ammonia gas were suggested based on the reaction products. These findings suggest that the mixture of hydrogen peroxide vapor and ammonia gas investigated in this study is an efficient decontaminant mixture for the removal of VX on solid surfaces at ambient temperature despite the formation of a toxic by-product in the reaction process.
Physiological and biochemical basis of salmon young ifshes migratory behavior
Institute of Scientific and Technical Information of China (English)
Vladimir Ivanovich Martemyanov
2016-01-01
The review presents data on structural changes, physiological and biochemical reactions occurring at salmon young fishes during smoltification. It is shown, that young salmon fishes located in fresh water, in the process of smoltification undergo a complex of structural, physiological and biochemical changes directed on preparation of the organism for living in the sea. These changes cause stress reaction which excites young fishes to migrate down the river towards the sea. Measures to improve reproduction of young salmon fishes at fish farms are offered.
Exact hybrid particle/population simulation of rule-based models of biochemical systems.
Directory of Open Access Journals (Sweden)
Justin S Hogg
2014-04-01
Full Text Available Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that
Keough, James M; Zuniga, Ashley N; Jenson, David L; Barry, Bridgette A
2013-02-07
In photosynthetic oxygen evolution, redox active tyrosine Z (YZ) plays an essential role in proton-coupled electron transfer (PCET) reactions. Four sequential photooxidation reactions are necessary to produce oxygen at a Mn(4)CaO(5) cluster. The sequentially oxidized states of this oxygen-evolving cluster (OEC) are called the S(n) states, where n refers to the number of oxidizing equivalents stored. The neutral radical, YZ•, is generated and then acts as an electron transfer intermediate during each S state transition. In the X-ray structure, YZ, Tyr161 of the D1 subunit, is involved in an extensive hydrogen bonding network, which includes calcium-bound water. In electron paramagnetic resonance experiments, we measured the YZ• recombination rate, in the presence of an intact Mn(4)CaO(5) cluster. We compared the S(0) and S(2) states, which differ in Mn oxidation state, and found a significant difference in the YZ• decay rate (t(1/2) = 3.3 ± 0.3 s in S(0); t(1/2) = 2.1 ± 0.3 s in S(2)) and in the solvent isotope effect (SIE) on the reaction (1.3 ± 0.3 in S(0); 2.1 ± 0.3 in S(2)). Although the YZ site is known to be solvent accessible, the recombination rate and SIE were pH independent in both S states. To define the origin of these effects, we measured the YZ• recombination rate in the presence of ammonia, which inhibits oxygen evolution and disrupts the hydrogen bond network. We report that ammonia dramatically slowed the YZ• recombination rate in the S(2) state but had a smaller effect in the S(0) state. In contrast, ammonia had no significant effect on YD•, the stable tyrosyl radical. Therefore, the alterations in YZ• decay, observed with S state advancement, are attributed to alterations in OEC hydrogen bonding and consequent differences in the YZ midpoint potential/pK(a). These changes may be caused by activation of metal-bound water molecules, which hydrogen bond to YZ. These observations document the importance of redox control in proton
Research on the Reaction Mechanism of Network Public Opinion Related to Procuratorate%涉检网络舆情反应机制研究
Institute of Scientific and Technical Information of China (English)
张亚力; 余芳
2011-01-01
新形势下的舆论环境要求检察机关提高处置网络舆情危机的能力。只有建立涉检网络舆情反应机制，敏锐地把握、驾驭应对舆论的能力，及时、坦诚地公开检务信息，才能培养人民群众对司法权威的认同感，提高检察工作的执法公信力，化解社会矛盾、维护社会和谐稳定。%Procuratorial organs are required to improve their abilities to deal with the crisis of network public opinion under the new situation. Only by establishing the reaction mechanism of network public opinion, mastering how to handle the public opinion keenly, opening the procuratorial information timely and frankly, people＇s sense of identity to judicial authority can be educated, the law enforcement credibility of procuratorial work can be improved, social contradictions can be defused, social harmony and stability can be maintained.
Li, Jin-Cheng; Zhao, Shi-Yong; Hou, Peng-Xiang; Fang, Ruo-Pian; Liu, Chang; Liang, Ji; Luan, Jian; Shan, Xu-Yi; Cheng, Hui-Ming
2015-12-01
A nitrogen-doped mesoporous carbon containing a network of carbon nanotubes (CNTs) was produced for use as a catalyst for the oxygen reduction reaction (ORR). SiO2 nanoparticles were decorated with clusters of Fe atoms to act as catalyst seeds for CNT growth, after which the material was impregnated with aniline. After polymerization of the aniline, the material was pyrolysed and the SiO2 was removed by acid treatment. The resulting carbon-based hybrid also contained some Fe from the CNT growth catalyst and was doped with N from the aniline. The Fe-N species act as active catalytic sites and the CNT network enables efficient electron transport in the material. Mesopores left by the removal of the SiO2 template provide short transport pathways and easy access to ions. As a result, the catalyst showed not only excellent ORR activity, with 59 mV more positive onset potential and 30 mV more positive half-wave potential than a Pt/C catalyst, but also much longer durability and stronger tolerance to methanol crossover than a Pt/C catalyst.
Xu, Yi; Jorissen, Alain; Chen, Guangling; Arnould, Marcel; 10.1051/0004-6361/201220537
2012-01-01
An update of a previous description of the BRUSLIB+NACRE package of nuclear data for astrophysics and of the web-based nuclear network generator NETGEN is presented. The new version of BRUSLIB contains the latest predictions of a wide variety of nuclear data based on the most recent version of the Brussels-Montreal Skyrme-HFB model. The nuclear masses, radii, spin/parities, deformations, single-particle schemes, matter densities, nuclear level densities, E1 strength functions, fission properties, and partition functions are provided for all nuclei lying between the proton and neutron drip lines over the 8<=Z<=110 range, whose evaluation is based on a unique microscopic model that ensures a good compromise between accuracy, reliability, and feasibility. In addition, these various ingredients are used to calculate about 100000 Hauser-Feshbach n-, p-, a-, and gamma-induced reaction rates based on the reaction code TALYS. NACRE is superseded by the NACRE II compilation for 15 charged-particle transfer react...
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Xi gang Yuan
2015-01-01
Full Text Available With system dynamics, we establish three-closed-loop supply chain distribution network system model which consists of supplier, manufacturer, two retailers, and products (parts recycler. We proposed that recycler make reflect for the government policy by adjusting the recycling ratio and recycling delay. We use vensim software to simulate this model and investigate how the products (parts recyclers behavior influences the loop supply chain distribution system. The result shows that (1 when recyclers respond positively to government policies, recycling will increase the proportion of recyclers. When recyclers respond negatively to government policy making, recycling will reduce the proportion of recyclers. (2 When the recovery percentage of recyclers improves, manufacturers, Retailer 1, and Retailer 2 quantity fluctuations will reduce and the bullwhip effect will diminish. (3 When the proportion of recycled parts recyclers is lowered, manufacturers, Retailer 1, and Retailer 2 inventory fluctuation will increase and the bullwhip effect will be enhanced. (4 When recyclers recycling product delays increased, volatility manufacturers order quantity will rise, but there is little change in the amount of fluctuation of orders of the two retailers. (5 When recycling parts recyclers delay increases, fluctuations in the supplier order quantity will rise, but there is little change in the amount of fluctuation of orders of the two retailers.
Energy Technology Data Exchange (ETDEWEB)
Lyubimov, Artem Y. [Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Sinsheimer Laboratories, 1156 High Street, Santa Cruz, CA 95064 (United States); Chen, Lin; Sampson, Nicole S. [Department of Chemistry, Stony Brook University, Stony Brook, NY 11794-3400 (United States); Vrielink, Alice, E-mail: alice.vrielink@uwa.edu.au [School of Biomedical, Biomolecular and Chemical Sciences, University of Western Australia, 35 Stirling Highway, Crawley, WA 6009 (Australia); Department of Molecular, Cell and Developmental Biology, University of California, Santa Cruz, Sinsheimer Laboratories, 1156 High Street, Santa Cruz, CA 95064 (United States)
2009-11-01
The importance of active-site electrostatics for oxidative and reductive half-reactions in a redox flavoenzyme (cholesterol oxidase) have been investigated by a combination of biochemistry and atomic resolution crystallography. A detailed examination of active-site dynamics demonstrates that the oxidation of substrate and the re-oxidation of the flavin cofactor by molecular oxygen are linked by a single active-site asparagine. Cholesterol oxidase is a flavoenzyme that catalyzes the oxidation and isomerization of 3β-hydroxysteroids. Structural and mutagenesis studies have shown that Asn485 plays a key role in substrate oxidation. The side chain makes an NH⋯π interaction with the reduced form of the flavin cofactor. A N485D mutant was constructed to further test the role of the amide group in catalysis. The mutation resulted in a 1800-fold drop in the overall k{sub cat}. Atomic resolution structures were determined for both the N485L and N485D mutants. The structure of the N485D mutant enzyme (at 1.0 Å resolution) reveals significant perturbations in the active site. As predicted, Asp485 is oriented away from the flavin moiety, such that any stabilizing interaction with the reduced flavin is abolished. Met122 and Glu361 form unusual hydrogen bonds to the functional group of Asp485 and are displaced from the positions they occupy in the wild-type active site. The overall effect is to disrupt the stabilization of the reduced FAD cofactor during catalysis. Furthermore, a narrow transient channel that is shown to form when the wild-type Asn485 forms the NH⋯π interaction with FAD and that has been proposed to function as an access route of molecular oxygen, is not observed in either of the mutant structures, suggesting that the dynamics of the active site are altered.
Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance
Thomas, Alex; Rahmanian, Sorena; Bordbar, Aarash; Palsson, Bernhard Ø.; Jamshidi, Neema
2014-01-01
Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.
Gonzalez-Diaz, Humberto; Arrasate, Sonia; Juan, Asier Gomez-San; Sotomayor, Nuria; Lete, Esther; Speck-Planche, Alejandro; Ruso, Juan M; Luan, Feng; Cordeiro, Maria Natalia Dias Soeiro
2014-01-01
The study of quantitative structure-property relationships (QSPR) is important to study complex networks of chemical reactions in drug synthesis or metabolism or drug-target interaction networks. A difficult but possible goal is the prediction of drug absorption, distribution, metabolism, and excretion (ADME) process with a single QSPR model. For this QSPR modelers need to use flexible structural parameters useful for the description of many different systems at different structural scales (multi-scale parameters). Also they need to use powerful analytical methods able to link in a single multi-scale hypothesis structural parameters of different target systems (multi-target modeling) with different experimental properties of these systems (multi-output models). In this sense, the QSPR study of complex bio-molecular systems may benefit substantially from the combined application of spectral moments of graph representations of complex systems with perturbation theory methods. On one hand, spectral moments are almost universal parameters that can be calculated to many different matrices used to represent the structure of the states of different systems. On the other hand, perturbation methods can be used to add "small" variation terms to parameters of a known state of a given system in order to approach to a solution of another state of the same or similar system with unknown properties. Here we present one state-of-art review about the different applications of spectral moments to describe complex bio-molecular systems. Next, we give some general ideas and formulate plausible linear models for a general-purpose perturbation theory of QSPR problems of complex systems. Last, we develop three new QSPR-Perturbation theory models based on spectral moments for three different problems with multiple in-out boundary conditions that are relevant to biomolecular sciences. The three models developed correctly classify more than pairs 115,600; 48,000; 134,900 cases of the
Institute of Scientific and Technical Information of China (English)
朱京科; 苏云
2001-01-01
在应用人工神经网络预测有机反应产率中，由于结合了统计方法，使人工神经网络易产生的随机性和过拟合作用造成的不利影响减小，从而提高了预测可靠性。%This work presents a backpropagation neural network trained toreproduce the reaction yield of aryl fluorides by the halex technique. The work shows that a ten-dimensional input space is able to reproduce reasonably the observed reaction yields by employing statistics in artificial neural system. By means of a number of multilayer feedforward (MLF) networks rather than one, the disadvantages caused by network randomness are limited greatly, and therefore the prediction quality is improved. The combined approach is suitable for relatively small training set, which often causes overfitting and leads to unreliable prediction results.
Weighting schemes in metabolic graphs for identifying biochemical routes.
Ghosh, S; Baloni, P; Vishveshwara, S; Chandra, N
2014-03-01
Metabolism forms an integral part of all cells and its study is important to understand the functioning of the system, to understand alterations that occur in disease state and hence for subsequent applications in drug discovery. Reconstruction of genome-scale metabolic graphs from genomics and other molecular or biochemical data is now feasible. Few methods have also been reported for inferring biochemical pathways from these networks. However, given the large scale and complex inter-connections in the networks, the problem of identifying biochemical routes is not trivial and some questions still remain open. In particular, how a given path is altered in perturbed conditions remains a difficult problem, warranting development of improved methods. Here we report a comparison of 6 different weighting schemes to derive node and edge weights for a metabolic graph, weights reflecting various kinetic, thermodynamic parameters as well as abundances inferred from transcriptome data. Using a network of 50 nodes and 107 edges of carbohydrate metabolism, we show that kinetic parameter derived weighting schemes [Formula: see text] fare best. However, these are limited by their extent of availability, highlighting the usefulness of omics data under such conditions. Interestingly, transcriptome derived weights yield paths with best scores, but are inadequate to discriminate the theoretical paths. The method is tested on a system of Escherichia coli stress response. The approach illustrated here is generic in nature and can be used in the analysis for metabolic network from any species and perhaps more importantly for comparing condition-specific networks.
The application of information theory to biochemical signaling systems.
Rhee, Alex; Cheong, Raymond; Levchenko, Andre
2012-08-01
Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell's signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon's information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling.
Microbridge structures for uniform interval control of flowing droplets in microfluidic networks
Lee, Do-Hyun; Lee, Wonhye; Um, Eujin; Park, Je-Kyun
2011-01-01
Precise temporal control of microfluidic droplets such as synchronization and combinatorial pairing of droplets is required to achieve a variety range of chemical and biochemical reactions inside microfluidic networks. Here, we present a facile and robust microfluidic platform enabling uniform interval control of flowing droplets for the precise temporal synchronization and pairing of picoliter droplets with a reagent. By incorporating microbridge structures interconnecting the droplet-carryi...
DEFF Research Database (Denmark)
Kjeldsen, Kjeld Raunkjær; Nielsen, J.
2009-01-01
A genome-scale metabolic model of the Gram-positive bacteria Corynebacterium glutamicum ATCC 13032 was constructed comprising 446 reactions and 411 metabolite, based on the annotated genome and available biochemical information. The network was analyzed using constraint based methods. The model...... and lactate. Comparable flux values between in silico model and experimental values were seen, although some differences in the phenotypic behavior between the model and the experimental data were observed,...
Enzyme and biochemical producing fungi
DEFF Research Database (Denmark)
Lübeck, Peter Stephensen; Lübeck, Mette; Nilsson, Lena
2010-01-01
factories for sustainable production of important molecules. For developing fungi into efficient cell factories, the project includes identification of important factors that control the flux through the pathways using metabolic flux analysis and metabolic engineering of biochemical pathways....
Mathematical Models of Biochemical Oscillations
Conrad, Emery David
1999-01-01
The goal of this paper is to explain the mathematics involved in modeling biochemical oscillations. We first discuss several important biochemical concepts fundamental to the construction of descriptive mathematical models. We review the basic theory of differential equations and stability analysis as it relates to two-variable models exhibiting oscillatory behavior. The importance of the Hopf Bifurcation will be discussed in detail for the central role it plays in limit cycle behavior and...
Xu, Y.; Goriely, S.; Jorissen, A.; Chen, G. L.; Arnould, M.
2013-01-01
An update of a previous description of the BRUSLIB + NACRE package of nuclear data for astrophysics and of the web-based nuclear network generator NETGEN is presented. The new version of BRUSLIB contains the latest predictions of a wide variety of nuclear data based on the most recent version of the Brussels-Montreal Skyrme-Hartree-Fock-Bogoliubov model. The nuclear masses, radii, spin/parities, deformations, single-particle schemes, matter densities, nuclear level densities, E1 strength functions, fission properties, and partition functions are provided for all nuclei lying between the proton and neutron drip lines over the 8 ≤ Z ≤ 110 range, whose evaluation is based on a unique microscopic model that ensures a good compromise between accuracy, reliability, and feasibility. In addition, these various ingredients are used to calculate about 100 000 Hauser-Feshbach neutron-, proton-, α-, and γ-induced reaction rates based on the reaction code TALYS. NACRE is superseded by the NACRE II compilation for 15 charged-particle transfer reactions and 19 charged-particle radiative captures on stable targets with mass numbers A < 16. NACRE II features the inclusion of experimental data made available after the publication of NACRE in 1999 and up to 2011. In addition, the extrapolation of the available data to the very low energies of astrophysical relevance is improved through the systematic use of phenomenological potential models. Uncertainties in the rates are also evaluated on this basis. Finally, the latest release v10.0 of the web-based tool NETGEN is presented. In addition to the data already used in the previous NETGEN package, it contains in a fully documented form the new BRUSLIB and NACRE II data, as well as new experiment-based radiative neutron capture cross sections. The full new versions of BRUSLIB, NACRE II, and NETGEN are available electronically from the nuclear database at http://www.astro.ulb.ac.be/NuclearData. The nuclear material is presented in
Diffusion Controlled Reactions, Fluctuation Dominated Kinetics, and Living Cell Biochemistry
Konkoli, Zoran
2009-01-01
In recent years considerable portion of the computer science community has focused its attention on understanding living cell biochemistry and efforts to understand such complication reaction environment have spread over wide front, ranging from systems biology approaches, through network analysis (motif identification) towards developing language and simulators for low level biochemical processes. Apart from simulation work, much of the efforts are directed to using mean field equations (equivalent to the equations of classical chemical kinetics) to address various problems (stability, robustness, sensitivity analysis, etc.). Rarely is the use of mean field equations questioned. This review will provide a brief overview of the situations when mean field equations fail and should not be used. These equations can be derived from the theory of diffusion controlled reactions, and emerge when assumption of perfect mixing is used.
Moro, Artur J; Pana, Ana-Maria; Cseh, Liliana; Costisor, Otilia; Parola, Jorge; Cunha-Silva, L; Puttreddy, Rakesh; Rissanen, Kari; Pina, Fernando
2014-08-14
The kinetics and thermodynamics of the 2,6-bis(2-hydroxybenzilidene)cyclohexanone chemical reactions network was studied at different pH values using NMR, UV-vis, continuous irradiation, and flash photolysis. The chemical behavior of the system partially resembles anthocyanins and their analogue compounds. 2,6-Bis(2-hydroxybenzilidene)cyclohexanone exhibits a slow color change from yellow to red styrylflavylium under extreme acidic conditions. The rate constant for this process (5 × 10(-5) s(-1)) is pH independent and controlled by the cis-trans isomerization barrier. However, the interesting feature is the appearance of the colorless compound, 7,8-dihydro-6H-chromeno[3,2-d]xanthene, isolated from solutions of acid to neutral range, characterized by (1)H NMR and single crystal X-ray diffraction. Light absorption by 2,6-bis(2-hydroxybenzilidene)cyclohexanone solutions immediately after preparation exclusively results in cis-isomer as photoproduct, which via hemiketal formation yields (i) red styrylflavylium by dehydration under extremely acidic solutions (pH < 1) and (ii) colorless 7,8-dihydro-6H-chromeno[3,2-d]xanthene by cyclization in solutions of acid to neutral range.
Nuccio, Sean-Paul; Bäumler, Andreas J
2014-03-18
The Salmonella genus comprises a group of pathogens associated with illnesses ranging from gastroenteritis to typhoid fever. We performed an in silico analysis of comparatively reannotated Salmonella genomes to identify genomic signatures indicative of disease potential. By removing numerous annotation inconsistencies and inaccuracies, the process of reannotation identified a network of 469 genes involved in central anaerobic metabolism, which was intact in genomes of gastrointestinal pathogens but degrading in genomes of extraintestinal pathogens. This large network contained pathways that enable gastrointestinal pathogens to utilize inflammation-derived nutrients as well as many of the biochemical reactions used for the enrichment and biochemical discrimination of Salmonella serovars. Thus, comparative genome analysis identifies a metabolic network that provides clues about the strategies for nutrient acquisition and utilization that are characteristic of gastrointestinal pathogens. IMPORTANCE While some Salmonella serovars cause infections that remain localized to the gut, others disseminate throughout the body. Here, we compared Salmonella genomes to identify characteristics that distinguish gastrointestinal from extraintestinal pathogens. We identified a large metabolic network that is functional in gastrointestinal pathogens but decaying in extraintestinal pathogens. While taxonomists have used traits from this network empirically for many decades for the enrichment and biochemical discrimination of Salmonella serovars, our findings suggest that it is part of a "business plan" for growth in the inflamed gastrointestinal tract. By identifying a large metabolic network characteristic of Salmonella serovars associated with gastroenteritis, our in silico analysis provides a blueprint for potential strategies to utilize inflammation-derived nutrients and edge out competing gut microbes.
Directory of Open Access Journals (Sweden)
M.R. Barros
2009-04-01
Full Text Available Lactobacilos foram isolados do inglúvio e cecos de reprodutoras pesadas e caracterizados como Gram-positivo, catalase negativo, produtores de gás em glicose e não produtores de H2S em triple sugar iron e pela fermentação de carboidratos. Utilizaram-se os iniciadores: Lac 1/23-10C para detecção de Lactobacillus acidophilus, L. crispatus, L. amylovorus, L. gasseri, L. helveticus e L. jensenii; Lac 2/LU-1' para L. acidophilus; Fer 3/Fer 4 para L. fermentum; Reu 1/Reu 2 para L. reuteri e Sal 1 e Sal 2 para L. salivarius. L. reuteri e L. salivarius foram identificados pela reação em cadeia de polimerase (PCR e pelo teste bioquímico, enquanto L. acidophilus, L. fermentum e Lactobacillus sp. somente pelo teste bioquímico. Os resultados obtidos na PCR foram mais precisos quando comparados aos obtidos com o método bioquímico, que demonstrou ser subjetivo devido às variações na fermentação de carboidratos, principalmente na diferenciação entre L. fermentum e L. reuteri.Lactobacilli were isolated from crops and ceca of broiler breeders and characterized by positive Gram staining, negative catalase test, production of gas from glucose, and negative for H2S production from triple sugar iron, and carbohydrates fermentation. Primers: Lac1/23-10C for detecting Lactobacillus acidophilus, L. crispatus, L. amylovorus, L. gasseri, L. helveticus, and L. jensenii; Lac2/LU-1' for L. acidophilus; Fer3/Fer4 for L. fermentum; Reu1/Reu2 for L. reuteri, and Sal1/Sal2 for L. salivarius were used. L. reuteri and L. salivarius were identified by both polymerase chain reaction (PCR and biochemical tests. However, L. acidophilus, L. fermentum, and Lactobacillus sp. were only identified by biochemical tests. PCR results were more precise, considering the variability of carbohydrate fermentation among the strains, especially for identifying L. fermentum and L. reuteri.
Large-scale networks in engineering and life sciences
Findeisen, Rolf; Flockerzi, Dietrich; Reichl, Udo; Sundmacher, Kai
2014-01-01
This edited volume provides insights into and tools for the modeling, analysis, optimization, and control of large-scale networks in the life sciences and in engineering. Large-scale systems are often the result of networked interactions between a large number of subsystems, and their analysis and control are becoming increasingly important. The chapters of this book present the basic concepts and theoretical foundations of network theory and discuss its applications in different scientific areas such as biochemical reactions, chemical production processes, systems biology, electrical circuits, and mobile agents. The aim is to identify common concepts, to understand the underlying mathematical ideas, and to inspire discussions across the borders of the various disciplines. The book originates from the interdisciplinary summer school “Large Scale Networks in Engineering and Life Sciences” hosted by the International Max Planck Research School Magdeburg, September 26-30, 2011, and will therefore be of int...
Signatures of arithmetic simplicity in metabolic network architecture.
Directory of Open Access Journals (Sweden)
William J Riehl
2010-04-01
Full Text Available Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that properties similar to those predicted for the artificial chemistry hold also for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity.
Biotin: biochemical, physiological and clinical aspects.
Said, Hamid M
2012-01-01
Significant progress has been made in our understanding of the biochemical, physiological and nutritional aspects of the water-soluble vitamin biotin (vitamin H). It is well know now that biotin plays important roles in a variety of critical metabolic reactions in the cell, and thus, is essential for normal human health, growth and development. This is underscored by the serious clinical abnormalities that occur in conditions of biotin deficiency, which include, among other things, growth retardation, neurological disorders, and dermatological abnormalities (reviewed in 1). Studies in animals have also shown that biotin deficiency during pregnancy leads to embryonic growth retardation, congenital malformation and death (Watanabe 1983; Cooper and Brown 1958; Mock et al. 2003; Zempleni and Mock 2000). The aim of this chapter is to provide coverage of current knowledge of the biochemical, physiological, and clinical aspects of biotin nutrition. Many sections of this chapter have been the subject of excellent recent reviews by others (Wolf 2001; McMahon 2002; Mock 2004; Rodriguez-Melendez and Zempleni 2003; Said 2004; Said et al. 2000; Said and Seetheram 2006), and thus, for more information the reader is advised to consider these additional sources.
Biochemical research elucidating metabolic pathways in Pneumocystis*
Directory of Open Access Journals (Sweden)
Kaneshiro E.S.
2010-12-01
Full Text Available Advances in sequencing the Pneumocystis carinii genome have helped identify potential metabolic pathways operative in the organism. Also, data from characterizing the biochemical and physiological nature of these organisms now allow elucidation of metabolic pathways as well as pose new challenges and questions that require additional experiments. These experiments are being performed despite the difficulty in doing experiments directly on this pathogen that has yet to be subcultured indefinitely and produce mass numbers of cells in vitro. This article reviews biochemical approaches that have provided insights into several Pneumocystis metabolic pathways. It focuses on 1 S-adenosyl-L-methionine (AdoMet; SAM, which is a ubiquitous participant in numerous cellular reactions; 2 sterols: focusing on oxidosqualene cyclase that forms lanosterol in P. carinii; SAM:sterol C-24 methyltransferase that adds methyl groups at the C-24 position of the sterol side chain; and sterol 14α-demethylase that removes a methyl group at the C-14 position of the sterol nucleus; and 3 synthesis of ubiquinone homologs, which play a pivotal role in mitochondrial inner membrane and other cellular membrane electron transport.
Vijaykumar, Adithya; Wolde, Pieter Rein ten; Bolhuis, Peter G
2016-01-01
The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic Molecular Dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P.G. Bolhuis and P.R. ten Wolde, J. Chem. Phys. {\\bf 43}, 21: 214102 (2015)]. Here we extend this multiscale BD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm we discuss its performance. The rotational BD-GFRD multiscale method will open up the possibility for large scale simulations of e.g. protein signalling networks.
BEST: Biochemical Engineering Simulation Technology
Energy Technology Data Exchange (ETDEWEB)
1996-01-01
The idea of developing a process simulator that can describe biochemical engineering (a relatively new technology area) was formulated at the National Renewable Energy Laboratory (NREL) during the late 1980s. The initial plan was to build a consortium of industrial and U.S. Department of Energy (DOE) partners to enhance a commercial simulator with biochemical unit operations. DOE supported this effort; however, before the consortium was established, the process simulator industry changed considerably. Work on the first phase of implementing various fermentation reactors into the chemical process simulator, ASPEN/SP-BEST, is complete. This report will focus on those developments. Simulation Sciences, Inc. (SimSci) no longer supports ASPEN/SP, and Aspen Technology, Inc. (AspenTech) has developed an add-on to its ASPEN PLUS (also called BioProcess Simulator [BPS]). This report will also explain the similarities and differences between BEST and BPS. ASPEN, developed by the Massachusetts Institute of Technology for DOE in the late 1970s, is still the state-of-the-art chemical process simulator. It was selected as the only simulator with the potential to be easily expanded into the biochemical area. ASPEN/SP, commercially sold by SimSci, was selected for the BEST work. SimSci completed work on batch, fed-batch, and continuous fermentation reactors in 1993, just as it announced it would no longer commercially support the complete ASPEN/SP product. BEST was left without a basic support program. Luckily, during this same time frame, AspenTech was developing a biochemical simulator with its version of ASPEN (ASPEN PLUS), which incorporates most BEST concepts. The future of BEST will involve developing physical property data and models appropriate to biochemical systems that are necessary for good biochemical process design.
Suk, Heejun
2016-08-01
This paper presents a semi-analytical procedure for solving coupled the multispecies reactive solute transport equations, with a sequential first-order reaction network on spatially or temporally varying flow velocities and dispersion coefficients involving distinct retardation factors. This proposed approach was developed to overcome the limitation reported by Suk (2013) regarding the identical retardation values for all reactive species, while maintaining the extensive capability of the previous Suk method involving spatially variable or temporally variable coefficients of transport, general initial conditions, and arbitrary temporal variable inlet concentration. The proposed approach sequentially calculates the concentration distributions of each species by employing only the generalized integral transform technique (GITT). Because the proposed solutions for each species' concentration distributions have separable forms in space and time, the solution for subsequent species (daughter species) can be obtained using only the GITT without the decomposition by change-of-variables method imposing the limitation of identical retardation values for all the reactive species by directly substituting solutions for the preceding species (parent species) into the transport equation of subsequent species (daughter species). The proposed solutions were compared with previously published analytical solutions or numerical solutions of the numerical code of the Two-Dimensional Subsurface Flow, Fate and Transport of Microbes and Chemicals (2DFATMIC) in three verification examples. In these examples, the proposed solutions were well matched with previous analytical solutions and the numerical solutions obtained by 2DFATMIC model. A hypothetical single-well push-pull test example and a scale-dependent dispersion example were designed to demonstrate the practical application of the proposed solution to a real field problem.
A model study of sequential enzyme reactions and electrostatic channeling.
Eun, Changsun; Kekenes-Huskey, Peter M; Metzger, Vincent T; McCammon, J Andrew
2014-03-14
We study models of two sequential enzyme-catalyzed reactions as a basic functional building block for coupled biochemical networks. We investigate the influence of enzyme distributions and long-range molecular interactions on reaction kinetics, which have been exploited in biological systems to maximize metabolic efficiency and signaling effects. Specifically, we examine how the maximal rate of product generation in a series of sequential reactions is dependent on the enzyme distribution and the electrostatic composition of its participant enzymes and substrates. We find that close proximity between enzymes does not guarantee optimal reaction rates, as the benefit of decreasing enzyme separation is countered by the volume excluded by adjacent enzymes. We further quantify the extent to which the electrostatic potential increases the efficiency of transferring substrate between enzymes, which supports the existence of electrostatic channeling in nature. Here, a major finding is that the role of attractive electrostatic interactions in confining intermediate substrates in the vicinity of the enzymes can contribute more to net reactive throughput than the directional properties of the electrostatic fields. These findings shed light on the interplay of long-range interactions and enzyme distributions in coupled enzyme-catalyzed reactions, and their influence on signaling in biological systems.
Toward the automated generation of genome-scale metabolic networks in the SEED
Directory of Open Access Journals (Sweden)
Gould John
2007-04-01
Full Text Available Abstract Background Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. Results We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis. We have implemented our tools and database within the SEED, an open-source software environment for comparative
Polettini, Matteo
2014-01-01
In this and a companion paper we outline a general framework for the thermodynamic description of open chemical reaction networks, with special regard to metabolic networks regulating cellular physiology and biochemical functions. We first introduce closed networks ``in a box'', whose thermodynamics is subjected to strict physical constraints: the mass-action law, elementarity of processes, and detailed balance. We further digress on the role of solvents and on the seemingly unacknowledged property of network independence of free energy landscapes. We then open the system by assuming that the concentrations of certain substrate species (the chemostats) are fixed, whether because promptly regulated by the environment via contact with reservoirs, or because nearly constant in a time window. As a result, the system is driven out of equilibrium. A rich algebraic and topological structure ensues in the network of internal species: Emergent irreversible cycles are associated to nonvanishing affinities, whose symmet...
Institute of Scientific and Technical Information of China (English)
朱群雄; 赵乃伟; 徐圆
2012-01-01
Chemical processes are complex, for which traditional neural network models usually can not lead to satisfactory accuracy. Selective neural network ensemble is an effective way to enhance the generalization accuracy of networks, but there are some problems, e.g., lacking of unified definition of diversity among component neural networks and difficult to improve the accuracy by selecting if the diversities of available networks are small. In this study, the output errors of networks are vectorized, the diversity of networks is defined based on the error vectors, and the size of ensemble is analyzed. Then an error vectorization based selective neural network ensemble (EVSNE) is proposed, in which the error vector of each network can offset that of the other networks by training the component networks orderly. Thus the component networks have large diversity. Experiments and comparisons over standard data sets and actual chemical process data set for production of high-density polyethylene demonstrate that EVSNE performs better in generalization ability.
Directory of Open Access Journals (Sweden)
Marco Villani
2013-09-01
Full Text Available In this work we introduce some preliminary analyses on the role of a semi-permeable membrane in the dynamics of a stochastic model of catalytic reaction sets (CRSs of molecules. The results of the simulations performed on ensembles of randomly generated reaction schemes highlight remarkable differences between this very simple protocell description model and the classical case of the continuous stirred-tank reactor (CSTR. In particular, in the CSTR case, distinct simulations with the same reaction scheme reach the same dynamical equilibrium, whereas, in the protocell case, simulations with identical reaction schemes can reach very different dynamical states, despite starting from the same initial conditions.
Noise Filtering and Prediction in Biological Signaling Networks
Hathcock, David; Weisenberger, Casey; Ilker, Efe; Hinczewski, Michael
2016-01-01
Information transmission in biological signaling circuits has often been described using the metaphor of a noise filter. Cellular systems need accurate, real-time data about their environmental conditions, but the biochemical reaction networks that propagate, amplify, and process signals work with noisy representations of that data. Biology must implement strategies that not only filter the noise, but also predict the current state of the environment based on information delayed due to the finite speed of chemical signaling. The idea of a biochemical noise filter is actually more than just a metaphor: we describe recent work that has made an explicit mathematical connection between signaling fidelity in cellular circuits and the classic theories of optimal noise filtering and prediction that began with Wiener, Kolmogorov, Shannon, and Bode. This theoretical framework provides a versatile tool, allowing us to derive analytical bounds on the maximum mutual information between the environmental signal and the re...
Biochemical markers of bone turnover
Energy Technology Data Exchange (ETDEWEB)
Kim, Deog Yoon [College of Medicine, Kyunghee Univ., Seoul (Korea, Republic of)
1999-08-01
Biochemical markers of bone turnover has received increasing attention over the past few years, because of the need for sensitivity and specific tool in the clinical investigation of osteoporosis. Bone markers should be unique to bone, reflect changes of bone less, and should be correlated with radiocalcium kinetics, histomorphometry, or changes in bone mass. The markers also should be useful in monitoring treatment efficacy. Although no bone marker has been established to meet all these criteria, currently osteocalcin and pyridinium crosslinks are the most efficient markers to assess the level of bone turnover in the menopausal and senile osteoporosis. Recently, N-terminal telopeptide (NTX), C-terminal telopeptide (CTX) and bone specific alkaline phosphatase are considered as new valid markers of bone turnover. Recent data suggest that CTX and free deoxypyridinoline could predict the subsequent risk of hip fracture of elderly women. Treatment of postmenopausal women with estrogen, calcitonin and bisphosphonates demonstrated rapid decrease of the levels of bone markers that correlated with the long-term increase of bone mass. Factors such as circadian rhythms, diet, age, sex, bone mass and renal function affect the results of biochemical markers and should be appropriately adjusted whenever possible. Each biochemical markers of bone turnover may have its own specific advantages and limitations. Recent advances in research will provide more sensitive and specific assays.
Biochemical adaptation to ocean acidification.
Stillman, Jonathon H; Paganini, Adam W
2015-06-01
The change in oceanic carbonate chemistry due to increased atmospheric PCO2 has caused pH to decline in marine surface waters, a phenomenon known as ocean acidification (OA). The effects of OA on organisms have been shown to be widespread among diverse taxa from a wide range of habitats. The majority of studies of organismal response to OA are in short-term exposures to future levels of PCO2 . From such studies, much information has been gathered on plastic responses organisms may make in the future that are beneficial or harmful to fitness. Relatively few studies have examined whether organisms can adapt to negative-fitness consequences of plastic responses to OA. We outline major approaches that have been used to study the adaptive potential for organisms to OA, which include comparative studies and experimental evolution. Organisms that inhabit a range of pH environments (e.g. pH gradients at volcanic CO2 seeps or in upwelling zones) have great potential for studies that identify adaptive shifts that have occurred through evolution. Comparative studies have advanced our understanding of adaptation to OA by linking whole-organism responses with cellular mechanisms. Such optimization of function provides a link between genetic variation and adaptive evolution in tuning optimal function of rate-limiting cellular processes in different pH conditions. For example, in experimental evolution studies of organisms with short generation times (e.g. phytoplankton), hundreds of generations of growth under future conditions has resulted in fixed differences in gene expression related to acid-base regulation. However, biochemical mechanisms for adaptive responses to OA have yet to be fully characterized, and are likely to be more complex than simply changes in gene expression or protein modification. Finally, we present a hypothesis regarding an unexplored area for biochemical adaptation to ocean acidification. In this hypothesis, proteins and membranes exposed to the
Motif analysis for small-number effects in chemical reaction dynamics.
Saito, Nen; Sughiyama, Yuki; Kaneko, Kunihiko
2016-09-07
The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a "mesoscopic" level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration.
Motif analysis for small-number effects in chemical reaction dynamics
Saito, Nen; Sughiyama, Yuki; Kaneko, Kunihiko
2016-09-01
The number of molecules involved in a cell or subcellular structure is sometimes rather small. In this situation, ordinary macroscopic-level fluctuations can be overwhelmed by non-negligible large fluctuations, which results in drastic changes in chemical-reaction dynamics and statistics compared to those observed under a macroscopic system (i.e., with a large number of molecules). In order to understand how salient changes emerge from fluctuations in molecular number, we here quantitatively define small-number effect by focusing on a "mesoscopic" level, in which the concentration distribution is distinguishable both from micro- and macroscopic ones and propose a criterion for determining whether or not such an effect can emerge in a given chemical reaction network. Using the proposed criterion, we systematically derive a list of motifs of chemical reaction networks that can show small-number effects, which includes motifs showing emergence of the power law and the bimodal distribution observable in a mesoscopic regime with respect to molecule number. The list of motifs provided herein is helpful in the search for candidates of biochemical reactions with a small-number effect for possible biological functions, as well as for designing a reaction system whose behavior can change drastically depending on molecule number, rather than concentration.
Biochemical filter with sigmoidal response: increasing the complexity of biomolecular logic.
Privman, Vladimir; Halámek, Jan; Arugula, Mary A; Melnikov, Dmitriy; Bocharova, Vera; Katz, Evgeny
2010-11-11
The first realization of a designed, rather than natural, biochemical filter process is reported and analyzed as a promising network component for increasing the complexity of biomolecular logic systems. Key challenge in biochemical logic research has been achieving scalability for complex network designs. Various logic gates have been realized, but a "toolbox" of analog elements for interconnectivity and signal processing has remained elusive. Filters are important as network elements that allow control of noise in signal transmission and conversion. We report a versatile biochemical filtering mechanism designed to have sigmoidal response in combination with signal-conversion process. Horseradish peroxidase-catalyzed oxidation of chromogenic electron donor by H(2)O(2) was altered by adding ascorbate, allowing to selectively suppress the output signal, modifying the response from convex to sigmoidal. A kinetic model was developed for evaluation of the quality of filtering. The results offer improved capabilities for design of scalable biomolecular information processing systems.
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...
Biochemical structure of Calendula officinalis.
Korakhashvili, A; Kacharava, T; Kiknavelidze, N
2007-01-01
Calendula officinalis is a well known medicinal herb. It is common knowledge that its medicinal properties are conditioned on biologically active complex substances of Carotin (Provitamin A), Stearin, Triterpiniod, Plavonoid, Kumarin, macro and micro compound elements. Because of constant need in raw material of Calendula officinalis, features of its ontogenetic development agro-biological qualities in various eco regions of Georgia were investigated. The data of biologically active compounds, biochemical structure and the maintenance both in flowers and in others parts of plant is presented; the pharmacological activity and importance in medicine was reviewed.
He, Li-Li; Zheng, Jie-Ning; Song, Pei; Zhong, Shu-Xian; Wang, Ai-Jun; Chen, Zhaojiang; Feng, Jiu-Ju
2015-02-01
In this work, a facile one-pot wet-chemical method is developed for preparation of bimetallic platinum-gold (Pt-Au) alloyed string-bead nanochain networks, using allantoin as a structure-directing agent, without any template, surfactant, or seed. The characterization experiments are mainly performed by transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) spectroscopy. The as-prepared Pt-Au nanocrystals show enhanced electrocatalytic performance toward oxygen reduction reaction (ORR) mainly predominated by a four-electron pathway, and display improved catalytic activity and high stability for methanol oxidation reaction (MOR) over commercial Pt black and Pt-Ru black.
Biochemical aspects of Huntington's chorea.
Caraceni, T; Calderini, G; Consolazione, A; Riva, E; Algeri, S; Girotti, F; Spreafico, R; Branciforti, A; Dall'olio, A; Morselli, P L
1977-01-01
Fifteen patients affected by Huntington's chorea were divided into two groups, 'slow' and 'fast', according to IQ scores on the Wechsler-Bellevue scale, and scores on some motor performance tests. A possible correlation was looked for between some biochemical data (cerebrospinal fluid (CSF), homovanillic acid (HVA), and 5-hydroxyindolacetic acid (5HIAA) levels, plasma dopamine-beta-hydroxylase (DBH), dopamine (DA) uptake by platelets), and clinical data (duration of illness, severity of symptoms, age of patients, IQ scores, 'slow' and 'fast' groups). The CSF, HVA, and 5HIAA levels were found to be significantly lowered in comparison with normal controls. DBH activity and DA uptake by platelets did not differ significantly from normal subjects. Treatment with haloperidol in all patients and with dipropylacetic acid in three patients did not appear to modify the CSF, HVA, and 5HIAA concentrations, the plasma DBH activity, or the DA uptake. There were no significant differences in the CSF, HVA, and 5HIAA contents between the two groups of patients, and there was no correlation between biochemical data and clinical features. PMID:143508
Mass transfer and chemical reaction in gas-liquid-liquid systems
Brilman, Derk Willem Frederik
1998-01-01
Gas-liquid-liquid reaction systems may be encountered in several important fields of application as e.g. hydroformylation, alkylation, carboxylation, polymerisation, hydrometallurgy, biochemical processes and fine chemicals manufacturing. However, the reaction engineering aspects of these systems ha
Heuristics-Guided Exploration of Reaction Mechanisms
Bergeler, Maike; Proppe, Jonny; Reiher, Markus
2015-01-01
For the investigation of chemical reaction networks, the efficient and accurate determination of all relevant intermediates and elementary reactions is inevitable. The complexity of such a network may grow rapidly, in particular if reactive species are involved that might cause a myriad of side reactions. Without automation, a complete investigation of complex reaction mechanisms is tedious and possibly unfeasible. Therefore, only the expected dominant reaction paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic cascade) are usually explored in practice. Here, we present a computational protocol that constructs such networks in a parallelized and automated manner. Molecular structures of reactive complexes are generated based on heuristic rules and subsequently optimized by electronic-structure methods. Pairs of reactive complexes related by an elementary reaction are then automatically detected and subjected to an automated search for the connecting transition state. The results are...
Spatio-temporal patterns with hyperchaotic dynamics in diffusively coupled biochemical oscillators
Directory of Open Access Journals (Sweden)
Gerold Baier
1997-01-01
Full Text Available We present three examples how complex spatio-temporal patterns can be linked to hyperchaotic attractors in dynamical systems consisting of nonlinear biochemical oscillators coupled linearly with diffusion terms. The systems involved are: (a a two-variable oscillator with two consecutive autocatalytic reactions derived from the Lotka–Volterra scheme; (b a minimal two-variable oscillator with one first-order autocatalytic reaction; (c a three-variable oscillator with first-order feedback lacking autocatalysis. The dynamics of a finite number of coupled biochemical oscillators may account for complex patterns in compartmentalized living systems like cells or tissue, and may be tested experimentally in coupled microreactors.
The application of information theory to biochemical signaling systems
Rhee, Alex; Cheong, Raymond; Levchenko, Andre
2012-01-01
Cell signaling can be thought of fundamentally as an information transmission problem in which chemical messengers relay information about the external environment to the decision centers within a cell. Due to the biochemical nature of cellular signal transduction networks, molecular noise will inevitably limit the fidelity of any messages received and processed by a cell’s signal transduction networks, leaving it with an imperfect impression of its environment. Fortunately, Shannon’s information theory provides a mathematical framework independent of network complexity that can quantify the amount of information that can be transmitted despite biochemical noise. In particular, the channel capacity can be used to measure the maximum number of stimuli a cell can distinguish based upon the noisy responses of its signaling systems. Here, we provide a primer for quantitative biologists that covers fundamental concepts of information theory, highlights several key considerations when experimentally measuring channel capacity, and describes successful examples of the application of information theoretic analysis to biological signaling. PMID:22872091
Wu, Zhanghan; Elgart, Vlad; Qian, Hong; Xing, Jianhua
2009-09-10
A protein undergoes conformational dynamics with multiple time scales, which results in fluctuating enzyme activities. Recent studies in single-molecule enzymology have observe this "age-old" dynamic disorder phenomenon directly. However, the single-molecule technique has its limitation. To be able to observe this molecular effect with real biochemical functions in situ, we propose to couple the fluctuations in enzymatic activity to noise propagations in small protein interaction networks such as a zeroth-order ultrasensitive phosphorylation-dephosphorylation cycle. We show that enzyme fluctuations can indeed be amplified by orders of magnitude into fluctuations in the level of substrate phosphorylation, a quantity of wide interest in cellular biology. Enzyme conformational fluctuations sufficiently slower than the catalytic reaction turnover rate result in a bimodal concentration distribution of the phosphorylated substrate. In return, this network-amplified single-enzyme fluctuation can be used as a novel biochemical "reporter" for measuring single-enzyme conformational fluctuation rates.
Catalytic Oxidized Reaction of Paraffin Wax Based on BP Neural Network%基于BP神经网络的石蜡催化氧化反应的研究
Institute of Scientific and Technical Information of China (English)
黄玮; 丛玉凤; 郭大鹏
2012-01-01
The oxidized wax was prepared by catalytic oxidized reaction of paraffin wax which used BP neural network to build mathematical model of acid value and saponification value influenced by the amount of reactive catalyst and accessory ingredient, airflow rate, reaction temperature and time, and utilized the model of neutral network to calculate the technology condition of preparing oxidized wax through catalyzing and oxidizing paraffin wax. Consequently, optimum technology conditions were gained in order to achieve the objective of reducing experimental number of times.%在石蜡催化氧化反应制备氧化蜡的研究中,利用BP神经网络建立反应催化剂用量、助剂用量、空气流量、反应温度和反应时间对酸值和皂化值影响的数学模型,并利用该神经网络模型对石蜡催化氧化制备氧化蜡的工艺条件进行预测,从而获得最优工艺条件,达到缩短实验次数的目的.
Institute of Scientific and Technical Information of China (English)
周凤燕
2012-01-01
研究了一类反应扩散广义时滞细胞神经网络在噪声干扰下的指数稳定性.利用Ito公式,Holder不等式,M矩阵性质和微分不等式技巧,给出了系统均值指数稳定的充分条件,并且判断方法简单易操作.最后给出了主要定理的两个应用实例,表明结论的有效性.%The exponential stability of a class of reaction-diffusion general cellular neural network with time delay and noise perturbation is studied. Using the Ito formula, Holder inequality, M-matric properties and a skill of differential inequality, some sufficient conditions are given to guarantee the mean value exponential stability of the equilibrium for the stochastic reaction-diffusion general cellular neural network with time delay and the sufficient conditions are easier to operate. In the end, two examples are given to illustrate the main theoretical results.
Network Biology (http://www.iaees.org/publications/journals/nb/online-version.asp
Directory of Open Access Journals (Sweden)
networkbiology@iaees.org
Full Text Available Network Biology ISSN 2220-8879 URL: http://www.iaees.org/publications/journals/nb/online-version.asp RSS: http://www.iaees.org/publications/journals/nb/rss.xml E-mail: networkbiology@iaees.org Editor-in-Chief: WenJun Zhang Aims and Scope NETWORK BIOLOGY (ISSN 2220-8879; CODEN NBEICS is an open access, peer-reviewed international journal that considers scientific articles in all different areas of network biology. It is the transactions of the International Society of Network Biology. It dedicates to the latest advances in network biology. The goal of this journal is to keep a record of the state-of-the-art research and promote the research work in these fast moving areas. The topics to be covered by Network Biology include, but are not limited to: •Theories, algorithms and programs of network analysis •Innovations and applications of biological networks •Ecological networks, food webs and natural equilibrium •Co-evolution, co-extinction, biodiversity conservation •Metabolic networks, protein-protein interaction networks, biochemical reaction networks, gene networks, transcriptional regulatory networks, cell cycle networks, phylogenetic networks, network motifs •Physiological networks •Network regulation of metabolic processes, human diseases and ecological systems •Social networks, epidemiological networks •System complexity, self-organized systems, emergence of biological systems, agent-based modeling, individual-based modeling, neural network modeling, and other network-based modeling, etc. We are also interested in short communications that clearly address a specific issue or completely present a new ecological network, food web, or metabolic or gene network, etc. Authors can submit their works to the email box of this journal, networkbiology@iaees.org. All manuscripts submitted to this journal must be previously unpublished and may not be considered for publication elsewhere at any time during review period of this journal
Goyette, Jesse; Salas, Citlali Solis; Coker-Gordon, Nicola; Bridge, Marcus; Isaacson, Samuel A.; Allard, Jun; Dushek, Omer
2017-01-01
Tethered enzymatic reactions are ubiquitous in signaling networks but are poorly understood. A previously unreported mathematical analysis is established for tethered signaling reactions in surface plasmon resonance (SPR). Applying the method to the phosphatase SHP-1 interacting with a phosphorylated tether corresponding to an immune receptor cytoplasmic tail provides five biophysical/biochemical constants from a single SPR experiment: two binding rates, two catalytic rates, and a reach parameter. Tether binding increases the activity of SHP-1 by 900-fold through a binding-induced allosteric activation (20-fold) and a more significant increase in local substrate concentration (45-fold). The reach parameter indicates that this local substrate concentration is exquisitely sensitive to receptor clustering. We further show that truncation of the tether leads not only to a lower reach but also to lower binding and catalysis. This work establishes a new framework for studying tethered signaling processes and highlights the tether as a control parameter in clustered receptor signaling.
Biochemical Abnormalities in Batten's Syndrome
DEFF Research Database (Denmark)
Clausen, Jytte Lene; Nielsen, Gunnar Gissel; Jensen, Gunde Egeskov;
1978-01-01
The present data indicate that a group of ten patients with Batten's syndrome showed reduced activity of erythrocyte glutathione (GSH) peroxidase (Px) (glutathione: H2O2 oxidoreductase, EC 1.1.1.9.) using H2O2 as peroxide donor. Assay of erythrocyte GSHPx using H2O2, cumene hydroperoxide and t......-butyl hydroperoxide as donors also makes it possible biochemically to divide Batten's syndrome into two types: (1) one type with decreased values when H2O2 and cumene hydroperoxide are used, and (2) one type with increased values when t-butyl hydroperoxide is used. Furthermore an increased content of palmitic, oleic...... in whole blood. In normal human beings a connection was found between the erythrocyte selenium content and GSHPx activity assayed by cumene hydroperoxide as a peroxide donor....
Biochemical nature of Russell Bodies.
Mossuto, Maria Francesca; Ami, Diletta; Anelli, Tiziana; Fagioli, Claudio; Doglia, Silvia Maria; Sitia, Roberto
2015-07-30
Professional secretory cells produce and release abundant proteins. Particularly in case of mutations and/or insufficient chaperoning, these can aggregate and become toxic within or amongst cells. Immunoglobulins (Ig) are no exception. In the extracellular space, certain Ig-L chains form fibrils causing systemic amyloidosis. On the other hand, Ig variants lacking the first constant domain condense in dilated cisternae of the early secretory compartment, called Russell Bodies (RB), frequently observed in plasma cell dyscrasias, autoimmune diseases and chronic infections. RB biogenesis can be recapitulated in lymphoid and non-lymphoid cells by expressing mutant Ig-μ, providing powerful models to investigate the pathophysiology of endoplasmic reticulum storage disorders. Here we analyze the aggregation propensity and the biochemical features of the intra- and extra-cellular Ig deposits in human cells, revealing β-aggregated features for RB.
Energy Technology Data Exchange (ETDEWEB)
Maggi, F.M.; Riley, W.J.
2009-11-01
We present a mathematical treatment of the kinetic equations that describe isotopologue and isotopomer speciation and fractionation during enzyme-catalyzed biochemical reactions. These equations, presented here with the name GEBIK (general equations for biochemical isotope kinetics) and GEBIF (general equations for biochemical isotope fractionation), take into account microbial biomass and enzyme dynamics, reaction stoichiometry, isotope substitution number, and isotope location within each isotopologue and isotopomer. In addition to solving the complete GEBIK and GEBIF, we also present and discuss two approximations to the full solutions under the assumption of biomass-free and enzyme steady-state, and under the quasi-steady-state assumption as applied to the complexation rate. The complete and approximate approaches are applied to observations of biological denitrification in soils. Our analysis highlights that the full GEBIK and GEBIF provide a more accurate description of concentrations and isotopic compositions of substrates and products throughout the reaction than do the approximate forms. We demonstrate that the isotopic effects of a biochemical reaction depend, in the most general case, on substrate and complex concentrations and, therefore, the fractionation factor is a function of time. We also demonstrate that inverse isotopic effects can occur for values of the fractionation factor smaller than 1, and that reactions that do not discriminate isotopes do not necessarily imply a fractionation factor equal to 1.
Heuristics-Guided Exploration of Reaction Mechanisms.
Bergeler, Maike; Simm, Gregor N; Proppe, Jonny; Reiher, Markus
2015-12-08
For the investigation of chemical reaction networks, the efficient and accurate determination of all relevant intermediates and elementary reactions is mandatory. The complexity of such a network may grow rapidly, in particular if reactive species are involved that might cause a myriad of side reactions. Without automation, a complete investigation of complex reaction mechanisms is tedious and possibly unfeasible. Therefore, only the expected dominant reaction paths of a chemical reaction network (e.g., a catalytic cycle or an enzymatic cascade) are usually explored in practice. Here, we present a computational protocol that constructs such networks in a parallelized and automated manner. Molecular structures of reactive complexes are generated based on heuristic rules derived from conceptual electronic-structure theory and subsequently optimized by quantum-chemical methods to produce stable intermediates of an emerging reaction network. Pairs of intermediates in this network that might be related by an elementary reaction according to some structural similarity measure are then automatically detected and subjected to an automated search for the connecting transition state. The results are visualized as an automatically generated network graph, from which a comprehensive picture of the mechanism of a complex chemical process can be obtained that greatly facilitates the analysis of the whole network. We apply our protocol to the Schrock dinitrogen-fixation catalyst to study alternative pathways of catalytic ammonia production.
A network perspective on metabolic inconsistency
Directory of Open Access Journals (Sweden)
Sonnenschein Nikolaus
2012-05-01
Full Text Available Abstract Background Integrating gene expression profiles and metabolic pathways under different experimental conditions is essential for understanding the coherence of these two layers of cellular organization. The network character of metabolic systems can be instrumental in developing concepts of agreement between expression data and pathways. A network-driven interpretation of gene expression data has the potential of suggesting novel classifiers for pathological cellular states and of contributing to a general theoretical understanding of gene regulation. Results Here, we analyze the coherence of gene expression patterns and a reconstruction of human metabolism, using consistency scores obtained from network and constraint-based analysis methods. We find a surprisingly strong correlation between the two measures, demonstrating that a substantial part of inconsistencies between metabolic processes and gene expression can be understood from a network perspective alone. Prompted by this finding, we investigate the topological context of the individual biochemical reactions responsible for the observed inconsistencies. On this basis, we are able to separate the differential contributions that bear physiological information about the system, from the unspecific contributions that unravel gaps in the metabolic reconstruction. We demonstrate the biological potential of our network-driven approach by analyzing transcriptome profiles of aldosterone producing adenomas that have been obtained from a cohort of Primary Aldosteronism patients. We unravel systematics in the data that could not have been resolved by conventional microarray data analysis. In particular, we discover two distinct metabolic states in the adenoma expression patterns. Conclusions The methodology presented here can help understand metabolic inconsistencies from a network perspective. It thus serves as a mediator between the topology of metabolic systems and their dynamical
Fibre optic system for biochemical and microbiological sensing
Energy Technology Data Exchange (ETDEWEB)
Penwill, L A; Slater, J H; Hayes, N W; Tremlett, C J [Evanes Co Ltd, 4 and 5 Forde Court, Newton Abbot, Devon, TQ12 4AD (United Kingdom)
2007-07-15
This poster will discuss state-of-the-art fibre optic sensors based on evanescent wave technology emphasising chemophotonic sensors for biochemical reactions and microbe detection. Devices based on antibody specificity and unique DNA sequences will be described. The development of simple sensor devices with disposable single use sensor probes will be illustrated with a view to providing cost effective field based or point of care analysis of major themes such as hospital acquired infections or bioterrorism events. This presentation will discuss the nature and detection thresholds required, the optical detection techniques investigated, results of sensor trials and the potential for wider commercial application.
TrypanoCyc: A community-led biochemical pathways database for Trypanosoma brucei
S. Shameer (Sanu); F.J. Logan-Klumpler (Flora J.); F. Vinson (Florence); L. Cottret (Ludovic); B. Merlet (Benjamin); F. Achcar (Fiona); M. Boshart (Michael); M. Berriman (Matthew); R. Breitling (Rainer); F. Bringaud (Frédéric); P. Bütikofer (Peter); A.M. Cattanach (Amy M.); B. Bannerman-Chukualim (Bridget); D.J. Creek (Darren J.); K. Crouch (Kathryn); H.P. De Koning (Harry P.); H. Denise (Hubert); C. Ebikeme (Charles); A.H. Fairlamb (Alan H.); M.A.J. Ferguson (Michael A. J.); M.L. Ginger (Michael L.); C. Hertz-Fowler (Christiane); E.J. Kerkhoven (Eduard); P. Mäser (Pascal); P.A.M. Michels (Paul); A. Nayak (Archana); D. Nes (DavidW.); D.P. Nolan (Derek P.); C. Olsen (Christian); F. Silva-Franco (Fatima); T.K. Smith (Terry K.); M.C. Taylor (Martin C.); A.G.M. Tielens (Aloysius); M.D. Urbaniak (Michael D.); J.J. van Hellemond (Jaap); I.M. Vincent (Isabel M.); S.R. Wilkinson (Shane R.); S. Wyllie (Susan); F.R. Opperdoes (Fred); M.P. Barrett (Michael P.); F. Jourdan (Fabien)
2015-01-01
textabstractThe metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individualmetabolic networks is increasing as we learn
TrypanoCyc : a community-led biochemical pathways database for Trypanosoma brucei
Shameer, Sanu; Logan-Klumpler, Flora J; Vinson, Florence; Cottret, Ludovic; Merlet, Benjamin; Achcar, Fiona; Boshart, Michael; Berriman, Matthew; Breitling, Rainer; Bringaud, Frédéric; Bütikofer, Peter; Cattanach, Amy M; Bannerman-Chukualim, Bridget; Creek, Darren J; Crouch, Kathryn; de Koning, Harry P; Denise, Hubert; Ebikeme, Charles; Fairlamb, Alan H; Ferguson, Michael A J; Ginger, Michael L; Hertz-Fowler, Christiane; Kerkhoven, Eduard J; Mäser, Pascal; Michels, Paul A M; Nayak, Archana; Nes, David W; Nolan, Derek P; Olsen, Christian; Silva-Franco, Fatima; Smith, Terry K; Taylor, Martin C; Tielens, Aloysius G M; Urbaniak, Michael D; van Hellemond, Jaap J; Vincent, Isabel M; Wilkinson, Shane R; Wyllie, Susan; Opperdoes, Fred R; Barrett, Michael P; Jourdan, Fabien
2015-01-01
The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about
Institute of Scientific and Technical Information of China (English)
傅育熙
1998-01-01
The paper proposes reaction graphs as graphical representations of computational objects.A reaction graph is a directed graph with all its arrows and some of its nodes labeled.Computations are modled by graph rewriting of a simple nature.The basic rewriting rules embody the essence of both the communications among processes and cut-eliminations in proofs.Calculi of graphs are ideentified to give a formal and algebraic account of reaction graphs in the spirit of process algebra.With the help of the calculi,it is demonstrated that reaction graphs capture many interesting aspects of computations.
SpringSaLaD: A Spatial, Particle-Based Biochemical Simulation Platform with Excluded Volume.
Michalski, Paul J; Loew, Leslie M
2016-02-02
We introduce Springs, Sites, and Langevin Dynamics (SpringSaLaD), a comprehensive software platform for spatial, stochastic, particle-based modeling of biochemical systems. SpringSaLaD models biomolecules in a coarse-grained manner as a group of linked spherical sites with excluded volume. This mesoscopic approach bridges the gap between highly detailed molecular dynamics simulations and the various methods used to study network kinetics and diffusion at the cellular level. SpringSaLaD is a standalone tool that supports model building, simulation, visualization, and data analysis, all through a user-friendly graphical user interface that should make it more accessible than tools built into more comprehensive molecular dynamics infrastructures. Importantly, for bimolecular reactions we derive an exact expression relating the macroscopic on-rate to the various microscopic parameters with the inclusion of excluded volume; this makes SpringSaLaD more accurate than other tools, which rely on approximate relationships between these parameters.
The fidelity of dynamic signaling by noisy biomolecular networks.
Directory of Open Access Journals (Sweden)
Clive G Bowsher
Full Text Available Cells live in changing, dynamic environments. To understand cellular decision-making, we must therefore understand how fluctuating inputs are processed by noisy biomolecular networks. Here we present a general methodology for analyzing the fidelity with which different statistics of a fluctuating input are represented, or encoded, in the output of a signaling system over time. We identify two orthogonal sources of error that corrupt perfect representation of the signal: dynamical error, which occurs when the network responds on average to other features of the input trajectory as well as to the signal of interest, and mechanistic error, which occurs because biochemical reactions comprising the signaling mechanism are stochastic. Trade-offs between these two errors can determine the system's fidelity. By developing mathematical approaches to derive dynamics conditional on input trajectories we can show, for example, that increased biochemical noise (mechanistic error can improve fidelity and that both negative and positive feedback degrade fidelity, for standard models of genetic autoregulation. For a group of cells, the fidelity of the collective output exceeds that of an individual cell and negative feedback then typically becomes beneficial. We can also predict the dynamic signal for which a given system has highest fidelity and, conversely, how to modify the network design to maximize fidelity for a given dynamic signal. Our approach is general, has applications to both systems and synthetic biology, and will help underpin studies of cellular behavior in natural, dynamic environments.
Noise Control in Gene Regulatory Networks with Negative Feedback.
Hinczewski, Michael; Thirumalai, D
2016-07-01
Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.
Positive roles of compartmentalization in internal reactions.
Ichihashi, Norikazu; Yomo, Tetsuya
2014-10-01
Recently, many researchers have attempted to construct artificial cell models using a bottom-up approach in which various biochemical reactions that involve a defined set of molecules are reconstructed in cell-like compartments, such as liposomes and water-in-oil droplets. In many of these studies, the cell-like compartments have acted only as containers for the encapsulated biochemical reactions, whereas other studies have indicated that compartmentalization improves the rates and yields of these reactions. Here, we introduce two ways in which compartmentalization can improve internal reactions: the isolation effect and the condensation effect. These positive effects of compartmentalization might have played an important role in the genesis of the first primitive cell on early Earth.
Metabolic constraint-based refinement of transcriptional regulatory networks.
Chandrasekaran, Sriram; Price, Nathan D
2013-01-01
There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10(-172)), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10(-14)) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to
Drousiotou, A; DiMeo, I; Mineri, R; Georgiou, Th; Stylianidou, G; Tiranti, V
2011-04-01
Ethylmalonic encephalopathy (EE, OMIM # 602473) is an autosomal recessive metabolic disorder of infancy affecting the brain, the gastrointestinal tract and peripheral vessels. It is caused by a defect in the ETHE1 gene product, which was recently shown to be part of a metabolic pathway devoted to sulphide detoxification. We report the application of improved biochemical and molecular approaches to the diagnosis of three cases of EE from two unrelated Cypriot families. The children presented all the typical biochemical hallmarks of the disease including elevated lactate and butyrylcarnitine in blood and elevated urinary excretion of ethylmalonic acid, 2-methylsuccinate, isobutyrylglycine and isovalerylglycine. We also detected an elevated level of thiosulphate in urine, which we propose as an additional biochemical marker of the disease. The proband of the first family was shown to be a compound heterozygote for a missense mutation in exon 5, L185R, and a deletion of exon 4. The deletion was identified using quantitative real-time polymerase chain reaction (qRT-PCR). Using the same technique, the proband of the second family was found to be homozygous for the exon 4 deletion. A prenatal diagnosis was performed for the second family using qRT-PCR, thus establishing the usefulness of RT-PCR in prenatal diagnosis.
Topographical mapping of biochemical properties of articular cartilage in the equine fetlock joint
Brama, P.A.J.; Tekoppele, J.M.; Bank, R.A.; Karssenberg, D.; Barneveld, A.; Weeren, P.R. van
2000-01-01
The aim of this study was to evaluate topographical differences in the biochemical composition of the extracellular matrix of articular cartilage of the normal equine fetlock joint. Water content, DNA content, glycosaminoglycan (GAG) content and a number of characteristics of the collagen network (t
Biochemical genetic markers in sugarcane.
Glaszmann, J C; Fautret, A; Noyer, J L; Feldmann, P; Lanaud, C
1989-10-01
Isozyme variation was used to identify biochemical markers of potential utility in sugarcane genetics and breeding. Electrophoretic polymorphism was surveyed for nine enzymes among 39 wild and noble sugarcane clones, belonging to the species most closely related to modern varieties. Up to 114 distinct bands showing presence versus absence type of variation were revealed and used for qualitative characterization of the materials. Multivariate analysis of the data isolated the Erianthus clone sampled and separated the Saccharum spontaneum clones from the S. robustum and S. officinarum clones; the latter two were not differentiated from one another. The analysis of self-progenies of a 2n=112 S. spontaneum and of a commercial variety showed examples of mono- and polyfactorial segregations. Within the progeny of the variety, co-segregation of two isozymes frequent in S. spontaneum led to them being assigned to a single chromosome initially contributed by a S. spontaneum donor. This illustrates how combined survey of ancestral species and segregation analysis in modern breeding materials should permit using the lack of interspecific cross-over to establish linkage groups in a sugarcane genome.
Kinetic modeling of reactions in Foods
Boekel, van M.A.J.S.
2008-01-01
The level of quality that food maintains as it travels down the production-to-consumption path is largely determined by the chemical, biochemical, physical, and microbiological changes that take place during its processing and storage. Kinetic Modeling of Reactions in Foods demonstrates how to effec
Condor-COPASI: high-throughput computing for biochemical networks
Kent Edward; Hoops Stefan; Mendes Pedro
2012-01-01
Abstract Background Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary experti...
Condor-COPASI: high-throughput computing for biochemical networks
Directory of Open Access Journals (Sweden)
Kent Edward
2012-07-01
Full Text Available Abstract Background Mathematical modelling has become a standard technique to improve our understanding of complex biological systems. As models become larger and more complex, simulations and analyses require increasing amounts of computational power. Clusters of computers in a high-throughput computing environment can help to provide the resources required for computationally expensive model analysis. However, exploiting such a system can be difficult for users without the necessary expertise. Results We present Condor-COPASI, a server-based software tool that integrates COPASI, a biological pathway simulation tool, with Condor, a high-throughput computing environment. Condor-COPASI provides a web-based interface, which makes it extremely easy for a user to run a number of model simulation and analysis tasks in parallel. Tasks are transparently split into smaller parts, and submitted for execution on a Condor pool. Result output is presented to the user in a number of formats, including tables and interactive graphical displays. Conclusions Condor-COPASI can effectively use a Condor high-throughput computing environment to provide significant gains in performance for a number of model simulation and analysis tasks. Condor-COPASI is free, open source software, released under the Artistic License 2.0, and is suitable for use by any institution with access to a Condor pool. Source code is freely available for download at http://code.google.com/p/condor-copasi/, along with full instructions on deployment and usage.
Gray box modeling of MSW degradation: Revealing its dominant (bio)chemical mechanism
Van Turnhout, A.G.; Heimovaara, T.J.; Kleerebezem, R.
2013-01-01
In this paper we present an approach to describe organic degradation within immobile water regions of Municipal Solid Waste (MSW) landfills which is best described by the term “gray box” model. We use a simplified set of dominant (bio)chemical and physical reactions and realistic environmental condi
Simulation methods with extended stability for stiff biochemical Kinetics
Directory of Open Access Journals (Sweden)
Rué Pau
2010-08-01
Full Text Available Abstract Background With increasing computer power, simulating the dynamics of complex systems in chemistry and biology is becoming increasingly routine. The modelling of individual reactions in (biochemical systems involves a large number of random events that can be simulated by the stochastic simulation algorithm (SSA. The key quantity is the step size, or waiting time, τ, whose value inversely depends on the size of the propensities of the different channel reactions and which needs to be re-evaluated after every firing event. Such a discrete event simulation may be extremely expensive, in particular for stiff systems where τ can be very short due to the fast kinetics of some of the channel reactions. Several alternative methods have been put forward to increase the integration step size. The so-called τ-leap approach takes a larger step size by allowing all the reactions to fire, from a Poisson or Binomial distribution, within that step. Although the expected value for the different species in the reactive system is maintained with respect to more precise methods, the variance at steady state can suffer from large errors as τ grows. Results In this paper we extend Poisson τ-leap methods to a general class of Runge-Kutta (RK τ-leap methods. We show that with the proper selection of the coefficients, the variance of the extended τ-leap can be well-behaved, leading to significantly larger step sizes. Conclusions The benefit of adapting the extended method to the use of RK frameworks is clear in terms of speed of calculation, as the number of evaluations of the Poisson distribution is still one set per time step, as in the original τ-leap method. The approach paves the way to explore new multiscale methods to simulate (biochemical systems.
Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks
Berestovsky, Natalie; Zhou, Wanding; Nagrath, Deepak; Nakhleh, Luay
2013-01-01
The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more
Modeling integrated cellular machinery using hybrid Petri-Boolean networks.
Directory of Open Access Journals (Sweden)
Natalie Berestovsky
Full Text Available The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Mannakee, Brian K; Gutenkunst, Ryan N
2016-07-01
The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.
Selection on Network Dynamics Drives Differential Rates of Protein Domain Evolution.
Directory of Open Access Journals (Sweden)
Brian K Mannakee
2016-07-01
Full Text Available The long-held principle that functionally important proteins evolve slowly has recently been challenged by studies in mice and yeast showing that the severity of a protein knockout only weakly predicts that protein's rate of evolution. However, the relevance of these studies to evolutionary changes within proteins is unknown, because amino acid substitutions, unlike knockouts, often only slightly perturb protein activity. To quantify the phenotypic effect of small biochemical perturbations, we developed an approach to use computational systems biology models to measure the influence of individual reaction rate constants on network dynamics. We show that this dynamical influence is predictive of protein domain evolutionary rate within networks in vertebrates and yeast, even after controlling for expression level and breadth, network topology, and knockout effect. Thus, our results not only demonstrate the importance of protein domain function in determining evolutionary rate, but also the power of systems biology modeling to uncover unanticipated evolutionary forces.
BIOCHEMICAL SCREENING OF DIABETIC NEPHROPATHY
Directory of Open Access Journals (Sweden)
Vivek
2016-01-01
Full Text Available Diabetic nephropathy is a clinical syndrome characterized by the following- Persistent albuminuria (>300mg/d or >200μg/min, that is confirmed on at least 2 occasions 3-6 months apart diabetic, progressive decline in the Glomerular Filtration Rate (GFR, elevated arterial blood pressure. The earliest biochemical criteria for the diagnosis of diabetic nephropathy is the presence of micro-albumin in the urine, which if left untreated will eventually lead to End-Stage Renal Disease (ESRD. Micro-albuminuria refers to the excretion of albumin in the urine at a rate that exceeds normal limits. The current study was conducted to establish the prevalence of micro-albuminuria in a sequential sample of diabetic patients attending hospital and OPD Clinic to determine its relationship with known and putative risk factors to identify micro- and normo-albuminuric patients in their sample for subsequent comparison in different age, sex, weight and creatinine clearance of the micro- and normo-albuminuric patients. This cross-sectional analytical study was conducted in one hundred patients at Saraswathi Institute of Medical Sciences, Anwarpur, Hapur, U. P. Patients having diabetes mellitus in different age group ranging from 30 to 70 years were selected. Data was analysed by SPSS software. Micro-albuminuria was observed in 35% in patients with type 2 diabetes mellitus. It was observed that 65% patients were free from any type of albuminuria. Also micro-albuminuria was present in 10% of the patients less than 50 yrs. of age, while 15% of the patients more than 50 yrs. of age were having micro-albuminuria. There was a statistically significant correlation of micro-albuminuria with duration of diabetes. Incidence of micro-albuminuria increases with age as well as increased duration of diabetes mellitus. Our study shows that only 5% patients developed macro-albuminuria. Glycosylated haemoglobin and fasting plasma glucose was significantly raised among all these
Yeast 5 – an expanded reconstruction of the Saccharomyces cerevisiae metabolic network
Directory of Open Access Journals (Sweden)
Heavner Benjamin D
2012-06-01
Full Text Available Abstract Background Efforts to improve the computational reconstruction of the Saccharomyces cerevisiae biochemical reaction network and to refine the stoichiometrically constrained metabolic models that can be derived from such a reconstruction have continued since the first stoichiometrically constrained yeast genome scale metabolic model was published in 2003. Continuing this ongoing process, we have constructed an update to the Yeast Consensus Reconstruction, Yeast 5. The Yeast Consensus Reconstruction is a product of efforts to forge a community-based reconstruction emphasizing standards compliance and biochemical accuracy via evidence-based selection of reactions. It draws upon models published by a variety of independent research groups as well as information obtained from biochemical databases and primary literature. Results Yeast 5 refines the biochemical reactions included in the reconstruction, particularly reactions involved in sphingolipid metabolism; updates gene-reaction annotations; and emphasizes the distinction between reconstruction and stoichiometrically constrained model. Although it was not a primary goal, this update also improves the accuracy of model prediction of viability and auxotrophy phenotypes and increases the number of epistatic interactions. This update maintains an emphasis on standards compliance, unambiguous metabolite naming, and computer-readable annotations available through a structured document format. Additionally, we have developed MATLAB scripts to evaluate the model’s predictive accuracy and to demonstrate basic model applications such as simulating aerobic and anaerobic growth. These scripts, which provide an independent tool for evaluating the performance of various stoichiometrically constrained yeast metabolic models using flux balance analysis, are included as Additional files 1, 2 and 3. Additional file 1 Function testYeastModel.m.m. Click here for file Additional file 2 Function model
Yu, Jie; Zhong, Yijun; Zhou, Wei; Shao, Zongping
2017-01-01
Efficient oxygen evolution reaction (OER) catalysts are required to facilitate the large-scale exploitation of renewable energy resources and applications in electrochemical energy conversion technologies. Here, we show that metal alloy-based hybrids can provide higher electrocatalytic activity than their individual metal-based hybrids. In particular, NiCo alloys encapsulated within nitrogen-doped carbon nanotubes (NiCo@NCNTs) showed higher OER activities in an alkaline solution than the individual metal hybrids (Ni@NCNTs and Co@NCNTs), highlighting a synergy between the Ni and Co components. NiCo@NCNTs pyrolyzed at 800 °C displayed an overpotential of ∼41 mV at a current density of 10 mA cm-2 and were more stable than IrO2 during 1000-cycle accelerated durability testing at a scan rate of 100 mV s-1.
Directory of Open Access Journals (Sweden)
Fernanda Oliveira Vieira da Cunha
2004-12-01
Full Text Available In this work was employed a 2³ factorial experiment design to evaluate the castor oil polyurethane-poly(methyl methacrylate semi-IPN synthesis. The reaction parameters used as independent variables were NCO/OH molar ratio, polyurethane polymerization time and methyl methacrylate (MMA content. The semi-IPNs were cured over 28 h using two thermal treatments. The polymers were characterized by infrared and Raman spectroscopy, thermal analysis and swelling profiles in n-hexane. The glass transition temperature (Tg and the swelling were more affect by the NCO/OH molar ratio variation. The semi-IPNs showed Tg from - 27 to - 6 °C and the swelling range was from 3 to 22%, according to the crosslink density. The IPN mechanical properties were dependent on the cure temperature and MMA content in it. Lower elastic modulus values were observed in IPNs cured at room temperature.
Directory of Open Access Journals (Sweden)
Marwan Wolfgang
2011-07-01
Full Text Available Abstract Background Network inference methods reconstruct mathematical models of molecular or genetic networks directly from experimental data sets. We have previously reported a mathematical method which is exclusively data-driven, does not involve any heuristic decisions within the reconstruction process, and deliveres all possible alternative minimal networks in terms of simple place/transition Petri nets that are consistent with a given discrete time series data set. Results We fundamentally extended the previously published algorithm to consider catalysis and inhibition of the reactions that occur in the underlying network. The results of the reconstruction algorithm are encoded in the form of an extended Petri net involving control arcs. This allows the consideration of processes involving mass flow and/or regulatory interactions. As a non-trivial test case, the phosphate regulatory network of enterobacteria was reconstructed using in silico-generated time-series data sets on wild-type and in silico mutants. Conclusions The new exact algorithm reconstructs extended Petri nets from time series data sets by finding all alternative minimal networks that are consistent with the data. It suggested alternative molecular mechanisms for certain reactions in the network. The algorithm is useful to combine data from wild-type and mutant cells and may potentially integrate physiological, biochemical, pharmacological, and genetic data in the form of a single model.
Catalyst Initiation in the Oscillatory Carbonylation Reaction
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Katarina Novakovic
2011-01-01
Full Text Available Palladium(II iodide is used as a catalyst in the phenylacetylene oxidative carbonylation reaction that has demonstrated oscillatory behaviour in both pH and heat of reaction. In an attempt to extract the reaction network responsible for the oscillatory nature of this reaction, the system was divided into smaller parts and they were studied. This paper focuses on understanding the reaction network responsible for the initial reactions of palladium(II iodide within this oscillatory reaction. The species researched include methanol, palladium(II iodide, potassium iodide, and carbon monoxide. Several chemical reactions were considered and applied in a modelling study. The study revealed the significant role played by traces of water contained in the standard HPLC grade methanol used.
A Program on Biochemical and Biomedical Engineering.
San, Ka-Yiu; McIntire, Larry V.
1989-01-01
Presents an introduction to the Biochemical and Biomedical Engineering program at Rice University. Describes the development of the academic and enhancement programs, including organizational structure and research project titles. (YP)
Introduction: Cancer Gene Networks.
Clarke, Robert
2017-01-01
Constructing, evaluating, and interpreting gene networks generally sits within the broader field of systems biology, which continues to emerge rapidly, particular with respect to its application to understanding the complexity of signaling in the context of cancer biology. For the purposes of this volume, we take a broad definition of systems biology. Considering an organism or disease within an organism as a system, systems biology is the study of the integrated and coordinated interactions of the network(s) of genes, their variants both natural and mutated (e.g., polymorphisms, rearrangements, alternate splicing, mutations), their proteins and isoforms, and the organic and inorganic molecules with which they interact, to execute the biochemical reactions (e.g., as enzymes, substrates, products) that reflect the function of that system. Central to systems biology, and perhaps the only approach that can effectively manage the complexity of such systems, is the building of quantitative multiscale predictive models. The predictions of the models can vary substantially depending on the nature of the model and its inputoutput relationships. For example, a model may predict the outcome of a specific molecular reaction(s), a cellular phenotype (e.g., alive, dead, growth arrest, proliferation, and motility), a change in the respective prevalence of cell or subpopulations, a patient or patient subgroup outcome(s). Such models necessarily require computers. Computational modeling can be thought of as using machine learning and related tools to integrate the very high dimensional data generated from modern, high throughput omics technologies including genomics (next generation sequencing), transcriptomics (gene expression microarrays; RNAseq), metabolomics and proteomics (ultra high performance liquid chromatography, mass spectrometry), and "subomic" technologies to study the kinome, methylome, and others. Mathematical modeling can be thought of as the use of ordinary
Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.
Directory of Open Access Journals (Sweden)
Christian L Barrett
2006-05-01
Full Text Available The number of complete, publicly available genome sequences is now greater than 200, and this number is expected to rapidly grow in the near future as metagenomic and environmental sequencing efforts escalate and the cost of sequencing drops. In order to make use of this data for understanding particular organisms and for discerning general principles about how organisms function, it will be necessary to reconstruct their various biochemical reaction networks. Principal among these will be transcriptional regulatory networks. Given the physical and logical complexity of these networks, the various sources of (often noisy data that can be utilized for their elucidation, the monetary costs involved, and the huge number of potential experiments approximately 10(12 that can be performed, experiment design algorithms will be necessary for synthesizing the various computational and experimental data to maximize the efficiency of regulatory network reconstruction. This paper presents an algorithm for experimental design to systematically and efficiently reconstruct transcriptional regulatory networks. It is meant to be applied iteratively in conjunction with an experimental laboratory component. The algorithm is presented here in the context of reconstructing transcriptional regulation for metabolism in Escherichia coli, and, through a retrospective analysis with previously performed experiments, we show that the produced experiment designs conform to how a human would design experiments. The algorithm is able to utilize probability estimates based on a wide range of computational and experimental sources to suggest experiments with the highest potential of discovering the greatest amount of new regulatory knowledge.
Biochemical software: Carbohydrates on Laboratory
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D.N. Heidrich
2005-07-01
Full Text Available Educators around the world are being challenged to develop and design better and more effective strategies for student learning using a variety of modern resources. In this present work, an educa- tional hypermedia software was constructed as a support tool to biochemistry teaching. Occurrence, structure, main characteristics and biological function of the biomolecule Carbohydrates were pre- sented through modules. The software was developed using concept maps, ISIS-Draw, and FLASH- MX animation program. The chapter Carbohydrates on Laboratory illustrates experimental methods of carbohydrates characterization, through animation of a laboratory scenery. The subject was de- veloped showing reactions as Bial, Benedict, Selliwanoff, Barfoed, Phenol Sulphuric, and Iodines, and also enzymatic reactions as glucose oxidase and amylase. There are also links with short texts in order to help the understanding of the contents and principles of laboratory practice as well as background reactions. Application of the software to undergraduate students and high school teachers showed an excellent acceptance. All of them considered the software a very good learning tool. Both teachers and students welcomed this program as it is more flexible, and allows the learning in a more individual rhythm. In addition, application of the software would be suitable to a more effective learning and it is less expensive than conventional experimental teaching.
Vasyunin, A I
2012-01-01
The observed gas-phase molecular inventory of hot cores is believed to be significantly impacted by the products of chemistry in interstellar ices. In this study, we report the construction of a full macroscopic Monte Carlo model of both the gas-phase chemistry and the chemistry occurring in the icy mantles of interstellar grains. Our model treats icy grain mantles in a layer-by-layer manner, which incorporates laboratory data on ice desorption correctly. The ice treatment includes a distinction between a reactive ice surface and an inert bulk. The treatment also distinguishes between zeroth and first order desorption, and includes the entrapment of volatile species in more refractory ice mantles. We apply the model to the investigation of the chemistry in hot cores, in which a thick ice mantle built up during the previous cold phase of protostellar evolution undergoes surface reactions and is eventually evaporated. For the first time, the impact of a detailed multilayer approach to grain mantle formation on ...
Matsuoka, Takeshi; Tanaka, Shigenori; Ebina, Kuniyoshi
2015-09-07
Photosystem II (PS II) is a protein complex which evolves oxygen and drives charge separation for photosynthesis employing electron and excitation-energy transfer processes over a wide timescale range from picoseconds to milliseconds. While the fluorescence emitted by the antenna pigments of this complex is known as an important indicator of the activity of photosynthesis, its interpretation was difficult because of the complexity of PS II. In this study, an extensive kinetic model which describes the complex and multi-timescale characteristics of PS II is analyzed through the use of the hierarchical coarse-graining method proposed in the authors׳ earlier work. In this coarse-grained analysis, the reaction center (RC) is described by two states, open and closed RCs, both of which consist of oxidized and neutral special pairs being in quasi-equilibrium states. Besides, the PS II model at millisecond scale with three-state RC, which was studied previously, could be derived by suitably adjusting the kinetic parameters of electron transfer between tyrosine and RC. Our novel coarse-grained model of PS II can appropriately explain the light-intensity dependent change of the characteristic patterns of fluorescence induction kinetics from O-J-I-P, which shows two inflection points, J and I, between initial point O and peak point P, to O-J-D-I-P, which shows a dip D between J and I inflection points.
Pita, Marcos; Privman, Vladimir; Arugula, Mary A; Melnikov, Dmitriy; Bocharova, Vera; Katz, Evgeny
2011-03-14
We realize a biochemical filtering process by introducing a buffer in a biocatalytic signal-transduction logic system based on the function of an enzyme, esterase. The input, ethyl butyrate, is converted into butyric acid--the output signal, which in turn is measured by the drop in the pH value. The developed approach offers a versatile "network element" for increasing the complexity of biochemical information processing systems. Evaluation of an optimal regime for quality filtering is accomplished in the framework of a kinetic rate-equation model.
Pita, Marcos; Privman, Vladimir; Arugula, Mary A.; Melnikov, Dmitriy; Bocharova, Vera; Katz, Evgeny
We realize a biochemical filtering process by introducing a buffer in a biocatalytic signal-transduction logic system based on the function of an enzyme, esterase. The input, ethyl butyrate, is converted into butyric acid-the output signal, which in turn is measured by the drop in the pH value. The developed approach offers a versatile "network element" for increasing the complexity of biochemical information processing systems. Evaluation of an optimal regime for quality filtering is accomplished in the framework of a kinetic rate-equation model.
Nonequilibrium steady state of biochemical cycle kinetics under non-isothermal conditions
Jin, Xiao
2016-01-01
Nonequilibrium steady state of isothermal biochemical cycle kinetics has been extensively studied, but much less investigated under non-isothermal conditions. However, once the heat exchange between subsystems is rather slow, the isothermal assumption of the whole system meets great challenge, which is indeed the case inside many kinds of living organisms. Here we generalize the nonequilibrium steady-state theory of isothermal biochemical cycle kinetics, in the master-equation models, to the situation in which the temperatures of subsystems can be far from uniform. We first obtain a new thermodynamic relation between the chemical reaction rates and thermodynamic potentials under such a non-isothermal circumstances, which immediately implies simply applying the isothermal transition-state rate formula for each chemical reaction in terms of only the reactants' temperature, is not thermodynamically consistent. Therefore, we mathematically derive several revised reaction-rate formulas which not only obey the new ...
Endt, P.M.
1956-01-01
Capture reactions will be considered here from the viewpoint of the nuclear spectroscopist. Especially important to him are the capture of neutrons, protons, and alpha particles, which may proceed through narrow resonances, offering a well defined initial state for the subsequent deexcitation proces
Stochastic analysis of biochemical systems
Anderson, David F
2015-01-01
This book focuses on counting processes and continuous-time Markov chains motivated by examples and applications drawn from chemical networks in systems biology. The book should serve well as a supplement for courses in probability and stochastic processes. While the material is presented in a manner most suitable for students who have studied stochastic processes up to and including martingales in continuous time, much of the necessary background material is summarized in the Appendix. Students and Researchers with a solid understanding of calculus, differential equations, and elementary probability and who are well-motivated by the applications will find this book of interest. David F. Anderson is Associate Professor in the Department of Mathematics at the University of Wisconsin and Thomas G. Kurtz is Emeritus Professor in the Departments of Mathematics and Statistics at that university. Their research is focused on probability and stochastic processes with applications in biology and other ar...
Energy Technology Data Exchange (ETDEWEB)
Polettini, Matteo, E-mail: matteo.polettini@uni.lu; Esposito, Massimiliano [Complex Systems and Statistical Mechanics, University of Luxembourg, Campus Limpertsberg, 162a avenue de la Faïencerie, L-1511 Luxembourg, G. D. Luxembourg (Luxembourg)
2014-07-14
In this paper and Paper II, we outline a general framework for the thermodynamic description of open chemical reaction networks, with special regard to metabolic networks regulating cellular physiology and biochemical functions. We first introduce closed networks “in a box”, whose thermodynamics is subjected to strict physical constraints: the mass-action law, elementarity of processes, and detailed balance. We further digress on the role of solvents and on the seemingly unacknowledged property of network independence of free energy landscapes. We then open the system by assuming that the concentrations of certain substrate species (the chemostats) are fixed, whether because promptly regulated by the environment via contact with reservoirs, or because nearly constant in a time window. As a result, the system is driven out of equilibrium. A rich algebraic and topological structure ensues in the network of internal species: Emergent irreversible cycles are associated with nonvanishing affinities, whose symmetries are dictated by the breakage of conservation laws. These central results are resumed in the relation a + b = s{sup Y} between the number of fundamental affinities a, that of broken conservation laws b and the number of chemostats s{sup Y}. We decompose the steady state entropy production rate in terms of fundamental fluxes and affinities in the spirit of Schnakenberg's theory of network thermodynamics, paving the way for the forthcoming treatment of the linear regime, of efficiency and tight coupling, of free energy transduction, and of thermodynamic constraints for network reconstruction.
Polettini, Matteo; Esposito, Massimiliano
2014-07-14
In this paper and Paper II, we outline a general framework for the thermodynamic description of open chemical reaction networks, with special regard to metabolic networks regulating cellular physiology and biochemical functions. We first introduce closed networks "in a box", whose thermodynamics is subjected to strict physical constraints: the mass-action law, elementarity of processes, and detailed balance. We further digress on the role of solvents and on the seemingly unacknowledged property of network independence of free energy landscapes. We then open the system by assuming that the concentrations of certain substrate species (the chemostats) are fixed, whether because promptly regulated by the environment via contact with reservoirs, or because nearly constant in a time window. As a result, the system is driven out of equilibrium. A rich algebraic and topological structure ensues in the network of internal species: Emergent irreversible cycles are associated with nonvanishing affinities, whose symmetries are dictated by the breakage of conservation laws. These central results are resumed in the relation a + b = s(Y) between the number of fundamental affinities a, that of broken conservation laws b and the number of chemostats s(Y). We decompose the steady state entropy production rate in terms of fundamental fluxes and affinities in the spirit of Schnakenberg's theory of network thermodynamics, paving the way for the forthcoming treatment of the linear regime, of efficiency and tight coupling, of free energy transduction, and of thermodynamic constraints for network reconstruction.
40 CFR 158.2010 - Biochemical pesticides data requirements.
2010-07-01
... 40 Protection of Environment 23 2010-07-01 2010-07-01 false Biochemical pesticides data...) PESTICIDE PROGRAMS DATA REQUIREMENTS FOR PESTICIDES Biochemical Pesticides § 158.2010 Biochemical pesticides... required to support registration of biochemical pesticides. Sections 158.2080 through 158.2084 identify...
Baronchelli, Andrea; Pastor-Satorras, Romualdo; Chater, Nick; Christiansen, Morten H
2013-01-01
Networks of interconnected nodes have long played a key role in cognitive science, from artificial neural networks to spreading activation models of semantic memory. Recently, however, a new Network Science has been developed, providing insights into the emergence of global, system-scale properties in contexts as diverse as the Internet, metabolic reactions or collaborations among scientists. Today, the inclusion of network theory into cognitive sciences, and the expansion of complex systems science, promises to significantly change the way in which the organization and dynamics of cognitive and behavioral processes are understood. In this paper, we review recent contributions of network theory at different levels and domains within the cognitive sciences.
Computational study of noise in a large signal transduction network
Directory of Open Access Journals (Sweden)
Ruohonen Keijo
2011-06-01
Full Text Available Abstract Background Biochemical systems are inherently noisy due to the discrete reaction events that occur in a random manner. Although noise is often perceived as a disturbing factor, the system might actually benefit from it. In order to understand the role of noise better, its quality must be studied in a quantitative manner. Computational analysis and modeling play an essential role in this demanding endeavor. Results We implemented a large nonlinear signal transduction network combining protein kinase C, mitogen-activated protein kinase, phospholipase A2, and β isoform of phospholipase C networks. We simulated the network in 300 different cellular volumes using the exact Gillespie stochastic simulation algorithm and analyzed the results in both the time and frequency domain. In order to perform simulations in a reasonable time, we used modern parallel computing techniques. The analysis revealed that time and frequency domain characteristics depend on the system volume. The simulation results also indicated that there are several kinds of noise processes in the network, all of them representing different kinds of low-frequency fluctuations. In the simulations, the power of noise decreased on all frequencies when the system volume was increased. Conclusions We concluded that basic frequency domain techniques can be applied to the analysis of simulation results produced by the Gillespie stochastic simulation algorithm. This approach is suited not only to the study of fluctuations but also to the study of pure noise processes. Noise seems to have an important role in biochemical systems and its properties can be numerically studied by simulating the reacting system in different cellular volumes. Parallel computing techniques make it possible to run massive simulations in hundreds of volumes and, as a result, accurate statistics can be obtained from computational studies.
Multiscale Quantum Mechanics/Molecular Mechanics Simulations with Neural Networks.
Shen, Lin; Wu, Jingheng; Yang, Weitao
2016-10-11
Molecular dynamics simulation with multiscale quantum mechanics/molecular mechanics (QM/MM) methods is a very powerful tool for understanding the mechanism of chemical and biological processes in solution or enzymes. However, its computational cost can be too high for many biochemical systems because of the large number of ab initio QM calculations. Semiempirical QM/MM simulations have much higher efficiency. Its accuracy can be improved with a correction to reach the ab initio QM/MM level. The computational cost on the ab initio calculation for the correction determines the efficiency. In this paper we developed a neural network method for QM/MM calculation as an extension of the neural-network representation reported by Behler and Parrinello. With this approach, the potential energy of any configuration along the reaction path for a given QM/MM system can be predicted at the ab initio QM/MM level based on the semiempirical QM/MM simulations. We further applied this method to three reactions in water to calculate the free energy changes. The free-energy profile obtained from the semiempirical QM/MM simulation is corrected to the ab initio QM/MM level with the potential energies predicted with the constructed neural network. The results are in excellent accordance with the reference data that are obtained from the ab initio QM/MM molecular dynamics simulation or corrected with direct ab initio QM/MM potential energies. Compared with the correction using direct ab initio QM/MM potential energies, our method shows a speed-up of 1 or 2 orders of magnitude. It demonstrates that the neural network method combined with the semiempirical QM/MM calculation can be an efficient and reliable strategy for chemical reaction simulations.
In vitro biochemical characterization of all barley endosperm starch synthases
DEFF Research Database (Denmark)
Cuesta-Seijo, Jose A.; Nielsen, Morten M.; Ruzanski, Christian;
2016-01-01
Starch is the main storage polysaccharide in cereals and the major source of calories in the human diet. It is synthesized by a panel of enzymes including five classes of starch synthases (SSs). While the overall starch synthase (SS) reaction is known, the functional differences between the five SS...... classes are poorly understood. Much of our knowledge comes from analyzing mutant plants with altered SS activities, but the resulting data are often difficult to interpret as a result of pleitropic effects, competition between enzymes, overlaps in enzyme activity and disruption of multi-enzyme complexes....... Here we provide a detailed biochemical study of the activity of all five classes of SSs in barley endosperm. Each enzyme was produced recombinantly in E. coli and the properties and modes of action in vitro were studied in isolation from other SSs and other substrate modifying activities. Our results...
Isolation of Clostridium absonum and its cultural and biochemical properties.
Hayase, M; Mitsui, N; Tamai, K; Nakamura, S; Nishida, S
1974-01-01
A new procedure for isolation of Clostridium absonum was devised. Sixtyseven strains of C. absonum were isolated from 135 soil samples, but no strain of C. absonum could be found from human fecal samples. The lecithinase, hemolysin, and lethal toxin in the culture filtrates of this species exhibited low avidity for C. perfringens type A antitoxin. The three activities were inseparable by the present method of purification. A reinvestigation of biochemical properties revealed that incomplete suppression of lecithinase reaction by C. perfringens type A antitoxin and no fermentation of raffinose, melibiose, and starch are useful criteria to differentiate C. absonum from C. perfringens, and that positive, although weak, gelatin liquefaction and fermentation of trehalose are useful to differentiate it from C. paraperfringens.
DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.
Energy Technology Data Exchange (ETDEWEB)
MASLOV,S.SNEPPEN,K.
2003-11-17
Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An
DETECTION OF TOPOLOGICAL PATTERNS IN PROTEIN NETWORKS.
Energy Technology Data Exchange (ETDEWEB)
MASLOV,S.SNEPPEN,K.
2003-11-17
Complex networks appear in biology on many different levels: (1) All biochemical reactions taking place in a single cell constitute its metabolic network, where nodes are individual metabolites, and edges are metabolic reactions converting them to each other. (2) Virtually every one of these reactions is catalyzed by an enzyme and the specificity of this catalytic function is ensured by the key and lock principle of its physical interaction with the substrate. Often the functional enzyme is formed by several mutually interacting proteins. Thus the structure of the metabolic network is shaped by the network of physical interactions of cell's proteins with their substrates and each other. (3) The abundance and the level of activity of each of the proteins in the physical interaction network in turn is controlled by the regulatory network of the cell. Such regulatory network includes all of the multiple mechanisms in which proteins in the cell control each other including transcriptional and translational regulation, regulation of mRNA editing and its transport out of the nucleus, specific targeting of individual proteins for degradation, modification of their activity e.g. by phosphorylation/dephosphorylation or allosteric regulation, etc. To get some idea about the complexity and interconnectedness of protein-protein regulations in baker's yeast Saccharomyces Cerevisiae in Fig. 1 we show a part of the regulatory network corresponding to positive or negative regulations that regulatory proteins exert on each other. (4) On yet higher level individual cells of a multicellular organism exchange signals with each other. This gives rise to several new networks such as e.g. nervous, hormonal, and immune systems of animals. The intercellular signaling network stages the development of a multicellular organism from the fertilized egg. (5) Finally, on the grandest scale, the interactions between individual species in ecosystems determine their food webs. An
Advances in Biochemical Indices of Zooplankton Production.
Yebra, L; Kobari, T; Sastri, A R; Gusmão, F; Hernández-León, S
2017-01-01
Several new approaches for measuring zooplankton growth and production rates have been developed since the publication of the ICES (International Council for the Exploration of the Sea) Zooplankton Methodology Manual (Harris et al., 2000). In this review, we summarize the advances in biochemical methods made in recent years. Our approach explores the rationale behind each method, the design of calibration experiments, the advantages and limitations of each method and their suitability as proxies for in situ rates of zooplankton community growth and production. We also provide detailed protocols for the existing methods and information relevant to scientists wanting to apply, calibrate or develop these biochemical indices for zooplankton production.
Final Technical Report "Multiscale Simulation Algorithms for Biochemical Systems"
Energy Technology Data Exchange (ETDEWEB)
Petzold, Linda R.
2012-10-25
Biochemical systems are inherently multiscale and stochastic. In microscopic systems formed by living cells, the small numbers of reactant molecules can result in dynamical behavior that is discrete and stochastic rather than continuous and deterministic. An analysis tool that respects these dynamical characteristics is the stochastic simulation algorithm (SSA, Gillespie, 1976), a numerical simulation procedure that is essentially exact for chemical systems that are spatially homogeneous or well stirred. Despite recent improvements, as a procedure that simulates every reaction event, the SSA is necessarily inefficient for most realistic problems. There are two main reasons for this, both arising from the multiscale nature of the underlying problem: (1) stiffness, i.e. the presence of multiple timescales, the fastest of which are stable; and (2) the need to include in the simulation both species that are present in relatively small quantities and should be modeled by a discrete stochastic process, and species that are present in larger quantities and are more efficiently modeled by a deterministic differential equation (or at some scale in between). This project has focused on the development of fast and adaptive algorithms, and the fun- damental theory upon which they must be based, for the multiscale simulation of biochemical systems. Areas addressed by this project include: (1) Theoretical and practical foundations for ac- celerated discrete stochastic simulation (tau-leaping); (2) Dealing with stiffness (fast reactions) in an efficient and well-justified manner in discrete stochastic simulation; (3) Development of adaptive multiscale algorithms for spatially homogeneous discrete stochastic simulation; (4) Development of high-performance SSA algorithms.
Biochemical evolution II: origin of life in tubular microstructures on weathered feldspar surfaces.
Parsons, I; Lee, M R; Smith, J V
1998-12-22
Mineral surfaces were important during the emergence of life on Earth because the assembly of the necessary complex biomolecules by random collisions in dilute aqueous solutions is implausible. Most silicate mineral surfaces are hydrophilic and organophobic and unsuitable for catalytic reactions, but some silica-rich surfaces of partly dealuminated feldspars and zeolites are organophilic and potentially catalytic. Weathered alkali feldspar crystals from granitic rocks at Shap, north west England, contain abundant tubular etch pits, typically 0.4-0.6 microm wide, forming an orthogonal honeycomb network in a surface zone 50 microm thick, with 2-3 x 10(6) intersections per mm2 of crystal surface. Surviving metamorphic rocks demonstrate that granites and acidic surface water were present on the Earth's surface by approximately 3.8 Ga. By analogy with Shap granite, honeycombed feldspar has considerable potential as a natural catalytic surface for the start of biochemical evolution. Biomolecules should have become available by catalysis of amino acids, etc. The honeycomb would have provided access to various mineral inclusions in the feldspar, particularly apatite and oxides, which contain phosphorus and transition metals necessary for energetic life. The organized environment would have protected complex molecules from dispersion into dilute solutions, from hydrolysis, and from UV radiation. Sub-micrometer tubes in the honeycomb might have acted as rudimentary cell walls for proto-organisms, which ultimately evolved a lipid lid giving further shelter from the hostile outside environment. A lid would finally have become a complete cell wall permitting detachment and flotation in primordial "soup." Etch features on weathered alkali feldspar from Shap match the shape of overlying soil bacteria.
Plante, Ianik; Devroye, Luc
2015-09-01
Several computer codes simulating chemical reactions in particles systems are based on the Green's functions of the diffusion equation (GFDE). Indeed, many types of chemical systems have been simulated using the exact GFDE, which has also become the gold standard for validating other theoretical models. In this work, a simulation algorithm is presented to sample the interparticle distance for partially diffusion-controlled reversible ABCD reaction. This algorithm is considered exact for 2-particles systems, is faster than conventional look-up tables and uses only a few kilobytes of memory. The simulation results obtained with this method are compared with those obtained with the independent reaction times (IRT) method. This work is part of our effort in developing models to understand the role of chemical reactions in the radiation effects on cells and tissues and may eventually be included in event-based models of space radiation risks. However, as many reactions are of this type in biological systems, this algorithm might play a pivotal role in future simulation programs not only in radiation chemistry, but also in the simulation of biochemical networks in time and space as well.
Energy Technology Data Exchange (ETDEWEB)
Plante, Ianik, E-mail: ianik.plante-1@nasa.gov [Wyle Science, Technology & Engineering, 1290 Hercules, Houston, TX 77058 (United States); Devroye, Luc, E-mail: lucdevroye@gmail.com [School of Computer Science, McGill University, 3480 University Street, Montreal H3A 0E9 (Canada)
2015-09-15
Several computer codes simulating chemical reactions in particles systems are based on the Green's functions of the diffusion equation (GFDE). Indeed, many types of chemical systems have been simulated using the exact GFDE, which has also become the gold standard for validating other theoretical models. In this work, a simulation algorithm is presented to sample the interparticle distance for partially diffusion-controlled reversible ABCD reaction. This algorithm is considered exact for 2-particles systems, is faster than conventional look-up tables and uses only a few kilobytes of memory. The simulation results obtained with this method are compared with those obtained with the independent reaction times (IRT) method. This work is part of our effort in developing models to understand the role of chemical reactions in the radiation effects on cells and tissues and may eventually be included in event-based models of space radiation risks. However, as many reactions are of this type in biological systems, this algorithm might play a pivotal role in future simulation programs not only in radiation chemistry, but also in the simulation of biochemical networks in time and space as well.
Artificial neural networks in medicine
Energy Technology Data Exchange (ETDEWEB)
Keller, P.E.
1994-07-01
This Technology Brief provides an overview of artificial neural networks (ANN). A definition and explanation of an ANN is given and situations in which an ANN is used are described. ANN applications to medicine specifically are then explored and the areas in which it is currently being used are discussed. Included are medical diagnostic aides, biochemical analysis, medical image analysis and drug development.
Biochemical Applications in the Analytical Chemistry Lab
Strong, Cynthia; Ruttencutter, Jeffrey
2004-01-01
An HPLC and a UV-visible spectrophotometer are identified as instruments that helps to incorporate more biologically-relevant experiments into the course, in order to increase the students understanding of selected biochemistry topics and enhances their ability to apply an analytical approach to biochemical problems. The experiment teaches…
2009 Biochemical Conversion Platform Review Report
Energy Technology Data Exchange (ETDEWEB)
Ferrell, John [Office of Energy Efficiency and Renewable Energy (EERE), Washington, DC (United States)
2009-12-01
This document summarizes the recommendations and evaluations provided by an independent external panel of experts at the U.S. Department of Energy Biomass Program’s Biochemical Conversion platform review meeting, held on April 14-16, 2009, at the Sheraton Denver Downtown, Denver, Colorado.
Biochemical and Anatomical Characteristics of Dolphin Muscles.
1984-01-01
hexosamines. These data suggest a biochemical difference in dolphin and rat tendon, which may be of relevance to the unique myofascial design of the...glycosaminoglycan (GAG) chains . The GAG chain is a polymer of repeating disaccharides, each disaccharide containing a hexosamine and either a
Biochemical Thermodynamics under near Physiological Conditions
Mendez, Eduardo
2008-01-01
The recommendations for nomenclature and tables in Biochemical Thermodynamics approved by IUBMB and IUPAC in 1994 can be easily introduced after the chemical thermodynamic formalism. Substitution of the usual standard thermodynamic properties by the transformed ones in the thermodynamic equations, and the use of appropriate thermodynamic tables…
Context-dependent metabolic networks
Beguerisse-Díaz, Mariano; Oyarzún, Diego; Picó, Jesús; Barahona, Mauricio
2016-01-01
Cells adapt their metabolism to survive changes in their environment. We present a framework for the construction and analysis of metabolic reaction networks that can be tailored to reflect different environmental conditions. Using context-dependent flux distributions from Flux Balance Analysis (FBA), we produce directed networks with weighted links representing the amount of metabolite flowing from a source reaction to a target reaction per unit time. Such networks are analyzed with tools from network theory to reveal salient features of metabolite flows in each biological context. We illustrate our approach with the directed network of the central carbon metabolism of Escherichia coli, and study its properties in four relevant biological scenarios. Our results show that both flow and network structure depend drastically on the environment: networks produced from the same metabolic model in different contexts have different edges, components, and flow communities, capturing the biological re-routing of metab...
Dynamics of the ethanolamine glycerophospholipid remodeling network.
Directory of Open Access Journals (Sweden)
Lu Zhang
Full Text Available Acyl chain remodeling in lipids is a critical biochemical process that plays a central role in disease. However, remodeling remains poorly understood, despite massive increases in lipidomic data. In this work, we determine the dynamic network of ethanolamine glycerophospholipid (PE remodeling, using data from pulse-chase experiments and a novel bioinformatic network inference approach. The model uses a set of ordinary differential equations based on the assumptions that (1 sn1 and sn2 acyl positions are independently remodeled; (2 remodeling reaction rates are constant over time; and (3 acyl donor concentrations are constant. We use a novel fast and accurate two-step algorithm to automatically infer model parameters and their values. This is the first such method applicable to dynamic phospholipid lipidomic data. Our inference procedure closely fits experimental measurements and shows strong cross-validation across six independent experiments with distinct deuterium-labeled PE precursors, demonstrating the validity of our assumptions. In contrast, fits of randomized data or fits using random model parameters are worse. A key outcome is that we are able to robustly distinguish deacylation and reacylation kinetics of individual acyl chain types at the sn1 and sn2 positions, explaining the established prevalence of saturated and unsaturated chains in the respective positions. The present study thus demonstrates that dynamic acyl chain remodeling processes can be reliably determined from dynamic lipidomic data.
The interplay of intrinsic and extrinsic bounded noises in biomolecular networks.
Directory of Open Access Journals (Sweden)
Giulio Caravagna
Full Text Available After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a biomolecular network. The influence of intrinsic and extrinsic noises on biomolecular networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: (i the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, (ii a model of enzymatic futile cycle and (iii a genetic toggle switch. In (ii and (iii we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possible functional role of bounded noises.
Geometric universality of currents in an open network of interacting particles
Energy Technology Data Exchange (ETDEWEB)
Sinitsyn, Nikolai A [Los Alamos National Laboratory; Chernyak, Vladimir Y [Los Alamos National Laboratory; Chertkov, Michael [Los Alamos National Laboratory
2010-01-01
We discuss a non-equilibrium statistical system on a graph or network. Identical particles are injected, interact with each other, traverse, and leave the graph in a stochastic manner described in terms of Poisson rates, possibly dependent on time and instantaneous occupation numbers at the nodes of the graph. We show that under the assumption of the relative rates constancy, the system demonstrates a profound statistical symmetry, resulting in geometric universality of the particle currents statistics. The phenomenon applies broadly to many man-made and natural open stochastic systems, such as queuing of packages over internet, transport of electrons and quasi-particles in mesoscopic systems, and chains of reactions in bio-chemical networks. We illustrate the utility of the general approach using two enabling examples from the two latter disciplines.
Teaching Biochemical Pathways Using Concept Maps
Directory of Open Access Journals (Sweden)
Simon Brown
2013-08-01
Full Text Available The interesting paper by Dinarvand and Vaisi-Raygan (1 makes valuable points about a particularly challenging aspect of biochemistry learning and teaching. Their work prompts me to ask two questions and make a comment. First, what do the authors mean by a concept map (CM? A pathway map could be considered a CM, but a CM could cover modes of regulation and kinetics in relation to particular reactions or pathways and there are many other possibilities. Irrespective of this, a CM can get extremely complex if more than a few concepts are involved (2, as can be seen in examples given by Novak (3. This is the fundamental problem of teaching and learning biochemistry (4, which combines the network of pathways, compartmentation, macromol¬ecular structure, regulation, kinetics and some fairly sophisticated chemical concepts.Second, how did the students go about preparing CMs? My experience is that students prefer to use a computer for most tasks, but standard CM software (5 may not be suitable. For example, they often struggle unnec¬essarily to use software to prepare a graphical summary of the structural features of a protein, its precursors and the gene encoding it. This distracts them from the material. My suggestions that pencil and paper might be sufficient are usually met with amazement. Third, as Dinarvand and Vaisi-Raygan (1 make clear, a coherent summary of the metabolism considered in a course in metabolic biochemistry is crucial if students are to appreciate the pathways and their interconn-ection and regulation. For many years I have used an approach in which students collaborate in tutorials to achieve this. The sessions are usually initiated by me drawing the plasma membrane and the mitochondrial membranes on a large board and inviting the students to fill in the blanks (I provide large sheets of paper so that students can make copies. With coaxing, someone volunteers and I explain that the volunteer is not alone because everyone is
Non-transcriptional regulatory processes shape transcriptional network dynamics
Ray, J. Christian J; Tabor, Jeffrey J.; Igoshin, Oleg A.
2011-01-01
Information about the extra- or intracellular environment is often captured as biochemical signals propagating through regulatory networks. These signals eventually drive phenotypic changes, typically by altering gene expression programs in the cell. Reconstruction of transcriptional regulatory networks has given a compelling picture of bacterial physiology, but transcriptional network maps alone often fail to describe phenotypes. In many cases, the dynamical performance of transcriptional re...
Aon, Miguel Antonio; O'Rourke, Brian; Cortassa, Sonia
2004-01-01
In this work, we highlight the links between fractals and scaling in cells and explore the kinetic consequences for biochemical reactions operating in fractal media. Based on the proposal that the cytoskeletal architecture is organized as a percolation lattice, with clusters emerging as fractal forms, the analysis of kinetics in percolation clusters is especially emphasized. A key consequence of this spatiotemporal cytoplasmic organization is that enzyme reactions following Michaelis-Menten or allosteric type kinetics exhibit higher rates in fractal media (for short times and at lower substrate concentrations) at the percolation threshold than in Euclidean media. As a result, considerably faster and higher amplification of enzymatic activity is obtained. Finally, we describe some of the properties bestowed by cytoskeletal organization and dynamics on metabolic networks.
Reconstruction and analysis of human liver-specific metabolic network based on CNHLPP data.
Zhao, Jing; Geng, Chao; Tao, Lin; Zhang, Duanfeng; Jiang, Ying; Tang, Kailin; Zhu, Ruixin; Yu, Hong; Zhang, Weidong; He, Fuchu; Li, Yixue; Cao, Zhiwei
2010-04-05
Liver is the largest internal organ in the body that takes central roles in metabolic homeostasis, detoxification of various substances, as well as in the synthesis and storage of nutrients. To fulfill these complex tasks, thousands of biochemical reactions are going on in liver to cope with a wide range of foods and environmental variations, which are densely interconnected into an intricate metabolic network. Here, the first human liver-specific metabolic network was reconstructed according to proteomics data from Chinese Human Liver Proteome Project (CNHLPP), and then investigated in the context of the genome-scale metabolic network of Homo sapiens. Topological analysis shows that this organ-specific metabolic network exhibits similar features as organism-specific networks, such as power-law degree distribution, small-world property, and bow-tie structure. Furthermore, the structure of liver network exhibits a modular organization where the modules are formed around precursors from primary metabolism or hub metabolites from derivative metabolism, respectively. Most of the modules are dominated by one major category of metabolisms, while enzymes within same modules have a tendency of being expressed concertedly at protein level. Network decomposition and comparison suggest that the liver network overlays a predominant area in the global metabolic network of H. sapiens genome; meanwhile the human network may develop extra modules to gain more specialized functionality out of liver. The results of this study would permit a high-level interpretation of the metabolite information flow in human liver and provide a basis for modeling the physiological and pathological metabolic states of liver.
Brama, P.A.J.; Tekoppele, J.M.; Bank, R.A.; Barneveld, A.; Weeren, P.R. van
2000-01-01
Biochemical heterogeneity of cartilage within a joint is well known in mature individuals. It has recently been reported that heterogeneity for proteoglycan content and chondrocyte metabolism in sheep develops postnatally under the influence of loading. No data exist on the collagen network in gener
Visualizing biological reaction intermediates with DNA curtains
Zhao, Yiling; Jiang, Yanzhou; Qi, Zhi
2017-04-01
Single-molecule approaches have tremendous potential analyzing dynamic biological reaction with heterogeneity that cannot be effectively accessed via traditional ensemble-level biochemical approaches. The approach of deoxyribonucleic acid (DNA) curtains developed by Dr Eric Greene and his research team at Columbia University is a high-throughput single-molecule technique that utilizes fluorescent imaging to visualize protein–DNA interactions directly and allows the acquisition of statistically relevant information from hundreds or even thousands of individual reactions. This review aims to summarize the past, present, and future of DNA curtains, with an emphasis on its applications to solve important biological questions.
Textural and biochemical changes during ripening of old-fashioned salted herrings
DEFF Research Database (Denmark)
Christensen, Mette; Andersen, Eva; Christensen, Line;
2011-01-01
BACKGROUND: Understanding of the biochemical reactions taking place during ripening of salted herring is still rather limited. Therefore, salted herrings were traditionally produced and the impact of the brine composition was evaluated in relation to the development of the characteristic texture ...... by oxidative reactions inducing myosin cross-linking followed by subsequent degradation of thesemyosin aggregates. The brine composition might play a role in the development of herring texture but this need to be investigated in more details. c 2010 Society of Chemical Industry...
Kinetic modelling of GlmU reactions - prioritization of reaction for therapeutic application.
Directory of Open Access Journals (Sweden)
Vivek K Singh
Full Text Available Mycobacterium tuberculosis(Mtu, a successful pathogen, has developed resistance against the existing anti-tubercular drugs necessitating discovery of drugs with novel action. Enzymes involved in peptidoglycan biosynthesis are attractive targets for antibacterial drug discovery. The bifunctional enzyme mycobacterial GlmU (Glucosamine 1-phosphate N-acetyltransferase/ N-acetylglucosamine-1-phosphate uridyltransferase has been a target enzyme for drug discovery. Its C- and N- terminal domains catalyze acetyltransferase (rxn-1 and uridyltransferase (rxn-2 activities respectively and the final product is involved in peptidoglycan synthesis. However, the bifunctional nature of GlmU poses difficulty in deciding which function to be intervened for therapeutic advantage. Genetic analysis showed this as an essential gene but it is still unclear whether any one or both of the activities are critical for cell survival. Often enzymatic activity with suitable high-throughput assay is chosen for random screening, which may not be the appropriate biological function inhibited for maximal effect. Prediction of rate-limiting function by dynamic network analysis of reactions could be an option to identify the appropriate function. With a view to provide insights into biochemical assays with appropriate activity for inhibitor screening, kinetic modelling studies on GlmU were undertaken. Kinetic model of Mtu GlmU-catalyzed reactions was built based on the available kinetic data on Mtu and deduction from Escherichia coli data. Several model variants were constructed including coupled/decoupled, varying metabolite concentrations and presence/absence of product inhibitions. This study demonstrates that in coupled model at low metabolite concentrations, inhibition of either of the GlmU reactions cause significant decrement in the overall GlmU rate. However at higher metabolite concentrations, rxn-2 showed higher decrement. Moreover, with available intracellular
Kombucha tea fermentation: Microbial and biochemical dynamics.
Chakravorty, Somnath; Bhattacharya, Semantee; Chatzinotas, Antonis; Chakraborty, Writachit; Bhattacharya, Debanjana; Gachhui, Ratan
2016-03-02
Kombucha tea, a non-alcoholic beverage, is acquiring significant interest due to its claimed beneficial properties. The microbial community of Kombucha tea consists of bacteria and yeast which thrive in two mutually non-exclusive compartments: the soup or the beverage and the biofilm floating on it. The microbial community and the biochemical properties of the beverage have so far mostly been described in separate studies. This, however, may prevent understanding the causal links between the microbial communities and the beneficial properties of Kombucha tea. Moreover, an extensive study into the microbial and biochemical dynamics has also been missing. In this study, we thus explored the structure and dynamics of the microbial community along with the biochemical properties of Kombucha tea at different time points up to 21 days of fermentation. We hypothesized that several biochemical properties will change during the course of fermentation along with the shifts in the yeast and bacterial communities. The yeast community of the biofilm did not show much variation over time and was dominated by Candida sp. (73.5-83%). The soup however, showed a significant shift in dominance from Candida sp. to Lachancea sp. on the 7th day of fermentation. This is the first report showing Candida as the most dominating yeast genus during Kombucha fermentation. Komagateibacter was identified as the single largest bacterial genus present in both the biofilm and the soup (~50%). The bacterial diversity was higher in the soup than in the biofilm with a peak on the seventh day of fermentation. The biochemical properties changed with the progression of the fermentation, i.e., beneficial properties of the beverage such as the radical scavenging ability increased significantly with a maximum increase at day 7. We further observed a significantly higher D-saccharic acid-1,4-lactone content and caffeine degradation property compared to previously described Kombucha tea fermentations. Our
Impact of flow velocity on biochemical processes – a laboratory experiment
Directory of Open Access Journals (Sweden)
A. Boisson
2014-08-01
Full Text Available Understanding and predicting hydraulic and chemical properties of natural environments are current crucial challenges. It requires considering hydraulic, chemical and biological processes and evaluating how hydrodynamic properties impact on biochemical reactions. In this context, an original laboratory experiment to study the impact of flow velocity on biochemical reactions along a one-dimensional flow streamline has been developed. Based on the example of nitrate reduction, nitrate-rich water passes through plastic tubes at several flow velocities (from 6.2 to 35 mm min−1, while nitrate concentration at the tube outlet is monitored for more than 500 h. This experimental setup allows assessing the biologically controlled reaction between a mobile electron acceptor (nitrate and an electron donor (carbon coming from an immobile phase (tube that produces carbon during its degradation by microorganisms. It results in observing a dynamic of the nitrate transformation associated with biofilm development which is flow-velocity dependent. It is proposed that the main behaviors of the reaction rates are related to phases of biofilm development through a simple analytical model including assimilation. Experiment results and their interpretation demonstrate a significant impact of flow velocity on reaction performance and stability and highlight the relevance of dynamic experiments over static experiments for understanding biogeochemical processes.
Achilles tendinosis: Changes in biochemical composition and collagen turnover rate
Mos, M. de; El, B. van; Groot, J. de; Jahr, H.; Schie, H.T.M. van; Arkel, E.R. van; Tol, H.; Heijboer, R.; Osch, G.J.V.M. van; Verhaar, J.A.N.
2007-01-01
Background: Understanding biochemical and structural changes of the extracellular matrix in Achilles tendinosis might be important for developing mechanism-based therapies. Hypothesis: In Achilles tendinosis, changes occur in biochemical composition and collagen turnover rate. Study Design: Descript
Achilles tendinosis - Changes in biochemical composition and collagen turnover rate
de Mos, Marieke; van El, Benno; DeGroot, Jeroen; Jahr, Holger; van Schie, Hans T. M.; van Arkel, Ewoud R.; Tol, Hans; Heijboer, Rien; van Osch, Gerjo J. V. M.; Verhaar, Jan A. N.
2007-01-01
Background: Understanding biochemical and structural changes of the extracellular matrix in Achilles tendinosis might be important for developing mechanism-based therapies. Hypothesis: In Achilles tendinosis, changes occur in biochemical composition and collagen turnover rate. Study Design: Descript
Advancement in biochemical assays in andrology
Institute of Scientific and Technical Information of China (English)
Wolf-BernhardSchill; RaftHenkel
1999-01-01
Determination of maikers of sperm function, accessory sex gland secretion and silent male genital tract inflammation is of considerable diagnostic value in the evaluation of male infertility. The introduction of biochemical tests into the analysis of male factor has the advantage that standardized assays with a coefficient of variafion characteristic of clinical chemistry are performed, in contrast to biological test systems with a large variability .Biochemical parameters may be used in clinical practice to evaluate the sperm fertitizing capacity (acrosin, aniline blue,ROS), to characterize male accessory sex gland secretinns (fructose, a-glucosidase, PSA), and to identify men with silent genital tract inflammation (elastase, C'3 complement component, coeruloplasmin, IgA, IgG, ROS). (As/an J Androl 1999 Jun; 1: 45-51)
Optical Slot-Waveguide Based Biochemical Sensors
Directory of Open Access Journals (Sweden)
Carlos Angulo Barrios
2009-06-01
Full Text Available Slot-waveguides allow light to be guided and strongly confined inside a nanometer-scale region of low refractive index. Thus stronger light-analyte interaction can be obtained as compared to that achievable by a conventional waveguide, in which the propagating beam is confined to the high-refractive-index core of the waveguide. In addition, slot-waveguides can be fabricated by employing CMOS compatible materials and technology, enabling miniaturization, integration with electronic, photonic and fluidic components in a chip, and mass production. These advantages have made the use of slot-waveguides for highly se