Conservation Laws in Biochemical Reaction Networks
DEFF Research Database (Denmark)
Mahdi, Adam; Ferragut, Antoni; Valls, Claudia
2017-01-01
We study the existence of linear and nonlinear conservation laws in biochemical reaction networks with mass-action kinetics. It is straightforward to compute the linear conservation laws as they are related to the left null-space of the stoichiometry matrix. The nonlinear conservation laws...... are difficult to identify and have rarely been considered in the context of mass-action reaction networks. Here, using the Darboux theory of integrability, we provide necessary structural (i.e., parameterindependent) conditions on a reaction network to guarantee the existence of nonlinear conservation laws...
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; Tempone, Raul; Vilanova, Pedro
2011-01-01
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
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.
Modeling stochasticity in biochemical reaction networks
International Nuclear Information System (INIS)
Constantino, P H; Vlysidis, M; Smadbeck, P; Kaznessis, Y N
2016-01-01
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. (topical review)
HSimulator: Hybrid Stochastic/Deterministic Simulation of Biochemical Reaction Networks
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Luca Marchetti
2017-01-01
Full Text Available HSimulator is a multithread simulator for mass-action biochemical reaction systems placed in a well-mixed environment. HSimulator provides optimized implementation of a set of widespread state-of-the-art stochastic, deterministic, and hybrid simulation strategies including the first publicly available implementation of the Hybrid Rejection-based Stochastic Simulation Algorithm (HRSSA. HRSSA, the fastest hybrid algorithm to date, allows for an efficient simulation of the models while ensuring the exact simulation of a subset of the reaction network modeling slow reactions. Benchmarks show that HSimulator is often considerably faster than the other considered simulators. The software, running on Java v6.0 or higher, offers a simulation GUI for modeling and visually exploring biological processes and a Javadoc-documented Java library to support the development of custom applications. HSimulator is released under the COSBI Shared Source license agreement (COSBI-SSLA.
International Nuclear Information System (INIS)
Marchetti, Luca; Priami, Corrado; Thanh, Vo Hong
2016-01-01
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.
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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.
Pantazis, Yannis; Katsoulakis, Markos A; Vlachos, Dionisios G
2013-10-22
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. 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. 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 FIM can allow to efficiently address
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.
A moment-convergence method for stochastic analysis of biochemical reaction networks
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Zhang, Jiajun [School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China); Nie, Qing [Department of Mathematics, University of California at Irvine, Irvine, California 92697 (United States); Zhou, Tianshou, E-mail: mcszhtsh@mail.sysu.edu.cn [School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China); Guangdong Province Key Laboratory of Computational Science and School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275 (China)
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.
Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks.
Rumschinski, Philipp; Borchers, Steffen; Bosio, Sandro; Weismantel, Robert; Findeisen, Rolf
2010-05-25
Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand. In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort. The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.
Herath, Narmada; Del Vecchio, Domitilla
2018-03-01
Biochemical reaction networks often involve reactions that take place on different time scales, giving rise to "slow" and "fast" system variables. This property is widely used in the analysis of systems to obtain dynamical models with reduced dimensions. In this paper, we consider stochastic dynamics of biochemical reaction networks modeled using the Linear Noise Approximation (LNA). Under time-scale separation conditions, we obtain a reduced-order LNA that approximates both the slow and fast variables in the system. We mathematically prove that the first and second moments of this reduced-order model converge to those of the full system as the time-scale separation becomes large. These mathematical results, in particular, provide a rigorous justification to the accuracy of LNA models derived using the stochastic total quasi-steady state approximation (tQSSA). Since, in contrast to the stochastic tQSSA, our reduced-order model also provides approximations for the fast variable stochastic properties, we term our method the "stochastic tQSSA+". Finally, we demonstrate the application of our approach on two biochemical network motifs found in gene-regulatory and signal transduction networks.
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.
Thomas, Philipp; Straube, Arthur V.; Grima, Ramon
2011-11-01
It is commonly believed that, whenever timescale separation holds, the predictions of reduced chemical master equations obtained using the stochastic quasi-steady-state approximation are in very good agreement with the predictions of the full master equations. We use the linear noise approximation to obtain a simple formula for the relative error between the predictions of the two master equations for the Michaelis-Menten reaction with substrate input. The reduced approach is predicted to overestimate the variance of the substrate concentration fluctuations by as much as 30%. The theoretical results are validated by stochastic simulations using experimental parameter values for enzymes involved in proteolysis, gluconeogenesis, and fermentation.
RMBNToolbox: random models for biochemical networks
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Niemi Jari
2007-05-01
Full Text Available Abstract Background There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models. Results We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language. Conclusion While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.
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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
Modeling of uncertainties in biochemical reactions.
Mišković, Ljubiša; Hatzimanikatis, Vassily
2011-02-01
Mathematical modeling is an indispensable tool for research and development in biotechnology and bioengineering. The formulation of kinetic models of biochemical networks depends on knowledge of the kinetic properties of the enzymes of the individual reactions. However, kinetic data acquired from experimental observations bring along uncertainties due to various experimental conditions and measurement methods. In this contribution, we propose a novel way to model the uncertainty in the enzyme kinetics and to predict quantitatively the responses of metabolic reactions to the changes in enzyme activities under uncertainty. The proposed methodology accounts explicitly for mechanistic properties of enzymes and physico-chemical and thermodynamic constraints, and is based on formalism from systems theory and metabolic control analysis. We achieve this by observing that kinetic responses of metabolic reactions depend: (i) on the distribution of the enzymes among their free form and all reactive states; (ii) on the equilibrium displacements of the overall reaction and that of the individual enzymatic steps; and (iii) on the net fluxes through the enzyme. Relying on this observation, we develop a novel, efficient Monte Carlo sampling procedure to generate all states within a metabolic reaction that satisfy imposed constrains. Thus, we derive the statistics of the expected responses of the metabolic reactions to changes in enzyme levels and activities, in the levels of metabolites, and in the values of the kinetic parameters. We present aspects of the proposed framework through an example of the fundamental three-step reversible enzymatic reaction mechanism. We demonstrate that the equilibrium displacements of the individual enzymatic steps have an important influence on kinetic responses of the enzyme. Furthermore, we derive the conditions that must be satisfied by a reversible three-step enzymatic reaction operating far away from the equilibrium in order to respond to
Biochemical reactions of the organism
International Nuclear Information System (INIS)
Fedorova, A.V.
1984-01-01
Effects of mercury, strontium chloride, GMDA, trichlorfon as well as some radionuclides ( 89 Sr, 137 Cs, 203 Hg) were studied on rats. Changes in biochemical parameters (histamine content, activity of cholinesterase and histaminase) are noted. Most noticeable changes were observed in enzymatic activity. Distortion of enzymatic systems and accumulation of intermediate exchange and decay products of tissues in excess quantities affecting other systems can be the reason for changes in the organism. The observed changes in biochemical parameters should be necessarily taken into account at hygienic regulations of harmful effects of enviroment
Simulation studies in biochemical signaling and enzyme reactions
Nelatury, Sudarshan R.; Vagula, Mary C.
2014-06-01
Biochemical pathways characterize various biochemical reaction schemes that involve a set of species and the manner in which they are connected. Determination of schematics that represent these pathways is an important task in understanding metabolism and signal transduction. Examples of these Pathways are: DNA and protein synthesis, and production of several macro-molecules essential for cell survival. A sustained feedback mechanism arises in gene expression and production of mRNA that lead to protein synthesis if the protein so synthesized serves as a transcription factor and becomes a repressor of the gene expression. The cellular regulations are carried out through biochemical networks consisting of reactions and regulatory proteins. Systems biology is a relatively new area that attempts to describe the biochemical pathways analytically and develop reliable mathematical models for the pathways. A complete understanding of chemical reaction kinetics is prohibitively hard thanks to the nonlinear and highly complex mechanisms that regulate protein formation, but attempting to numerically solve some of the governing differential equations seems to offer significant insight about their biochemical picture. To validate these models, one can perform simple experiments in the lab. This paper introduces fundamental ideas in biochemical signaling and attempts to take first steps into the understanding of biochemical oscillations. Initially, the two-pool model of calcium is used to describe the dynamics behind the oscillations. Later we present some elementary results showing biochemical oscillations arising from solving differential equations of Elowitz and Leibler using MATLAB software.
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. © 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
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Elston Timothy C
2004-03-01
Full Text Available Abstract Background Intrinsic fluctuations due to the stochastic nature of biochemical reactions can have large effects on the response of biochemical networks. This is particularly true for pathways that involve transcriptional regulation, where generally there are two copies of each gene and the number of messenger RNA (mRNA molecules can be small. Therefore, there is a need for computational tools for developing and investigating stochastic models of biochemical networks. Results We have developed the software package Biochemical Network Stochastic Simulator (BioNetS for efficientlyand accurately simulating stochastic models of biochemical networks. BioNetS has a graphical user interface that allows models to be entered in a straightforward manner, and allows the user to specify the type of random variable (discrete or continuous for each chemical species in the network. The discrete variables are simulated using an efficient implementation of the Gillespie algorithm. For the continuous random variables, BioNetS constructs and numerically solvesthe appropriate chemical Langevin equations. The software package has been developed to scale efficiently with network size, thereby allowing large systems to be studied. BioNetS runs as a BioSpice agent and can be downloaded from http://www.biospice.org. BioNetS also can be run as a stand alone package. All the required files are accessible from http://x.amath.unc.edu/BioNetS. Conclusions We have developed BioNetS to be a reliable tool for studying the stochastic dynamics of large biochemical networks. Important features of BioNetS are its ability to handle hybrid models that consist of both continuous and discrete random variables and its ability to model cell growth and division. We have verified the accuracy and efficiency of the numerical methods by considering several test systems.
Probabilistic sensitivity analysis of biochemical reaction systems.
Zhang, Hong-Xuan; Dempsey, William P; Goutsias, John
2009-09-07
Sensitivity analysis is an indispensable tool for studying the robustness and fragility properties of biochemical reaction systems as well as for designing optimal approaches for selective perturbation and intervention. Deterministic sensitivity analysis techniques, using derivatives of the system response, have been extensively used in the literature. However, these techniques suffer from several drawbacks, which must be carefully considered before using them in problems of systems biology. We develop here a probabilistic approach to sensitivity analysis of biochemical reaction systems. The proposed technique employs a biophysically derived model for parameter fluctuations and, by using a recently suggested variance-based approach to sensitivity analysis [Saltelli et al., Chem. Rev. (Washington, D.C.) 105, 2811 (2005)], it leads to a powerful sensitivity analysis methodology for biochemical reaction systems. The approach presented in this paper addresses many problems associated with derivative-based sensitivity analysis techniques. Most importantly, it produces thermodynamically consistent sensitivity analysis results, can easily accommodate appreciable parameter variations, and allows for systematic investigation of high-order interaction effects. By employing a computational model of the mitogen-activated protein kinase signaling cascade, we demonstrate that our approach is well suited for sensitivity analysis of biochemical reaction systems and can produce a wealth of information about the sensitivity properties of such systems. The price to be paid, however, is a substantial increase in computational complexity over derivative-based techniques, which must be effectively addressed in order to make the proposed approach to sensitivity analysis more practical.
Ricard, Jacques
2010-01-01
The present article discusses the possibility that catalysed chemical networks can evolve. Even simple enzyme-catalysed chemical reactions can display this property. The example studied is that of a two-substrate proteinoid, or enzyme, reaction displaying random binding of its substrates A and B. The fundamental property of such a system is to display either emergence or integration depending on the respective values of the probabilities that the enzyme has bound one of its substrate regardless it has bound the other substrate, or, specifically, after it has bound the other substrate. There is emergence of information if p(A)>p(AB) and p(B)>p(BA). Conversely, if p(A)equilibrium. Moreover, in such systems, emergence results in an increase of the energy level of the ternary EAB complex that becomes closer to the transition state of the reaction, thus leading to the enhancement of catalysis. Hence a drift from quasi-equilibrium is, to a large extent, responsible for the production of information and enhancement of catalysis. Non-equilibrium of these simple systems must be an important aspect that leads to both self-organization and evolutionary processes. These conclusions can be extended to networks of catalysed chemical reactions. Such networks are, in fact, networks of networks, viz. meta-networks. In this formal representation, nodes are chemical reactions catalysed by poorly specific proteinoids, and links can be identified to the transport of metabolites from proteinoid to proteinoid. The concepts of integration and emergence can be applied to such situations and can be used to define the identity of these networks and therefore their evolution. Defined as open non-equilibrium structures, such biochemical networks possess two remarkable properties: (1) the probability of occurrence of their nodes is dependant upon the input and output of matter in, and from, the system and (2) the probability of occurrence of the nodes is strictly linked to their degree of
Hierarchical thinking in network biology: the unbiased modularization of biochemical networks.
Papin, Jason A; Reed, Jennifer L; Palsson, Bernhard O
2004-12-01
As reconstructed biochemical reaction networks continue to grow in size and scope, there is a growing need to describe the functional modules within them. Such modules facilitate the study of biological processes by deconstructing complex biological networks into conceptually simple entities. The definition of network modules is often based on intuitive reasoning. As an alternative, methods are being developed for defining biochemical network modules in an unbiased fashion. These unbiased network modules are mathematically derived from the structure of the whole network under consideration.
Optimal Information Processing in Biochemical Networks
Wiggins, Chris
2012-02-01
A variety of experimental results over the past decades provide examples of near-optimal information processing in biological networks, including in biochemical and transcriptional regulatory networks. Computing information-theoretic quantities requires first choosing or computing the joint probability distribution describing multiple nodes in such a network --- for example, representing the probability distribution of finding an integer copy number of each of two interacting reactants or gene products while respecting the `intrinsic' small copy number noise constraining information transmission at the scale of the cell. I'll given an overview of some recent analytic and numerical work facilitating calculation of such joint distributions and the associated information, which in turn makes possible numerical optimization of information flow in models of noisy regulatory and biochemical networks. Illustrating cases include quantification of form-function relations, ideal design of regulatory cascades, and response to oscillatory driving.
SABIO-RK: A data warehouse for biochemical reactions and their kinetics
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Krebs Olga
2007-03-01
Full Text Available Systems biology is an emerging field that aims at obtaining a system-level understanding of biological processes. The modelling and simulation of networks of biochemical reactions have great and promising application potential but require reliable kinetic data. In order to support the systems biology community with such data we have developed SABIO-RK (System for the Analysis of Biochemical Pathways - Reaction Kinetics, a curated database with information about biochemical reactions and their kinetic properties, which allows researchers to obtain and compare kinetic data and to integrate them into models of biochemical networks. SABIO-RK is freely available for academic use at http://sabio.villa-bosch.de/SABIORK/.
Hu, Qian-Nan; Deng, Zhe; Hu, Huanan; Cao, Dong-Sheng; Liang, Yi-Zeng
2011-09-01
Biochemical reactions play a key role to help sustain life and allow cells to grow. RxnFinder was developed to search biochemical reactions from KEGG reaction database using three search criteria: molecular structures, molecular fragments and reaction similarity. RxnFinder is helpful to get reference reactions for biosynthesis and xenobiotics metabolism. RxnFinder is freely available via: http://sdd.whu.edu.cn/rxnfinder. qnhu@whu.edu.cn.
Estimation of parameter sensitivities for stochastic reaction networks
Gupta, Ankit
2016-01-01
Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a
Comparing chemical reaction networks
DEFF Research Database (Denmark)
Cardelli, Luca; Tribastone, Mirco; Tschaikowski, Max
2017-01-01
We study chemical reaction networks (CRNs) as a kernel model of concurrency provided with semantics based on ordinary differential equations. We investigate the problem of comparing two CRNs, i.e., to decide whether the solutions of a source and of a target CRN can be matched for an appropriate...... choice of initial conditions. Using a categorical framework, we extend and unify model-comparison approaches based on dynamical (semantic) and structural (syntactic) properties of CRNs. Then, we provide an algorithm to compare CRNs, running linearly in time with respect to the cardinality of all possible...... comparisons. Finally, using a prototype implementation, CAGE, we apply our results to biological models from the literature....
On the Adaptive Design Rules of Biochemical Networks in Evolution
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Bor-Sen Chen
2007-01-01
Full Text Available Biochemical networks are the backbones of physiological systems of organisms. Therefore, a biochemical network should be sufficiently robust (not sensitive to tolerate genetic mutations and environmental changes in the evolutionary process. In this study, based on the robustness and sensitivity criteria of biochemical networks, the adaptive design rules are developed for natural selection in the evolutionary process. This will provide insights into the robust adaptive mechanism of biochemical networks in the evolutionary process. We find that if a mutated biochemical network satisfies the robustness and sensitivity criteria of natural selection, there is a high probability for the biochemical network to prevail during natural selection in the evolutionary process. Since there are various mutated biochemical networks that can satisfy these criteria but have some differences in phenotype, the biochemical networks increase their diversities in the evolutionary process. The robustness of a biochemical network enables co-option so that new phenotypes can be generated in evolution. The proposed robust adaptive design rules of natural selection gain much insight into the evolutionary mechanism and provide a systematic robust biochemical circuit design method of biochemical networks for biotechnological and therapeutic purposes in the future.
Structuring evolution: biochemical networks and metabolic diversification in birds.
Morrison, Erin S; Badyaev, Alexander V
2016-08-25
Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a "global" carotenoid network - comprising of all known enzymatic reactions among naturally occurring carotenoids - with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds. We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network - compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution. The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.
Thermodynamics of random reaction networks.
Directory of Open Access Journals (Sweden)
Jakob Fischer
Full Text Available Reaction networks are useful for analyzing reaction systems occurring in chemistry, systems biology, or Earth system science. Despite the importance of thermodynamic disequilibrium for many of those systems, the general thermodynamic properties of reaction networks are poorly understood. To circumvent the problem of sparse thermodynamic data, we generate artificial reaction networks and investigate their non-equilibrium steady state for various boundary fluxes. We generate linear and nonlinear networks using four different complex network models (Erdős-Rényi, Barabási-Albert, Watts-Strogatz, Pan-Sinha and compare their topological properties with real reaction networks. For similar boundary conditions the steady state flow through the linear networks is about one order of magnitude higher than the flow through comparable nonlinear networks. In all networks, the flow decreases with the distance between the inflow and outflow boundary species, with Watts-Strogatz networks showing a significantly smaller slope compared to the three other network types. The distribution of entropy production of the individual reactions inside the network follows a power law in the intermediate region with an exponent of circa -1.5 for linear and -1.66 for nonlinear networks. An elevated entropy production rate is found in reactions associated with weakly connected species. This effect is stronger in nonlinear networks than in the linear ones. Increasing the flow through the nonlinear networks also increases the number of cycles and leads to a narrower distribution of chemical potentials. We conclude that the relation between distribution of dissipation, network topology and strength of disequilibrium is nontrivial and can be studied systematically by artificial reaction networks.
Thermodynamics of random reaction networks.
Fischer, Jakob; Kleidon, Axel; Dittrich, Peter
2015-01-01
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.
Markovian dynamics on complex reaction networks
Energy Technology Data Exchange (ETDEWEB)
Goutsias, J., E-mail: goutsias@jhu.edu; Jenkinson, G., E-mail: jenkinson@jhu.edu
2013-08-10
Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.
Markovian dynamics on complex reaction networks
International Nuclear Information System (INIS)
Goutsias, J.; Jenkinson, G.
2013-01-01
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 underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples
Kulasiri, Don
2011-01-01
We discuss the quantification of molecular fluctuations in the biochemical reaction systems within the context of intracellular processes associated with gene expression. We take the molecular reactions pertaining to circadian rhythms to develop models of molecular fluctuations in this chapter. There are a significant number of studies on stochastic fluctuations in intracellular genetic regulatory networks based on single cell-level experiments. In order to understand the fluctuations associated with the gene expression in circadian rhythm networks, it is important to model the interactions of transcriptional factors with the E-boxes in the promoter regions of some of the genes. The pertinent aspects of a near-equilibrium theory that would integrate the thermodynamical and particle dynamic characteristics of intracellular molecular fluctuations would be discussed, and the theory is extended by using the theory of stochastic differential equations. We then model the fluctuations associated with the promoter regions using general mathematical settings. We implemented ubiquitous Gillespie's algorithms, which are used to simulate stochasticity in biochemical networks, for each of the motifs. Both the theory and the Gillespie's algorithms gave the same results in terms of the time evolution of means and variances of molecular numbers. As biochemical reactions occur far away from equilibrium-hence the use of the Gillespie algorithm-these results suggest that the near-equilibrium theory should be a good approximation for some of the biochemical reactions. © 2011 Elsevier Inc. All rights reserved.
Thermodynamically consistent Bayesian analysis of closed biochemical reaction systems
Directory of Open Access Journals (Sweden)
Goutsias John
2010-11-01
Full Text Available Abstract Background Estimating the rate constants of a biochemical reaction system with known stoichiometry from noisy time series measurements of molecular concentrations is an important step for building predictive models of cellular function. Inference techniques currently available in the literature may produce rate constant values that defy necessary constraints imposed by the fundamental laws of thermodynamics. As a result, these techniques may lead to biochemical reaction systems whose concentration dynamics could not possibly occur in nature. Therefore, development of a thermodynamically consistent approach for estimating the rate constants of a biochemical reaction system is highly desirable. Results We introduce a Bayesian analysis approach for computing thermodynamically consistent estimates of the rate constants of a closed biochemical reaction system with known stoichiometry given experimental data. Our method employs an appropriately designed prior probability density function that effectively integrates fundamental biophysical and thermodynamic knowledge into the inference problem. Moreover, it takes into account experimental strategies for collecting informative observations of molecular concentrations through perturbations. The proposed method employs a maximization-expectation-maximization algorithm that provides thermodynamically feasible estimates of the rate constant values and computes appropriate measures of estimation accuracy. We demonstrate various aspects of the proposed method on synthetic data obtained by simulating a subset of a well-known model of the EGF/ERK signaling pathway, and examine its robustness under conditions that violate key assumptions. Software, coded in MATLAB®, which implements all Bayesian analysis techniques discussed in this paper, is available free of charge at http://www.cis.jhu.edu/~goutsias/CSS%20lab/software.html. Conclusions Our approach provides an attractive statistical methodology for
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.
BioNessie - a grid enabled biochemical networks simulation environment
Liu, X.; Jiang, J.; Ajayi, O.; Gu, X.; Gilbert, D.; Sinnott, R.O.
2008-01-01
The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scale simulations.
Limits for Stochastic Reaction Networks
DEFF Research Database (Denmark)
Cappelletti, Daniele
Reaction systems have been introduced in the 70s to model biochemical systems. Nowadays their range of applications has increased and they are fruitfully used in dierent elds. The concept is simple: some chemical species react, the set of chemical reactions form a graph and a rate function...... is associated with each reaction. Such functions describe the speed of the dierent reactions, or their propensities. Two modelling regimes are then available: the evolution of the dierent species concentrations can be deterministically modelled through a system of ODE, while the counts of the dierent species...... at a certain time are stochastically modelled by means of a continuous-time Markov chain. Our work concerns primarily stochastic reaction systems, and their asymptotic properties. In Paper I, we consider a reaction system with intermediate species, i.e. species that are produced and fast degraded along a path...
Fleming, R M T; Maes, C M; Saunders, M A; Ye, Y; Palsson, B Ø
2012-01-07
We derive a convex optimization problem on a steady-state nonequilibrium network of biochemical reactions, with the property that energy conservation and the second law of thermodynamics both hold at the problem solution. This suggests a new variational principle for biochemical networks that can be implemented in a computationally tractable manner. We derive the Lagrange dual of the optimization problem and use strong duality to demonstrate that a biochemical analogue of Tellegen's theorem holds at optimality. Each optimal flux is dependent on a free parameter that we relate to an elementary kinetic parameter when mass action kinetics is assumed. Copyright © 2011 Elsevier Ltd. All rights reserved.
Effect of Modifying Factors on Radiosensitive Biochemical Reactions
Energy Technology Data Exchange (ETDEWEB)
Romantsev, E. F.; Filippovich, I. V.; Zhulanova, Z. I.; Blokhina, V. D.; Trebenok, Z. A.; Kolesnikov, E. E.; Sheremetyevskaya, T. N.; Nikolsky, A. V.; Zymaleva, O. G. [Institute of Biophysics, USSR Ministry of Health, Moscow, USSR (Russian Federation)
1971-03-15
Some of the radioprotective aminothiols are now routine pharmacopoeial drugs and are used in clinics to decrease the radiation reaction which appears as a side effect during the radiotherapy of cancer. The action of effective modifying agents on radiosensitive biochemical reactions in the organisms of mammals, in principle, cannot be different from the same effects of the protectors on biochemical systems of the human organism. The effect of modifying agents is mediated by biochemical systems. The administration of radioprotective doses of MEA to rats before irradiation results in a significant normalization of the excretion in urine of degradation products of nucleic acids (so-called Dische-positive compounds), the excretion of which sharply rises after irradiation. The curve of the radioprotective effect of MEA (survival rate after administration of radioprotectors at different intervals of time) completely corresponds to curves of the accumulation of MEA which is bound (by mixed disulphide links) to the proteins of liver mitochondria, to proteins of the nuclear-sap, to the hyaloplasm of rat thymus and to the nuclear ribosomes of the spleen. After MEA administration the curve of the biosynthesis of deoxycytidine represents a mirror reflection of the curve of MEA bound to proteins of the thymus hyaloplasm by means of mixed disulphide links. The mechanism of action of such modifying factors as MEA in experiments on mammals is mediated to a great degree through the temporary formation of mixed disulphide links between the aminothiol and the protein component of enzymes in different biochemical systems. (author)
Autocatalytic sets in a partitioned biochemical network.
Smith, Joshua I; Steel, Mike; Hordijk, Wim
2014-01-01
In previous work, RAF theory has been developed as a tool for making theoretical progress on the origin of life question, providing insight into the structure and occurrence of self-sustaining and collectively autocatalytic sets within catalytic polymer networks. We present here an extension in which there are two "independent" polymer sets, where catalysis occurs within and between the sets, but there are no reactions combining polymers from both sets. Such an extension reflects the interaction between nucleic acids and peptides observed in modern cells and proposed forms of early life. We present theoretical work and simulations which suggest that the occurrence of autocatalytic sets is robust to the partitioned structure of the network. We also show that autocatalytic sets remain likely even when the molecules in the system are not polymers, and a low level of inhibition is present. Finally, we present a kinetic extension which assigns a rate to each reaction in the system, and show that identifying autocatalytic sets within such a system is an NP-complete problem. Recent experimental work has challenged the necessity of an RNA world by suggesting that peptide-nucleic acid interactions occurred early in chemical evolution. The present work indicates that such a peptide-RNA world could support the spontaneous development of autocatalytic sets and is thus a feasible alternative worthy of investigation.
Modeling of fluctuating reaction networks
International Nuclear Information System (INIS)
Lipshtat, A.; Biham, O.
2004-01-01
Full Text:Various dynamical systems are organized as reaction networks, where the population size of one component affects the populations of all its neighbors. Such networks can be found in interstellar surface chemistry, cell biology, thin film growth and other systems. I cases where the populations of reactive species are large, the network can be modeled by rate equations which provide all reaction rates within mean field approximation. However, in small systems that are partitioned into sub-micron size, these populations strongly fluctuate. Under these conditions rate equations fail and the master equation is needed for modeling these reactions. However, the number of equations in the master equation grows exponentially with the number of reactive species, severely limiting its feasibility for complex networks. Here we present a method which dramatically reduces the number of equations, thus enabling the incorporation of the master equation in complex reaction networks. The method is examplified in the context of reaction network on dust grains. Its applicability for genetic networks will be discussed. 1. Efficient simulations of gas-grain chemistry in interstellar clouds. Azi Lipshtat and Ofer Biham, Phys. Rev. Lett. 93 (2004), 170601. 2. Modeling of negative autoregulated genetic networks in single cells. Azi Lipshtat, Hagai B. Perets, Nathalie Q. Balaban and Ofer Biham, Gene: evolutionary genomics (2004), In press
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. Copyright © 2016 Elsevier Ltd. All rights reserved.
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.
Moraes, Alvaro
2015-01-01
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
Random catalytic reaction networks
Stadler, Peter F.; Fontana, Walter; Miller, John H.
1993-03-01
We study networks that are a generalization of replicator (or Lotka-Volterra) equations. They model the dynamics of a population of object types whose binary interactions determine the specific type of interaction product. Such a system always reduces its dimension to a subset that contains production pathways for all of its members. The network equation can be rewritten at a level of collectives in terms of two basic interaction patterns: replicator sets and cyclic transformation pathways among sets. Although the system contains well-known cases that exhibit very complicated dynamics, the generic behavior of randomly generated systems is found (numerically) to be extremely robust: convergence to a globally stable rest point. It is easy to tailor networks that display replicator interactions where the replicators are entire self-sustaining subsystems, rather than structureless units. A numerical scan of random systems highlights the special properties of elementary replicators: they reduce the effective interconnectedness of the system, resulting in enhanced competition, and strong correlations between the concentrations.
Accelerating rejection-based simulation of biochemical reactions with bounded acceptance probability
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); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento, Trento (Italy)
2016-06-14
Stochastic simulation of large biochemical reaction networks is often computationally expensive due to the disparate reaction rates and high variability of population of chemical species. An approach to accelerate the simulation is to allow multiple reaction firings before performing update by assuming that reaction propensities are changing of a negligible amount during a time interval. Species with small population in the firings of fast reactions significantly affect both performance and accuracy of this simulation approach. It is even worse when these small population species are involved in a large number of reactions. We present in this paper a new approximate algorithm to cope with this problem. It is based on bounding the acceptance probability of a reaction selected by the exact rejection-based simulation algorithm, which employs propensity bounds of reactions and the rejection-based mechanism to select next reaction firings. The reaction is ensured to be selected to fire with an acceptance rate greater than a predefined probability in which the selection becomes exact if the probability is set to one. Our new algorithm improves the computational cost for selecting the next reaction firing and reduces the updating the propensities of reactions.
The human sunburn reaction: histologic and biochemical studies
International Nuclear Information System (INIS)
Gilchrest, B.A.; Soter, N.A.; Stoff, J.S.; Mihm, M.C. Jr.
1981-01-01
The ultraviolet-induced erythema reaction was investigated histologically and biochemically in four subjects, utilizing suction blister aspirates, analyzed for histamine and prostaglandin E2 (PGE2), and Epon-embedded 1-mu skin biopsy sections from control skin and from irradiated skin at intervals for 72 hours after exposure to a Hanovia lamp. Major histologic alterations in the epidermis included dyskeratotic and vacuolated keratinocytes (sunburn cells), and disappearance of Langerhans cells. In the dermis the major changes were vascular, involving both the superficial and deep venular plexuses. Endothelial cell enlargement was first apparent within 30 minutes of irradiation, peaked at 24 hours, and persisted throughout the 72-hour study period. Mast cell degranulation and associated perivenular edema were first apparent at 1 hour and striking at the onset of erythema, 3 to 4 hours postirradiation; edema was absent and mast cells were again normal in number and granule content at 24 hours. Histamine levels rose approximately fourfold above control values immediately after the onset of erythema and returned to baseline within 24 hours. PGE2 levels were statistically elevated even before the onset of erythema and reached approximately 150% of the control value at 24 hours. These data provide the first evidence that histamine may mediate the early phase of the human sunburn reaction and increase our understanding of its complex histologic and biochemical sequelae
Directory of Open Access Journals (Sweden)
Goutsias John
2010-05-01
Full Text Available Abstract Background Sensitivity analysis is an indispensable tool for the analysis of complex systems. In a recent paper, we have introduced a thermodynamically consistent variance-based sensitivity analysis approach for studying the robustness and fragility properties of biochemical reaction systems under uncertainty in the standard chemical potentials of the activated complexes of the reactions and the standard chemical potentials of the molecular species. In that approach, key sensitivity indices were estimated by Monte Carlo sampling, which is computationally very demanding and impractical for large biochemical reaction systems. Computationally efficient algorithms are needed to make variance-based sensitivity analysis applicable to realistic cellular networks, modeled by biochemical reaction systems that consist of a large number of reactions and molecular species. Results We present four techniques, derivative approximation (DA, polynomial approximation (PA, Gauss-Hermite integration (GHI, and orthonormal Hermite approximation (OHA, for analytically approximating the variance-based sensitivity indices associated with a biochemical reaction system. By using a well-known model of the mitogen-activated protein kinase signaling cascade as a case study, we numerically compare the approximation quality of these techniques against traditional Monte Carlo sampling. Our results indicate that, although DA is computationally the most attractive technique, special care should be exercised when using it for sensitivity analysis, since it may only be accurate at low levels of uncertainty. On the other hand, PA, GHI, and OHA are computationally more demanding than DA but can work well at high levels of uncertainty. GHI results in a slightly better accuracy than PA, but it is more difficult to implement. OHA produces the most accurate approximation results and can be implemented in a straightforward manner. It turns out that the computational cost of the
Modular verification of chemical reaction network encodings via serializability analysis
Lakin, Matthew R.; Stefanovic, Darko; Phillips, Andrew
2015-01-01
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. PMID:27325906
Propagating semantic information in biochemical network models
Directory of Open Access Journals (Sweden)
Schulz Marvin
2012-01-01
Full Text Available Abstract Background To enable automatic searches, alignments, and model combination, the elements of systems biology models need to be compared and matched across models. Elements can be identified by machine-readable biological annotations, but assigning such annotations and matching non-annotated elements is tedious work and calls for automation. Results A new method called "semantic propagation" allows the comparison of model elements based not only on their own annotations, but also on annotations of surrounding elements in the network. One may either propagate feature vectors, describing the annotations of individual elements, or quantitative similarities between elements from different models. Based on semantic propagation, we align partially annotated models and find annotations for non-annotated model elements. Conclusions Semantic propagation and model alignment are included in the open-source library semanticSBML, available on sourceforge. Online services for model alignment and for annotation prediction can be used at http://www.semanticsbml.org.
Computing with competition in biochemical networks.
Genot, Anthony J; Fujii, Teruo; Rondelez, Yannick
2012-11-16
Cells rely on limited resources such as enzymes or transcription factors to process signals and make decisions. However, independent cellular pathways often compete for a common molecular resource. Competition is difficult to analyze because of its nonlinear global nature, and its role remains unclear. Here we show how decision pathways such as transcription networks may exploit competition to process information. Competition for one resource leads to the recognition of convex sets of patterns, whereas competition for several resources (overlapping or cascaded regulons) allows even more general pattern recognition. Competition also generates surprising couplings, such as correlating species that share no resource but a common competitor. The mechanism we propose relies on three primitives that are ubiquitous in cells: multiinput motifs, competition for a resource, and positive feedback loops.
Emergence of switch-like behavior in a large family of simple biochemical networks.
Directory of Open Access Journals (Sweden)
Dan Siegal-Gaskins
2011-05-01
Full Text Available Bistability plays a central role in the gene regulatory networks (GRNs controlling many essential biological functions, including cellular differentiation and cell cycle control. However, establishing the network topologies that can exhibit bistability remains a challenge, in part due to the exceedingly large variety of GRNs that exist for even a small number of components. We begin to address this problem by employing chemical reaction network theory in a comprehensive in silico survey to determine the capacity for bistability of more than 40,000 simple networks that can be formed by two transcription factor-coding genes and their associated proteins (assuming only the most elementary biochemical processes. We find that there exist reaction rate constants leading to bistability in ∼90% of these GRN models, including several circuits that do not contain any of the TF cooperativity commonly associated with bistable systems, and the majority of which could only be identified as bistable through an original subnetwork-based analysis. A topological sorting of the two-gene family of networks based on the presence or absence of biochemical reactions reveals eleven minimal bistable networks (i.e., bistable networks that do not contain within them a smaller bistable subnetwork. The large number of previously unknown bistable network topologies suggests that the capacity for switch-like behavior in GRNs arises with relative ease and is not easily lost through network evolution. To highlight the relevance of the systematic application of CRNT to bistable network identification in real biological systems, we integrated publicly available protein-protein interaction, protein-DNA interaction, and gene expression data from Saccharomyces cerevisiae, and identified several GRNs predicted to behave in a bistable fashion.
Molecular codes in biological and chemical reaction networks.
Directory of Open Access Journals (Sweden)
Dennis Görlich
Full Text Available Shannon's theory of communication has been very successfully applied for the analysis of biological information. However, the theory neglects semantic and pragmatic aspects and thus cannot directly be applied to distinguish between (bio- chemical systems able to process "meaningful" information from those that do not. Here, we present a formal method to assess a system's semantic capacity by analyzing a reaction network's capability to implement molecular codes. We analyzed models of chemical systems (martian atmosphere chemistry and various combustion chemistries, biochemical systems (gene expression, gene translation, and phosphorylation signaling cascades, an artificial chemistry, and random reaction networks. Our study suggests that different chemical systems possess different semantic capacities. No semantic capacity was found in the model of the martian atmosphere chemistry, the studied combustion chemistries, and highly connected random networks, i.e. with these chemistries molecular codes cannot be implemented. High semantic capacity was found in the studied biochemical systems and in random reaction networks where the number of second order reactions is twice the number of species. We conclude that our approach can be applied to evaluate the information processing capabilities of a chemical system and may thus be a useful tool to understand the origin and evolution of meaningful information, e.g. in the context of the origin of life.
Biophysical constraints on the computational capacity of biochemical signaling networks
Wang, Ching-Hao; Mehta, Pankaj
Biophysics fundamentally constrains the computations that cells can carry out. Here, we derive fundamental bounds on the computational capacity of biochemical signaling networks that utilize post-translational modifications (e.g. phosphorylation). To do so, we combine ideas from the statistical physics of disordered systems and the observation by Tony Pawson and others that the biochemistry underlying protein-protein interaction networks is combinatorial and modular. Our results indicate that the computational capacity of signaling networks is severely limited by the energetics of binding and the need to achieve specificity. We relate our results to one of the theoretical pillars of statistical learning theory, Cover's theorem, which places bounds on the computational capacity of perceptrons. PM and CHW were supported by a Simons Investigator in the Mathematical Modeling of Living Systems Grant, and NIH Grant No. 1R35GM119461 (both to PM).
Bowsher, Clive G
2011-02-15
Understanding the encoding and propagation of information by biochemical reaction networks and the relationship of such information processing properties to modular network structure is of fundamental importance in the study of cell signalling and regulation. However, a rigorous, automated approach for general biochemical networks has not been available, and high-throughput analysis has therefore been out of reach. Modularization Identification by Dynamic Independence Algorithms (MIDIA) is a user-friendly, extensible R package that performs automated analysis of how information is processed by biochemical networks. An important component is the algorithm's ability to identify exact network decompositions based on both the mass action kinetics and informational properties of the network. These modularizations are visualized using a tree structure from which important dynamic conditional independence properties can be directly read. Only partial stoichiometric information needs to be used as input to MIDIA, and neither simulations nor knowledge of rate parameters are required. When applied to a signalling network, for example, the method identifies the routes and species involved in the sequential propagation of information between its multiple inputs and outputs. These routes correspond to the relevant paths in the tree structure and may be further visualized using the Input-Output Path Matrix tool. MIDIA remains computationally feasible for the largest network reconstructions currently available and is straightforward to use with models written in Systems Biology Markup Language (SBML). The package is distributed under the GNU General Public License and is available, together with a link to browsable Supplementary Material, at http://code.google.com/p/midia. Further information is at www.maths.bris.ac.uk/~macgb/Software.html.
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. © 2016 Elsevier Inc. All rights reserved.
A network dynamics approach to chemical reaction networks
van der Schaft, Abraham; Rao, S.; Jayawardhana, B.
2016-01-01
A treatment of chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a
Efficient Parallel Statistical Model Checking of Biochemical Networks
Directory of Open Access Journals (Sweden)
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.
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.
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...... that the elasticity coefficients can be calculated directly from the pool levels of metabolites at steady state. The only requirement is that one thermodynamic parameter be known, namely the reaction affinity at the intercept of the tangent in the inflection point of the curve of reaction rate against reaction...... of the thermokinetic description of reaction rates to include the influence of effecters. Here the reaction rate is written as a linear function of the logarithm of the metabolite concentrations. With this type of rate function it is shown that the approach of Delgado and Liao [Biochem. J. (1992) 282, 919-927] can...
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.
Efficient rejection-based simulation of biochemical reactions with stochastic noise and delays
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 (Italy); Zunino, Roberto, E-mail: roberto.zunino@unitn.it [Department of Mathematics, University of Trento (Italy)
2014-10-07
We propose a new exact stochastic rejection-based simulation algorithm for biochemical reactions and extend it to systems with delays. Our algorithm accelerates the simulation by pre-computing reaction propensity bounds to select the next reaction to perform. Exploiting such bounds, we are able to avoid recomputing propensities every time a (delayed) reaction is initiated or finished, as is typically necessary in standard approaches. Propensity updates in our approach are still performed, but only infrequently and limited for a small number of reactions, saving computation time and without sacrificing exactness. We evaluate the performance improvement of our algorithm by experimenting with concrete biological models.
Estimation of parameter sensitivities for stochastic reaction networks
Gupta, Ankit
2016-01-07
Quantification of the effects of parameter uncertainty is an important and challenging problem in Systems Biology. We consider this problem in the context of stochastic models of biochemical reaction networks where the dynamics is described as a continuous-time Markov chain whose states represent the molecular counts of various species. For such models, effects of parameter uncertainty are often quantified by estimating the infinitesimal sensitivities of some observables with respect to model parameters. The aim of this talk is to present a holistic approach towards this problem of estimating parameter sensitivities for stochastic reaction networks. Our approach is based on a generic formula which allows us to construct efficient estimators for parameter sensitivity using simulations of the underlying model. We will discuss how novel simulation techniques, such as tau-leaping approximations, multi-level methods etc. can be easily integrated with our approach and how one can deal with stiff reaction networks where reactions span multiple time-scales. We will demonstrate the efficiency and applicability of our approach using many examples from the biological literature.
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.
'BioNessie(G) - a grid enabled biochemical networks simulation environment
Liu, X; Jiang, J; Ajayi, O; Gu, X; Gilbert, D; Sinnott, R
2008-01-01
The simulation of biochemical networks provides insight and understanding about the underlying biochemical processes and pathways used by cells and organisms. BioNessie is a biochemical network simulator which has been developed at the University of Glasgow. This paper describes the simulator and focuses in particular on how it has been extended to benefit from a wide variety of high performance compute resources across the UK through Grid technologies to support larger scal...
Neutral theory of chemical reaction networks
International Nuclear Information System (INIS)
Lee, Sang Hoon; Holme, Petter; Minnhagen, Petter; Bernhardsson, Sebastian; Kim, Beom Jun
2012-01-01
To what extent do the characteristic features of a chemical reaction network reflect its purpose and function? In general, one argues that correlations between specific features and specific functions are key to understanding a complex structure. However, specific features may sometimes be neutral and uncorrelated with any system-specific purpose, function or causal chain. Such neutral features are caused by chance and randomness. Here we compare two classes of chemical networks: one that has been subjected to biological evolution (the chemical reaction network of metabolism in living cells) and one that has not (the atmospheric planetary chemical reaction networks). Their degree distributions are shown to share the very same neutral system-independent features. The shape of the broad distributions is to a large extent controlled by a single parameter, the network size. From this perspective, there is little difference between atmospheric and metabolic networks; they are just different sizes of the same random assembling network. In other words, the shape of the degree distribution is a neutral characteristic feature and has no functional or evolutionary implications in itself; it is not a matter of life and death. (paper)
Contributions of cell growth and biochemical reactions to nongenetic variability of cells.
Schwabe, A.; Bruggeman, F.J.
2014-01-01
Cell-to-cell variability in the molecular composition of isogenic, steady-state growing cells arises spontaneously from the inherent stochasticity of intracellular biochemical reactions and cell growth. Here, we present a general decomposition of the total variance in the copy number per cell of a
A network dynamics approach to chemical reaction networks
van der Schaft, A. J.; Rao, S.; Jayawardhana, B.
2016-04-01
A treatment of a chemical reaction network theory is given from the perspective of nonlinear network dynamics, in particular of consensus dynamics. By starting from the complex-balanced assumption, the reaction dynamics governed by mass action kinetics can be rewritten into a form which allows for a very simple derivation of a number of key results in the chemical reaction network theory, and which directly relates to the thermodynamics and port-Hamiltonian formulation of the system. Central in this formulation is the definition of a balanced Laplacian matrix on the graph of chemical complexes together with a resulting fundamental inequality. This immediately leads to the characterisation of the set of equilibria and their stability. Furthermore, the assumption of complex balancedness is revisited from the point of view of Kirchhoff's matrix tree theorem. Both the form of the dynamics and the deduced behaviour are very similar to consensus dynamics, and provide additional perspectives to the latter. Finally, using the classical idea of extending the graph of chemical complexes by a 'zero' complex, a complete steady-state stability analysis of mass action kinetics reaction networks with constant inflows and mass action kinetics outflows is given, and a unified framework is provided for structure-preserving model reduction of this important class of open reaction networks.
Formal balancing of chemical reaction networks
van der Schaft, Abraham; Rao, S.; Jayawardhana, B.
2016-01-01
In this paper we recall and extend the main results of Van der Schaft, Rao, Jayawardhana (2015) concerning the use of Kirchhoff’s Matrix Tree theorem in the explicit characterization of complex-balanced reaction networks and the notion of formal balancing. The notion of formal balancing corresponds
Directory of Open Access Journals (Sweden)
E. I. Ponomareva
2013-01-01
Full Text Available Researches are devoted to identifying changes in the chemical composition of whole-grain wheat bread during baking and to forecasting of food value of bakery products by mathematical modeling of biochemical transformations. The received model represents the invariant composition, considering speed of biochemical reactions at a batch of bakery products, and allowing conduct virtual experiments to develop new types of bread for various categories of the population, including athletes. The offered way of modeling of biochemical transformations at a stage of heat treatment allows to predict food value of bakery products, without spending funds for raw materials and large volume of experiment that will provide possibility of economy of material resources at a stage of development of new types of bakery products and possibility of production efficiency increase.
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.
On the network thermodynamics of mass action chemical reaction networks
Schaft, A.J. van der; Rao, S.; Jayawardhana, B.
In this paper we elaborate on the mathematical formulation of mass action chemical reaction networks as recently given in van der Schaft, Rao, Jayawardhana (2012). We show how the reference chemical potentials define a specific thermodynamical equilibrium, and we discuss the port-Hamiltonian
Switching dynamics in reaction networks induced by molecular discreteness
International Nuclear Information System (INIS)
Togashi, Yuichi; Kaneko, Kunihiko
2007-01-01
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 concentrations on the system size, which is caused by transitions to discreteness-induced novel states
Graphical reduction of reaction networks by linear elimination of species
DEFF Research Database (Denmark)
Saez Cornellana, Meritxell; Wiuf, Carsten; Feliu, Elisenda
2017-01-01
We give a graphically based procedure to reduce a reaction network to a smaller reaction network with fewer species after linear elimination of a set of noninteracting species. We give a description of the reduced reaction network, its kinetics and conservations laws, and explore properties...
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. Copyright © 2016 Elsevier Ltd. All rights reserved.
Timasheff, Serge N
2002-07-23
Solvent additives (cosolvents, osmolytes) modulate biochemical reactions if, during the course of the reaction, there is a change in preferential interactions of solvent components with the reacting system. Preferential interactions can be expressed in terms of preferential binding of the cosolvent or its preferential exclusion (preferential hydration). The driving force is the perturbation by the protein of the chemical potential of the cosolvent. It is shown that the measured change of the amount of water in contact with protein during the course of the reaction modulated by an osmolyte is a change in preferential hydration that is strictly a measure of the cosolvent chemical potential perturbation by the protein in the ternary water-protein-cosolvent system. It is not equal to the change in water of hydration, because water of hydration is a reflection strictly of protein-water forces in a binary system. There is no direct relation between water of preferential hydration and water of hydration.
International Network of Nuclear Reaction Data Centres
International Nuclear Information System (INIS)
Otsuka, Naohiko; Dunaeva, Svetlana
2010-11-01
The activities of fourteen nuclear data centres are summarized, and their cooperation under the auspices of the International Atomic Energy Agency is described. Each of the centres provides coverage for different geographical zones and/or specific types of nuclear data, thus together providing a complete service for users worldwide. The International Network of Nuclear Reaction Data Centres (NRDC) was established with the objective of providing nuclear physics databases that are required for nuclear technology (encompassing energy and non-energy applications) by coordinating the collection, compilation and dissemination of nuclear data on an international scale. (author)
Nuclear Forensics and Radiochemistry: Reaction Networks
Energy Technology Data Exchange (ETDEWEB)
Rundberg, Robert S. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2017-11-22
In the intense neutron flux of a nuclear explosion the production of isotopes may occur through successive neutron induced reactions. The pathway to these isotopes illustrates both the complexity of the problem and the need for high quality nuclear data. The growth and decay of radioactive isotopes can follow a similarly complex network. The Bateman equation will be described and modified to apply to the transmutation of isotopes in a high flux reactor. A alternative model of growth and decay, the GD code, that can be applied to fission products will also be described.
SABIO-RK: an updated resource for manually curated biochemical reaction kinetics
Rey, Maja; Weidemann, Andreas; Kania, Renate; Müller, Wolfgang
2018-01-01
Abstract SABIO-RK (http://sabiork.h-its.org/) is a manually curated database containing data about biochemical reactions and their reaction kinetics. The data are primarily extracted from scientific literature and stored in a relational database. The content comprises both naturally occurring and alternatively measured biochemical reactions and is not restricted to any organism class. The data are made available to the public by a web-based search interface and by web services for programmatic access. In this update we describe major improvements and extensions of SABIO-RK since our last publication in the database issue of Nucleic Acid Research (2012). (i) The website has been completely revised and (ii) allows now also free text search for kinetics data. (iii) Additional interlinkages with other databases in our field have been established; this enables users to gain directly comprehensive knowledge about the properties of enzymes and kinetics beyond SABIO-RK. (iv) Vice versa, direct access to SABIO-RK data has been implemented in several systems biology tools and workflows. (v) On request of our experimental users, the data can be exported now additionally in spreadsheet formats. (vi) The newly established SABIO-RK Curation Service allows to respond to specific data requirements. PMID:29092055
A probabilistic approach to identify putative drug targets in biochemical networks.
Murabito, E.; Smalbone, K.; Swinton, J.; Westerhoff, H.V.; Steuer, R.
2011-01-01
Network-based drug design holds great promise in clinical research as a way to overcome the limitations of traditional approaches in the development of drugs with high efficacy and low toxicity. This novel strategy aims to study how a biochemical network as a whole, rather than its individual
Hidden long evolutionary memory in a model biochemical network
Ali, Md. Zulfikar; Wingreen, Ned S.; Mukhopadhyay, Ranjan
2018-04-01
We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.
Open complex-balanced mass action chemical reaction networks
Rao, Shodhan; van der Schaft, Arjan; Jayawardhana, Bayu
We consider open chemical reaction networks, i.e. ones with inflows and outflows. We assume that all the inflows to the network are constant and all outflows obey the mass action kinetics rate law. We define a complex-balanced open reaction network as one that admits a complex-balanced steady state.
Johnston, Matthew D
2017-12-01
Recent work of Johnston et al. has produced sufficient conditions on the structure of a chemical reaction network which guarantee that the corresponding discrete state space system exhibits an extinction event. The conditions consist of a series of systems of equalities and inequalities on the edges of a modified reaction network called a domination-expanded reaction network. In this paper, we present a computational implementation of these conditions written in Python and apply the program on examples drawn from the biochemical literature. We also run the program on 458 models from the European Bioinformatics Institute's BioModels Database and report our results. Copyright © 2017 Elsevier Inc. All rights reserved.
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.
Vickram, A S; Kamini, A Rao; Das, Raja; Pathy, M Ramesh; Parameswari, R; Archana, K; Sridharan, T B
2016-08-01
Seminal fluid is the secretion from many glands comprised of several organic and inorganic compounds including free amino acids, proteins, fructose, glucosidase, zinc, and other scavenging elements like Mg(2+), Ca(2+), K(+), and Na(+). Therefore, in the view of development of novel approaches and proper diagnosis to male infertility, overall understanding of the biochemical and molecular composition and its role in regulation of sperm quality is highly desirable. Perhaps this can be achieved through artificial intelligence. This study was aimed to elucidate and predict various biochemical markers present in human seminal plasma with three different neural network models. A total of 177 semen samples were collected for this research (both fertile and infertile samples) and immediately processed to prepare a semen analysis report, based on the protocol of the World Health Organization (WHO [2010]). The semen samples were then categorized into oligoasthenospermia (n=35), asthenospermia (n=35), azoospermia (n=22), normospermia (n=34), oligospermia (n=34), and control (n=17). The major biochemical parameters like total protein content, fructose, glucosidase, and zinc content were elucidated by standard protocols. All the biochemical markers were predicted by using three different artificial neural network (ANN) models with semen parameters as inputs. Of the three models, the back propagation neural network model (BPNN) yielded the best results with mean absolute error 0.025, -0.080, 0.166, and -0.057 for protein, fructose, glucosidase, and zinc, respectively. This suggests that BPNN can be used to predict biochemical parameters for the proper diagnosis of male infertility in assisted reproductive technology (ART) centres. AAS: absorption spectroscopy; AI: artificial intelligence; ANN: artificial neural networks; ART: assisted reproductive technology; BPNN: back propagation neural network model; DT: decision tress; MLP: multilayer perceptron; PESA: percutaneous
Identification of neutral biochemical network models from time series data
Directory of Open Access Journals (Sweden)
Maia Marco
2009-05-01
Full Text Available Abstract Background The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. Results In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. Conclusion The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Identification of neutral biochemical network models from time series data.
Vilela, Marco; Vinga, Susana; Maia, Marco A Grivet Mattoso; Voit, Eberhard O; Almeida, Jonas S
2009-05-05
The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, i.e., if it is constructed according to strict guidelines. In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity. The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium Lactococcus lactis and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.
Efficient Characterization of Parametric Uncertainty of Complex (Biochemical Networks.
Directory of Open Access Journals (Sweden)
Claudia Schillings
2015-08-01
Full Text Available Parametric uncertainty is a particularly challenging and relevant aspect of systems analysis in domains such as systems biology where, both for inference and for assessing prediction uncertainties, it is essential to characterize the system behavior globally in the parameter space. However, current methods based on local approximations or on Monte-Carlo sampling cope only insufficiently with high-dimensional parameter spaces associated with complex network models. Here, we propose an alternative deterministic methodology that relies on sparse polynomial approximations. We propose a deterministic computational interpolation scheme which identifies most significant expansion coefficients adaptively. We present its performance in kinetic model equations from computational systems biology with several hundred parameters and state variables, leading to numerical approximations of the parametric solution on the entire parameter space. The scheme is based on adaptive Smolyak interpolation of the parametric solution at judiciously and adaptively chosen points in parameter space. As Monte-Carlo sampling, it is "non-intrusive" and well-suited for massively parallel implementation, but affords higher convergence rates. This opens up new avenues for large-scale dynamic network analysis by enabling scaling for many applications, including parameter estimation, uncertainty quantification, and systems design.
Exact model reduction of combinatorial reaction networks
Directory of Open Access Journals (Sweden)
Fey Dirk
2008-08-01
Full Text Available Abstract Background Receptors and scaffold proteins usually possess a high number of distinct binding domains inducing the formation of large multiprotein signaling complexes. Due to combinatorial reasons the number of distinguishable species grows exponentially with the number of binding domains and can easily reach several millions. Even by including only a limited number of components and binding domains the resulting models are very large and hardly manageable. A novel model reduction technique allows the significant reduction and modularization of these models. Results We introduce methods that extend and complete the already introduced approach. For instance, we provide techniques to handle the formation of multi-scaffold complexes as well as receptor dimerization. Furthermore, we discuss a new modeling approach that allows the direct generation of exactly reduced model structures. The developed methods are used to reduce a model of EGF and insulin receptor crosstalk comprising 5,182 ordinary differential equations (ODEs to a model with 87 ODEs. Conclusion The methods, presented in this contribution, significantly enhance the available methods to exactly reduce models of combinatorial reaction networks.
Grima, R
2010-07-21
Chemical master equations provide a mathematical description of stochastic reaction kinetics in well-mixed conditions. They are a valid description over length scales that are larger than the reactive mean free path and thus describe kinetics in compartments of mesoscopic and macroscopic dimensions. The trajectories of the stochastic chemical processes described by the master equation can be ensemble-averaged to obtain the average number density of chemical species, i.e., the true concentration, at any spatial scale of interest. For macroscopic volumes, the true concentration is very well approximated by the solution of the corresponding deterministic and macroscopic rate equations, i.e., the macroscopic concentration. However, this equivalence breaks down for mesoscopic volumes. These deviations are particularly significant for open systems and cannot be calculated via the Fokker-Planck or linear-noise approximations of the master equation. We utilize the system-size expansion including terms of the order of Omega(-1/2) to derive a set of differential equations whose solution approximates the true concentration as given by the master equation. These equations are valid in any open or closed chemical reaction network and at both the mesoscopic and macroscopic scales. In the limit of large volumes, the effective mesoscopic rate equations become precisely equal to the conventional macroscopic rate equations. We compare the three formalisms of effective mesoscopic rate equations, conventional rate equations, and chemical master equations by applying them to several biochemical reaction systems (homodimeric and heterodimeric protein-protein interactions, series of sequential enzyme reactions, and positive feedback loops) in nonequilibrium steady-state conditions. In all cases, we find that the effective mesoscopic rate equations can predict very well the true concentration of a chemical species. This provides a useful method by which one can quickly determine the
International Nuclear Information System (INIS)
Pal, Krishnendu; Das, Biswajit; Banerjee, Kinshuk; Gangopadhyay, Gautam
2015-01-01
We have introduced an approach to nonequilibrium thermodynamics of an open chemical reaction network in terms of the propensities of the individual elementary reactions and the corresponding reverse reactions. The method is a microscopic formulation of the dissipation function in terms of the relative entropy or Kullback-Leibler distance which is based on the analogy of phase space trajectory with the path of elementary reactions in a network of chemical process. We have introduced here a fluctuation theorem valid for each opposite pair of elementary reactions which is useful in determining the contribution of each sub-reaction on the nonequilibrium thermodynamics of overall reaction. The methodology is applied to an oligomeric enzyme kinetics at a chemiostatic condition that leads the reaction to a nonequilibrium steady state for which we have estimated how each step of the reaction is energy driven or entropy driven to contribute to the overall reaction. (paper)
Consumer Activities and Reactions to Social Network Marketing
Bistra Vassileva
2017-01-01
The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM) referral behaviour, and purchase intentions. Consumers are investigated ...
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
Accelerated Sensitivity Analysis in High-Dimensional Stochastic Reaction Networks.
Directory of Open Access Journals (Sweden)
Georgios Arampatzis
Full Text Available 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
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.
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.
SkyNet: Modular nuclear reaction network library
Lippuner, Jonas; Roberts, Luke F.
2017-10-01
The general-purpose nuclear reaction network SkyNet evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. Any list of isotopes can be evolved and SkyNet supports various different types of nuclear reactions. SkyNet is modular, permitting new or existing physics, such as nuclear reactions or equations of state, to be easily added or modified.
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...
International Nuclear Information System (INIS)
Qian, Hong
2011-01-01
The nonlinear dynamics of biochemical reactions in a small-sized system on the order of a cell are stochastic. Assuming spatial homogeneity, the populations of n molecular species follow a multi-dimensional birth-and-death process on Z n . We introduce the Delbrück–Gillespie process, a continuous-time Markov jump process, whose Kolmogorov forward equation has been known as the chemical master equation, and whose stochastic trajectories can be computed via the Gillespie algorithm. Using simple models, we illustrate that a system of nonlinear ordinary differential equations on R n emerges in the infinite system size limit. For finite system size, transitions among multiple attractors of the nonlinear dynamical system are rare events with exponentially long transit times. There is a separation of time scales between the deterministic ODEs and the stochastic Markov jumps between attractors. No diffusion process can provide a global representation that is accurate on both short and long time scales for the nonlinear, stochastic population dynamics. On the short time scale and near deterministic stable fixed points, Ornstein–Uhlenbeck Gaussian processes give linear stochastic dynamics that exhibit time-irreversible circular motion for open, driven chemical systems. Extending this individual stochastic behaviour-based nonlinear population theory of molecular species to other biological systems is discussed. (invited article)
The Forward-Reverse Algorithm for Stochastic Reaction Networks
Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro
2015-01-01
In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem
National Research Council Canada - National Science Library
Frazier, John; Chusak, Yaroslav; Foy, Brent
2008-01-01
.... The software uses either exact or approximate stochastic simulation algorithms for generating Monte Carlo trajectories that describe the time evolution of the behavior of biomolecular reaction networks...
Least-squares methods for identifying biochemical regulatory networks from noisy measurements
Directory of Open Access Journals (Sweden)
Heslop-Harrison Pat
2007-01-01
Full Text Available Abstract Background We consider the problem of identifying the dynamic interactions in biochemical networks from noisy experimental data. Typically, approaches for solving this problem make use of an estimation algorithm such as the well-known linear Least-Squares (LS estimation technique. We demonstrate that when time-series measurements are corrupted by white noise and/or drift noise, more accurate and reliable identification of network interactions can be achieved by employing an estimation algorithm known as Constrained Total Least Squares (CTLS. The Total Least Squares (TLS technique is a generalised least squares method to solve an overdetermined set of equations whose coefficients are noisy. The CTLS is a natural extension of TLS to the case where the noise components of the coefficients are correlated, as is usually the case with time-series measurements of concentrations and expression profiles in gene networks. Results The superior performance of the CTLS method in identifying network interactions is demonstrated on three examples: a genetic network containing four genes, a network describing p53 activity and mdm2 messenger RNA interactions, and a recently proposed kinetic model for interleukin (IL-6 and (IL-12b messenger RNA expression as a function of ATF3 and NF-κB promoter binding. For the first example, the CTLS significantly reduces the errors in the estimation of the Jacobian for the gene network. For the second, the CTLS reduces the errors from the measurements that are corrupted by white noise and the effect of neglected kinetics. For the third, it allows the correct identification, from noisy data, of the negative regulation of (IL-6 and (IL-12b by ATF3. Conclusion The significant improvements in performance demonstrated by the CTLS method under the wide range of conditions tested here, including different levels and types of measurement noise and different numbers of data points, suggests that its application will enable
Elimination of intermediate species in multiscale stochastic reaction networks
DEFF Research Database (Denmark)
Cappelletti, Daniele; Wiuf, Carsten
2016-01-01
such as the substrate-enzyme complex in the Michaelis-Menten mechanism. Such species are virtually in all real-world networks, they are typically short-lived, degraded at a fast rate and hard to observe experimentally. We provide conditions under which the Markov process of a multiscale reaction network...
Directory of Open Access Journals (Sweden)
Ankit Gupta
2014-06-01
Full Text Available Reaction networks are systems in which the populations of a finite number of species evolve through predefined interactions. Such networks are found as modeling tools in many biological disciplines such as biochemistry, ecology, epidemiology, immunology, systems biology and synthetic biology. It is now well-established that, for small population sizes, stochastic models for biochemical reaction networks are necessary to capture randomness in the interactions. The tools for analyzing such models, however, still lag far behind their deterministic counterparts. In this paper, we bridge this gap by developing a constructive framework for examining the long-term behavior and stability properties of the reaction dynamics in a stochastic setting. In particular, we address the problems of determining ergodicity of the reaction dynamics, which is analogous to having a globally attracting fixed point for deterministic dynamics. We also examine when the statistical moments of the underlying process remain bounded with time and when they converge to their steady state values. The framework we develop relies on a blend of ideas from probability theory, linear algebra and optimization theory. We demonstrate that the stability properties of a wide class of biological networks can be assessed from our sufficient theoretical conditions that can be recast as efficient and scalable linear programs, well-known for their tractability. It is notably shown that the computational complexity is often linear in the number of species. We illustrate the validity, the efficiency and the wide applicability of our results on several reaction networks arising in biochemistry, systems biology, epidemiology and ecology. The biological implications of the results as well as an example of a non-ergodic biological network are also discussed.
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.
A computational framework for the automated construction of glycosylation reaction networks.
Liu, Gang; Neelamegham, Sriram
2014-01-01
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 biochemistry. All
A computational framework for the automated construction of glycosylation reaction networks.
Directory of Open Access Journals (Sweden)
Gang Liu
Full Text Available Glycosylation is among the most common and complex post-translational modifications identified to date. It proceeds through the catalytic action of multiple enzyme families that include the glycosyltransferases that add monosaccharides to growing glycans, and glycosidases which remove sugar residues to trim glycans. The expression level and specificity of these enzymes, in part, regulate the glycan distribution or glycome of specific cell/tissue systems. Currently, there is no systematic method to describe the enzymes and cellular reaction networks that catalyze glycosylation. To address this limitation, we present a streamlined machine-readable definition for the glycosylating enzymes and additional methodologies to construct and analyze glycosylation reaction networks. In this computational framework, the enzyme class is systematically designed to store detailed specificity data such as enzymatic functional group, linkage and substrate specificity. The new classes and their associated functions enable both single-reaction inference and automated full network reconstruction, when given a list of reactants and/or products along with the enzymes present in the system. In addition, graph theory is used to support functions that map the connectivity between two or more species in a network, and that generate subset models to identify rate-limiting steps regulating glycan biosynthesis. Finally, this framework allows the synthesis of biochemical reaction networks using mass spectrometry (MS data. The features described above are illustrated using three case studies that examine: i O-linked glycan biosynthesis during the construction of functional selectin-ligands; ii automated N-linked glycosylation pathway construction; and iii the handling and analysis of glycomics based MS data. Overall, the new computational framework enables automated glycosylation network model construction and analysis by integrating knowledge of glycan structure and enzyme
Structural parameter identifiability analysis for dynamic reaction networks
DEFF Research Database (Denmark)
Davidescu, Florin Paul; Jørgensen, Sten Bay
2008-01-01
method based on Lie derivatives. The proposed systematic two phase methodology is illustrated on a mass action based model for an enzymatically catalyzed reaction pathway network where only a limited set of variables is measured. The methodology clearly pinpoints the structurally identifiable parameters...... where for a given set of measured variables it is desirable to investigate which parameters may be estimated prior to spending computational effort on the actual estimation. This contribution addresses the structural parameter identifiability problem for the typical case of reaction network models....... The proposed analysis is performed in two phases. The first phase determines the structurally identifiable reaction rates based on reaction network stoichiometry. The second phase assesses the structural parameter identifiability of the specific kinetic rate expressions using a generating series expansion...
Modelling biochemical networks with intrinsic time delays: a hybrid semi-parametric approach
Directory of Open Access Journals (Sweden)
Oliveira Rui
2010-09-01
Full Text Available Abstract Background This paper presents a method for modelling dynamical biochemical networks with intrinsic time delays. Since the fundamental mechanisms leading to such delays are many times unknown, non conventional modelling approaches become necessary. Herein, a hybrid semi-parametric identification methodology is proposed in which discrete time series are incorporated into fundamental material balance models. This integration results in hybrid delay differential equations which can be applied to identify unknown cellular dynamics. Results The proposed hybrid modelling methodology was evaluated using two case studies. The first of these deals with dynamic modelling of transcriptional factor A in mammalian cells. The protein transport from the cytosol to the nucleus introduced a delay that was accounted for by discrete time series formulation. The second case study focused on a simple network with distributed time delays that demonstrated that the discrete time delay formalism has broad applicability to both discrete and distributed delay problems. Conclusions Significantly better prediction qualities of the novel hybrid model were obtained when compared to dynamical structures without time delays, being the more distinctive the more significant the underlying system delay is. The identification of the system delays by studies of different discrete modelling delays was enabled by the proposed structure. Further, it was shown that the hybrid discrete delay methodology is not limited to discrete delay systems. The proposed method is a powerful tool to identify time delays in ill-defined biochemical networks.
Scalable rule-based modelling of allosteric proteins and biochemical networks.
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Julien F Ollivier
2010-11-01
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.
Extracting reaction networks from databases-opening Pandora's box.
Fearnley, Liam G; Davis, Melissa J; Ragan, Mark A; Nielsen, Lars K
2014-11-01
Large quantities of information describing the mechanisms of biological pathways continue to be collected in publicly available databases. At the same time, experiments have increased in scale, and biologists increasingly use pathways defined in online databases to interpret the results of experiments and generate hypotheses. Emerging computational techniques that exploit the rich biological information captured in reaction systems require formal standardized descriptions of pathways to extract these reaction networks and avoid the alternative: time-consuming and largely manual literature-based network reconstruction. Here, we systematically evaluate the effects of commonly used knowledge representations on the seemingly simple task of extracting a reaction network describing signal transduction from a pathway database. We show that this process is in fact surprisingly difficult, and the pathway representations adopted by various knowledge bases have dramatic consequences for reaction network extraction, connectivity, capture of pathway crosstalk and in the modelling of cell-cell interactions. Researchers constructing computational models built from automatically extracted reaction networks must therefore consider the issues we outline in this review to maximize the value of existing pathway knowledge. © The Author 2013. Published by Oxford University Press.
Consumer Activities and Reactions to Social Network Marketing
Directory of Open Access Journals (Sweden)
Bistra Vassileva
2017-06-01
Full Text Available The purpose of this paper is to understand consumer behavioural models with respect to their reactions to social network marketing. Theoretical background is focused on online and social network usage, motivations and behaviour. The research goal is to explore consumer reactions to the exposure of social network marketing based on the following criteria: level of brand engagement, word-of-mouth (WOM referral behaviour, and purchase intentions. Consumers are investigated based on their attitudes toward social network marketing and basic socio-demographic covariates using data from a sample size of 700 Bulgarian respondents (age group 21–54 years, Internet users, urban inhabitants. Factor and cluster analyses are applied. It is found that consumers are willing to receive information about brands and companies through social networks. They like to talk in social networks about these brands and companies and to share information as well (factor 2, brand engagement. Internet users are willing to share information received through social network advertising (factor 1, wom referral behaviour but they would not buy a certain brand as a result of brand communication activities in social networks (factor 3, purchase intention. Several practical implications regarding marketing activities through social networks are drawn.
Integration of metabolome data with metabolic networks reveals reporter reactions
DEFF Research Database (Denmark)
Çakir, Tunahan; Patil, Kiran Raosaheb; Önsan, Zeynep Ilsen
2006-01-01
Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic...... network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from...... is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through...
Shape, size, and robustness: feasible regions in the parameter space of biochemical networks.
Directory of Open Access Journals (Sweden)
Adel Dayarian
2009-01-01
Full Text Available The concept of robustness of regulatory networks has received much attention in the last decade. One measure of robustness has been associated with the volume of the feasible region, namely, the region in the parameter space in which the system is functional. In this paper, we show that, in addition to volume, the geometry of this region has important consequences for the robustness and the fragility of a network. We develop an approximation within which we could algebraically specify the feasible region. We analyze the segment polarity gene network to illustrate our approach. The study of random walks in the parameter space and how they exit the feasible region provide us with a rich perspective on the different modes of failure of this network model. In particular, we found that, between two alternative ways of activating Wingless, one is more robust than the other. Our method provides a more complete measure of robustness to parameter variation. As a general modeling strategy, our approach is an interesting alternative to Boolean representation of biochemical networks.
Modularization of biochemical networks based on classification of Petri net t-invariants.
Grafahrend-Belau, Eva; Schreiber, Falk; Heiner, Monika; Sackmann, Andrea; Junker, Björn H; Grunwald, Stefanie; Speer, Astrid; Winder, Katja; Koch, Ina
2008-02-08
Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior.With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant) manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t-clusters), which can be interpreted as modules. To find
Modularization of biochemical networks based on classification of Petri net t-invariants
Directory of Open Access Journals (Sweden)
Grunwald Stefanie
2008-02-01
Full Text Available Abstract Background Structural analysis of biochemical networks is a growing field in bioinformatics and systems biology. The availability of an increasing amount of biological data from molecular biological networks promises a deeper understanding but confronts researchers with the problem of combinatorial explosion. The amount of qualitative network data is growing much faster than the amount of quantitative data, such as enzyme kinetics. In many cases it is even impossible to measure quantitative data because of limitations of experimental methods, or for ethical reasons. Thus, a huge amount of qualitative data, such as interaction data, is available, but it was not sufficiently used for modeling purposes, until now. New approaches have been developed, but the complexity of data often limits the application of many of the methods. Biochemical Petri nets make it possible to explore static and dynamic qualitative system properties. One Petri net approach is model validation based on the computation of the system's invariant properties, focusing on t-invariants. T-invariants correspond to subnetworks, which describe the basic system behavior. With increasing system complexity, the basic behavior can only be expressed by a huge number of t-invariants. According to our validation criteria for biochemical Petri nets, the necessary verification of the biological meaning, by interpreting each subnetwork (t-invariant manually, is not possible anymore. Thus, an automated, biologically meaningful classification would be helpful in analyzing t-invariants, and supporting the understanding of the basic behavior of the considered biological system. Methods Here, we introduce a new approach to automatically classify t-invariants to cope with network complexity. We apply clustering techniques such as UPGMA, Complete Linkage, Single Linkage, and Neighbor Joining in combination with different distance measures to get biologically meaningful clusters (t
Li, X Y; Yang, G W; Zheng, D S; Guo, W S; Hung, W N N
2015-04-28
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.
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.
Injectivity, multiple zeros, and multistationarity in reaction networks
DEFF Research Database (Denmark)
Feliu, Elisenda
2015-01-01
Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parametrized by many (unknown) parameters...
Silva, Pedro J.; Ramos, Maria João
2011-01-01
The performance of 18 different DFT functionals in the prediction of absolute and relative energies of organic and biochemical acid/base and redox reactions was evaluated, using MP2 extrapolated to the complete basis set limit and CCSD(T)/aug-cc-pVTZ energies as benchmark. Absolute reduction energies were predicted with relatively large average errors (2–4 kcal mol−1) except for the best functional, PBE0 (1.3 ± 1.2 kcal mol−1). The DFT predictions of relative reduction energies afforded mean ...
Fabrication of a pen-shaped portable biochemical reaction system based on magnetic bead manipulation
International Nuclear Information System (INIS)
Shikida, Mitsuhiro; Inagaki, Noriyuki; Okochi, Mina; Honda, Hiroyuki; Sato, Kazuo
2011-01-01
A pen-shaped platform that is similar to a mechanical pencil is proposed for producing a portable reaction system. A reaction unit, as the key component in the system, was produced by using a heat shrinkable tube. A mechanical pencil supplied by Mitsubishi Pencil Co. Ltd was used as the pen-shaped platform for driving the reaction cylinder. It was actuated using an inchworm motion. We confirmed that the magnetic beads were successfully manipulated in the droplet in the cylinder-shaped reaction units. (technical note)
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.
Brandt-Pollmann, U; Lebiedz, D; Diehl, M; Sager, S; Schlöder, J
2005-09-01
Theoretical and experimental studies related to manipulation of pattern formation in self-organizing reaction-diffusion processes by appropriate control stimuli become increasingly important both in chemical engineering and cellular biochemistry. In a model study, we demonstrate here exemplarily the application of an efficient nonlinear model predictive control (NMPC) algorithm to real-time optimal feedback control of pattern formation in a bacterial chemotaxis system modeled by nonlinear partial differential equations. The corresponding drift-diffusion model type is representative for many (bio)chemical systems involving nonlinear reaction dynamics and nonlinear diffusion. We show how the computed optimal feedback control strategy exploits the system inherent physical property of wave propagation to achieve desired control aims. We discuss various applications of our approach to optimal control of spatiotemporal dynamics.
Directory of Open Access Journals (Sweden)
Sagar Indurkhya
Full Text Available ODE simulations of chemical systems perform poorly when some of the species have extremely low concentrations. Stochastic simulation methods, which can handle this case, have been impractical for large systems due to computational complexity. We observe, however, that when modeling complex biological systems: (1 a small number of reactions tend to occur a disproportionately large percentage of the time, and (2 a small number of species tend to participate in a disproportionately large percentage of reactions. We exploit these properties in LOLCAT Method, a new implementation of the Gillespie Algorithm. First, factoring reaction propensities allows many propensities dependent on a single species to be updated in a single operation. Second, representing dependencies between reactions with a bipartite graph of reactions and species requires only storage for reactions, rather than the required for a graph that includes only reactions. Together, these improvements allow our implementation of LOLCAT Method to execute orders of magnitude faster than currently existing Gillespie Algorithm variants when simulating several yeast MAPK cascade models.
Indurkhya, Sagar; Beal, Jacob
2010-01-01
ODE simulations of chemical systems perform poorly when some of the species have extremely low concentrations. Stochastic simulation methods, which can handle this case, have been impractical for large systems due to computational complexity. We observe, however, that when modeling complex biological systems: (1) a small number of reactions tend to occur a disproportionately large percentage of the time, and (2) a small number of species tend to participate in a disproportionately large percentage of reactions. We exploit these properties in LOLCAT Method, a new implementation of the Gillespie Algorithm. First, factoring reaction propensities allows many propensities dependent on a single species to be updated in a single operation. Second, representing dependencies between reactions with a bipartite graph of reactions and species requires only storage for reactions, rather than the required for a graph that includes only reactions. Together, these improvements allow our implementation of LOLCAT Method to execute orders of magnitude faster than currently existing Gillespie Algorithm variants when simulating several yeast MAPK cascade models. PMID:20066048
Indurkhya, Sagar; Beal, Jacob
2010-01-06
ODE simulations of chemical systems perform poorly when some of the species have extremely low concentrations. Stochastic simulation methods, which can handle this case, have been impractical for large systems due to computational complexity. We observe, however, that when modeling complex biological systems: (1) a small number of reactions tend to occur a disproportionately large percentage of the time, and (2) a small number of species tend to participate in a disproportionately large percentage of reactions. We exploit these properties in LOLCAT Method, a new implementation of the Gillespie Algorithm. First, factoring reaction propensities allows many propensities dependent on a single species to be updated in a single operation. Second, representing dependencies between reactions with a bipartite graph of reactions and species requires only storage for reactions, rather than the required for a graph that includes only reactions. Together, these improvements allow our implementation of LOLCAT Method to execute orders of magnitude faster than currently existing Gillespie Algorithm variants when simulating several yeast MAPK cascade models.
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.
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P Zieliński
2003-03-01
Full Text Available The aim of this work was to determine the influence of the menstruation phase on changes of respective indicators of the gas exchange and on biochemical parameters of blood during physical efforts with the sub-maximal intensity. Fifteen female students of the Academy of Physical Education took part in the study. Girls were aged from 19 to 22 years old and did not practice sports. The effort tests were conducted in the follicular and luteal phase of two succeeding menstrual cycles. As far the aerobic capacity determination is concerned, one cyclo-ergometric test with graded effort was conducted and it was performed till the “refusal”. It allowed to mark a threshold (TDMA and a maximal level of physiological and biochemical indicators. Basing on the results of the graded test individual loads were determined for every next effort trial (repeated 4 times in every phase of the two succeeding menstrual cycles. The aim of this trial was to evaluate the reaction of women’s constitution on work with the sub-maximal intensity. The above trial consisted on two 10 min efforts divided with the 2 min pause (the first effort with the intensity of 80% of the TDMA threshold, second with the intensity bigger about 30-40% of difference between TDMA and a maximal load established by the graded test. The research did not reveal statistically significant differentiation as considering effort changes of basic physiological and biochemical indicators, determining reaction of women’s organisms on work with the sub- and over- threshold intensity (TDMA. It showed that menstruation has not significant effect on the level of changes of analysed parameters caused by the physical effort with the sub-maximal intensity.
Mélykúti, Bence; Burrage, Kevin; Zygalakis, Konstantinos C.
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
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.
Miwa, Makoto; Ohta, Tomoko; Rak, Rafal; Rowley, Andrew; Kell, Douglas B.; Pyysalo, Sampo; Ananiadou, Sophia
2013-01-01
Motivation: To create, verify and maintain pathway models, curators must discover and assess knowledge distributed over the vast body of biological literature. Methods supporting these tasks must understand both the pathway model representations and the natural language in the literature. These methods should identify and order documents by relevance to any given pathway reaction. No existing system has addressed all aspects of this challenge. Method: We present novel methods for associating pathway model reactions with relevant publications. Our approach extracts the reactions directly from the models and then turns them into queries for three text mining-based MEDLINE literature search systems. These queries are executed, and the resulting documents are combined and ranked according to their relevance to the reactions of interest. We manually annotate document-reaction pairs with the relevance of the document to the reaction and use this annotation to study several ranking methods, using various heuristic and machine-learning approaches. Results: Our evaluation shows that the annotated document-reaction pairs can be used to create a rule-based document ranking system, and that machine learning can be used to rank documents by their relevance to pathway reactions. We find that a Support Vector Machine-based system outperforms several baselines and matches the performance of the rule-based system. The success of the query extraction and ranking methods are used to update our existing pathway search system, PathText. Availability: An online demonstration of PathText 2 and the annotated corpus are available for research purposes at http://www.nactem.ac.uk/pathtext2/. Contact: makoto.miwa@manchester.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23813008
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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.
Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models
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Joseph A. Wayman
2015-03-01
Full Text Available Cell-free systems offer many advantages for the study, manipulation and modeling of metabolism compared to in vivo processes. Many of the challenges confronting genome-scale kinetic modeling can potentially be overcome in a cell-free system. For example, there is no complex transcriptional regulation to consider, transient metabolic measurements are easier to obtain, and we no longer have to consider cell growth. Thus, cell-free operation holds several significant advantages for model development, identification and validation. Theoretically, genome-scale cell-free kinetic models may be possible for industrially important organisms, such as E. coli, if a simple, tractable framework for integrating allosteric regulation with enzyme kinetics can be formulated. Toward this unmet need, we present an effective biochemical network modeling framework for building dynamic cell-free metabolic models. The key innovation of our approach is the integration of simple effective rules encoding complex allosteric regulation with traditional kinetic pathway modeling. We tested our approach by modeling the time evolution of several hypothetical cell-free metabolic networks. We found that simple effective rules, when integrated with traditional enzyme kinetic expressions, captured complex allosteric patterns such as ultrasensitivity or non-competitive inhibition in the absence of mechanistic information. Second, when integrated into network models, these rules captured classic regulatory patterns such as product-induced feedback inhibition. Lastly, we showed, at least for the network architectures considered here, that we could simultaneously estimate kinetic parameters and allosteric connectivity from synthetic data starting from an unbiased collection of possible allosteric structures using particle swarm optimization. However, when starting with an initial population that was heavily enriched with incorrect structures, our particle swarm approach could converge
SkyNet: A Modular Nuclear Reaction Network Library
Lippuner, Jonas; Roberts, Luke F.
2017-12-01
Almost all of the elements heavier than hydrogen that are present in our solar system were produced by nuclear burning processes either in the early universe or at some point in the life cycle of stars. In all of these environments, there are dozens to thousands of nuclear species that interact with each other to produce successively heavier elements. In this paper, we present SkyNet, a new general-purpose nuclear reaction network that evolves the abundances of nuclear species under the influence of nuclear reactions. SkyNet can be used to compute the nucleosynthesis evolution in all astrophysical scenarios where nucleosynthesis occurs. SkyNet is free and open source, and aims to be easy to use and flexible. Any list of isotopes can be evolved, and SkyNet supports different types of nuclear reactions. SkyNet is modular so that new or existing physics, like nuclear reactions or equations of state, can easily be added or modified. Here, we present in detail the physics implemented in SkyNet with a focus on a self-consistent transition to and from nuclear statistical equilibrium to non-equilibrium nuclear burning, our implementation of electron screening, and coupling of the network to an equation of state. We also present comprehensive code tests and comparisons with existing nuclear reaction networks. We find that SkyNet agrees with published results and other codes to an accuracy of a few percent. Discrepancies, where they exist, can be traced to differences in the physics implementations.
Inoue, Kentaro; Shimozono, Shinichi; Yoshida, Hideaki; Kurata, Hiroyuki
2012-01-01
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. 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. 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.
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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.
Schreibersite: an effective catalyst in the formose reaction network
Pallmann, S.; Šteflová (neé Svobodová, J.; Haas, M.; Lamour, S.; Henß, A.; Trapp, O.
2018-05-01
We report on the ability of the meteoritic material schreibersite to catalyze the generation of higher sugars from simple carbohydrates in the formose reaction network. Since the analysis of carbonaceous meteorites like the Murchison meteorite it has become generally accepted that a substantial amount of organic material has been delivered to the early earth and, therefore, ought to be considered in scenarios for the origin(s) of life. Also for the open question of accessible phosphorus sources, an extraterrestrial material called schreibersite has been identified that is capable of releasing soluble and reactive phosphorus oxyanions that would react with organics to form for instance nucleotides and membrane associated molecules. We have reinvestigated this material using capillary electrophoresis to monitor its corrosion process in water and probed its ability to phosphorylate a wide range of organics. Although showing a poor reactivity of schreibersite, we have found that the material catalyzes the aldol reaction of small carbohydrates forming larger sugar molecules. This reaction in the formose reaction network is a prebiotically likely route to biologically relevant sugars. The results of our study present one of the first instances of connecting extraterrestrial material to prebiotic chemistry on the early earth.
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.
Noise transmission and delay-induced stochastic oscillations in biochemical network motifs
International Nuclear Information System (INIS)
Liu Sheng-Jun; Wang Qi; Liu Bo; Yan Shi-Wei; Sakata Fumihiko
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. (interdisciplinary physics and related areas of science and technology)
Recent developments in research on catalytic reaction networks
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Roberto Serra
2013-09-01
Full Text Available Over the last years, analyses performed on a stochastic model of catalytic reaction networks have provided some indications about the reasons why wet-lab experiments hardly ever comply with the phase transition typically predicted by theoretical models with regard to the emergence of collectively self-replicating sets of molecule (also defined as autocatalytic sets, ACSs, a phenomenon that is often observed in nature and that is supposed to have played a major role in the emergence of the primitive forms of life. The model at issue has allowed to reveal that the emerging ACSs are characterized by a general dynamical fragility, which might explain the difficulty to observe them in lab experiments. In this work, the main results of the various analyses are reviewed, with particular regard to the factors able to affect the generic properties of catalytic reactions network, for what concerns, not only the probability of ACSs to be observed, but also the overall activity of the system, in terms of production of new species, reactions and matter.
Hahl, Sayuri K; Kremling, Andreas
2016-01-01
In the mathematical modeling of biochemical reactions, a convenient standard approach is to use ordinary differential equations (ODEs) that follow the law of mass action. However, this deterministic ansatz is based on simplifications; in particular, it neglects noise, which is inherent to biological processes. In contrast, the stochasticity of reactions is captured in detail by the discrete chemical master equation (CME). Therefore, the CME is frequently applied to mesoscopic systems, where copy numbers of involved components are small and random fluctuations are thus significant. Here, we compare those two common modeling approaches, aiming at identifying parallels and discrepancies between deterministic variables and possible stochastic counterparts like the mean or modes of the state space probability distribution. To that end, a mathematically flexible reaction scheme of autoregulatory gene expression is translated into the corresponding ODE and CME formulations. We show that in the thermodynamic limit, deterministic stable fixed points usually correspond well to the modes in the stationary probability distribution. However, this connection might be disrupted in small systems. The discrepancies are characterized and systematically traced back to the magnitude of the stoichiometric coefficients and to the presence of nonlinear reactions. These factors are found to synergistically promote large and highly asymmetric fluctuations. As a consequence, bistable but unimodal, and monostable but bimodal systems can emerge. This clearly challenges the role of ODE modeling in the description of cellular signaling and regulation, where some of the involved components usually occur in low copy numbers. Nevertheless, systems whose bimodality originates from deterministic bistability are found to sustain a more robust separation of the two states compared to bimodal, but monostable systems. In regulatory circuits that require precise coordination, ODE modeling is thus still
Patterns of Stochastic Behavior in Dynamically Unstable High-Dimensional Biochemical Networks
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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.
The Forward-Reverse Algorithm for Stochastic Reaction Networks
Bayer, Christian
2015-01-07
In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which we solve a set of deterministic optimization problems where the SRNs are replaced by the classical ODE rates; then, during the second phase, the Monte Carlo version of the EM algorithm is applied starting from the output of the previous phase. Starting from a set of over-dispersed seeds, the output of our two-phase method is a cluster of maximum likelihood estimates obtained by using convergence assessment techniques from the theory of Markov chain Monte Carlo.
Biochemical toxicology studies of methomyl and its kinetic reaction with cholinesterase in rats
International Nuclear Information System (INIS)
Tawfik, S.M.; Abdel-Hamid, F.M.
2001-01-01
The effect of the pesticide methomyl on acetylcholinesterase activities in brain and blood of male rats in vivo and the kinetics involved in their reaction in vitro were studied. Also, its effect on peroxidase action of catalase in vivo was studied using 14C - formate. The results showed that methomyl is a competitive inhibitor for acetylcholinesterase and the concentration levels that caused 50% inhibition of the enzyme activity were 2.1 x 10-2 M and 1.9 x 10-4 for brain and blood- Ache, respectively. The inhibition of acetylcholinesterase activity decreased to 67.5 % at the time of appearance of toxicity symptoms. The radioactivity eliminated in both the expired air and in urine was reduced
Explicit integration of extremely stiff reaction networks: partial equilibrium methods
International Nuclear Information System (INIS)
Guidry, M W; Hix, W R; Billings, J J
2013-01-01
In two preceding papers (Guidry et al 2013 Comput. Sci. Disc. 6 015001 and Guidry and Harris 2013 Comput. Sci. Disc. 6 015002), we have shown that when reaction networks are well removed from equilibrium, explicit asymptotic and quasi-steady-state approximations can give algebraically stabilized integration schemes that rival standard implicit methods in accuracy and speed for extremely stiff systems. However, we also showed that these explicit methods remain accurate but are no longer competitive in speed as the network approaches equilibrium. In this paper, we analyze this failure and show that it is associated with the presence of fast equilibration timescales that neither asymptotic nor quasi-steady-state approximations are able to remove efficiently from the numerical integration. Based on this understanding, we develop a partial equilibrium method to deal effectively with the approach to equilibrium and show that explicit asymptotic methods, combined with the new partial equilibrium methods, give an integration scheme that can plausibly deal with the stiffest networks, even in the approach to equilibrium, with accuracy and speed competitive with that of implicit methods. Thus we demonstrate that such explicit methods may offer alternatives to implicit integration of even extremely stiff systems and that these methods may permit integration of much larger networks than have been possible before in a number of fields. (paper)
Hierarchical feedback modules and reaction hubs in cell signaling networks.
Xu, Jianfeng; Lan, Yueheng
2015-01-01
Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.
Hierarchical feedback modules and reaction hubs in cell signaling networks.
Directory of Open Access Journals (Sweden)
Jianfeng Xu
Full Text Available Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks.
Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks
Xu, Jianfeng; Lan, Yueheng
2015-01-01
Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347
Conditions for extinction events in chemical reaction networks with discrete state spaces.
Johnston, Matthew D; Anderson, David F; Craciun, Gheorghe; Brijder, Robert
2018-05-01
We study chemical reaction networks with discrete state spaces and present sufficient conditions on the structure of the network that guarantee the system exhibits an extinction event. The conditions we derive involve creating a modified chemical reaction network called a domination-expanded reaction network and then checking properties of this network. Unlike previous results, our analysis allows algorithmic implementation via systems of equalities and inequalities and suggests sequences of reactions which may lead to extinction events. We apply the results to several networks including an EnvZ-OmpR signaling pathway in Escherichia coli.
A Multilevel Adaptive Reaction-splitting Simulation Method for Stochastic Reaction Networks
Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro
2016-01-01
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.
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.
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.
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.
A Data-Driven Sparse-Learning Approach to Model Reduction in Chemical Reaction Networks
Harirchi, Farshad; Khalil, Omar A.; Liu, Sijia; Elvati, Paolo; Violi, Angela; Hero, Alfred O.
2017-01-01
In this paper, we propose an optimization-based sparse learning approach to identify the set of most influential reactions in a chemical reaction network. This reduced set of reactions is then employed to construct a reduced chemical reaction mechanism, which is relevant to chemical interaction network modeling. The problem of identifying influential reactions is first formulated as a mixed-integer quadratic program, and then a relaxation method is leveraged to reduce the computational comple...
Experimental (Network) and Evaluated Nuclear Reaction Data at NDS
International Nuclear Information System (INIS)
Otsuka, N.; Semkova, V.; Simakov, S.P.; Zerkin, V.
2011-01-01
Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.
Experimental (Network) and Evaluated Nuclear Reaction Data at NDS
Energy Technology Data Exchange (ETDEWEB)
Otsuka, N; Semkova, V; Simakov, S P; Zerkin, V [Nuclear Data Services Unit, Nuclear Data Section, IAEA, Vienna (Austria)
2011-11-15
Dr Simakov of Nuclear Data Services Unit in the Nuclear Data Section (NDS) gave a brief overview of the data compilation and evaluation activities in the nuclear data community: experimental nuclear reaction data (EXFOR, http://www-nds.iaea.org/exfor/) and evaluated nuclear reaction data (ENDF, http://www-nds.iaea.org/endf). The International Network of Nuclear Reaction Data Centres (NRDC) coordinated by NDS includes 14 Centres in 8 Countries (China, Hungary, India, Japan, Korea, Russian, Ukraine, USA) and 2 International Organizations (NEA, IAEA). It had the first meeting of four core centres (Brookhaven, Saclay, Obninsk, Vienna) in 1966 and the EXFOR was adopted as an official data exchange format. In 2000, IAEA implemented the EXFOR database as a relational multiform database and the EXFOR is a trusted, increasing and living database with 19100 experimental works (as of September 2011) and 141600 data tables. The EXFOR provides a compilation control system for selection of articles and compilation of data and the NRDC home page provides manuals, documents and codes. The nuclear data can be retrieved by the web-retrieval system or distributed on a DVD on request. The EXFOR data play a critical role in the development of evaluated nuclear reaction data. There are several major general purpose libraries: ENDF (US), CENDL (China), JEFF (EU), JENDL (Japan) and RUSFOND (Russia). In addition, there are special libraries for particular applications: EAF (European Activation File), FENDL (Fusion Evaluated Nuclear Data Library for ITER neutronics), IBANDL (Ion Beam Analysis Nuclear Data Library for surface analysis of solids), IRDF, DXS (Dosimetry, radiation damage and gas production data) and Medical portal. Dr V. Zerkin of NDS demonstrated the data retrieval from the EXFOR database and the ENDF library.
Westerterp, K.R.; Westerink, E.J.
1990-01-01
A method is developed to design a tank reactor in which a network of reactions is carried out. The network is a combination of parallel and consecutive reactions. The method ensures unique operation. Dimensionless groups are used which are either representative of properties of the reaction system
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
Iitani, Kenta; Chien, Po-Jen; Suzuki, Takuma; Toma, Koji; Arakawa, Takahiro; Iwasaki, Yasuhiko; Mitsubayashi, Kohji
2018-02-23
Volatile organic compounds (VOCs) exhaled in breath have huge potential as indicators of diseases and metabolisms. Application of breath analysis for disease screening and metabolism assessment is expected since breath samples can be noninvasively collected and measured. In this research, a highly sensitive and selective biochemical gas sensor (bio-sniffer) for gaseous acetaldehyde (AcH) was developed. In the AcH bio-sniffer, a reverse reaction of alcohol dehydrogenase (ADH) was employed for reducing AcH to ethanol and simultaneously consuming a coenzyme, reduced form of nicotinamide adenine dinucleotide (NADH). The concentration of AcH can be quantified by fluorescence detection of NADH that was consumed by reverse reaction of ADH. The AcH bio-sniffer was composed of an ultraviolet light-emitting diode (UV-LED) as an excitation light source, a photomultiplier tube (PMT) as a fluorescence detector, and an optical fiber probe, and these three components were connected with a bifurcated optical fiber. A gas-sensing region of the fiber probe was developed with a flow-cell and an ADH-immobilized membrane. In the experiment, after optimization of the enzyme reaction conditions, the selectivity and dynamic range of the AcH bio-sniffer were investigated. The AcH bio-sniffer showed a short measurement time (within 2 min) and a broad dynamic range for determination of gaseous AcH, 0.02-10 ppm, which encompassed a typical AcH concentration in exhaled breath (1.2-6.0 ppm). Also, the AcH bio-sniffer exhibited a high selectivity to gaseous AcH based on the specificity of ADH. The sensor outputs were observed only from AcH-contained standard gaseous samples. Finally, the AcH bio-sniffer was applied to measure the concentration of AcH in exhaled breath from healthy subjects after ingestion of alcohol. As a result, a significant difference of AcH concentration between subjects with different aldehyde dehydrogenase type 2 (ALDH2) phenotypes was observed. The AcH bio-sniffer can be
Hybrid Multilevel Monte Carlo Simulation of Stochastic Reaction Networks
Moraes, Alvaro
2015-01-07
Stochastic reaction networks (SRNs) is a class of continuous-time Markov chains intended to describe, from the kinetic point of view, the time-evolution of chemical systems in which molecules of different chemical species undergo a finite set of reaction channels. This talk is based on articles [4, 5, 6], where we are interested in the following problem: given a SRN, X, defined though its set of reaction channels, and its initial state, x0, estimate E (g(X(T))); that is, the expected value of a scalar observable, g, of the process, X, at a fixed time, T. This problem lead us to define a series of Monte Carlo estimators, M, such that, with high probability can produce values close to the quantity of interest, E (g(X(T))). More specifically, given a user-selected tolerance, TOL, and a small confidence level, η, find an estimator, M, based on approximate sampled paths of X, such that, P (|E (g(X(T))) − M| ≤ TOL) ≥ 1 − η; even more, we want to achieve this objective with near optimal computational work. We first introduce a hybrid path-simulation scheme based on the well-known stochastic simulation algorithm (SSA)[3] and the tau-leap method [2]. Then, we introduce a Multilevel Monte Carlo strategy that allows us to achieve a computational complexity of order O(T OL−2), this is the same computational complexity as in an exact method but with a smaller constant. We provide numerical examples to show our results.
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
Schaft, Arjan van der; Rao, Shodhan; Jayawardhana, Bayu
2013-01-01
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
Model reduction of detailed-balanced reaction networks by clustering linkage classes
Rao, Shodhan; Jayawardhana, Bayu; van der Schaft, Abraham; Findeisen, Rolf; Bullinger, Eric; Balsa-Canto, Eva; Bernaerts, Kristel
2016-01-01
We propose a model reduction method that involves sequential application of clustering of linkage classes and Kron reduction. This approach is specifically useful for chemical reaction networks with each linkage class having less number of reactions. In case of detailed balanced chemical reaction
International Nuclear Information System (INIS)
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. Lastly, 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.
Preclusion of switch behavior in reaction networks with mass-action kinetics
DEFF Research Database (Denmark)
Feliu, Elisenda; Wiuf, C.
2012-01-01
We study networks taken with mass-action kinetics and provide a Jacobian criterion that applies to an arbitrary network to preclude the existence of multiple positive steady states within any stoichiometric class for any choice of rate constants. We are concerned with the characterization...... precludes the existence of degenerate steady states. Further, we relate injectivity of a network to that of the network obtained by adding outflow, or degradation, reactions for all species....
Dynamic Modeling of Cell-Free Biochemical Networks Using Effective Kinetic Models
2015-03-03
based whole-cell models of E. coli [6]. Conversely , highly abstracted kinetic frameworks, such as the cybernetic framework, represented a paradigm shift...metabolic objective function has been the optimization of biomass formation [18], although other metabolic objectives have also been estimated [19...experimental data. Toward these questions, we explored five hypothetical cell-free networks. Each network shared the same enzymatic connectivity, but
Sheng, Yin; Zhang, Hao; Zeng, Zhigang
2017-10-01
This paper is concerned with synchronization for a class of reaction-diffusion neural networks with Dirichlet boundary conditions and infinite discrete time-varying delays. By utilizing theories of partial differential equations, Green's formula, inequality techniques, and the concept of comparison, algebraic criteria are presented to guarantee master-slave synchronization of the underlying reaction-diffusion neural networks via a designed controller. Additionally, sufficient conditions on exponential synchronization of reaction-diffusion neural networks with finite time-varying delays are established. The proposed criteria herein enhance and generalize some published ones. Three numerical examples are presented to substantiate the validity and merits of the obtained theoretical results.
Unravelling the Maillard reaction network by multiresponse kinetic modelling
Martins, S.I.F.S.
2003-01-01
The Maillard reaction is an important reaction in food industry. It is responsible for the formation of colour and aroma, as well as toxic compounds as the recent discovered acrylamide. The knowledge of kinetic parameters, such as rate constants and activation energy, is necessary to predict its
An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks
Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro
2016-01-01
In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem
Drift-Implicit Multi-Level Monte Carlo Tau-Leap Methods for Stochastic Reaction Networks
Ben Hammouda, Chiheb
2015-01-01
-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
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.
International Nuclear Information System (INIS)
Liang Jinling; Cao Jinde
2003-01-01
Employing general Halanay inequality, we analyze the global exponential stability of a class of reaction-diffusion recurrent neural networks with time-varying delays. Several new sufficient conditions are obtained to ensure existence, uniqueness and global exponential stability of the equilibrium point of delayed reaction-diffusion recurrent neural networks. The results extend and improve the earlier publications. In addition, an example is given to show the effectiveness of the obtained result
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...
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
Alarcon, R A
2012-10-01
A hypothesis suggesting the existence of a ubiquitous physiological anticancer system created by two highly reactive oxidative stress inducers with anticancer properties, acrolein and hydroxyl radical, is reported in this communication. Both components can originate separately or together in several biochemical interactions, among them, the enzymatic oxidation of the polyamine spermine, which appear to be their main source. The foundations of this hypothesis encompass our initial search for growth-inhibitors or anticancer compounds in biological material leading to the isolation of spermine, a polyamine that became highly cytotoxic through the generation of acrolein, when enzymatically oxidized. Findings complemented with pertinent literature data by other workers and observed anticancer activities by sources capable of producing acrolein and hydroxyl radical. This hypothesis obvious implication: spermine enzymatic oxidations or other biochemical interactions that would co-generate acrolein and hydroxyl radical, the anticancer system components, should be tried as treatments for any given cancer. The biochemical generation of acrolein observed was totally unexpected, since this aldehyde was known; as a very toxic and highly reactive xenobiotic chemical produced in the pyrolysis of fats and other organic material, found as an atmospheric pollutant, in tobacco smoke and car emissions, and mainly used as a pesticide or aquatic herbicide. Numerous studies on acrolein, considered after our work a biological product, as well, followed. In them, acrolein widespread presence, its effects on diverse cellular proteins, such as, growth factors, and its anticancer activities, were additionally reported. Regarding hydroxyl radical, the second component of the proposed anticancer system, and another cytotoxic product in normal cell metabolism, it co-generates with acrolein in several biochemical interactions, occurrences suggesting that these products might jointly fulfill some
International Nuclear Information System (INIS)
Wang Jian; Lu Junguo
2008-01-01
In this paper, we study the global exponential stability of fuzzy cellular neural networks with delays and reaction-diffusion terms. By constructing a suitable Lyapunov functional and utilizing some inequality techniques, we obtain a sufficient condition for the uniqueness and global exponential stability of the equilibrium solution for a class of fuzzy cellular neural networks with delays and reaction-diffusion terms. The result imposes constraint conditions on the network parameters independently of the delay parameter. The result is also easy to check and plays an important role in the design and application of globally exponentially stable fuzzy neural circuits
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
Can shoulder joint reaction forces be estimated by neural networks?
de Vries, W.H.K.; Veeger, H.E.J.; Baten, C.T.M.; van der Helm, F.C.T.
2016-01-01
To facilitate the development of future shoulder endoprostheses, a long term load profile of the shoulder joint is desired. A musculoskeletal model using 3D kinematics and external forces as input can estimate the mechanical load on the glenohumeral joint, in terms of joint reaction forces. For long
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.
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.
Television violence--reactions from physicians, advertisers and the networks.
Feingold, M; Johnson, G T
1977-02-24
In response to our call for letters on television violence we received more than 1500 letters from readers of the Journal. Seventy-two per cent of the leading television advertisers responded to a subsequent letter requesting a description of their policies regarding content of the programs they sponsor. Their responses included exculpating factors such as lack of control over programming, the limited amount of available advertising time and censorship. We presented these responses to network representatives. They commented on the difficulty in defining violence, the current decrease in the amount of violence shown and their activities in response to this issue. We maintain that the burden of proof that television violence does not harm lies with those who introduce it into society. Advertisers and networks will respond, we believe, to the problem of television violence if continuous public pressure is maintained.
Directory of Open Access Journals (Sweden)
Eva Skiöldebrand
2018-01-01
Full Text Available Chondrocytes are effectively involved in the pathophysiological processes of inflammation in joints. They form cellular processes in the superficial layer of the articular cartilage and form gap junction coupled syncytium to facilitate cell-to-cell communication. However, very little is known about their physiological cellular identity and communication. The aim with the present work is to evaluate the physiological behavior after stimulation with the inflammatory inducers interleukin-1β and lipopolysaccharide. The cytoskeleton integrity and intracellular Ca2+ release were assessed as indicators of inflammatory state. Cytoskeleton integrity was analyzed through cartilage oligomeric matrix protein and actin labeling with an Alexa 488-conjugated phalloidin probe. Ca2+ responses were assessed through the Ca2+ sensitive fluorophore Fura-2/AM. Western blot analyses of several inflammatory markers were performed. The results show reorganization of the actin filaments. Glutamate, 5-hydoxytryptamine, and ATP evoked intracellular Ca2+ release changed from single peaks to oscillations after inflammatory induction in the chondrocytes. The expression of toll-like receptor 4, the glutamate transporters GLAST and GLT-1, and the matrix metalloproteinase-13 increased. This work demonstrates that chondrocytes are a key part in conditions that lead to inflammation in the cartilage. The inflammatory inducers modulate the cytoskeleton, the Ca2+ signaling, and several inflammatory parameters. In conclusion, our data show that the cellular responses to inflammatory insults from healthy and inflammatory chondrocytes resemble those previously observed in astrocyte and cardiac fibroblasts networks.
The US nuclear reaction data network. Summary of the first meeting, March 13 ampersand 14 1996
International Nuclear Information System (INIS)
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
Liang, Jing; Han, Qian; Ding, Haizhen; Li, Jianyong
2017-12-01
In available insect genomes, there are several L-3,4-dihydroxyphenylalanine (L-dopa) decarboxylase (DDC)-like or aromatic amino acid decarboxylase (AAAD) sequences. This contrasts to those of mammals whose genomes contain only one DDC. Our previous experiments established that two DDC-like proteins from Drosophila actually mediate a complicated decarboxylation-oxidative deamination process of dopa in the presence of oxygen, leading to the formation of 3,4-dihydroxyphenylacetaldehyde (DHPA), CO 2 , NH 3, and H 2 O 2 . This contrasts to the typical DDC-catalyzed reaction, which produces CO 2 and dopamine. These DDC-like proteins were arbitrarily named DHPA synthases based on their critical role in insect soft cuticle formation. Establishment of reactions catalyzed by these AAAD-like proteins solved a puzzle that perplexed researchers for years, but to tell a true DHPA synthase from a DDC in the insect AAAD family remains problematic due to high sequence similarity. In this study, we performed extensive structural and biochemical comparisons between DHPA synthase and DDC. These comparisons identified several target residues potentially dictating DDC-catalyzed and DHPA synthase-catalyzed reactions, respectively. Comparison of DHPA synthase homology models with crystal structures of typical DDC proteins, particularly residues in the active sites, provided further insights for the roles these identified target residues play. Subsequent site-directed mutagenesis of the tentative target residues and activity evaluations of their corresponding mutants determined that active site His192 and Asn192 are essential signature residues for DDC- and DHPA synthase-catalyzed reactions, respectively. Oxygen is required in DHPA synthase-mediated process and this oxidizing agent is reduced to H 2 O 2 in the process. Biochemical assessment established that H 2 O 2 , formed in DHPA synthase-mediated process, can be reused as oxidizing agent and this active oxygen species is reduced to H 2
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 acquired immune response
Han, Da; Wu, Cuichen; You, Mingxu; Zhang, Tao; Wan, Shuo; Chen, Tao; Qiu, Liping; Zheng, Zheng; Liang, Hao; Tan, Weihong
2015-01-01
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 which we call Adaptive Immune Response Simulator (AIRS). DNA and enzymes are used as simple artificial analogues of the components of the AIS to create a system which responds to specific molecular stimuli in vitro. We show that this network of reactions can function in a manner which is superficially similar to the most basic responses of the vertebrate acquired immune system, 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. PMID:26391084
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.
Metabolomics in epidemiology: from metabolite concentrations to integrative reaction networks.
Fearnley, Liam G; Inouye, Michael
2016-10-01
Metabolomics is becoming feasible for population-scale studies of human disease. In this review, we survey epidemiological studies that leverage metabolomics and multi-omics to gain insight into disease mechanisms. We outline key practical, technological and analytical limitations while also highlighting recent successes in integrating these data. The use of multi-omics to infer reaction rates is discussed as a potential future direction for metabolomics research, as a means of identifying biomarkers as well as inferring causality. Furthermore, we highlight established analysis approaches as well as simulation-based methods currently used in single- and multi-cell levels in systems biology. © The Author 2016. Published by Oxford University Press on behalf of the International Epidemiological Association.
Report on the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres
International Nuclear Information System (INIS)
Forrest, R.; Dunaeva, S.; Otsuka, N.
2010-07-01
This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Japan Nuclear Reaction Data Centre, Hokkaido University, Sapporo, Japan, from 20 - 23 April 2010. The meeting was attended by 27 participants from 12 cooperating data centres of seven Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. (author)
International Nuclear Information System (INIS)
Li Zuoan; Li Kelin
2009-01-01
In this paper, we investigate a class of impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. By employing the delay differential inequality with impulsive initial conditions and M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive fuzzy cellular neural networks with distributed delays and reaction-diffusion terms. In particular, the estimate of the exponential converging index is also provided, which depends on the system parameters. An example is given to show the effectiveness of the results obtained here.
van der Heijden, R T; Heijnen, J J; Hellinga, C; Romein, B; Luyben, K C
1994-01-05
Measurements provide the basis for process monitoring and control as well as for model development and validation. Systematic approaches to increase the accuracy and credibility of the empirical data set are therefore of great value. In (bio)chemical conversions, linear conservation relations such as the balance equations for charge, enthalpy, and/or chemical elements, can be employed to relate conversion rates. In a pactical situation, some of these rates will be measured (in effect, be calculated directly from primary measurements of, e.g., concentrations and flow rates), as others can or cannot be calculated from the measured ones. When certain measured rates can also be calculated from other measured rates, the set of equations, the accuracy and credibility of the measured rates can indeed be improved by, respectively, balancing and gross error diagnosis. The balanced conversion rates are more accurate, and form a consistent set of data, which is more suitable for further application (e.g., to calculate nonmeasured rates) than the raw measurements. Such an approach has drawn attention in previous studies. The current study deals mainly with the problem of mathematically classifying the conversion rates into balanceable and calculable rates, given the subset of measured rates. The significance of this problem is illustrated with some examples. It is shown that a simple matrix equation can be derived that contains the vector of measured conversion rates and the redundancy matrix R. Matrix R plays a predominant role in the classification problem. In supplementary articles, significance of the redundancy matrix R for an improved gross error diagnosis approach will be shown. In addition, efficient equations have been derived to calculate the balanceable and/or calculable rates. The method is completely based on matrix algebra (principally different from the graph-theoretical approach), and it is easily implemented into a computer program. (c) 1994 John Wiley & Sons
Lenártová, Petra; Kopčeková, Jana; Gažarová, Martina; Mrázová, Jana; Wyka, Joanna
Chronic obstructive pulmonary disease (COPD) is an airway inflammatory disease caused by inhalation of toxic particles, mainly cigarette smoking, and now is accepted as a disease associated with systemic characteristics. The aim of this work was to investigate and compare selected biochemical parameters in patients with and without COPD. Observation group consisted of clinically stable patients with COPD (n = 60). The control group was healthy persons from the general population, without COPD, who were divided into two subgroups – smokers (n = 30) and non-smokers (n = 30). Laboratory parameters were investigated by automated clinical chemistry analyzer LISA 200th. Albumin in our measurements showed an average value of 39.55 g.l-1 in the patient population; 38.89 g.l-1 in smokers and in non-smokers group 44.65 g.l-1. The average value of pre-albumin in the group of patients was 0.28 ± 0.28 g.l-1 and 0.30 ± 0.04 g.l-1 in smokers group. The average value of the orosomucoid in patients was about 1.11 ± 0.90 mg.ml-1. In the group of smokers, the mean value of orosomucoid was 0.60 ± 0.13 mg.ml-1. The level of C-reactive protein (CRP) in the patient group reached an average value of 15.31 ± 22.04 mg.l-1, in the group of smokers was 5.18 ± 4.58 mg. l-1. Prognostic inflammatory and nutritional index (PINI) in the group of patients showed a mean value of 4.65 ± 10.77 and 0.026 ± 0.025 in smokers. The results of this work show, that the values of index PINI in COPD patients are significantly higher than in smokers (P COPD.
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.
Passivity analysis for uncertain BAM neural networks with time delays and reaction-diffusions
Zhou, Jianping; Xu, Shengyuan; Shen, Hao; Zhang, Baoyong
2013-08-01
This article deals with the problem of passivity analysis for delayed reaction-diffusion bidirectional associative memory (BAM) neural networks with weight uncertainties. By using a new integral inequality, we first present a passivity condition for the nominal networks, and then extend the result to the case with linear fractional weight uncertainties. The proposed conditions are expressed in terms of linear matrix inequalities, and thus can be checked easily. Examples are provided to demonstrate the effectiveness of the proposed results.
International Nuclear Information System (INIS)
Lou, X.; Cui, B.
2008-01-01
In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)
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, María
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.
Schnell, Santiago; Chappell, Michael J; Evans, Neil D; Roussel, Marc R
2006-01-01
A theoretical analysis of the distinguishability problem of two rival models of the single enzyme-single substrate reaction, the Michaelis-Menten and Henri mechanisms, is presented. We also outline a general approach for analysing the structural indistinguishability between two mechanisms. The approach involves constructing, if possible, a smooth mapping between the two candidate models. Evans et al. [N.D. Evans, M.J. Chappell, M.J. Chapman, K.R. Godfrey, Structural indistinguishability between uncontrolled (autonomous) nonlinear analytic systems, Automatica 40 (2004) 1947-1953] have shown that if, in addition, either of the mechanisms satisfies a particular criterion then such a transformation always exists when the models are indistinguishable from their experimentally observable outputs. The approach is applied to the single enzyme-single substrate reaction mechanism. In principle, mechanisms can be distinguished using this analysis, but we show that our ability to distinguish mechanistic models depends both on the precise measurements made, and on our knowledge of the system prior to performing the kinetics experiments.
Toward a self-consistent and unitary reaction network for big bang nucleosynthesis
Energy Technology Data Exchange (ETDEWEB)
Paris, Mark W.; Brown, Lowell S.; Hale, Gerald M.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Kawano, Toshihiko, E-mail: mparis@lanl.gov [Los Alamos National Laboratory, Los Alamos, New Mexico (United States); Fuller, George M.; Grohs, Evan B. [Department of Physics, University of California, San Diego, La Jolla, CA (United States); Kunieda, Satoshi [Nuclear Data Center, Japan Atomic Energy Agency, Tokai-mura Naka-gun, Ibaraki (Japan)
2014-07-01
Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the {sup 9}B compound nuclear system in the resonant destruction of {sup 7}Li during primordial nucleosynthesis. We have studied reactions in the {sup 9}B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic {sup 6}Li({sup 3}He,{sup 3}He){sup 6}Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for {sup 6}Li({sup 3}He,p){sup 8}Be* and from 0.4 to 5.0 MeV for the {sup 6}Li({sup 3}He,γ){sup 7}Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for {sup 6}Li({sup 3}He,γ){sup 9}B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)
Toward a self-consistent and unitary reaction network for big bang nucleosynthesis
International Nuclear Information System (INIS)
Paris, Mark W.; Brown, Lowell S.; Hale, Gerald M.; Hayes-Sterbenz, Anna C.; Jungman, Gerard; Kawano, Toshihiko; Fuller, George M.; Grohs, Evan B.; Kunieda, Satoshi
2014-01-01
Unitarity, the mathematical expression of the conservation of probability in multichannel reactions, is an essential ingredient in the development of accurate nuclear reaction networks appropriate for nucleosynthesis in a variety of environments. We describe our ongoing program to develop a 'unitary reaction network' for the big-bang nucleosynthesis environment and look at an example of the need and power of unitary parametrizations of nuclear scattering and reaction data. Recent attention has been focused on the possible role of the 9 B compound nuclear system in the resonant destruction of 7 Li during primordial nucleosynthesis. We have studied reactions in the 9 B compound system with a multichannel, two-body unitary R-matrix code (EDA) using the known elastic and reaction data, in a four-channel treatment. The data include elastic 6 Li( 3 He, 3 He) 6 Li differential cross sections from 0.7 to 2.0 MeV, integrated reaction cross sections for energies from 0.7 to 5.0 MeV for 6 Li( 3 He,p) 8 Be* and from 0.4 to 5.0 MeV for the 6 Li( 3 He,γ) 7 Be reaction. Capture data have been added to the previous analysis with integrated cross section measurements from 0.7 to 0.825 MeV for 6 Li( 3 He,γ) 9 B. The resulting resonance parameters are compared with tabulated values from TUNL Nuclear Data Group analyses. Previously unidentified resonances are noted and the relevance of this analysis and a unitary reaction network for big-bang nucleosynthesis are emphasized. (author)
Ivanciuc, Ovidiu; Ivanciuc, Teodora; Klein, Douglas J
2013-06-01
Usual quantitative structure-activity relationship (QSAR) models are computed from unstructured input data, by using a vector of molecular descriptors for each chemical in the dataset. Another alternative is to consider the structural relationships between the chemical structures, such as molecular similarity, presence of certain substructures, or chemical transformations between compounds. We defined a class of network-QSAR models based on molecular networks induced by a sequence of substitution reactions on a chemical structure that generates a partially ordered set (or poset) oriented graph that may be used to predict various molecular properties with quantitative superstructure-activity relationships (QSSAR). The network-QSAR interpolation models defined on poset graphs, namely average poset, cluster expansion, and spline poset, were tested with success for the prediction of several physicochemical properties for diverse chemicals. We introduce the flow network QSAR, a new poset regression model in which the dataset of chemicals, represented as a reaction poset, is transformed into an oriented network of electrical resistances in which the current flow results in a potential at each node. The molecular property considered in the QSSAR model is represented as the electrical potential, and the value of this potential at a particular node is determined by the electrical resistances assigned to each edge and by a system of batteries. Each node with a known value for the molecular property is attached to a battery that sets the potential on that node to the value of the respective molecular property, and no external battery is attached to nodes from the prediction set, representing chemicals for which the values of the molecular property are not known or are intended to be predicted. The flow network QSAR algorithm determines the values of the molecular property for the prediction set of molecules by applying Ohm's law and Kirchhoff's current law to the poset
Adaptive exponential synchronization of delayed neural networks with reaction-diffusion terms
International Nuclear Information System (INIS)
Sheng Li; Yang Huizhong; Lou Xuyang
2009-01-01
This paper presents an exponential synchronization scheme for a class of neural networks with time-varying and distributed delays and reaction-diffusion terms. An adaptive synchronization controller is derived to achieve the exponential synchronization of the drive-response structure of neural networks by using the Lyapunov stability theory. At the same time, the update laws of parameters are proposed to guarantee the synchronization of delayed neural networks with all parameters unknown. It is shown that the approaches developed here extend and improve the ideas presented in recent literatures.
Impact parameter determination for 40Ca + 40Ca reactions using a neural network
International Nuclear Information System (INIS)
Haddad, F.; Hagel, K.; Li, J.; Mdeiwayeh, N.; Natowitz, J.B.; Wada, R.; Xiao, B.; David, C.; Freslier, M.; Aichelin, J.
1995-01-01
A neural network is used for the impact parameter determination in 40 Ca + 40 Ca reactions at energies between 35 and 70 AMeV. A special attention is devoted to the effect of experimental constraints such as the detection efficiency. An overall improvement of the impact parameter determination of 25% is obtained with the neural network. The neural network technique is then used in the analysis of the Ca+Ca data at 35 AMeV and allows separation of three different class of events among the selected 'complete' events. (authors). 8 refs., 5 figs
Yaniv, Yael; Ganesan, Ambhighainath; Yang, Dongmei; Ziman, Bruce D.; Lyashkov, Alexey E.; Levchenko, Andre; Zhang, Jin; Lakatta, Edward G.
2015-01-01
cAMP-PKA protein kinase is a key nodal signaling pathway that regulates a wide range of heart pacemaker cell functions. These functions are predicted to be involved in regulation of spontaneous action potential (AP) generation of these cells. Here we investigate if the kinetics and stoichiometry of increase in PKA activity match the increase in AP firing rate in response to β-adrenergic receptor (β-AR) stimulation or phosphodiesterase (PDE) inhibition, that alter the AP firing rate of heart sinoatrial pacemaker cells. In cultured adult rabbit pacemaker cells infected with an adenovirous expressing the FRET sensor AKAR3, the EC50 in response to graded increases in the intensity of β-AR stimulation (by Isoproterenol) the magnitude of the increases in PKA activity and the spontaneous AP firing rate were similar (0.4±0.1nM vs. 0.6±0.15nM, respectively). Moreover, the kinetics (t1/2) of the increases in PKA activity and spontaneous AP firing rate in response to β-AR stimulation or PDE inhibition were tightly linked. We characterized the system rate-limiting biochemical reactions by integrating these experimentally derived data into mechanistic-computational model. Model simulations predicted that phospholamban phosphorylation is a potent target of the increase in PKA activity that links to increase in spontaneous AP firing rate. In summary, the kinetics and stoichiometry of increases in PKA activity in response to a physiological (β-AR stimulation) or pharmacological (PDE inhibitor) stimuli match those of changes in the AP firing rate. Thus Ca2+-cAMP/PKA-dependent phosphorylation limits the rate and magnitude of increase in spontaneous AP firing rate. PMID:26241846
Vilanova, Pedro
2016-01-01
reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ
International Nuclear Information System (INIS)
Dunaeva, S.; Otsuka, N.; Schwerer, O.
2009-08-01
An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at the IAEA Headquarters in Vienna from 25 to 26 May 2009. The meeting was attended by 23 participants from 13 cooperating data centres. A summary of the meeting is given in this report, along with the conclusions, actions, and status report of the participating data centres. (author)
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...
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...
Dynamical Behaviors of Stochastic Reaction-Diffusion Cohen-Grossberg Neural Networks with Delays
Directory of Open Access Journals (Sweden)
Li Wan
2012-01-01
Full Text Available This paper investigates dynamical behaviors of stochastic Cohen-Grossberg neural network with delays and reaction diffusion. By employing Lyapunov method, Poincaré inequality and matrix technique, some sufficient criteria on ultimate boundedness, weak attractor, and asymptotic stability are obtained. Finally, a numerical example is given to illustrate the correctness and effectiveness of our theoretical results.
MSU SINP CDFE nuclear data activities in the nuclear reaction data centres network
International Nuclear Information System (INIS)
Boboshin, I.N.; Varlamov, V.V.; Komarov, S.Yu.; Peskov, N.N.; Semin, S.B.; Stepanov, M.E.; Chesnokov, V.V.
2002-01-01
This paper is the progress report of the Centre for Photonuclear Experiments Data, Moscow. It is a short review of the works carried out by the CDFE concerning the IAEA nuclear reaction data centers network activities from May 2001 until May 2002. and the description of the main results obtained. (a.n.)
Ensafi, Ali A; Jamei, Hamid Reza; Heydari-Bafrooei, Esmaeil; Rezaei, B
2016-10-01
This paper presents the results of an experimental investigation of voltammetric and impedimetric DNA-based biosensors for monitoring biological and chemical redox cycling reactions involving free radical intermediates. The concept is based on associating the amounts of radicals generated with the electrochemical signals produced, using differential pulse voltammetry (DPV) and electrochemical impedance spectroscopy (EIS). For this purpose, a pencil graphite electrode (PGE) modified with multiwall carbon nanotubes and poly-diallydimethlammonium chloride decorated with double stranded fish sperm DNA was prepared to detect DNA damage induced by the radicals generated from a redox cycling quinone (i.e., menadione (MD; 2-methyl-1,4-naphthoquinone)). Menadione was employed as a model compound to study the redox cycling of quinones. A direct relationship was found between free radical production and DNA damage. The relationship between MD-induced DNA damage and free radical generation was investigated in an attempt to identify the possible mechanism(s) involved in the action of MD. Results showed that DPV and EIS were appropriate, simple and inexpensive techniques for the quantitative and qualitative comparisons of different reducing reagents. These techniques may be recommended for monitoring DNA damages and investigating the mechanisms involved in the production of redox cycling compounds. Copyright © 2016 Elsevier B.V. All rights reserved.
International Nuclear Information System (INIS)
Petcu, I.; Moisoi, N.; Savu, D.; Constantinescu, B.
1999-01-01
The experiment intended to examine whether adaptive response could be elicited in vivo by low level internal contamination of rats with tritiated water and subsequently observed after a challenging irradiation with fast neutrons. Rats have been pre-contaminated for 3 weeks to total doses of 7 cGy and 35 cGy. Subsequently they were irradiated 1 Gy by fast neutrons obtained from deuterons (13.5 MeV) on a Be target. After 24 h the rats were sacrificed and the lipid peroxidation level was determined in liver, kidney, small intestine, spleen, bone marrow and plasma. The reduced glutathion and the glucose-6-phosphate dehydrogenase activity was determined in erythrocytes. Concurrently an in vivo assay was performed to observe the modifications of the thymidine intake in the isolated bone marrow cells. For the rats pre-exposed to tritiated water the lipid peroxides level was significantly decreased only for liver and kidney and only after the highest preirradiation dose (35 cGy). The thymidine incorporation assay revealed a putative adaptive reaction also for the 35 cGy preirradiated rats. The glutathion content was found to be increased (back to the normal level) for the tritium pre-contaminated and neutron irradiated animals as compared to those exposed only to fast neutrons. (authors)
Critical regimes driven by recurrent mobility patterns of reaction-diffusion processes in networks
Gómez-Gardeñes, J.; Soriano-Paños, D.; Arenas, A.
2018-04-01
Reaction-diffusion processes1 have been widely used to study dynamical processes in epidemics2-4 and ecology5 in networked metapopulations. In the context of epidemics6, reaction processes are understood as contagions within each subpopulation (patch), while diffusion represents the mobility of individuals between patches. Recently, the characteristics of human mobility7, such as its recurrent nature, have been proven crucial to understand the phase transition to endemic epidemic states8,9. Here, by developing a framework able to cope with the elementary epidemic processes, the spatial distribution of populations and the commuting mobility patterns, we discover three different critical regimes of the epidemic incidence as a function of these parameters. Interestingly, we reveal a regime of the reaction-diffussion process in which, counter-intuitively, mobility is detrimental to the spread of disease. We analytically determine the precise conditions for the emergence of any of the three possible critical regimes in real and synthetic networks.
Abrams , Robert H.; Loague, Keith; Kent, Douglas B.
1998-01-01
The work reported here is the first part of a larger effort focused on efficient numerical simulation of redox zone development in contaminated aquifers. The sequential use of various electron acceptors, which is governed by the energy yield of each reaction, gives rise to redox zones. The large difference in energy yields between the various redox reactions leads to systems of equations that are extremely ill-conditioned. These equations are very difficult to solve, especially in the context of coupled fluid flow, solute transport, and geochemical simulations. We have developed a general, rational method to solve such systems where we focus on the dominant reactions, compartmentalizing them in a manner that is analogous to the redox zones that are often observed in the field. The compartmentalized approach allows us to easily solve a complex geochemical system as a function of time and energy yield, laying the foundation for our ongoing work in which we couple the reaction network, for the development of redox zones, to a model of subsurface fluid flow and solute transport. Our method (1) solves the numerical system without evoking a redox parameter, (2) improves the numerical stability of redox systems by choosing which compartment and thus which reaction network to use based upon the concentration ratios of key constituents, (3) simulates the development of redox zones as a function of time without the use of inhibition factors or switching functions, and (4) can reduce the number of transport equations that need to be solved in space and time. We show through the use of various model performance evaluation statistics that the appropriate compartment choice under different geochemical conditions leads to numerical solutions without significant error. The compartmentalized approach described here facilitates the next phase of this effort where we couple the redox zone reaction network to models of fluid flow and solute transport.
Wang, Ting; Plecháč, Petr
2017-12-01
Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.
Kouvaris, Nikos E; Sebek, Michael; Iribarne, Albert; Díaz-Guilera, Albert; Kiss, István Z
2017-04-01
We present theoretical and experimental studies on pattern formation with bistable dynamical units coupled in a star network configuration. By applying a localized perturbation to the central or the peripheral elements, we demonstrate the subsequent spreading, pinning, or retraction of the activations; such analysis enables the characterization of the formation of stationary patterns of localized activity. The results are interpreted with a theoretical analysis of a simplified bistable reaction-diffusion model. Weak coupling results in trivial pinned states where the activation cannot propagate. At strong coupling, a uniform state is expected with active or inactive elements at small or large degree networks, respectively. A nontrivial stationary spatial pattern, corresponding to an activation pinning, is predicted to occur at an intermediate number of peripheral elements and at intermediate coupling strengths, where the central activation of the network is pinned, but the peripheral activation propagates toward the center. The results are confirmed in experiments with star networks of bistable electrochemical reactions. The experiments confirm the existence of the stationary spatial patterns and the dependence of coupling strength on the number of peripheral elements for transitions between pinned and retreating or spreading fronts in forced network configurations (where the central or periphery elements are forced to maintain their states).
Wang, Ting; Plecháč, Petr
2017-12-21
Stochastic reaction networks that exhibit bistable behavior are common in systems biology, materials science, and catalysis. Sampling of stationary distributions is crucial for understanding and characterizing the long-time dynamics of bistable stochastic dynamical systems. However, simulations are often hindered by the insufficient sampling of rare transitions between the two metastable regions. In this paper, we apply the parallel replica method for a continuous time Markov chain in order to improve sampling of the stationary distribution in bistable stochastic reaction networks. The proposed method uses parallel computing to accelerate the sampling of rare transitions. Furthermore, it can be combined with the path-space information bounds for parametric sensitivity analysis. With the proposed methodology, we study three bistable biological networks: the Schlögl model, the genetic switch network, and the enzymatic futile cycle network. We demonstrate the algorithmic speedup achieved in these numerical benchmarks. More significant acceleration is expected when multi-core or graphics processing unit computer architectures and programming tools such as CUDA are employed.
An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks
Bayer, Christian
2016-01-06
In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce an efficient two-phase algorithm in which the first phase is deterministic and it is intended to provide a starting point for the second phase which is the Monte Carlo EM Algorithm.
Design of an Adaptive-Neural Network Attitude Controller of a Satellite using Reaction Wheels
Directory of Open Access Journals (Sweden)
Abbas Ajorkar
2015-04-01
Full Text Available In this paper, an adaptive attitude control algorithm is developed based on neural network for a satellite using four reaction wheels in a tetrahedron configuration. Then, an attitude control based on feedback linearization control has been designed and uncertainties in the moment of inertia matrix and disturbances torque have been considered. In order to eliminate the effect of these uncertainties, a multilayer neural network with back-propagation law is designed. In this structure, the parameters of the moment of inertia matrix and external disturbances are estimated and used in feedback linearization control law. Finally, the performance of the designed attitude controller is investigated by several simulations.
Report on the IAEA technical meeting on network of nuclear reaction data centres
Energy Technology Data Exchange (ETDEWEB)
Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria); Henriksson, H [NEA Data Bank, Issy-les-Moulineaux (France)
2005-01-15
This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)
Report on the IAEA technical meeting on network of nuclear reaction data centres
International Nuclear Information System (INIS)
Schwerer, O.; Henriksson, H.
2005-01-01
This report summarizes the IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting), held at the Brookhaven National Laboratory, Upton, NY, USA from 4-7 October 2004. The meeting was attended by 20 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, status reports of the participating data centres, and a revised technical protocol for the cooperation of the network. (author)
International Nuclear Information System (INIS)
Wang Linshan; Zhang Zhe; Wang Yangfan
2008-01-01
Some criteria for the global stochastic exponential stability of the delayed reaction-diffusion recurrent neural networks with Markovian jumping parameters are presented. The jumping parameters considered here are generated from a continuous-time discrete-state homogeneous Markov process, which are governed by a Markov process with discrete and finite state space. By employing a new Lyapunov-Krasovskii functional, a linear matrix inequality (LMI) approach is developed to establish some easy-to-test criteria of global exponential stability in the mean square for the stochastic neural networks. The criteria are computationally efficient, since they are in the forms of some linear matrix inequalities
Metadislocation reactions and metadislocation networks in the complex metallic alloy ξ'-Al-Pd-Mn
International Nuclear Information System (INIS)
Heggen, Marc; Feuerbacher, Michael
2005-01-01
Metadislocations are novel structural defects firstly observed in the complex metallic alloy ξ'-Al-Pd-Mn. We present a transmission electron microscopy study on metadislocation reactions and networks. It is shown that metadislocations can dissociate into partials, which leads to a decrease of the elastic line energy. Connected groups of metadislocations can assume large and complex network structures with large total Burgers vectors. However, the local elastic strain at the individual metadislocation cores as well as the fault-plane energies remain small. By this mechanism, effective large Burgers vectors, contributing massively to plastic strain, can be distributed over a large portion of the material
Event-triggered synchronization for reaction-diffusion complex networks via random sampling
Dong, Tao; Wang, Aijuan; Zhu, Huiyun; Liao, Xiaofeng
2018-04-01
In this paper, the synchronization problem of the reaction-diffusion complex networks (RDCNs) with Dirichlet boundary conditions is considered, where the data is sampled randomly. An event-triggered controller based on the sampled data is proposed, which can reduce the number of controller and the communication load. Under this strategy, the synchronization problem of the diffusion complex network is equivalently converted to the stability of a of reaction-diffusion complex dynamical systems with time delay. By using the matrix inequality technique and Lyapunov method, the synchronization conditions of the RDCNs are derived, which are dependent on the diffusion term. Moreover, it is found the proposed control strategy can get rid of the Zeno behavior naturally. Finally, a numerical example is given to verify the obtained results.
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.
International Nuclear Information System (INIS)
Gaikar, Vilas G.; Thaore, Vaishali
2014-01-01
The recovery and purification of plutonium (Pu) from uranium (U) and of U from Thorium (Th) in spent nuclear fuel reprocessing is accomplished by processes that employ organophosphorous compounds as extractants.The main objective of the present work was to develop a suitable kinetic model and to determine the kinetic parameters of the set of reactions involved in the pyrolysis of amides by fitting the experimental data in the reaction network model. The experimental data and analysis are expected to be useful in the steam pyrolysis of amide waste in fuel reprocessing in the nuclear industry. The basic approach was to understand the reaction mechanism of the steam pyrolysis of amides and then to estimate the reaction rate constants for the generation and consumption of different species by solving the model equations, allowing for the determination of important species in the reaction network
Report on the IAEA technical meeting of the International Network of Nuclear Reaction Data Centres
Energy Technology Data Exchange (ETDEWEB)
Schwerer, O; Dunaeva, S [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria); org, S Dunaeva@iaea [eds.
2007-11-15
An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at IAEA Headquarters, Vienna, Austria, from 8 to 10 October 2007. The meeting was attended by 19 participants from 11 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)
Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres
International Nuclear Information System (INIS)
Otsuka, Naohiko
2012-06-01
This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the OECD Nuclear Energy Agency (NEA) in Issy-les-Moulineaux, France from 16 to 19 April 2012. The meeting was attended by twenty-three participants representing thirteen cooperative centres from eight Member States and two International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)
Report on the IAEA technical meeting on the network of nuclear reaction data centres
Energy Technology Data Exchange (ETDEWEB)
Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria)
2006-02-15
Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)
Report on the IAEA technical meeting on network of nuclear reaction data centres
Energy Technology Data Exchange (ETDEWEB)
Schwerer, O [IAEA Nuclear Data Section, Vienna (Austria)
2006-12-15
An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)
Report on the IAEA technical meeting on network of nuclear reaction data centres
International Nuclear Information System (INIS)
Pronyaev, V.G.; Schwerer, O.; Nichols, A.L.
2002-08-01
An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (and the biennial Data Centre Heads' Meeting) was held at the OECD Nuclear Energy Agency, Issy-les-Moulineaux (near Paris), France, from 27 to 30 May 2002. The meeting was attended by 21 participants from 12 co-operating data centres of six Member States and two international organizations. This report contains the meeting summary, conclusions and actions, status reports of the participating data centres, and working papers considered. (author)
Report on the IAEA technical meeting on the network of nuclear reaction data centres
International Nuclear Information System (INIS)
Schwerer, O.
2006-02-01
Results of the IAEA Technical meeting on the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 12 to 14 October 2005, are summarized in this report. The meeting was attended by 16 participants from 11 co-operating data centres of six Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, and status reports of the participating data centres. (author)
Report on the IAEA technical meeting on network of nuclear reaction data centres
International Nuclear Information System (INIS)
Schwerer, O.
2006-12-01
An IAEA Technical Meeting on the Network of Nuclear Reaction Data Centres (biennial Data Centre Heads Meeting) was held at IAEA Headquarters, Vienna, Austria, from 25 to 28 September 2006. The meeting was attended by 19 participants from 10 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)
Report on the IAEA technical meeting of the International Network of Nuclear Reaction Data Centres
International Nuclear Information System (INIS)
Schwerer, O.; Dunaeva, S.; S.Dunaeva@iaea.org
2007-11-01
An IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres was held at IAEA Headquarters, Vienna, Austria, from 8 to 10 October 2007. The meeting was attended by 19 participants from 11 cooperating data centres of six Member States and two international organizations. A summary of the meeting is given in this report, along with the conclusions, actions, and status reports of the participating data centres. (author)
Report on the IAEA advisory group meeting on network of nuclear reaction data centres
Energy Technology Data Exchange (ETDEWEB)
Pronyaev, V G; Schwerer, O [International Atomic Energy Agency, Nuclear Data Section, Vienna (Austria)
2000-08-01
This report summarizes the IAEA Advisory Group Meeting (AGM) on Network of Nuclear Reaction Data Centres, hold at the Institute of Physics and Power Engineering, Obninsk, Russia, 15 to 19 May 2000. The meeting was attended by 28 participants from 13 co-operating data centres from seven Member States and two International Organizations. The report contains a meeting summary, the conclusions and actions, progress and status reports of the participating data centres and working papers considered at the meeting. (author)
International Nuclear Information System (INIS)
Otsuka, Naohiko
2011-07-01
This report summarizes the IAEA Technical Meeting of the International Network of Nuclear Reaction Data Centres, held at the IAEA Headquarters in Vienna, Austria from 23 - 24 May 2011. The meeting was attended by 25 participants from 13 cooperating data centres of nine Member States and two International Organizations. The report contains a summary of the meeting, the conclusions and actions, the lists of working papers and presentations presented at the meeting. (author)
Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres
International Nuclear Information System (INIS)
Otsuka, Naohiko
2013-07-01
This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres, held at the IAEA Headquarters in Vienna, Austria from 23 to 25 April 2013. The meeting was attended by 24 participants representing 13 cooperative centres from 8 Member States and 2 International Organisations. A summary of the meeting is given in this report along with the conclusions and actions. (author)
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)
Andrea De Martino
Full Text Available Within a fully microscopic setting, we derive a variational principle for the non-equilibrium steady states of chemical reaction networks, valid for time-scales over which chemical potentials can be taken to be slowly varying: at stationarity the system minimizes a global function of the reaction fluxes with the form of a Hopfield Hamiltonian with hebbian couplings, that is explicitly seen to correspond to the rate of decay of entropy production over time. Guided by this analogy, we show that reaction networks can be formally re-cast as systems of interacting reactions that optimize the use of the available compounds by competing for substrates, akin to agents competing for a limited resource in an optimal allocation problem. As an illustration, we analyze the scenario that emerges in two simple cases: that of toy (random reaction networks and that of a metabolic network model of the human red blood cell.
Traveling and Pinned Fronts in Bistable Reaction-Diffusion Systems on Networks
Kouvaris, Nikos E.; Kori, Hiroshi; Mikhailov, Alexander S.
2012-01-01
Traveling fronts and stationary localized patterns in bistable reaction-diffusion systems have been broadly studied for classical continuous media and regular lattices. Analogs of such non-equilibrium patterns are also possible in networks. Here, we consider traveling and stationary patterns in bistable one-component systems on random Erdös-Rényi, scale-free and hierarchical tree networks. As revealed through numerical simulations, traveling fronts exist in network-organized systems. They represent waves of transition from one stable state into another, spreading over the entire network. The fronts can furthermore be pinned, thus forming stationary structures. While pinning of fronts has previously been considered for chains of diffusively coupled bistable elements, the network architecture brings about significant differences. An important role is played by the degree (the number of connections) of a node. For regular trees with a fixed branching factor, the pinning conditions are analytically determined. For large Erdös-Rényi and scale-free networks, the mean-field theory for stationary patterns is constructed. PMID:23028746
Applicant reactions to social network web use in personnel selection and assessment
Directory of Open Access Journals (Sweden)
David Aguado
2016-12-01
Full Text Available Human Resource (HR professionals are increasingly using Social Networking Websites (SNWs for personnel recruitment and selection processes. However, evidence is required regarding their psychometric properties and their impact on applicant reactions. In this paper we present and discuss the results of exploring applicant reactions to either the use of a professional SNW (such as LinkedIn or a non-professional SNW (such as Facebook. A scale for assessing applicant reactions was applied to 124 professionals. The results showed more positive attitudes to the use of professional SNWs compared with non-professional SNWs. Both gender and age moderated these results, with females and young applicants having a less positive attitude than males and older participants towards the use of non-professional SNWs.
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...
FERN - a Java framework for stochastic simulation and evaluation of reaction networks.
Erhard, Florian; Friedel, Caroline C; Zimmer, Ralf
2008-08-29
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. 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. FERN addresses shortcomings of currently available stochastic simulation programs in several ways. First, it provides a broad range of efficient and accurate algorithms both for exact and approximate stochastic simulation and a simple interface for extending to new algorithms. FERN's implementations are considerably faster than the C implementations of gillespie2 or the Java implementations of ISBJava. Second, it can be used in a straightforward way both as a stand-alone program and within new
Segmenting Costumers Based on Their Reactions to Social Networks Marketing on Instagram
Directory of Open Access Journals (Sweden)
rashin ghahreman
2017-09-01
Full Text Available Since customers react differently to business and marketing on social networks, the researcher is looking for segmenting customers into different categories according to their reaction to marketing in social networks. The present study is a descriptive-exploratory research and the data were collected through a questionnaire. The population of 14,000 follower of the researcher’s personal page on Instagram were analyzed and a sample 224 members were randomly selected. To analyze the data, a two-step clustering method was applied. As a result, five distinct clusters (the active, the talker, the hesitant, the passive and the averse were identified. Two segments were reported to be highly influenced by social networks marketing in terms of brand engagement, purchase intention and word of mouth advertisement (WOM. The "Active" are the most influenced group including 18.3% of the population most of whom are single girls or women. The next group that are influenced the most by social networks marketing is the "Talker". This group represents 24.1% of the population, the most populated group. The "Talker" are different from the "Active" in term of their intention to purchase. Totally, 42.2% of the population are reported to be influenced by social networks marketing.
Huang, Liang-Chin; Wu, Xiaogang; Chen, Jake Y
2013-01-01
The prediction of adverse drug reactions (ADRs) has become increasingly important, due to the rising concern on serious ADRs that can cause drugs to fail to reach or stay in the market. We proposed a framework for predicting ADR profiles by integrating protein-protein interaction (PPI) networks with drug structures. We compared ADR prediction performances over 18 ADR categories through four feature groups-only drug targets, drug targets with PPI networks, drug structures, and drug targets with PPI networks plus drug structures. The results showed that the integration of PPI networks and drug structures can significantly improve the ADR prediction performance. The median AUC values for the four groups were 0.59, 0.61, 0.65, and 0.70. We used the protein features in the best two models, "Cardiac disorders" (median-AUC: 0.82) and "Psychiatric disorders" (median-AUC: 0.76), to build ADR-specific PPI networks with literature supports. For validation, we examined 30 drugs withdrawn from the U.S. market to see if our approach can predict their ADR profiles and explain why they were withdrawn. Except for three drugs having ADRs in the categories we did not predict, 25 out of 27 withdrawn drugs (92.6%) having severe ADRs were successfully predicted by our approach. © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
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. Copyright © 2012 Elsevier Ltd. All rights reserved.
African Journals Online (AJOL)
abp
19 oct. 2017 ... Reaction to Mohamed Said Nakhli et al. concerning the article: "When the axillary block remains the only alternative in a 5 year old child". .... Bertini L1, Savoia G, De Nicola A, Ivani G, Gravino E, Albani A et al ... 2010;7(2):101-.
Elucidation of Diels-Alder Reaction Network of 2,5-Dimethylfuran and Ethylene on HY Zeolite Catalyst
Energy Technology Data Exchange (ETDEWEB)
Do, Phuong T. M. [Univ. of Delaware, Newark, DE (United States); McAtee, Jesse R. [Univ. of Delaware, Newark, DE (United States); Watson, Donald A. [Univ. of Delaware, Newark, DE (United States); Lobo, Raul F. [Univ. of Delaware, Newark, DE (United States)
2012-12-12
The reaction of 2,5-dimethylfuran and ethylene to produce p-xylene represents a potentially important route for the conversion of biomass to high-value organic chemicals. Current preparation methods suffer from low selectivity and produce a number of byproducts. Using modern separation and analytical techniques, the structures of many of the byproducts produced in this reaction when HY zeolite is employed as a catalyst have been identified. From these data, a detailed reaction network is proposed, demonstrating that hydrolysis and electrophilic alkylation reactions compete with the desired Diels–Alder/dehydration sequence. This information will allow the rational identification of more selective catalysts and more selective reaction conditions.
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
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.
A Reaction-Diffusion-Based Coding Rate Control Mechanism for Camera Sensor Networks
Directory of Open Access Journals (Sweden)
Naoki Wakamiya
2010-08-01
Full Text Available A wireless camera sensor network is useful for surveillance and monitoring for its visibility and easy deployment. However, it suffers from the limited capacity of wireless communication and a network is easily overflown with a considerable amount of video traffic. In this paper, we propose an autonomous video coding rate control mechanism where each camera sensor node can autonomously determine its coding rate in accordance with the location and velocity of target objects. For this purpose, we adopted a biological model, i.e., reaction-diffusion model, inspired by the similarity of biological spatial patterns and the spatial distribution of video coding rate. Through simulation and practical experiments, we verify the effectiveness of our proposal.
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.
The Adaptive QSE-reduced Nuclear Reaction Network for Silicon Burning
International Nuclear Information System (INIS)
Parete-Koon, Suzanne; Hix, William Raphael; Thielemann, Friedrich-Karl W.
2008-01-01
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 2 QSE groups at appropriately high temperature, then progressing through, 3 and 3* 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
Summary Report of the Technical Meeting on International Network of Nuclear Reaction Data Centres
International Nuclear Information System (INIS)
Otuka, Naohiko; Herman, Michal
2016-07-01
This report summarizes the IAEA Technical Meeting on the International Network of Nuclear Reaction Data Centres held at the China Hall of Science and Technology in Beijing, China from 7 to 10 June 2016. The meeting was attended by 23 participants representing 13 cooperative Centres from 8 Member States (China, Hungary, India, Japan, Korea, Russia, Ukraine and USA) and 2 International Organisations (NEA, IAEA) as well as two participants from Kazakhstan. A summary of the meeting is given in this report along with the conclusions and actions. (author)
Neural network approach to time-dependent dividing surfaces in classical reaction dynamics
Schraft, Philippe; Junginger, Andrej; Feldmaier, Matthias; Bardakcioglu, Robin; Main, Jörg; Wunner, Günter; Hernandez, Rigoberto
2018-04-01
In a dynamical system, the transition between reactants and products is typically mediated by an energy barrier whose properties determine the corresponding pathways and rates. The latter is the flux through a dividing surface (DS) between the two corresponding regions, and it is exact only if it is free of recrossings. For time-independent barriers, the DS can be attached to the top of the corresponding saddle point of the potential energy surface, and in time-dependent systems, the DS is a moving object. The precise determination of these direct reaction rates, e.g., using transition state theory, requires the actual construction of a DS for a given saddle geometry, which is in general a demanding methodical and computational task, especially in high-dimensional systems. In this paper, we demonstrate how such time-dependent, global, and recrossing-free DSs can be constructed using neural networks. In our approach, the neural network uses the bath coordinates and time as input, and it is trained in a way that its output provides the position of the DS along the reaction coordinate. An advantage of this procedure is that, once the neural network is trained, the complete information about the dynamical phase space separation is stored in the network's parameters, and a precise distinction between reactants and products can be made for all possible system configurations, all times, and with little computational effort. We demonstrate this general method for two- and three-dimensional systems and explain its straightforward extension to even more degrees of freedom.
Flows, scaling, and the control of moment hierarchies for stochastic chemical reaction networks
Smith, Eric; Krishnamurthy, Supriya
2017-12-01
Stochastic chemical reaction networks (CRNs) are complex systems that combine the features of concurrent transformation of multiple variables in each elementary reaction event and nonlinear relations between states and their rates of change. Most general results concerning CRNs are limited to restricted cases where a topological characteristic known as deficiency takes a value 0 or 1, implying uniqueness and positivity of steady states and surprising, low-information forms for their associated probability distributions. Here we derive equations of motion for fluctuation moments at all orders for stochastic CRNs at general deficiency. We show, for the standard base case of proportional sampling without replacement (which underlies the mass-action rate law), that the generator of the stochastic process acts on the hierarchy of factorial moments with a finite representation. Whereas simulation of high-order moments for many-particle systems is costly, this representation reduces the solution of moment hierarchies to a complexity comparable to solving a heat equation. At steady states, moment hierarchies for finite CRNs interpolate between low-order and high-order scaling regimes, which may be approximated separately by distributions similar to those for deficiency-zero networks and connected through matched asymptotic expansions. In CRNs with multiple stable or metastable steady states, boundedness of high-order moments provides the starting condition for recursive solution downward to low-order moments, reversing the order usually used to solve moment hierarchies. A basis for a subset of network flows defined by having the same mean-regressing property as the flows in deficiency-zero networks gives the leading contribution to low-order moments in CRNs at general deficiency, in a 1 /n expansion in large particle numbers. Our results give a physical picture of the different informational roles of mean-regressing and non-mean-regressing flows and clarify the dynamical
Van Turnhout, A.G.; Kleerebezem, R.; Heimovaara, T.J.
2015-01-01
In this study, we aim to validate the reaction network with an idealized experiment. We want to show that 1) rate controlling parameters are identifiable from the measured data by inverse modeling, and 2) that this network is able to predict the measured emissions in the experiment given the initial
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.
Vilanova, Pedro
2016-01-07
In this work, we present an extension of the forward-reverse representation introduced in Simulation of forward-reverse stochastic representations for conditional diffusions , a 2014 paper by Bayer and Schoenmakers 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, i.e., 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 over-dispersed 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.
International Nuclear Information System (INIS)
Lu Junguo
2008-01-01
In this paper, the global exponential stability and periodicity for a class of reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are addressed by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential converge to 0 of the difference between any two solutions of the original reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Furthermore, we prove periodicity of the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions. Sufficient conditions ensuring the global exponential stability and the existence of periodic oscillatory solutions for the reaction-diffusion delayed recurrent neural networks with Dirichlet boundary conditions are given. These conditions are easy to check and have important leading significance in the design and application of reaction-diffusion recurrent neural networks with delays. Finally, two numerical examples are given to show the effectiveness of the obtained results
Directory of Open Access Journals (Sweden)
Ignat Drozdov
Full Text Available Small intestinal (SI neuroendocrine tumors (NET are increasing in incidence, however little is known about their biology. High throughput techniques such as inference of gene regulatory networks from microarray experiments can objectively define signaling machinery in this disease. Genome-wide co-expression analysis was used to infer gene relevance network in SI-NETs. The network was confirmed to be non-random, scale-free, and highly modular. Functional analysis of gene co-expression modules revealed processes including 'Nervous system development', 'Immune response', and 'Cell-cycle'. Importantly, gene network topology and differential expression analysis identified over-expression of the GPCR signaling regulators, the cAMP synthetase, ADCY2, and the protein kinase A, PRKAR1A. Seven CREB response element (CRE transcripts associated with proliferation and secretion: BEX1, BICD1, CHGB, CPE, GABRB3, SCG2 and SCG3 as well as ADCY2 and PRKAR1A were measured in an independent SI dataset (n = 10 NETs; n = 8 normal preparations. All were up-regulated (p<0.035 with the exception of SCG3 which was not differently expressed. Forskolin (a direct cAMP activator, 10(-5 M significantly stimulated transcription of pCREB and 3/7 CREB targets, isoproterenol (a selective ß-adrenergic receptor agonist and cAMP activator, 10(-5 M stimulated pCREB and 4/7 targets while BIM-53061 (a dopamine D(2 and Serotonin [5-HT(2] receptor agonist, 10(-6 M stimulated 100% of targets as well as pCREB; CRE transcription correlated with the levels of cAMP accumulation and PKA activity; BIM-53061 stimulated the highest levels of cAMP and PKA (2.8-fold and 2.5-fold vs. 1.8-2-fold for isoproterenol and forskolin. Gene network inference and graph topology analysis in SI NETs suggests that SI NETs express neural GPCRs that activate different CRE targets associated with proliferation and secretion. In vitro studies, in a model NET cell system, confirmed that transcriptional
Basílio, Nuno; Pina, Fernando
2014-08-04
In moderately acidic aqueous solutions, flavylium compounds undergo a pH-, and in some cases, light-dependent array of reversible chemical reactions. This network can be described as a single acid-base reaction involving a flavylium cation (acidic form) and a mixture of basic forms (quinoidal base, hemiketal and cis and trans chalcones). The apparent pK'a of the system and the relative mole fractions of the basic forms can be modulated by the interaction with cucurbit[7]uril. The system is studied by using (1) H NMR spectroscopy, UV/Vis spectroscopy, flash photolysis, and steady-state irradiation. Of all the network species, the flavylium cation possesses the highest affinity for cucurbit[7]uril. The rate of interconversion between flavylium cation and the basic species (where trans-chalcone is dominant) is approximately nine times lower inside the cucurbit[7]uril. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Plasmid-derived DNA Strand Displacement Gates for Implementing Chemical Reaction Networks.
Chen, Yuan-Jyue; Rao, Sundipta D; Seelig, Georg
2015-11-25
DNA nanotechnology requires large amounts of highly pure DNA as an engineering material. Plasmid DNA could meet this need since it is replicated with high fidelity, is readily amplified through bacterial culture and can be stored indefinitely in the form of bacterial glycerol stocks. However, the double-stranded nature of plasmid DNA has so far hindered its efficient use for construction of DNA nanostructures or devices that typically contain single-stranded or branched domains. In recent work, it was found that nicked double stranded DNA (ndsDNA) strand displacement gates could be sourced from plasmid DNA. The following is a protocol that details how these ndsDNA gates can be efficiently encoded in plasmids and can be derived from the plasmids through a small number of enzymatic processing steps. Also given is a protocol for testing ndsDNA gates using fluorescence kinetics measurements. NdsDNA gates can be used to implement arbitrary chemical reaction networks (CRNs) and thus provide a pathway towards the use of the CRN formalism as a prescriptive molecular programming language. To demonstrate this technology, a multi-step reaction cascade with catalytic kinetics is constructed. Further it is shown that plasmid-derived components perform better than identical components assembled from synthetic DNA.
International Nuclear Information System (INIS)
Wang Xiaohu; Xu Daoyi
2009-01-01
In this paper, the global exponential stability of impulsive fuzzy cellular neural networks with mixed delays and reaction-diffusion terms is considered. By establishing an integro-differential inequality with impulsive initial condition and using the properties of M-cone and eigenspace of the spectral radius of nonnegative matrices, several new sufficient conditions are obtained to ensure the global exponential stability of the equilibrium point for fuzzy cellular neural networks with delays and reaction-diffusion terms. These results extend and improve the earlier publications. Two examples are given to illustrate the efficiency of the obtained results.
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
Zhang, Rui; El-Refaei, Sayed M.; Russo, Patrícia A.; Pinna, Nicola
2018-05-01
The hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER) play key roles in the conversion of energy derived from renewable energy sources into chemical energy. Efficient, robust, and inexpensive electrocatalysts are necessary for driving these reactions at high rates at low overpotentials and minimize energetic losses. Recently, electrocatalysts derived from hybrid metal phosphonate compounds have shown high activity for the HER or OER. We review here the utilization of metal phosphonate coordination networks and metal-organic frameworks as precursors/templates for transition-metal phosphides, phosphates, or oxyhydroxides generated in situ in alkaline solutions, and their electrocatalytic performance in HER or OER.
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
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
CORREIA, RION BRATTIG; LI, LANG; ROCHA, LUIS M.
2015-01-01
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
Vijaykumar, A.; Ouldridge, T.E.; ten Wolde, P.R.; Bolhuis, P.G.
2017-01-01
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
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. Copyright Â© 2016 Elsevier Inc. All rights reserved.
Bronstein, Leo; Koeppl, Heinz
2018-01-01
Approximate solutions of the chemical master equation and the chemical Fokker-Planck equation are an important tool in the analysis of biomolecular reaction networks. Previous studies have highlighted a number of problems with the moment-closure approach used to obtain such approximations, calling it an ad hoc method. In this article, we give a new variational derivation of moment-closure equations which provides us with an intuitive understanding of their properties and failure modes and allows us to correct some of these problems. We use mixtures of product-Poisson distributions to obtain a flexible parametric family which solves the commonly observed problem of divergences at low system sizes. We also extend the recently introduced entropic matching approach to arbitrary ansatz distributions and Markov processes, demonstrating that it is a special case of variational moment closure. This provides us with a particularly principled approximation method. Finally, we extend the above approaches to cover the approximation of multi-time joint distributions, resulting in a viable alternative to process-level approximations which are often intractable.
Ramaswamy, Rajesh; Sbalzarini, Ivo F; González-Segredo, Nélido
2011-01-01
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. Confi...
Multi-scale simulation of reaction-diffusion systems
Vijaykumar, A.
2017-01-01
In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function
LiHe{sup +} IN THE EARLY UNIVERSE: A FULL ASSESSMENT OF ITS REACTION NETWORK AND FINAL ABUNDANCES
Energy Technology Data Exchange (ETDEWEB)
Bovino, Stefano; Tacconi, Mario; Gianturco, Francesco A. [Department of Chemistry, Universita degli Studi di Roma ' La Sapienza' , Piazzale A. Moro 5, 00185 Roma (Italy); Curik, Roman [J. Heyrovsky Institute of Physical Chemistry, Dolejskova 3, Prague (Czech Republic); Galli, Daniele, E-mail: fa.gianturco@caspur.it [INAF-Osservatorio Astrofisico di Arcetri, Largo E. Fermi 5, 50125 Firenze (Italy)
2012-06-10
We present the results of quantum calculations based on entirely ab initio methods for a variety of molecular processes and chemical reactions involving the LiHe{sup +} ionic polar molecule. With the aid of these calculations, we derive accurate reaction rates and fitting expressions valid over a range of gas temperatures representative of the typical conditions of the pregalactic gas. With the help of a full chemical network, we then compute the evolution of the abundance of LiHe{sup +} as function of redshift in the early universe. Finally, we compare the relative abundance of LiHe{sup +} with that of other polar cations formed in the same redshift interval.
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.
Cocos, Anne; Fiks, Alexander G; Masino, Aaron J
2017-07-01
Social media is an important pharmacovigilance data source for adverse drug reaction (ADR) identification. Human review of social media data is infeasible due to data quantity, thus natural language processing techniques are necessary. Social media includes informal vocabulary and irregular grammar, which challenge natural language processing methods. Our objective is to develop a scalable, deep-learning approach that exceeds state-of-the-art ADR detection performance in social media. We developed a recurrent neural network (RNN) model that labels words in an input sequence with ADR membership tags. The only input features are word-embedding vectors, which can be formed through task-independent pretraining or during ADR detection training. Our best-performing RNN model used pretrained word embeddings created from a large, non-domain-specific Twitter dataset. It achieved an approximate match F-measure of 0.755 for ADR identification on the dataset, compared to 0.631 for a baseline lexicon system and 0.65 for the state-of-the-art conditional random field model. Feature analysis indicated that semantic information in pretrained word embeddings boosted sensitivity and, combined with contextual awareness captured in the RNN, precision. Our model required no task-specific feature engineering, suggesting generalizability to additional sequence-labeling tasks. Learning curve analysis showed that our model reached optimal performance with fewer training examples than the other models. ADR detection performance in social media is significantly improved by using a contextually aware model and word embeddings formed from large, unlabeled datasets. The approach reduces manual data-labeling requirements and is scalable to large social media datasets. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com
Yaniv, Yael; Ganesan, Ambhighainath; Yang, Dongmei; Ziman, Bruce D; Lyashkov, Alexey E; Levchenko, Andre; Zhang, Jin; Lakatta, Edward G
2015-09-01
cAMP-PKA protein kinase is a key nodal signaling pathway that regulates a wide range of heart pacemaker cell functions. These functions are predicted to be involved in regulation of spontaneous action potential (AP) generation of these cells. Here we investigate if the kinetics and stoichiometry of increase in PKA activity match the increase in AP firing rate in response to β-adrenergic receptor (β-AR) stimulation or phosphodiesterase (PDE) inhibition, that alters the AP firing rate of heart sinoatrial pacemaker cells. In cultured adult rabbit pacemaker cells infected with an adenovirus expressing the FRET sensor AKAR3, the EC50 in response to graded increases in the intensity of β-AR stimulation (by Isoproterenol) the magnitude of the increases in PKA activity and the spontaneous AP firing rate were similar (0.4±0.1nM vs. 0.6±0.15nM, respectively). Moreover, the kinetics (t1/2) of the increases in PKA activity and spontaneous AP firing rate in response to β-AR stimulation or PDE inhibition were tightly linked. We characterized the system rate-limiting biochemical reactions by integrating these experimentally derived data into a mechanistic-computational model. Model simulations predicted that phospholamban phosphorylation is a potent target of the increase in PKA activity that links to increase in spontaneous AP firing rate. In summary, the kinetics and stoichiometry of increases in PKA activity in response to a physiological (β-AR stimulation) or pharmacological (PDE inhibitor) stimuli match those of changes in the AP firing rate. Thus Ca(2+)-cAMP/PKA-dependent phosphorylation limits the rate and magnitude of increase in spontaneous AP firing rate. Copyright © 2015 Elsevier Ltd. All rights reserved.
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.
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. © 2015 Wiley Periodicals, Inc.
Chen, Wu-Hua; Luo, Shixian; Zheng, Wei Xing
2016-12-01
This paper presents a new impulsive synchronization criterion of two identical reaction-diffusion neural networks with discrete and unbounded distributed delays. The new criterion is established by applying an impulse-time-dependent Lyapunov functional combined with the use of a new type of integral inequality for treating the reaction-diffusion terms. The impulse-time-dependent feature of the proposed Lyapunov functional can capture more hybrid dynamical behaviors of the impulsive reaction-diffusion neural networks than the conventional impulse-time-independent Lyapunov functions/functionals, while the new integral inequality, which is derived from Wirtinger's inequality, overcomes the conservatism introduced by the integral inequality used in the previous results. Numerical examples demonstrate the effectiveness of the proposed method. Later, the developed impulsive synchronization method is applied to build a spatiotemporal chaotic cryptosystem that can transmit an encrypted image. The experimental results verify that the proposed image-encrypting cryptosystem has the advantages of large key space and high security against some traditional attacks.
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
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.
Kamimura, Atsushi; Kaneko, Kunihiko
2018-03-01
Explanation of exponential growth in self-reproduction is an important step toward elucidation of the origins of life because optimization of the growth potential across rounds of selection is necessary for Darwinian evolution. To produce another copy with approximately the same composition, the exponential growth rates for all components have to be equal. How such balanced growth is achieved, however, is not a trivial question, because this kind of growth requires orchestrated replication of the components in stochastic and nonlinear catalytic reactions. By considering a mutually catalyzing reaction in two- and three-dimensional lattices, as represented by a cellular automaton model, we show that self-reproduction with exponential growth is possible only when the replication and degradation of one molecular species is much slower than those of the others, i.e., when there is a minority molecule. Here, the synergetic effect of molecular discreteness and crowding is necessary to produce the exponential growth. Otherwise, the growth curves show superexponential growth because of nonlinearity of the catalytic reactions or subexponential growth due to replication inhibition by overcrowding of molecules. Our study emphasizes that the minority molecular species in a catalytic reaction network is necessary for exponential growth at the primitive stage of life.
International Nuclear Information System (INIS)
Lu Junguo; Lu Linji
2009-01-01
In this paper, global exponential stability and periodicity of a class of reaction-diffusion recurrent neural networks with distributed delays and Dirichlet boundary conditions are studied by constructing suitable Lyapunov functionals and utilizing some inequality techniques. We first prove global exponential convergence to 0 of the difference between any two solutions of the original neural networks, the existence and uniqueness of equilibrium is the direct results of this procedure. This approach is different from the usually used one where the existence, uniqueness of equilibrium and stability are proved in two separate steps. Secondly, we prove periodicity. Sufficient conditions ensuring the existence, uniqueness, and global exponential stability of the equilibrium and periodic solution are given. These conditions are easy to verify and our results play an important role in the design and application of globally exponentially stable neural circuits and periodic oscillatory neural circuits.
Delay-induced Turing-like waves for one-species reaction-diffusion model on a network
Petit, Julien; Carletti, Timoteo; Asllani, Malbor; Fanelli, Duccio
2015-09-01
A one-species time-delay reaction-diffusion system defined on a complex network is studied. Traveling waves are predicted to occur following a symmetry-breaking instability of a homogeneous stationary stable solution, subject to an external nonhomogeneous perturbation. These are generalized Turing-like waves that materialize in a single-species populations dynamics model, as the unexpected byproduct of the imposed delay in the diffusion part. Sufficient conditions for the onset of the instability are mathematically provided by performing a linear stability analysis adapted to time-delayed differential equations. The method here developed exploits the properties of the Lambert W-function. The prediction of the theory are confirmed by direct numerical simulation carried out for a modified version of the classical Fisher model, defined on a Watts-Strogatz network and with the inclusion of the delay.
International Nuclear Information System (INIS)
Schwerer, O.
2003-08-01
Results of the IAEA Technical Meeting on the Co-ordination of the Network of Nuclear Reaction Data Centres held at the IAEA Headquarters, Vienna, Austria, 17 to 19 June 2003, are summarised in this report. The meeting was attended by 14 participants from 9 cooperating data centres of five member states and two International Organizations. A meeting summary, the conclusions and actions, progress and status reports of the participating data centres, and working papers considered at the meeting, are given in the relevant sections. (author)
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
Bayer, Christian; Moraes, Alvaro; Tempone, Raul; Vilanova, Pedro
2016-01-01
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
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 of...
Tensor methods for parameter estimation and bifurcation analysis of stochastic reaction networks
Czech Academy of Sciences Publication Activity Database
Liao, S.; Vejchodský, Tomáš; Erban, R.
2015-01-01
Roč. 12, č. 108 (2015), s. 20150233 ISSN 1742-5689 EU Projects: European Commission(XE) 328008 - STOCHDETBIOMODEL Institutional support: RVO:67985840 Keywords : gene regulatory networks * stochastic modelling * parametric analysis Subject RIV: BA - General Mathematics Impact factor: 3.818, year: 2015 http://rsif.royalsocietypublishing.org/content/12/108/20150233
A new building block for DNA network formation by self-assembly and polymerase chain reaction.
Bußkamp, Holger; Keller, Sascha; Robotta, Marta; Drescher, Malte; Marx, Andreas
2014-01-01
The predictability of DNA self-assembly is exploited in many nanotechnological approaches. Inspired by naturally existing self-assembled DNA architectures, branched DNA has been developed that allows self-assembly to predesigned architectures with dimensions on the nanometer scale. DNA is an attractive material for generation of nanostructures due to a plethora of enzymes which modify DNA with high accuracy, providing a toolbox for many different manipulations to construct nanometer scaled objects. We present a straightforward synthesis of a rigid DNA branching building block successfully used for the generation of DNA networks by self-assembly and network formation by enzymatic DNA synthesis. The Y-shaped 3-armed DNA construct, bearing 3 primer strands is accepted by Taq DNA polymerase. The enzyme uses each arm as primer strand and incorporates the branched construct into large assemblies during PCR. The networks were investigated by agarose gel electrophoresis, atomic force microscopy, dynamic light scattering, and electron paramagnetic resonance spectroscopy. The findings indicate that rather rigid DNA networks were formed. This presents a new bottom-up approach for DNA material formation and might find applications like in the generation of functional hydrogels.
A compartmentalized out-of-equilibrium enzymatic reaction network for sustained autonomous movement
Nijemeisland, M.; Abdelmohsen, L.K.E.A.; Huck, W.T.S.; Wilson, D.A.; van Hest, J.C.M.
2016-01-01
Every living cell is a compartmentalized out-ofequilibrium system exquisitely able to convert chemical energy into function. In order to maintain homeostasis, the flux of metabolites is tightly controlled by regulatory enzymatic networks. A crucial prerequisite for the development of lifelike
International Nuclear Information System (INIS)
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
Liu, Xuan-He; Wang, Dong; Wan, Li-Jun
2013-10-01
Control over the assembly of molecules on a surface is of great importance for the fabrication of molecule-based miniature devices. Melamine (MA) and molecules with terminal MA units are promising candidates for supramolecular interfacial packing patterning, owing to their multiple hydrogen-bonding sites. Herein, we report the formation of self-assembled structures of MA-capped molecules through a simple on-surface synthetic route. MA terminal groups were successfully fabricated onto rigid molecular cores with 2-fold and 3-fold symmetry through interfacial Schiff-base reactions between MA and aldehyde groups. Sub-molecular scanning tunneling microscopy (STM) imaging of the resultant adlayer revealed the formation of nanoporous networks. Detailed structural analysis indicated that strong hydrogen-bonding interactions between the MA groups persistently drove the formation of nanoporous networks. Herein, we demonstrate that functional groups with strong hydrogen-bond-formation ability are promising building blocks for the guided assembly of nanoporous networks and other hierarchical 2D assemblies. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Directory of Open Access Journals (Sweden)
S. Rafajlović
2009-06-01
Full Text Available The goal of this study was to test the applicability of accelerometer as the sensor for assessment of the walking. We present here the comparison of gait phases detected from the data recorded by force sensing resistors mounted in the shoe insoles, non-processed acceleration and processed acceleration perpendicular to the direction of the foot. The gait phases in all three cases were detected by means of a neural network. The output from the neural network was the gait phase, while the inputs were data from the sensors. The results show that the errors were in the ranges: 30 ms (2.7% – force sensors; 150 ms (13.6% – nonprocessed acceleration, and 120 ms (11% – processed acceleration data. This result suggests that it is possible to use the accelerometer as the gait phase detector, however, with the knowledge that the gait phases are time shifted for about 100 ms with respect the neural network predicted times.
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.
International Nuclear Information System (INIS)
Bonnet, J.; Bounor-Legaré, V.; Alcouffe, P.; Cassagnau, P.
2012-01-01
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 (λ polysiloxane/EVA = 4.0 × 10 −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 −1 ) with a low concentration of SiH ([SiH]/[VA] −3 ). -- Highlights: ► Carbonyl hydrosilylation reaction was found to enhance EVA/polysiloxane immiscible blends. ► EVA crosslinking was obtained with a low molar mass polysiloxane. ► EVA compatibilisation was obtained with a high molar mass polysiloxane. ► Shear rate was found to improve the hydrosilylation reaction at the interface. ► A two-phase morphology of the blends was observed after reaction with fine polysiloxane nodules.
BISEN: Biochemical simulation environment
Vanlier, J.; Wu, F.; Qi, F.; Vinnakota, K.C.; Han, Y.; Dash, R.K.; Yang, F.; Beard, D.A.
2009-01-01
The Biochemical Simulation Environment (BISEN) is a suite of tools for generating equations and associated computer programs for simulating biochemical systems in the MATLAB® computing environment. This is the first package that can generate appropriate systems of differential equations for
Katsahian, Sandrine; Simond Moreau, Erica; Leprovost, Damien; Lardon, Jeremy; Bousquet, Cedric; Kerdelhué, Gaétan; Abdellaoui, Redhouane; Texier, Nathalie; Burgun, Anita; Boussadi, Abdelali; Faviez, Carole
2015-01-01
Suspected adverse drug reactions (ADR) reported by patients through social media can be a complementary tool to already existing ADRs signal detection processes. However, several studies have shown that the quality of medical information published online varies drastically whatever the health topic addressed. The aim of this study is to use an existing rating tool on a set of social network web sites in order to assess the capabilities of these tools to guide experts for selecting the most adapted social network web site to mine ADRs. First, we reviewed and rated 132 Internet forums and social networks according to three major criteria: the number of visits, the notoriety of the forum and the number of messages posted in relation with health and drug therapy. Second, the pharmacist reviewed the topic-oriented message boards with a small number of drug names to ensure that they were not off topic. Six experts have been chosen to assess the selected internet forums using a French scoring tool: Net scoring. Three different scores and the agreement between experts according to each set of scores using weighted kappa pooled using mean have been computed. Three internet forums were chosen at the end of the selection step. Some criteria get high score (scores 3-4) no matter the website evaluated like accessibility (45-46) or design (34-36), at the opposite some criteria always have bad scores like quantitative (40-42) and ethical aspect (43-44), hyperlinks actualization (30-33). Kappa were positives but very small which corresponds to a weak agreement between experts. The personal opinion of the expert seems to have a major impact, undermining the relevance of the criterion. Our future work is to collect results given by this evaluation grid and proposes a new scoring tool for Internet social networks assessment.
Tutubalina, Elena; Nikolenko, Sergey
2017-01-01
Adverse drug reactions (ADRs) are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.
Directory of Open Access Journals (Sweden)
Elena Tutubalina
2017-01-01
Full Text Available Adverse drug reactions (ADRs are an essential part of the analysis of drug use, measuring drug use benefits, and making policy decisions. Traditional channels for identifying ADRs are reliable but very slow and only produce a small amount of data. Text reviews, either on specialized web sites or in general-purpose social networks, may lead to a data source of unprecedented size, but identifying ADRs in free-form text is a challenging natural language processing problem. In this work, we propose a novel model for this problem, uniting recurrent neural architectures and conditional random fields. We evaluate our model with a comprehensive experimental study, showing improvements over state-of-the-art methods of ADR extraction.
Schmitz, Guy; Kolar-Anić, Ljiljana Z; Anić, Slobodan R; Cupić, Zeljko D
2008-12-25
The stoichiometric network analysis (SNA) introduced by B. L. Clarke is applied to a simplified model of the complex oscillating Bray-Liebhafsky reaction under batch conditions, which was not examined by this method earlier. This powerful method for the analysis of steady-states stability is also used to transform the classical differential equations into dimensionless equations. This transformation is easy and leads to a form of the equations combining the advantages of classical dimensionless equations with the advantages of the SNA. The used dimensionless parameters have orders of magnitude given by the experimental information about concentrations and currents. This simplifies greatly the study of the slow manifold and shows which parameters are essential for controlling its shape and consequently have an important influence on the trajectories. The effectiveness of these equations is illustrated on two examples: the study of the bifurcations points and a simple sensitivity analysis, different from the classical one, more based on the chemistry of the studied system.
Energy Technology Data Exchange (ETDEWEB)
Kolb, Brian [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 (United States); Zhao, Bin; Guo, Hua, E-mail: hguo@unm.edu [Department of Chemistry and Chemical Biology, University of New Mexico, Albuquerque, New Mexico 87131 (United States); Li, Jun [School of Chemistry and Chemical Engineering, Chongqing University, Chongqing 401331 (China); Jiang, Bin [Department of Chemical Physics, University of Science and Technology of China, Hefei, Anhui 230026 (China)
2016-06-14
The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H{sub 2} → H{sub 2} + H, H + H{sub 2}O → H{sub 2} + OH, and H + CH{sub 4} → H{sub 2} + CH{sub 3}. A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.
International Nuclear Information System (INIS)
Kolb, Brian; Zhao, Bin; Guo, Hua; Li, Jun; Jiang, Bin
2016-01-01
The applicability and accuracy of the Behler-Parrinello atomistic neural network method for fitting reactive potential energy surfaces is critically examined in three systems, H + H 2 → H 2 + H, H + H 2 O → H 2 + OH, and H + CH 4 → H 2 + CH 3 . A pragmatic Monte Carlo method is proposed to make efficient choice of the atom-centered mapping functions. The accuracy of the potential energy surfaces is not only tested by fitting errors but also validated by direct comparison in dynamically important regions and by quantum scattering calculations. Our results suggest this method is both accurate and efficient in representing multidimensional potential energy surfaces even when dissociation continua are involved.
Yuan, Fanshu; Huang, Yang; Fan, Mengmeng; Chen, Chuntao; Qian, Jieshu; Hao, Qingli; Yang, Jiazhi; Sun, Dongping
2018-02-06
The large-scale, low-cost preparation of Pt-based catalysts with high activity and durability for the methanol oxidation reaction is still challenging. The key to achieving this aim is finding suitable supporting materials. In this paper, N-doped carbon nanofibrous networks are prepared by annealing a gel containing two inexpensive and ecofriendly precursors, that is, bacterial cellulose and urea, for the loading of Pt nanoparticles. An undoped analogue is also prepared for comparison. Meanwhile, the effect of the annealing temperature on the performance of the catalysts is evaluated. The results show that the N doping and higher annealing temperature can improve the electron conductivity of the catalyst and provide more active sites for the loading of ultrafine Pt nanoparticles with a narrow size distribution. The best catalyst exhibits a remarkably high electrocatalytic activity (627 mA mg -1 ), excellent poison tolerance, and high durability. This work demonstrates an ideal Pt supporting material for the methanol oxidation reaction. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
Directory of Open Access Journals (Sweden)
Zixiang Xu
Full Text Available Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective.
Winkelmann, Stefanie; Schütte, Christof
2017-09-01
Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.
Winkelmann, Stefanie; Schütte, Christof
2017-09-21
Well-mixed stochastic chemical kinetics are properly modeled by the chemical master equation (CME) and associated Markov jump processes in molecule number space. If the reactants are present in large amounts, however, corresponding simulations of the stochastic dynamics become computationally expensive and model reductions are demanded. The classical model reduction approach uniformly rescales the overall dynamics to obtain deterministic systems characterized by ordinary differential equations, the well-known mass action reaction rate equations. For systems with multiple scales, there exist hybrid approaches that keep parts of the system discrete while another part is approximated either using Langevin dynamics or deterministically. This paper aims at giving a coherent overview of the different hybrid approaches, focusing on their basic concepts and the relation between them. We derive a novel general description of such hybrid models that allows expressing various forms by one type of equation. We also check in how far the approaches apply to model extensions of the CME for dynamics which do not comply with the central well-mixed condition and require some spatial resolution. A simple but meaningful gene expression system with negative self-regulation is analysed to illustrate the different approximation qualities of some of the hybrid approaches discussed. Especially, we reveal the cause of error in the case of small volume approximations.
Energy Technology Data Exchange (ETDEWEB)
Cotter, Simon L., E-mail: simon.cotter@manchester.ac.uk
2016-10-15
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.
Chemical networks with inflows and outflows: a positive linear differential inclusions approach.
Angeli, David; De Leenheer, Patrick; Sontag, Eduardo D
2009-01-01
Certain mass-action kinetics models of biochemical reaction networks, although described by nonlinear differential equations, may be partially viewed as state-dependent linear time-varying systems, which in turn may be modeled by convex compact valued positive linear differential inclusions. A result is provided on asymptotic stability of such inclusions, and applied to a ubiquitous biochemical reaction network with inflows and outflows, known as the futile cycle. We also provide a characterization of exponential stability of general homogeneous switched systems which is not only of interest in itself, but also plays a role in the analysis of the futile cycle. 2009 American Institute of Chemical Engineers
Deterministic modelling and stochastic simulation of biochemical pathways using MATLAB.
Ullah, M; Schmidt, H; Cho, K H; Wolkenhauer, O
2006-03-01
The analysis of complex biochemical networks is conducted in two popular conceptual frameworks for modelling. The deterministic approach requires the solution of ordinary differential equations (ODEs, reaction rate equations) with concentrations as continuous state variables. The stochastic approach involves the simulation of differential-difference equations (chemical master equations, CMEs) with probabilities as variables. This is to generate counts of molecules for chemical species as realisations of random variables drawn from the probability distribution described by the CMEs. Although there are numerous tools available, many of them free, the modelling and simulation environment MATLAB is widely used in the physical and engineering sciences. We describe a collection of MATLAB functions to construct and solve ODEs for deterministic simulation and to implement realisations of CMEs for stochastic simulation using advanced MATLAB coding (Release 14). The program was successfully applied to pathway models from the literature for both cases. The results were compared to implementations using alternative tools for dynamic modelling and simulation of biochemical networks. The aim is to provide a concise set of MATLAB functions that encourage the experimentation with systems biology models. All the script files are available from www.sbi.uni-rostock.de/ publications_matlab-paper.html.
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.
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. PMID:22064852
Directory of Open Access Journals (Sweden)
Rajesh Ramaswamy
2011-01-01
Full Text Available 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.
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.
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
Diversity in the dynamical behaviour of a compartmentalized programmable biochemical oscillator.
Weitz, Maximilian; Kim, Jongmin; Kapsner, Korbinian; Winfree, Erik; Franco, Elisa; Simmel, Friedrich C
2014-04-01
In vitro compartmentalization of biochemical reaction networks is a crucial step towards engineering artificial cell-scale devices and systems. At this scale the dynamics of molecular systems becomes stochastic, which introduces several engineering challenges and opportunities. Here we study a programmable transcriptional oscillator system that is compartmentalized into microemulsion droplets with volumes between 33 fl and 16 pl. Simultaneous measurement of large populations of droplets reveals major variations in the amplitude, frequency and damping of the oscillations. Variability increases for smaller droplets and depends on the operating point of the oscillator. Rather than reflecting the stochastic kinetics of the chemical reaction network itself, the variability can be attributed to the statistical variation of reactant concentrations created during their partitioning into droplets. We anticipate that robustness to partitioning variability will be a critical challenge for engineering cell-scale systems, and that highly parallel time-series acquisition from microemulsion droplets will become a key tool for characterization of stochastic circuit function.
Stamova, Ivanka; Stamov, Gani
2017-12-01
In this paper, we propose a fractional-order neural network system with time-varying delays and reaction-diffusion terms. We first develop a new Mittag-Leffler synchronization strategy for the controlled nodes via impulsive controllers. Using the fractional Lyapunov method sufficient conditions are given. We also study the global Mittag-Leffler synchronization of two identical fractional impulsive reaction-diffusion neural networks using linear controllers, which was an open problem even for integer-order models. Since the Mittag-Leffler stability notion is a generalization of the exponential stability concept for fractional-order systems, our results extend and improve the exponential impulsive control theory of neural network system with time-varying delays and reaction-diffusion terms to the fractional-order case. The fractional-order derivatives allow us to model the long-term memory in the neural networks, and thus the present research provides with a conceptually straightforward mathematical representation of rather complex processes. Illustrative examples are presented to show the validity of the obtained results. We show that by means of appropriate impulsive controllers we can realize the stability goal and to control the qualitative behavior of the states. An image encryption scheme is extended using fractional derivatives. Copyright © 2017 Elsevier Ltd. All rights reserved.
Kim, Daesang; Lindquist, W. Brent
2013-01-01
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
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
A Simple Approach to Study Designs in Complex Biochemical ...
Indian Academy of Sciences (India)
Somdatta Sinha
Protein sequences. • Biochemical & Genetic information. REVERSE ENGINEERING. LARGE NETWORKS. FORWARD ENGINEERING. All designs that are not physically forbidden are realizable, but not all realizable designs are functionally effective. (in relation to context and constraints of the system and environment).
Reconstructing biochemical pathways from time course data.
Srividhya, Jeyaraman; Crampin, Edmund J; McSharry, Patrick E; Schnell, Santiago
2007-03-01
Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.
Biochemical activity of fullerenes and related derivatives
International Nuclear Information System (INIS)
Huczko, A.; Lange, H.; Calko, E.
1999-01-01
An astonishing scientific interest, embodied in over 15000 research articles so far, has been encountered since 1985 when fullerenes were discovered. From new superconductors to a rich electrochemistry and reaction chemistry, fullerene nanostructures continue to excite the scientific world, and new findings continue at record pace. This review presents many examples of the biochemical activities of fullerenes and derivatives, e. g. cytotoxic activity, selective DNA cleavage and antiviral activity against HIV. We also present some results of our testing which show that, despite its chemical and biochemical activity, fullerene matter does not present any health hazard directly related to skin irritation and allergic risks. (author)
Ouroboros - Playing A Biochemical
Directory of Open Access Journals (Sweden)
D. T. Rodrigues
2014-08-01
Full Text Available Ouroboros: Playing A Biochemical RODRIGUES,D.T.1,2;GAYER, M.C.1,2; ESCOTO, D.F.1; DENARDIN, E.L.G.2, ROEHRS, R.1,2 1Interdisciplinary Research Group on Teaching Practice, Graduate Program in Biochemistry, Unipampa, RS, Brazil 2Laboratory of Physicochemical Studies and Natural Products, Post Graduate Program in Biochemistry, Unipampa, RS, Brazil Introduction: Currently, teachers seek different alternatives to enhance the teaching-learning process. Innovative teaching methodologies are increasingly common tools in educational routine. The use of games, electronic or conventional, is an effective tool to assist in learning and also to raise the social interaction between students. Objective: In this sense our work aims to evaluate the card game and "Ouroboros" board as a teaching and learning tool in biochemistry for a graduating class in Natural Sciences. Materials and methods: The class gathered 22 students of BSc in Natural Sciences. Each letter contained a question across the board that was drawn to a group to answer within the allotted time. The questions related concepts of metabolism, organic and inorganic chemical reactions, bioenergetics, etc.. Before the game application, students underwent a pre-test with four issues involving the content that was being developed. Soon after, the game was applied. Then again questions were asked. Data analysis was performed from the ratio of the number of correct pre-test and post-test answers. Results and discussion: In the pre-test 18.1% of the students knew all issues, 18.1% got 3 correct answers, 40.9% answered only 2 questions correctly and 22.7% did not hit any. In post-test 45.4% answered all the questions right, 31.8% got 3 questions and 22.7% got 2 correct answers. The results show a significant improvement of the students about the field of content taught through the game. Conclusion: Generally, traditional approaches of chemistry and biochemistry are abstract and complex. Thus, through games
Efficient simulation of intrinsic, extrinsic and external noise in biochemical systems
Pischel, Dennis; Sundmacher, Kai; Flassig, Robert J.
2017-01-01
Abstract Motivation: Biological cells operate in a noisy regime influenced by intrinsic, extrinsic and external noise, which leads to large differences of individual cell states. Stochastic effects must be taken into account to characterize biochemical kinetics accurately. Since the exact solution of the chemical master equation, which governs the underlying stochastic process, cannot be derived for most biochemical systems, approximate methods are used to obtain a solution. Results: In this study, a method to efficiently simulate the various sources of noise simultaneously is proposed and benchmarked on several examples. The method relies on the combination of the sigma point approach to describe extrinsic and external variability and the τ-leaping algorithm to account for the stochasticity due to probabilistic reactions. The comparison of our method to extensive Monte Carlo calculations demonstrates an immense computational advantage while losing an acceptable amount of accuracy. Additionally, the application to parameter optimization problems in stochastic biochemical reaction networks is shown, which is rarely applied due to its huge computational burden. To give further insight, a MATLAB script is provided including the proposed method applied to a simple toy example of gene expression. Availability and implementation: MATLAB code is available at Bioinformatics online. Contact: flassig@mpi-magdeburg.mpg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881987
Multistate Model Builder (MSMB): a flexible editor for compact biochemical models.
Palmisano, Alida; Hoops, Stefan; Watson, Layne T; Jones, Thomas C; Tyson, John J; Shaffer, Clifford A
2014-04-04
Building models of molecular regulatory networks is challenging not just because of the intrinsic difficulty of describing complex biological processes. Writing a model is a creative effort that calls for more flexibility and interactive support than offered by many of today's biochemical model editors. Our model editor MSMB - Multistate Model Builder - supports multistate models created using different modeling styles. MSMB provides two separate advances on existing network model editors. (1) A simple but powerful syntax is used to describe multistate species. This reduces the number of reactions needed to represent certain molecular systems, thereby reducing the complexity of model creation. (2) Extensive feedback is given during all stages of the model creation process on the existing state of the model. Users may activate error notifications of varying stringency on the fly, and use these messages as a guide toward a consistent, syntactically correct model. MSMB default values and behavior during model manipulation (e.g., when renaming or deleting an element) can be adapted to suit the modeler, thus supporting creativity rather than interfering with it. MSMB's internal model representation allows saving a model with errors and inconsistencies (e.g., an undefined function argument; a syntactically malformed reaction). A consistent model can be exported to SBML or COPASI formats. We show the effectiveness of MSMB's multistate syntax through models of the cell cycle and mRNA transcription. Using multistate reactions reduces the number of reactions need to encode many biochemical network models. This reduces the cognitive load for a given model, thereby making it easier for modelers to build more complex models. The many interactive editing support features provided by MSMB make it easier for modelers to create syntactically valid models, thus speeding model creation. Complete information and the installation package can be found at http
Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.
2016-01-01
Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845
Directory of Open Access Journals (Sweden)
Bushell Michael E
2011-05-01
Full Text Available Abstract Background Constraint-based approaches facilitate the prediction of cellular metabolic capabilities, based, in turn on predictions of the repertoire of enzymes encoded in the genome. Recently, genome annotations have been used to reconstruct genome scale metabolic reaction networks for numerous species, including Homo sapiens, which allow simulations that provide valuable insights into topics, including predictions of gene essentiality of pathogens, interpretation of genetic polymorphism in metabolic disease syndromes and suggestions for novel approaches to microbial metabolic engineering. These constraint-based simulations are being integrated with the functional genomics portals, an activity that requires efficient implementation of the constraint-based simulations in the web-based environment. Results Here, we present Acorn, an open source (GNU GPL grid computing system for constraint-based simulations of genome scale metabolic reaction networks within an interactive web environment. The grid-based architecture allows efficient execution of computationally intensive, iterative protocols such as Flux Variability Analysis, which can be readily scaled up as the numbers of models (and users increase. The web interface uses AJAX, which facilitates efficient model browsing and other search functions, and intuitive implementation of appropriate simulation conditions. Research groups can install Acorn locally and create user accounts. Users can also import models in the familiar SBML format and link reaction formulas to major functional genomics portals of choice. Selected models and simulation results can be shared between different users and made publically available. Users can construct pathway map layouts and import them into the server using a desktop editor integrated within the system. Pathway maps are then used to visualise numerical results within the web environment. To illustrate these features we have deployed Acorn and created a
Ding, Jieting; Ji, Shan; Wang, Hui; Key, Julian; Brett, Dan J. L.; Wang, Rongfang
2018-01-01
Network-like metallic alloys of solid nanoparticles have been frequently reported as promising electrocatalysts for fuel cells. The three-dimensional structure of such networks is rich in pores in the form of voids between nanoparticles, which collectively expose a large surface area for catalytic activity. Herein, we present a novel solution to this problem using a precursor comprising a flocculent core-shell PtNi@Ni to produce PtNi network catalysts with nanoparticle intraporosity after carefully controlled electrochemical dealloying. Physical characterization shows a hierarchical level of nanoporosity (intrapores within nanoparticles and pores between them) evolves during the controlled electrochemical dealloying, and that a Pt-rich surface also forms after 22 cycles of Ni leaching. In ORR cycling, the PtNi networks gain 4-fold activity in both jECSA and jmass over a state of the art Pt/C electrocatalyst, and also significantly exceed previously reported PtNi networks. In ORR degradation tests, the PtNi networks also proved stable, dropping by 30.4% and 62.6% in jECSA and jmass respectively. The enhanced performance of the catalyst is evident, and we also propose that the presented synthesis procedure can be generally applied to developing other metallic networks.
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…
The biochemical, physiological and therapeutic roles of ascorbic acid
African Journals Online (AJOL)
Ascorbic acid is an important micronutrient necessary for a significant number of metabolic reactions in humans and other primates. It is a strong reducing agent involved in reduction reaction and it is structurally related to glucose. Experimental and epidemiological studies have documented the biochemical, physiological ...
Cocco, Alex P.; Nakajo, Arata; Chiu, Wilson K. S.
2017-12-01
We present a fully analytical, heuristic model - the "Analytical Transport Network Model" - for steady-state, diffusive, potential flow through a 3-D network. Employing a combination of graph theory, linear algebra, and geometry, the model explicitly relates a microstructural network's topology and the morphology of its channels to an effective material transport coefficient (a general term meant to encompass, e.g., conductivity or diffusion coefficient). The model's transport coefficient predictions agree well with those from electrochemical fin (ECF) theory and finite element analysis (FEA), but are computed 0.5-1.5 and 5-6 orders of magnitude faster, respectively. In addition, the theory explicitly relates a number of morphological and topological parameters directly to the transport coefficient, whereby the distributions that characterize the structure are readily available for further analysis. Furthermore, ATN's explicit development provides insight into the nature of the tortuosity factor and offers the potential to apply theory from network science and to consider the optimization of a network's effective resistance in a mathematically rigorous manner. The ATN model's speed and relative ease-of-use offer the potential to aid in accelerating the design (with respect to transport), and thus reducing the cost, of energy materials.
Shi, Ya-Cheng; Mei, Li-Ping; Wang, Ai-Jun; Yuan, Tao; Chen, Sai-Sai; Feng, Jiu-Ju
2017-10-15
In this work, bimetallic platinum-palladium sheet-assembled alloy networks (PtPd SAANs) were facilely synthesized by an eco-friendly one-pot aqueous approach under the guidance of l-glutamic acid at room temperature, without any additive, seed, toxic or organic solvent involved. l-Glutamic acid was served as the green shape-director and weak-stabilizing agent. A series of characterization techniques were employed to examine the morphology, structure and formation mechanism of the product. The architectures exhibited improved electrocatalytic activity and durable ability toward methanol oxidation reaction (MOR) and oxygen reduction reaction (ORR) in contrast with commercial Pt black and Pd black catalysts. This is ascribed to the unique structures of the obtained PtPd SAANs and the synergistic effects of the bimetals. These results demonstrate the potential application of the prepared catalyst in fuel cells. Copyright © 2017 Elsevier Inc. All rights reserved.
Multidimensional biochemical information processing of dynamical patterns.
Hasegawa, Yoshihiko
2018-02-01
Cells receive signaling molecules by receptors and relay information via sensory networks so that they can respond properly depending on the type of signal. Recent studies have shown that cells can extract multidimensional information from dynamical concentration patterns of signaling molecules. We herein study how biochemical systems can process multidimensional information embedded in dynamical patterns. We model the decoding networks by linear response functions, and optimize the functions with the calculus of variations to maximize the mutual information between patterns and output. We find that, when the noise intensity is lower, decoders with different linear response functions, i.e., distinct decoders, can extract much information. However, when the noise intensity is higher, distinct decoders do not provide the maximum amount of information. This indicates that, when transmitting information by dynamical patterns, embedding information in multiple patterns is not optimal when the noise intensity is very large. Furthermore, we explore the biochemical implementations of these decoders using control theory and demonstrate that these decoders can be implemented biochemically through the modification of cascade-type networks, which are prevalent in actual signaling pathways.
Statistical physics approaches to subnetwork dynamics in biochemical systems
Bravi, B.; Sollich, P.
2017-08-01
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 signalling pathway is provided to probe the increased prediction accuracy and computational efficiency of our method.
GKIN: a tool for drawing genetic networks
Directory of Open Access Journals (Sweden)
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/.
Wang, Hui-Fang; Liu, Zhi-Pan
2008-08-20
Ethanol oxidation on Pt is a typical multistep and multiselectivity heterogeneous catalytic process. A comprehensive understanding of this fundamental reaction would greatly benefit design of catalysts for use in direct ethanol fuel cells and the degradation of biomass-derived oxygenates. In this work, the reaction network of ethanol oxidation on different Pt surfaces, including close-packed Pt{111}, stepped Pt{211}, and open Pt{100}, is explored thoroughly with an efficient reaction path searching method, which integrates our new transition-state searching technique with periodic density functional theory calculations. Our new technique enables the location of the transition state and saddle points for most surface reactions simply and efficiently by optimization of local minima. We show that the selectivity of ethanol oxidation on Pt depends markedly on the surface structure, which can be attributed to the structure-sensitivity of two key reaction steps: (i) the initial dehydrogenation of ethanol and (ii) the oxidation of acetyl (CH3CO). On open surface sites, ethanol prefers C-C bond cleavage via strongly adsorbed intermediates (CH2CO or CHCO), which leads to complete oxidation to CO2. However, only partial oxidizations to CH3CHO and CH3COOH occur on Pt{111}. Our mechanism points out that the open surface Pt{100} is the best facet to fully oxidize ethanol at low coverages, which sheds light on the origin of the remarkable catalytic performance of Pt tetrahexahedra nanocrystals found recently. The physical origin of the structure-selectivity is rationalized in terms of both thermodynamics and kinetics. Two fundamental quantities that dictate the selectivity of ethanol oxidation are identified: (i) the ability of surface metal atoms to bond with unsaturated C-containing fragments and (ii) the relative stability of hydroxyl at surface atop sites with respect to other sites.
Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise
2016-01-01
The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...
Sabbar, Shaho; Hyun, Daiwon
2016-01-01
After a piece of information is put into a network, its fate depends on the behaviors of the nodes of the network; nodes that are equipped with the hardware and software of the age of information and are more powerful than any time in the past. This study suggests that a useful research for communication, marketing and advertising would be one that looks for patterns in the reactions of the nodes toward different pieces of information. This study has used Facebook to see how people have reacted to different types of messages in terms of liking, sharing and commenting. Rather than looking for universal, generalizable patterns we have tried to examine the practicality of the proposed method. The practical aspect of the study comes after a short theoretical discussion on the issue of flow of information in a digital world. The results revealed dozens of significant relations between the examined variables. This study, its theoretical discussion and results suggest that it would be practical to study the relations between the characteristics of Facebook messages and the type of reactions (liking, sharing and commenting) that they attract.
Biochemical thermodynamics: applications of Mathematica.
Alberty, Robert A
2006-01-01
reactants. Thus loading this package makes available 774 mathematical functions for these properties. These functions can be added and subtracted to obtain changes in these properties in biochemical reactions and apparent equilibrium constants.
Tang, Jin-Yun; Riley, William J.
2017-09-01
Several land biogeochemical models used for studying carbon-climate feedbacks have begun explicitly representing microbial dynamics. However, to our knowledge, there has been no theoretical work on how to achieve a consistent scaling of the complex biogeochemical reactions from microbial individuals to populations, communities, and interactions with plants and mineral soils. We focus here on developing a mathematical formulation of the substrate-consumer relationships for consumer-mediated redox reactions of the form A + BE→ products, where products could be, e.g., microbial biomass or bioproducts. Under the quasi-steady-state approximation, these substrate-consumer relationships can be formulated as the computationally difficult full equilibrium chemistry problem or approximated analytically with the dual Monod (DM) or synthesizing unit (SU) kinetics. We find that DM kinetics is scaling inconsistently for reaction networks because (1) substrate limitations are not considered, (2) contradictory assumptions are made regarding the substrate processing rate when transitioning from single- to multi-substrate redox reactions, and (3) the product generation rate cannot be scaled from one to multiple substrates. In contrast, SU kinetics consistently scales the product generation rate from one to multiple substrates but predicts unrealistic results as consumer abundances reach large values with respect to their substrates. We attribute this deficit to SU's failure to incorporate substrate limitation in its derivation. To address these issues, we propose SUPECA (SU plus the equilibrium chemistry approximation - ECA) kinetics, which consistently imposes substrate and consumer mass balance constraints. We show that SUPECA kinetics satisfies the partition principle, i.e., scaling invariance across a network of an arbitrary number of reactions (e.g., as in Newton's law of motion and Dalton's law of partial pressures). We tested SUPECA kinetics with the equilibrium chemistry
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.
Temporal motifs in time-dependent networks
International Nuclear Information System (INIS)
Kovanen, Lauri; Karsai, Márton; Kaski, Kimmo; Kertész, János; Saramäki, Jari
2011-01-01
Temporal networks are commonly used to represent systems where connections between elements are active only for restricted periods of time, such as telecommunication, neural signal processing, biochemical reaction and human social interaction networks. We introduce the framework of temporal motifs to study the mesoscale topological–temporal structure of temporal networks in which the events of nodes do not overlap in time. Temporal motifs are classes of similar event sequences, where the similarity refers not only to topology but also to the temporal order of the events. We provide a mapping from event sequences to coloured directed graphs that enables an efficient algorithm for identifying temporal motifs. We discuss some aspects of temporal motifs, including causality and null models, and present basic statistics of temporal motifs in a large mobile call network
Energy Technology Data Exchange (ETDEWEB)
1980-01-01
The Proceedings of the Conference comprises 12 papers dealing with the following themes: the history of apparent power; active and apparent power of periodical currents in single-phase and multiphase systems with periodical voltages of any shape of curve; power values in unsteady state processes; power and harmonic relations in mains-controlled direct frequency converters; power electronic devices for apparent power compensation; electric arc effects on power system: characteristics, measurement methods, compensation; harmonic compensation by means of impedance wave trap filters in rectifiers; compensation of apparent power by filter circuits; apparent power effects on ac railroad power systems; effects of apparent power in low-voltage networks of public power supply.
Implantable biochemical fuel cell
Energy Technology Data Exchange (ETDEWEB)
Richter, G; Rao, J R
1978-01-05
Implantable biochemical fuel cells for the operation of heart pacemakers or artificial hearts convert oxidisable body substances such as glucose on the anode side and reduce the oxygen contained in body fluids at the cathode. The anode and cathode are separated by membranes which are impermeable to albumen and blood corpuscles in body fluids. A chemical shortcircuit cannot occur in practice if, according to the invention, one or more selective oxygen electrodes with carbon as catalyst are arranged so that the mixture which diffuses into the cell from body fluids during operation reaches the fuel cell electrode through the porous oxygen electrode. The membranes used must be permeable to water. Cellulose, polymerised polyvinyl alcohol or an ion exchanger with a buffering capacity between pH5 and 8 act as permeable materials.
Politis, C; Wiersum, J C; Richardson, C; Robillard, P; Jorgensen, J; Renaudier, P; Faber, J-C; Wood, E M
2016-11-01
The International Haemovigilance Network's ISTARE is an online database for surveillance of all adverse reactions (ARs) and adverse events (AEs) associated with donation of blood and transfusion of blood components, irrespective of severity or the harm caused. ISTARE aims to unify the collection and sharing of information with a view to harmonizing best practices for haemovigilance systems around the world. Adverse reactionss and adverse events are recorded by blood component, type of reaction, severity and imputability to transfusion, using internationally agreed standard definitions. From 2006 to 2012, 125 national sets of annual aggregated data were received from 25 countries, covering 132.8 million blood components issued. The incidence of all ARs was 77.5 per 100 000 components issued, of which 25% were severe (19.1 per 100 000). Of 349 deaths (0.26 per 100 000), 58% were due to the three ARs related to the respiratory system: transfusion-associated circulatory overload (TACO, 27%), transfusion-associated acute lung injury (TRALI, 19%) and transfusion-associated dyspnoea (TAD, 12%). Cumulatively, 594 477 donor complications were reported (rate 660 per 100 000), of which 2.9% were severe. ISTARE is a well-established surveillance tool offering important contributions to international efforts to maximize transfusion safety. © 2016 International Society of Blood Transfusion.
International Nuclear Information System (INIS)
Aaen, Sara Bjørn; Kerndrup, Søren; Lyhne, Ivar
2016-01-01
This article adds to the growing insight into public acceptance by presenting a novel approach to how citizens make sense of new energy infrastructure. We claim that to understand public acceptance, we need to go beyond the current thinking of citizens framed as passive respondents to proposed projects, and instead view infrastructure projects as enacted by citizens in their local settings. We propose a combination of sensemaking theory and actor–network theory that allows insight into how citizens enact entities from experiences and surroundings in order to create meaning and form a reaction to new infrastructure projects. Empirically, we analyze how four citizens make sense of an electricity cable project through a conversation process with a representative from the infrastructure developer. Interestingly, the formal participation process and the materiality of the cable play minor roles in citizens' sensemaking process. We conclude that insight into the way citizens are making sense of energy infrastructure processes can improve and help to overcome shortcomings in the current thinking about public acceptance and public participation. - Highlights: •Attention to citizens' sensemaking enables greater insight into the decision-making process. •A combination of sensemaking and actor-network theory (ANT) is relevant for studies of public acceptance. •Sensemaking explains why citizens facing similar situations act differently. •Complexity of citizens' sensemaking challenges the predictability of processes.
International Nuclear Information System (INIS)
Lyubimov, Artem Y.; Chen, Lin; Sampson, Nicole S.; Vrielink, Alice
2009-01-01
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 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
Hierarchical analysis of dependency in metabolic networks.
Gagneur, Julien; Jackson, David B; Casari, Georg
2003-05-22
Elucidation of metabolic networks for an increasing number of organisms reveals that even small networks can contain thousands of reactions and chemical species. The intimate connectivity between components complicates their decomposition into biologically meaningful sub-networks. Moreover, traditional higher-order representations of metabolic networks as metabolic pathways, suffers from the lack of rigorous definition, yielding pathways of disparate content and size. We introduce a hierarchical representation that emphasizes the gross organization of metabolic networks in largely independent pathways and sub-systems at several levels of independence. The approach highlights the coupling of different pathways and the shared compounds responsible for those couplings. By assessing our results on Escherichia coli (E.coli metabolic reactions, Genetic Circuits Research Group, University of California, San Diego, http://gcrg.ucsd.edu/organisms/ecoli.html, 'model v 1.01. reactions') against accepted biochemical annotations, we provide the first systematic synopsis of an organism's metabolism. Comparison with operons of E.coli shows that low-level clusters are reflected in genome organization and gene regulation. Source code, data sets and supplementary information are available at http://www.mas.ecp.fr/labo/equipe/gagneur/hierarchy/hierarchy.html
Environmental versatility promotes modularity in genome-scale metabolic networks.
Samal, Areejit; Wagner, Andreas; Martin, Olivier C
2011-08-24
The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple
Environmental versatility promotes modularity in genome-scale metabolic networks
Directory of Open Access Journals (Sweden)
Wagner Andreas
2011-08-01
Full Text Available Abstract Background The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Results Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Conclusions Our work shows that modularity in metabolic networks can be a by-product of functional
Sleeve reaction chamber system
Northrup, M Allen [Berkeley, CA; Beeman, Barton V [San Mateo, CA; Benett, William J [Livermore, CA; Hadley, Dean R [Manteca, CA; Landre, Phoebe [Livermore, CA; Lehew, Stacy L [Livermore, CA; Krulevitch, Peter A [Pleasanton, CA
2009-08-25
A chemical reaction chamber system that combines devices such as doped polysilicon for heating, bulk silicon for convective cooling, and thermoelectric (TE) coolers to augment the heating and cooling rates of the reaction chamber or chambers. In addition the system includes non-silicon-based reaction chambers such as any high thermal conductivity material used in combination with a thermoelectric cooling mechanism (i.e., Peltier device). The heat contained in the thermally conductive part of the system can be used/reused to heat the device, thereby conserving energy and expediting the heating/cooling rates. The system combines a micromachined silicon reaction chamber, for example, with an additional module/device for augmented heating/cooling using the Peltier effect. This additional module is particularly useful in extreme environments (very hot or extremely cold) where augmented heating/cooling would be useful to speed up the thermal cycling rates. The chemical reaction chamber system has various applications for synthesis or processing of organic, inorganic, or biochemical reactions, including the polymerase chain reaction (PCR) and/or other DNA reactions, such as the ligase chain reaction.
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....
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.
Lanthanide ions as absorption spectral probes in biochemical reactions
International Nuclear Information System (INIS)
Misra, S.N.
1989-01-01
The interactions of adenine, adenosine, adenosine 5'-monophosphate, 5'-diphosphate and 5'-triphosphate with Pr(III) and Nd(III) in different stoichiometries and at varying pH levels have been investigated by electronic spectral studies. The intra 4f-4f transitions yield sharp bands which were analysed individually by Gaussian curve analysis. The energy interaction (Fsup(k),Esup(k)) spin orbit interaction (ζ4f), bonding(b), nephelauxetic (β,δ) and intensity parameters (Tsub(τ.P)) have been computed on HP-1000/45 computer using regression analyses refined by least square fit. The nature of bonding, coordination environment, outer and inner sphere coordination have been interpreted in terms of the magnitude of these parameters as compared to the lanthanide free ion. In order to supplicate the solution studies the crystalline compounds of AMP, ADP and ATP with Pr(III) and Nd(III) have been isolated and characterized by IR, 1 H and 31 P NMR studies. The infrared spectral data indicated weak interaction with the imidazole nitrogen of adenine moiety and bidentate attachment of oxygen. (author). 10 refs
Microsecond time-scale kinetics of transient biochemical reactions
Mitic, S.; Strampraad, M.J.F.; Hagen, W.R.; de Vries, S.
2017-01-01
To afford mechanistic studies in enzyme kinetics and protein folding in the microsecond time domain we have developed a continuous-flow microsecond time-scale mixing instrument with an unprecedented dead-time of 3.8 ± 0.3 μs. The instrument employs a micro-mixer with a mixing time of 2.7 μs
Reaction Decoder Tool (RDT): extracting features from chemical reactions.
Rahman, Syed Asad; Torrance, Gilliean; Baldacci, Lorenzo; Martínez Cuesta, Sergio; Fenninger, Franz; Gopal, Nimish; Choudhary, Saket; May, John W; Holliday, Gemma L; Steinbeck, Christoph; Thornton, Janet M
2016-07-01
Extracting chemical features like Atom-Atom Mapping (AAM), Bond Changes (BCs) and Reaction Centres from biochemical reactions helps us understand the chemical composition of enzymatic reactions. Reaction Decoder is a robust command line tool, which performs this task with high accuracy. It supports standard chemical input/output exchange formats i.e. RXN/SMILES, computes AAM, highlights BCs and creates images of the mapped reaction. This aids in the analysis of metabolic pathways and the ability to perform comparative studies of chemical reactions based on these features. This software is implemented in Java, supported on Windows, Linux and Mac OSX, and freely available at https://github.com/asad/ReactionDecoder : asad@ebi.ac.uk or s9asad@gmail.com. © The Author 2016. Published by Oxford University Press.
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...
Computing chemical organizations in biological networks.
Centler, Florian; Kaleta, Christoph; di Fenizio, Pietro Speroni; Dittrich, Peter
2008-07-15
Novel techniques are required to analyze computational models of intracellular processes as they increase steadily in size and complexity. The theory of chemical organizations has recently been introduced as such a technique that links the topology of biochemical reaction network models to their dynamical repertoire. The network is decomposed into algebraically closed and self-maintaining subnetworks called organizations. They form a hierarchy representing all feasible system states including all steady states. We present three algorithms to compute the hierarchy of organizations for network models provided in SBML format. Two of them compute the complete organization hierarchy, while the third one uses heuristics to obtain a subset of all organizations for large models. While the constructive approach computes the hierarchy starting from the smallest organization in a bottom-up fashion, the flux-based approach employs self-maintaining flux distributions to determine organizations. A runtime comparison on 16 different network models of natural systems showed that none of the two exhaustive algorithms is superior in all cases. Studying a 'genome-scale' network model with 762 species and 1193 reactions, we demonstrate how the organization hierarchy helps to uncover the model structure and allows to evaluate the model's quality, for example by detecting components and subsystems of the model whose maintenance is not explained by the model. All data and a Java implementation that plugs into the Systems Biology Workbench is available from http://www.minet.uni-jena.de/csb/prj/ot/tools.
Physiological and biochemical aspects of the effect of ionizing radiations on the lung parenchyma
International Nuclear Information System (INIS)
Pasquier, Christian.
1975-03-01
Concerning the biochemical reactions of the lung parenchyma to irradiation the following points have been developed. Role of biochemically active substances (histamine, serotonin, kinins, catecholamines, prostaglandins) in the early reaction of the lung to irradiation, their common feature being their vascular impact point. Lung irradiation and lipids (fatty acids and lipid metabolism in general); irradiation, by raising the proportion of unsaturated at the expense of saturated fatty acids, may give rise to serious physiological respiratory disorders. Lung irradiation and blood fluidity (fibrinolytic activity, heparin, platelet factors). Pulmonary interstitium and irradiation (of the three interstitium components collagen plays a preferential part). Irradiation and immunological lung reaction (reasons behind the immunological theory, immunological assistance, immunological mechanism of pulmonary reactions towards pollutants). Enzymatic lung radiolesion indicators. Three kinds of physiological changes have been considered. Vascular physiology disturbances caused by the initial biochemical reactions; anomalies of physiological or functional trials, images of the lesion formed; disorders of the cell physiology of carcinogenesis [fr
Vijaykumar, Adithya; Ouldridge, Thomas E.; ten Wolde, Pieter Rein; Bolhuis, Peter G.
2017-03-01
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. 143, 214102 (2015)]. Here we extend this multiscale MD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We present the novel algorithm focusing on Brownian dynamics only, although the methodology is generic. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm, we discuss its performance. The rotational Brownian dynamics MD-GFRD multiscale method will open up the possibility for large scale simulations of protein signalling networks.
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.
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.
Modular rate laws for enzymatic reactions: thermodynamics, elasticities and implementation.
Liebermeister, Wolfram; Uhlendorf, Jannis; Klipp, Edda
2010-06-15
Standard rate laws are a key requisite for systematically turning metabolic networks into kinetic models. They should provide simple, general and biochemically plausible formulae for reaction velocities and reaction elasticities. At the same time, they need to respect thermodynamic relations between the kinetic constants and the metabolic fluxes and concentrations. We present a family of reversible rate laws for reactions with arbitrary stoichiometries and various types of regulation, including mass-action, Michaelis-Menten and uni-uni reversible Hill kinetics as special cases. With a thermodynamically safe parameterization of these rate laws, parameter sets obtained by model fitting, sampling or optimization are guaranteed to lead to consistent chemical equilibrium states. A reformulation using saturation values yields simple formulae for rates and elasticities, which can be easily adjusted to the given stationary flux distributions. Furthermore, this formulation highlights the role of chemical potential differences as thermodynamic driving forces. We compare the modular rate laws to the thermodynamic-kinetic modelling formalism and discuss a simplified rate law in which the reaction rate directly depends on the reaction affinity. For automatic handling of modular rate laws, we propose a standard syntax and semantic annotations for the Systems Biology Markup Language. An online tool for inserting the rate laws into SBML models is freely available at www.semanticsbml.org. Supplementary data are available at Bioinformatics online.
Complexity of generic biochemical circuits: topology versus strength of interactions
Tikhonov, Mikhail; Bialek, William
2016-12-01
The historical focus on network topology as a determinant of biological function is still largely maintained today, illustrated by the rise of structure-only approaches to network analysis. However, biochemical circuits and genetic regulatory networks are defined both by their topology and by a multitude of continuously adjustable parameters, such as the strength of interactions between nodes, also recognized as important. Here we present a class of simple perceptron-based Boolean models within which comparing the relative importance of topology versus interaction strengths becomes a quantitatively well-posed problem. We quantify the intuition that for generic networks, optimization of interaction strengths is a crucial ingredient of achieving high complexity, defined here as the number of fixed points the network can accommodate. We propose a new methodology for characterizing the relative role of parameter optimization for topologies of a given class.
Circadian Clocks: Unexpected Biochemical Cogs.
Mori, Tetsuya; Mchaourab, Hassane; Johnson, Carl Hirschie
2015-10-05
A circadian oscillation can be reconstituted in vitro from three proteins that cycles with a period of ∼ 24 h. Two recent studies provide surprising biochemical answers to why this remarkable oscillator has such a long time constant and how it can switch effortlessly between alternating enzymatic modes. Copyright © 2015 Elsevier Ltd. All rights reserved.
Circadian Clocks: Unexpected Biochemical Cogs
Mori, Tetsuya; Mchaourab, Hassane; Johnson, Carl Hirschie
2015-01-01
A circadian oscillation can be reconstituted in vitro from three proteins that cycles with a period of ~24 h. Two recent studies provide surprising biochemical answers to why this remarkable oscillator has such a long time constant and how it can switch effortlessly between alternating enzymatic modes.
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.
Toward the automated generation of genome-scale metabolic networks in the SEED.
DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron
2007-04-26
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. 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 genome annotation and analysis. Our method sets the
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
Isotopes in oxidation reactions
International Nuclear Information System (INIS)
Stewart, R.
1976-01-01
The use of isotopes in the study of organic oxidation mechanisms is discussed. The help provided by tracer studies to demonstrate the two-equivalent path - hydride transfer, is illustrated by the examples of carbonium oxidants and the Wacker reaction. The role of kinetic isotope effects in the study of the scission of carbon-hydrogen bonds is illustrated by hydride abstraction, hydrogen atom abstraction, proton abstraction and quantum mechanical tunnelling. Isotopic studies on the oxidation of alcohols, carbonyl compounds, amines and hydrocarbons are discussed. The role of isotopes in the study of biochemical oxidation is illustrated with a discussion on nicotinamide and flavin coenzymes. (B.R.H.)
Biochemical Process Development and Integration | Bioenergy | NREL
Biochemical Process Development and Integration Biochemical Process Development and Integration Our conversion and separation processes to pilot-scale integrated process development and scale up. We also Publications Accounting for all sugar produced during integrated production of ethanol from lignocellulosic
Biochemical markers of bone turnover
International Nuclear Information System (INIS)
Kim, Deog Yoon
1999-01-01
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 toxicology of environmental agents
International Nuclear Information System (INIS)
Bruin, A. de
1976-01-01
A thorough and up-to-date account of the molecular-biological aspects of harmful agents - both chemical and physical - is given. This current treatise is principally intended to serve as an informative reference work for researchers in various areas of the field. In the pursuit of this aim, a devision of the entire field into 42 chapters has been made. Each chapter starts with a short introductory account dealing with the biochemical essentials of the particular subject. Radiation effects are discussed briefly at the end of each treatise. In order to make the treatise useful as a source book, a substantial collection of pertinent literature references is provided which are numbered in order of citation in the text. Initial chapters are devoted to the metabolic fate of the major classes of xenobiotic compounds. Peripheral topics, closely related to metabolism and dealing with modification of xenobiotic-metabolizing ability, as well as interaction phenomena follow (chs. 5-8). Subjects that draw heavily on the practical field of occupational hygiene are dealt with in chapters 9 and 10. The systematic treatment of how chemical and physical agents interact with the various biochemical and enzymatic systems they encounter during their passage through the organism occupies quantitatively the main part of the book (chs. 11-36). Finally, radiation biochemistry is discussed from the viewpoint of its high degree of scientific advancement, and secondly because the type of biochemical changes produced in vivo by X-rays closely parallel those evoked by chemical agents
Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks.
Morrison, Erin S; Badyaev, Alexander V
2018-05-01
Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components. © 2018 European Society For Evolutionary Biology. Journal of Evolutionary Biology © 2018 European Society For Evolutionary Biology.
International Nuclear Information System (INIS)
Lane, A.M.
1980-01-01
In reviewing work at Harwell over the past 25 years on nuclear reactions it is stated that a balance has to be struck in both experiment and theory between work on cross-sections of direct practical relevance to reactors and on those relevant to an overall understanding of reaction processes. The compound nucleus and direct process reactions are described. Having listed the contributions from AERE, Harwell to developments in nuclear reaction research in the period, work on the optical model, neutron capture theory, reactions at doorway states with fine structure, and sum-rules for spectroscopic factors are considered in more detail. (UK)
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...
Slot-waveguide biochemical sensor.
Barrios, Carlos A; Gylfason, Kristinn B; Sánchez, Benito; Griol, Amadeu; Sohlström, H; Holgado, M; Casquel, R
2007-11-01
We report an experimental demonstration of an integrated biochemical sensor based on a slot-waveguide microring resonator. The microresonator is fabricated on a Si3N4-SiO2 platform and operates at a wavelength of 1.3 microm. The transmission spectrum of the sensor is measured with different ambient refractive indices ranging from n=1.33 to 1.42. A linear shift of the resonant wavelength with increasing ambient refractive index of 212 nm/refractive index units (RIU) is observed. The sensor detects a minimal refractive index variation of 2x10(-4) RIU.
Thermodynamic analysis of biochemical systems
International Nuclear Information System (INIS)
Yuan, Y.; Fan, L.T.; Shieh, J.H.
1989-01-01
Introduction of the concepts of the availability (or exergy), datum level materials, and the dead state has been regarded as some of the most significant recent developments in classical thermodynamics. Not only the available energy balance but also the material and energy balances of a biological system may be established in reference to the datum level materials in the dead state or environment. In this paper these concepts are illustrated with two examples of fermentation and are shown to be useful in identifying sources of thermodynamic inefficiency, thereby leading naturally to the rational definition of thermodynamic efficiency of a biochemical process
Using chemistry and microfluidics to understand the spatial dynamics of complex biological networks.
Kastrup, Christian J; Runyon, Matthew K; Lucchetta, Elena M; Price, Jessica M; Ismagilov, Rustem F
2008-04-01
Understanding the spatial dynamics of biochemical networks is both fundamentally important for understanding life at the systems level and also has practical implications for medicine, engineering, biology, and chemistry. Studies at the level of individual reactions provide essential information about the function, interactions, and localization of individual molecular species and reactions in a network. However, analyzing the spatial dynamics of complex biochemical networks at this level is difficult. Biochemical networks are nonequilibrium systems containing dozens to hundreds of reactions with nonlinear and time-dependent interactions, and these interactions are influenced by diffusion, flow, and the relative values of state-dependent kinetic parameters. To achieve an overall understanding of the spatial dynamics of a network and the global mechanisms that drive its function, networks must be analyzed as a whole, where all of the components and influential parameters of a network are simultaneously considered. Here, we describe chemical concepts and microfluidic tools developed for network-level investigations of the spatial dynamics of these networks. Modular approaches can be used to simplify these networks by separating them into modules, and simple experimental or computational models can be created by replacing each module with a single reaction. Microfluidics can be used to implement these models as well as to analyze and perturb the complex network itself with spatial control on the micrometer scale. We also describe the application of these network-level approaches to elucidate the mechanisms governing the spatial dynamics of two networkshemostasis (blood clotting) and early patterning of the Drosophila embryo. To investigate the dynamics of the complex network of hemostasis, we simplified the network by using a modular mechanism and created a chemical model based on this mechanism by using microfluidics. Then, we used the mechanism and the model to
ACRE: Absolute concentration robustness exploration in module-based combinatorial networks
Kuwahara, Hiroyuki; Umarov, Ramzan; Almasri, Islam; Gao, Xin
2017-01-01
To engineer cells for industrial-scale application, a deep understanding of how to design molecular control mechanisms to tightly maintain functional stability under various fluctuations is crucial. Absolute concentration robustness (ACR) is a category of robustness in reaction network models in which the steady-state concentration of a molecular species is guaranteed to be invariant even with perturbations in the other molecular species in the network. Here, we introduce a software tool, absolute concentration robustness explorer (ACRE), which efficiently explores combinatorial biochemical networks for the ACR property. ACRE has a user-friendly interface, and it can facilitate efficient analysis of key structural features that guarantee the presence and the absence of the ACR property from combinatorial networks. Such analysis is expected to be useful in synthetic biology as it can increase our understanding of how to design molecular mechanisms to tightly control the concentration of molecular species. ACRE is freely available at https://github.com/ramzan1990/ACRE.
ACRE: Absolute concentration robustness exploration in module-based combinatorial networks
Kuwahara, Hiroyuki
2017-03-01
To engineer cells for industrial-scale application, a deep understanding of how to design molecular control mechanisms to tightly maintain functional stability under various fluctuations is crucial. Absolute concentration robustness (ACR) is a category of robustness in reaction network models in which the steady-state concentration of a molecular species is guaranteed to be invariant even with perturbations in the other molecular species in the network. Here, we introduce a software tool, absolute concentration robustness explorer (ACRE), which efficiently explores combinatorial biochemical networks for the ACR property. ACRE has a user-friendly interface, and it can facilitate efficient analysis of key structural features that guarantee the presence and the absence of the ACR property from combinatorial networks. Such analysis is expected to be useful in synthetic biology as it can increase our understanding of how to design molecular mechanisms to tightly control the concentration of molecular species. ACRE is freely available at https://github.com/ramzan1990/ACRE.
Szalanski, P; Marganeic, A; Gledenov, Yu M; Sedyshev, P V; Machrafi, R; Oprea, A; Padureanu, I; Aranghel, D
2002-01-01
This paper presents results of calculations for nuclear network in S-Cl region during helium burning in massive stars (25 M_{\\odot}) using integrated mathematical systems. The authors also examine other application of presented method in different physical tasks.
International Nuclear Information System (INIS)
Szalanski, P.; Stepinski, M.; Marganiec, A.; Gledenov, Yu.M.; Sedyshev, P.V.; Machrafi, R.; Oprea, A.; Padureanu, I.; Aranghel, D.
2002-01-01
This paper presents results of calculations for nuclear network in S - Cl region during helium burning in massive stars (25 solar mass) using integrated mathematical systems. The authors also examine other application of the presented method in different physical tasks. (author)
Silicon-based sleeve devices for chemical reactions
Northrup, M. Allen; Mariella, Jr., Raymond P.; Carrano, Anthony V.; Balch, Joseph W.
1996-01-01
A silicon-based sleeve type chemical reaction chamber that combines heaters, such as doped polysilicon for heating, and bulk silicon for convection cooling. The reaction chamber combines a critical ratio of silicon and silicon nitride to the volume of material to be heated (e.g., a liquid) in order to provide uniform heating, yet low power requirements. The reaction chamber will also allow the introduction of a secondary tube (e.g., plastic) into the reaction sleeve that contains the reaction mixture thereby alleviating any potential materials incompatibility issues. The reaction chamber may be utilized in any chemical reaction system for synthesis or processing of organic, inorganic, or biochemical reactions, such as the polymerase chain reaction (PCR) and/or other DNA reactions, such as the ligase chain reaction, which are examples of a synthetic, thermal-cycling-based reaction. The reaction chamber may also be used in synthesis instruments, particularly those for DNA amplification and synthesis.
Maggi, Federico; Riley, William J.
2010-03-01
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.
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.
Biochemical Markers in Neurocritical Care
Directory of Open Access Journals (Sweden)
Omidvar Rezae
2016-07-01
Full Text Available During the past two decades, a variety of serum or cerebrospinal fluid (CSF biochemical markers in daily clinical practice have been recommended to diagnose and monitor diverse diseases or pathologic situations. It will be essential to develop a panel of biomarkers, to be suitable for evaluation of treatment efficacy, representing distinct phases of injury and recovery and consider the temporal profile of those. Among the possible and different biochemical markers, S100b appeared to fulfill many of optimized criteria of an ideal marker. S100b, a cytosolic low molecular weight dimeric calciumbinding protein from chromosome 21, synthesized in glial cells throughout the CNS, an homodimeric diffusible, belongs to a family of closely related protein, predominantly expressed by astrocytes and Schwann cells and a classic immunohistochemical marker for these cells, is implicated in brain development and neurophysiology. Of the 3 isoforms of S-100, the BB subunit (S100B is present in high concentrations in central and peripheral glial and Schwann cells, Langerhans and anterior pituitary cells, fat, muscle, and bone marrow tissues. The biomarker has shown to be a sensitive marker of clinical and subclinical cerebral damage, such as stroke, traumatic brain injury, and spinal cord injury. Increasing evidence suggests that the biomarker plays a double function as an intracellular regulator and an extracellular signal of the CNS. S100b is found in the cytoplasm in a soluble form and also is associated with intracellular membranes, centrosomes, microtubules, and type III intermediate filaments. Their genomic organization now is known, and many of their target proteins have been identified, although the mechanisms of regulating S100b secretion are not completely understood and appear to be related to many factors, such as the proinflammatory cytokines, tumor necrosis factor alpha (TNF-a, interleukin (IL-1b, and metabolic stress.
A case study of evolutionary computation of biochemical adaptation
International Nuclear Information System (INIS)
François, Paul; Siggia, Eric D
2008-01-01
Simulations of evolution have a long history, but their relation to biology is questioned because of the perceived contingency of evolution. Here we provide an example of a biological process, adaptation, where simulations are argued to approach closer to biology. Adaptation is a common feature of sensory systems, and a plausible component of other biochemical networks because it rescales upstream signals to facilitate downstream processing. We create random gene networks numerically, by linking genes with interactions that model transcription, phosphorylation and protein–protein association. We define a fitness function for adaptation in terms of two functional metrics, and show that any reasonable combination of them will yield the same adaptive networks after repeated rounds of mutation and selection. Convergence to these networks is driven by positive selection and thus fast. There is always a path in parameter space of continuously improving fitness that leads to perfect adaptation, implying that the actual mutation rates we use in the simulation do not bias the results. Our results imply a kinetic view of evolution, i.e., it favors gene networks that can be learned quickly from the random examples supplied by mutation. This formulation allows for deductive predictions of the networks realized in nature
Fibre optic system for biochemical and microbiological sensing
International Nuclear Information System (INIS)
Penwill, L A; Slater, J H; Hayes, N W; Tremlett, C J
2007-01-01
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
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|info:eu-repo/dai/nl/069043035; 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
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
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
Bistable responses in bacterial genetic networks: Designs and dynamical consequences
Tiwari, Abhinav; Ray, J. Christian J.; Narula, Jatin; Igoshin, Oleg A.
2011-01-01
A key property of living cells is their ability to react to stimuli with specific biochemical responses. These responses can be understood through the dynamics of underlying biochemical and genetic networks. Evolutionary design principles have been well studied in networks that display graded responses, with a continuous relationship between input signal and system output. Alternatively, biochemical networks can exhibit bistable responses so that over a range of signals the network possesses two stable steady states. In this review, we discuss several conceptual examples illustrating network designs that can result in a bistable response of the biochemical network. Next, we examine manifestations of these designs in bacterial master-regulatory genetic circuits. In particular, we discuss mechanisms and dynamic consequences of bistability in three circuits: two-component systems, sigma-factor networks, and a multistep phosphorelay. Analyzing these examples allows us to expand our knowledge of evolutionary design principles for networks with bistable responses. PMID:21385588
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
International Nuclear Information System (INIS)
Henning, W.
1979-01-01
Quasielastic reaction studies, because of their capability to microscopically probe nuclear structure, are still of considerable interest in heavy-ion reactions. The recent progress in understanding various aspects of the reaction mechanism make this aim appear closer. The relation between microscopic and macroscopic behavior, as suggested, for example, by the single proton transfer data to individual final states or averaged excitation energy intervals, needs to be explored. It seems particularly useful to extend measurements to higher incident energies, to explore and understand nuclear structure aspects up to the limit of the energy range where they are important
Wang, Shufen; Yuan, Jiuchuang; Li, Huixing; Chen, Maodu
2017-08-02
In order to study the dynamics of the reaction H( 2 S) + NaH(X 1 Σ + ) → Na( 2 S) + H 2 (X 1 Σ g + ), a new potential energy surface (PES) for the ground state of the NaH 2 system is constructed based on 35 730 ab initio energy points. Using basis sets of quadruple zeta quality, multireference configuration interaction calculations with Davidson correction were carried out to obtain the ab initio energy points. The neural network method is used to fit the PES, and the root mean square error is very small (0.00639 eV). The bond lengths, dissociation energies, zero-point energies and spectroscopic constants of H 2 (X 1 Σ g + ) and NaH(X 1 Σ + ) obtained on the new NaH 2 PES are in good agreement with the experiment data. On the new PES, the reactant coordinate-based time-dependent wave packet method is applied to study the reaction dynamics of H( 2 S) + NaH(X 1 Σ + ) → Na( 2 S) + H 2 (X 1 Σ g + ), and the reaction probabilities, integral cross-sections (ICSs) and differential cross-sections (DCSs) are obtained. There is no threshold in the reaction due to the absence of an energy barrier on the minimum energy path. When the collision energy increases, the ICSs decrease from a high value at low collision energy. The DCS results show that the angular distribution of the product molecules tends to the forward direction. Compared with the LiH 2 system, the NaH 2 system has a larger mass and the PES has a larger well at the H-NaH configuration, which leads to a higher ICS value in the H( 2 S) + NaH(X 1 Σ + ) → Na( 2 S) + H 2 (X 1 Σ g + ) reaction. Because the H( 2 S) + NaH(X 1 Σ + ) → Na( 2 S) + H 2 (X 1 Σ g + ) reaction releases more energy, the product molecules can be excited to a higher vibrational state.
PHYSIOLOGICAL AND BIOCHEMICAL MARKERS OF SALINITY TOLERANCE IN PLANTS
Directory of Open Access Journals (Sweden)
Mustafa YILDIZ
2011-02-01
Full Text Available Salt stress limits plant productivity in arid and semi arid regions. Salt stress causes decrease in plant growth by adversely affecting physiological processes, especially photosynthesis. Salinity tolerance is defined as the ability of plant to maintain normal rowth and development under salt conditions. Salt stress results in accumulation of low molecular weight compounds, termed compatible solutes, which do not interfere with the normal biochemical reactions. These compatible solutes such as carbohydrates, polyols, amino acids and amides, quaternary ammonium compounds, polyamines andsoluble proteins may play a crucial role in osmotic adjustment, protection of macromolecules, maintenance of cellular pH and detoxification of free radicals. On the other hand, plants subjected to environmental stresses such as salinity produce reactive oxygen species (ROS and these ROS are efficiently eliminated by antioxidant enzyme systems. In plant breeding studies, the use of some physiological and biochemical markers for improving the salt tolerance in plants is crucial. In this review, the possibility of using some physiological and biochemical markers as selection criteria for salt tolerance is discussed.
International Nuclear Information System (INIS)
Goldberg, Robert N.; Lang, Brian E.; Lo, Catherine; Ross, David J.; Tewari, Yadu B.
2009-01-01
Microcalorimetry, high-performance liquid chromatography (h.p.l.c.), and an enzymatic assay have been used to conduct a thermodynamic investigation of five phosphate hydrolysis reactions: {α-D-galactose 1-phosphate(aq) + H 2 O(l) = D-galactose(aq) + orthophosphate(aq)} (1), {sn-glycerol 3-phosphate(aq) + H 2 O(l) = glycerol(aq) + orthophosphate(aq)} (2), {4-nitrophenyl phosphate(aq) + H 2 O(l) = 4-nitrophenol(aq) + orthophosphate(aq)} (3), {phosphocreatine(aq) + H 2 O(l) = creatine(aq) + orthophosphate(aq)} (4), and {3-phospho-D-glycerate(aq) + H 2 O(l) = D-glycerate(aq) + orthophosphate(aq)} (5). Calorimetrically determined enthalpies of reaction Δ r H(cal) were measured for reactions (1)-(5) and the apparent equilibrium constant K' was measured for reaction (2). The pKs and standard enthalpies of reaction Δ r H 0 for the H + and Mg 2+ binding reactions of the reactants and products in the aforementioned reactions were obtained either from the literature or by estimation. A chemical equilibrium model was then used to calculate standard equilibrium constants K and standard enthalpies of reaction Δ r H 0 for chemical reference reactions that correspond to the overall biochemical reactions that were studied experimentally. Property values from the literature and thermodynamic network calculations were used to obtain values of the equilibrium constants for the chemical reference reactions that correspond to the overall biochemical reactions (1). These values were compared with other results from the literature and also correlated with structural features. The results obtained in this study can be used in the chemical equilibrium model to calculate values of K', the standard apparent Gibbs free energy changes Δ r G '0 , the standard apparent enthalpy changes Δ r H '0 , changes in binding of the proton Δ r N(H + ), and the position of equilibrium for the overall biochemical reactions considered in this study over a reasonably wide range of temperature, pH, p
Prevalence of biochemical and immunological abnormalities in ...
African Journals Online (AJOL)
Tile prevalence of biochemical and immunological abnormalities was studied in a group of 256 patients with rheumatoid arthritis (104 coloureds, 100 whites and 52 blacks). The most common biochemical abnormalities detected were a reduction in the serum creatinine value (43,4%), raised globulins (39,7%), raised serum ...
Indian Academy of Sciences (India)
Unknown
Molecular Modeling Group, Organic Chemical Sciences, Indian Institute of Chemical Technology,. Hyderabad ... thus obtained are helpful to model the regioselectivity ... compromise to model Diels–Alder reactions involving ...... acceptance.
Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models
Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.
2014-01-01
Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891
Directory of Open Access Journals (Sweden)
Yimai Chen
2017-11-01
Full Text Available Three-dimensional nitrogen-doped carbon (3D-NCN has been synthesized via the ionothermal carbonization method using waste soybean dregs (SD as the precursor. N2 adsorption/desorption isotherms show that the as-prepared 3D-NCN formed a hierarchically porous structure with a specific BET surface area of 1093.4 m2 g−1 and a total pore volume of 1.77 cm3 g−1. The TEM images clearly show that graphene-like carbon sheets were formed on the edge of the networks. The characterization of the samples collected at different temperature indicated that salt melt plays the key role in the formation of the network structure and rich pores. When 3D-NCN is as electrocatalyst for ORR, it shows an onset potential of 0.945 V with a more positive half-wave potential (0.846 V, which is comparable to that of commercial Pt/C. In addition, the long-term cycle results show that the onset potential and half-wave potential only negatively shifted by 6 mV and 8 mV after 10,000 cycles respectively, which are smaller than those values of commercial Pt/C. Due to its high ORR activity, durability, and low-cost, producing 3D-NCN from SD in molten salt medium provides a promising approach to replace the Pt-based catalysts for use in fuel cells.
[Individual physical performance capacity with physiological and biochemical indicators of stress].
Bergert, K D; Nestler, K; Böttger, H; Schettler, R
1989-09-01
22 health male subjects were exposed by a combination of physical exercises and heat. Strain related physiological and biochemical parameters were measured. Different individual reactions were obtained under controlled conditions. In dependence on the individual performance an increased mobilisation of lactat, free fatty acids and catecholamines were found. The determination of aerob physical performance can be applied for the evaluation of working capacity.
Organic and biochemical synthesis group
International Nuclear Information System (INIS)
Anon.
1976-01-01
Stable isotopes, because of their unique properties and non-radioactive nature, have great potential for many fields of science and technology. In particular, isotopes of carbon, nitrogen, oxygen, and sulfur (the basic building blocks of all biological molecules) would be widely used in biomedical and environmental research if they were economically available in sufficient quantities and in the required chemical forms. The major objective of our program continues to be stimulation of the widespread utilization of stable isotopes and commercial involvement through development and demonstration of applications which have potential requirements for large quantities of isotopes. Thus, demand will be created which is necessary for large-scale production of stable isotopes and labeled compounds and concomitant low unit costs. The program continues to produce a variety of labeled materials needed for clinical, biomedical, chemical, and environmental applications which serve as effective demonstrations of unique and advantageous utilization of stable isotopes. Future commercial involvement should benefit, and is a consideration in our research and development, from the technology transfer that can readily be made as a result of our organic and biochemical syntheses and also of various techniques involved in applications
Microfabricated sleeve devices for chemical reactions
Northrup, M. Allen
2003-01-01
A silicon-based sleeve type chemical reaction chamber that combines heaters, such as doped polysilicon for heating, and bulk silicon for convection cooling. The reaction chamber combines a critical ratio of silicon and non-silicon based materials to provide the thermal properties desired. For example, the chamber may combine a critical ratio of silicon and silicon nitride to the volume of material to be heated (e.g., a liquid) in order to provide uniform heating, yet low power requirements. The reaction chamber will also allow the introduction of a secondary tube (e.g., plastic) into the reaction sleeve that contains the reaction mixture thereby alleviating any potential materials incompatibility issues. The reaction chamber may be utilized in any chemical reaction system for synthesis or processing of organic, inorganic, or biochemical reactions, such as the polymerase chain reaction (PCR) and/or other DNA reactions, such as the ligase chain reaction, which are examples of a synthetic, thermal-cycling-based reaction. The reaction chamber may also be used in synthesis instruments, particularly those for DNA amplification and synthesis.
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.
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.
Modeling chemical reactions for drug design.
Gasteiger, Johann
2007-01-01
Chemical reactions are involved at many stages of the drug design process. This starts with the analysis of biochemical pathways that are controlled by enzymes that might be downregulated in certain diseases. In the lead discovery and lead optimization process compounds have to be synthesized in order to test them for their biological activity. And finally, the metabolism of a drug has to be established. A better understanding of chemical reactions could strongly help in making the drug design process more efficient. We have developed methods for quantifying the concepts an organic chemist is using in rationalizing reaction mechanisms. These methods allow a comprehensive modeling of chemical reactivity and thus are applicable to a wide variety of chemical reactions, from gas phase reactions to biochemical pathways. They are empirical in nature and therefore allow the rapid processing of large sets of structures and reactions. We will show here how methods have been developed for the prediction of acidity values and of the regioselectivity in organic reactions, for designing the synthesis of organic molecules and of combinatorial libraries, and for furthering our understanding of enzyme-catalyzed reactions and of the metabolism of drugs.
Enumeration of smallest intervention strategies in genome-scale metabolic networks.
Directory of Open Access Journals (Sweden)
Axel von Kamp
2014-01-01
Full Text Available One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions in genome-scale metabolic network models. For this we combine two approaches, namely (i the mapping of MCSs to EMs in a dual network, and (ii a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth than reported previously. The strength of the presented approach is that smallest intervention strategies can be
Energy Technology Data Exchange (ETDEWEB)
Austern, N. [University of Pittsburgh, Pittsburgh, PA (United States)
1963-01-15
In order to give a unified presentation of one point of view, these lectures are devoted only to a detailed development of the standard theories of direct reactions, starting from basic principles. Discussion is given of the present status of the theories, of the techniques used for practical calculation, and of possible future developments. The direct interaction (DI) aspects of a reaction are those which involve only a few of the many degrees of freedom of a nucleus. In fact the minimum number of degrees of freedom which must be involved in a reaction are those required to describe the initial and final channels, and DI studies typically consider these degrees of freedom and no others. Because of this simplicity DI theories may be worked out in painstaking detail. DI processes concern only part of the wave function for a problem. The other part involves complicated excitations of many degrees of freedom, and gives the compound nucleus (CN) effects. While it is extremely interesting to learn how to separate DI and CN effects in an orderly manner, if they are both present in a reaction, no suitable method has yet been found. Instead, current work stresses the kinds of reactions and the kinds of final states in which DI effects dominate and in which CN effects may almost be forgotten. The DI cross-sections which are studied are often extremely large, comparable to elastic scattering cross-sections. (author)
Catalyst Initiation in the Oscillatory Carbonylation Reaction
Directory of Open Access Journals (Sweden)
Katarina Novakovic
2011-01-01
Full Text Available Palladium(II iodide is used as a catalyst in the phenylacetylene oxidative carbonylation reaction that has demonstrated oscillatory behaviour in both pH and heat of reaction. In an attempt to extract the reaction network responsible for the oscillatory nature of this reaction, the system was divided into smaller parts and they were studied. This paper focuses on understanding the reaction network responsible for the initial reactions of palladium(II iodide within this oscillatory reaction. The species researched include methanol, palladium(II iodide, potassium iodide, and carbon monoxide. Several chemical reactions were considered and applied in a modelling study. The study revealed the significant role played by traces of water contained in the standard HPLC grade methanol used.
Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling
International Nuclear Information System (INIS)
Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R.; Jijakli, Kenan; Salehi-Ashtiani, Kourosh
2014-01-01
Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
Microalgal Metabolic Network Model Refinement through High-Throughput Functional Metabolic Profiling
Energy Technology Data Exchange (ETDEWEB)
Chaiboonchoe, Amphun; Dohai, Bushra Saeed; Cai, Hong; Nelson, David R. [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Jijakli, Kenan [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates); Engineering Division, Biofinery, Manhattan, KS (United States); Salehi-Ashtiani, Kourosh, E-mail: ksa3@nyu.edu [Division of Science and Math, New York University Abu Dhabi, Abu Dhabi (United Arab Emirates); Center for Genomics and Systems Biology (CGSB), New York University Abu Dhabi Institute, Abu Dhabi (United Arab Emirates)
2014-12-10
Metabolic modeling provides the means to define metabolic processes at a systems level; however, genome-scale metabolic models often remain incomplete in their description of metabolic networks and may include reactions that are experimentally unverified. This shortcoming is exacerbated in reconstructed models of newly isolated algal species, as there may be little to no biochemical evidence available for the metabolism of such isolates. The phenotype microarray (PM) technology (Biolog, Hayward, CA, USA) provides an efficient, high-throughput method to functionally define cellular metabolic activities in response to a large array of entry metabolites. The platform can experimentally verify many of the unverified reactions in a network model as well as identify missing or new reactions in the reconstructed metabolic model. The PM technology has been used for metabolic phenotyping of non-photosynthetic bacteria and fungi, but it has not been reported for the phenotyping of microalgae. Here, we introduce the use of PM assays in a systematic way to the study of microalgae, applying it specifically to the green microalgal model species Chlamydomonas reinhardtii. The results obtained in this study validate a number of existing annotated metabolic reactions and identify a number of novel and unexpected metabolites. The obtained information was used to expand and refine the existing COBRA-based C. reinhardtii metabolic network model iRC1080. Over 254 reactions were added to the network, and the effects of these additions on flux distribution within the network are described. The novel reactions include the support of metabolism by a number of d-amino acids, l-dipeptides, and l-tripeptides as nitrogen sources, as well as support of cellular respiration by cysteamine-S-phosphate as a phosphorus source. The protocol developed here can be used as a foundation to functionally profile other microalgae such as known microalgae mutants and novel isolates.
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
International Nuclear Information System (INIS)
Nguyen Trong Anh
1988-01-01
The 1988 progress report of the Reaction Mechanisms laboratory (Polytechnic School, France), is presented. The research topics are: the valence bond methods, the radical chemistry, the modelling of the transition states by applying geometric constraints, the long range interactions (ion - molecule) in gaseous phase, the reaction sites in gaseous phase and the mass spectroscopy applications. The points of convergence between the investigations of the mass spectroscopy and the theoretical chemistry teams, as well as the purposes guiding the research programs, are discussed. The published papers, the conferences, the congress communications and the thesis, are also reported [fr
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
Metabolite coupling in genome-scale metabolic networks
Directory of Open Access Journals (Sweden)
Palsson Bernhard Ø
2006-03-01
Full Text Available Abstract Background Biochemically detailed stoichiometric matrices have now been reconstructed for various bacteria, yeast, and for the human cardiac mitochondrion based on genomic and proteomic data. These networks have been manually curated based on legacy data and elementally and charge balanced. Comparative analysis of these well curated networks is now possible. Pairs of metabolites often appear together in several network reactions, linking them topologically. This co-occurrence of pairs of metabolites in metabolic reactions is termed herein "metabolite coupling." These metabolite pairs can be directly computed from the stoichiometric matrix, S. Metabolite coupling is derived from the matrix ŜŜT, whose off-diagonal elements indicate the number of reactions in which any two metabolites participate together, where Ŝ is the binary form of S. Results Metabolite coupling in the studied networks was found to be dominated by a relatively small group of highly interacting pairs of metabolites. As would be expected, metabolites with high individual metabolite connectivity also tended to be those with the highest metabolite coupling, as the most connected metabolites couple more often. For metabolite pairs that are not highly coupled, we show that the number of reactions a pair of metabolites shares across a metabolic network closely approximates a line on a log-log scale. We also show that the preferential coupling of two metabolites with each other is spread across the spectrum of metabolites and is not unique to the most connected metabolites. We provide a measure for determining which metabolite pairs couple more often than would be expected based on their individual connectivity in the network and show that these metabolites often derive their principal biological functions from existing in pairs. Thus, analysis of metabolite coupling provides information beyond that which is found from studying the individual connectivity of individual
... that don't bother most people (such as venom from bee stings and certain foods, medicines, and pollens) can ... person. If the allergic reaction is from a bee sting, scrape the ... more venom. If the person has emergency allergy medicine on ...
Morphological, physiological and biochemical studies on Pyricularia ...
African Journals Online (AJOL)
SARAH
2014-02-28
Feb 28, 2014 ... compounds seem to reflect inherent biochemical and physiological differences among P. grisea isolates .... solutions for imaging and microscopy, soft image system .... characteristics among 12 P. grisea isolates from rice were.
Biochemical changes in blood caused by radioisotopes
International Nuclear Information System (INIS)
Zapol'skaya, N.A.; Fedorova, A.V.
1975-01-01
The changes were studied occurring in some biochemical indicators in blood at chronic peroral administration of strontium-90, cesium-137 and iodine-131 in amounts resulting in accumulation of commensurable doses in critical organs corresponding to each isotope
Biochemical and kinetic characterization of geranylgeraniol 18 ...
African Journals Online (AJOL)
Suchart
2015-07-22
Jul 22, 2015 ... biochemical characterization of GGOH 18-hydroxylase activity in the microsomal fraction from C. .... method as previously described (Chanama et al., 2009). Briefly, 30 g of frozen ..... Catalytic properties of the plant cytochrome.
Short Report Biochemical derangements prior to emergency ...
African Journals Online (AJOL)
MMJ VOL 29 (1): March 2017. Biochemical derangements prior to emergency laparotomy at QECH 55. Malawi Medical Journal 29 (1): March 2017 ... Venepuncture was performed preoperatively for urgent cases, defined as those requiring.
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)
Raman spectroscopic biochemical mapping of tissues
Stone, Nicholas; Hart Prieto, Maria C.; Kendall, Catherine A.; Shetty, Geeta; Barr, Hugh
2006-02-01
Advances in technologies have brought us closer to routine spectroscopic diagnosis of early malignant disease. However, there is still a poor understanding of the carcinogenesis process. For example it is not known whether many cancers follow a logical sequence from dysplasia, to carcinoma in situ, to invasion. Biochemical tissue changes, triggered by genetic mutations, precede morphological and structural changes. These can be probed using Raman or FTIR microspectroscopy and the spectra analysed for biochemical constituents. Local microscopic distribution of various constituents can then be visualised. Raman mapping has been performed on a number of tissues including oesophagus, breast, bladder and prostate. The biochemical constituents have been calculated at each point using basis spectra and least squares analysis. The residual of the least squares fit indicates any unfit spectral components. The biochemical distribution will be compared with the defined histopathological boundaries. The distribution of nucleic acids, glycogen, actin, collagen I, III, IV, lipids and others appear to follow expected patterns.
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
[Biochemical diagnostics of fatal opium intoxication].
Papyshev, I P; Astashkina, O G; Tuchik, E S; Nikolaev, B S; Cherniaev, A L
2013-01-01
Biochemical diagnostics of fatal opium intoxication remains a topical problem in forensic medical science and practice. We investigated materials obtained in the course of forensic medical expertise of the cases of fatal opium intoxication. The study revealed significant differences between myoglobin levels in blood, urine, myocardium, and skeletal muscles. The proposed approach to biochemical diagnostics of fatal opium intoxication enhances the accuracy and the level of evidence of expert conclusions.
Stoichiometric estimates of the biochemical conversion efficiencies in tsetse metabolism
Directory of Open Access Journals (Sweden)
Custer Adrian V
2005-08-01
Full Text Available Abstract Background The time varying flows of biomass and energy in tsetse (Glossina can be examined through the construction of a dynamic mass-energy budget specific to these flies but such a budget depends on efficiencies of metabolic conversion which are unknown. These efficiencies of conversion determine the overall yields when food or storage tissue is converted into body tissue or into metabolic energy. A biochemical approach to the estimation of these efficiencies uses stoichiometry and a simplified description of tsetse metabolism to derive estimates of the yields, for a given amount of each substrate, of conversion product, by-products, and exchanged gases. This biochemical approach improves on estimates obtained through calorimetry because the stoichiometric calculations explicitly include the inefficiencies and costs of the reactions of conversion. However, the biochemical approach still overestimates the actual conversion efficiency because the approach ignores all the biological inefficiencies and costs such as the inefficiencies of leaky membranes and the costs of molecular transport, enzyme production, and cell growth. Results This paper presents estimates of the net amounts of ATP, fat, or protein obtained by tsetse from a starting milligram of blood, and provides estimates of the net amounts of ATP formed from the catabolism of a milligram of fat along two separate pathways, one used for resting metabolism and one for flight. These estimates are derived from stoichiometric calculations constructed based on a detailed quantification of the composition of food and body tissue and on a description of the major metabolic pathways in tsetse simplified to single reaction sequences between substrates and products. The estimates include the expected amounts of uric acid formed, oxygen required, and carbon dioxide released during each conversion. The calculated estimates of uric acid egestion and of oxygen use compare favorably to
Improving Marine Ecosystem Models with Biochemical Tracers
Pethybridge, Heidi R.; Choy, C. Anela; Polovina, Jeffrey J.; Fulton, Elizabeth A.
2018-01-01
Empirical data on food web dynamics and predator-prey interactions underpin ecosystem models, which are increasingly used to support strategic management of marine resources. These data have traditionally derived from stomach content analysis, but new and complementary forms of ecological data are increasingly available from biochemical tracer techniques. Extensive opportunities exist to improve the empirical robustness of ecosystem models through the incorporation of biochemical tracer data and derived indices, an area that is rapidly expanding because of advances in analytical developments and sophisticated statistical techniques. Here, we explore the trophic information required by ecosystem model frameworks (species, individual, and size based) and match them to the most commonly used biochemical tracers (bulk tissue and compound-specific stable isotopes, fatty acids, and trace elements). Key quantitative parameters derived from biochemical tracers include estimates of diet composition, niche width, and trophic position. Biochemical tracers also provide powerful insight into the spatial and temporal variability of food web structure and the characterization of dominant basal and microbial food web groups. A major challenge in incorporating biochemical tracer data into ecosystem models is scale and data type mismatches, which can be overcome with greater knowledge exchange and numerical approaches that transform, integrate, and visualize data.
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.
Network-level architecture and the evolutionary potential of underground metabolism.
Notebaart, Richard A; Szappanos, Balázs; Kintses, Bálint; Pál, Ferenc; Györkei, Ádám; Bogos, Balázs; Lázár, Viktória; Spohn, Réka; Csörgő, Bálint; Wagner, Allon; Ruppin, Eytan; Pál, Csaba; Papp, Balázs
2014-08-12
A central unresolved issue in evolutionary biology is how metabolic innovations emerge. Low-level enzymatic side activities are frequent and can potentially be recruited for new biochemical functions. However, the role of such underground reactions in adaptation toward novel environments has remained largely unknown and out of reach of computational predictions, not least because these issues demand analyses at the level of the entire metabolic network. Here, we provide a comprehensive computational model of the underground metabolism in Escherichia coli. Most underground reactions are not isolated and 45% of them can be fully wired into the existing network and form novel pathways that produce key precursors for cell growth. This observation allowed us to conduct an integrated genome-wide in silico and experimental survey to characterize the evolutionary potential of E. coli to adapt to hundreds of nutrient conditions. We revealed that underground reactions allow growth in new environments when their activity is increased. We estimate that at least ∼20% of the underground reactions that can be connected to the existing network confer a fitness advantage under specific environments. Moreover, our results demonstrate that the genetic basis of evolutionary adaptations via underground metabolism is computationally predictable. The approach used here has potential for various application areas from bioengineering to medical genetics.
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...
Biochemical Post-Irradiation Changes and Radiation Indicators: A Review
Energy Technology Data Exchange (ETDEWEB)
Streffer, G. [Radiologisches Institut, University of Freiburg, Freiburg im Breisgau, Federal Republic of Germany (Germany)
1971-03-15
In a number of mammalian tissues a breakdown of the biological macromolecules, as nucleic acids and proteins, is observed after irradiation. This degradation appears in lymphatic tissues even after a radiation exposure less than 100 R and proceeds with increasing exposure to about 600 R. On the other hand, other biochemical effects are found after the whole-body irradiation of mammals which seem to have some relation to the functional status of the organs and the organisms. It appears therefore useful to classify the biochemical effects of radiation into two groups: (1) Observations which are a consequence of the degradation processes; (2) observations which are a consequence of functional changes. Most investigations in the field of biochemical indicators after irradiation have been concerned with the first class of reactions. The excretion of deoxycytidine, thymidine and pseudouridine have been extensively investigated in animals during recent years. However, in humans the normal deoxycytidine excretion is very low, as this substance is metabolized to a higher degree in humans than in other species. {beta}-aminoisobutyric acid (BAIBA), a metabolite of thymine, was also investigated in this connection. Besides nucleic acids, proteins are broken down in these radiosensitive organs. This leads to an increased content of amino acids for instance in the lymphatic tissues and to an increase in urinary excretion of amino acids. There are two amino acids which play a special role for this discussion: cysteine and tryptophan. Taurine, a metabolite of cysteine, is excreted in the urine to an increased extent after the irradiation of mice, rats and man. Again the enhancement is proportional to the radiation exposure between 100 and 300 R. Another substance which has been extensively studied is creatine. It has been shown that there is a good relationship between radiation exposure (up to about 600 R) and the increased excretion of creatine/creatinine in the urine of rats
Bolhuis, Peter
Important reaction-diffusion processes, such as biochemical networks in living cells, or self-assembling soft matter, span many orders in length and time scales. In these systems, the reactants' spatial dynamics at mesoscopic length and time scales of microns and seconds is coupled to the reactions between the molecules at microscopic length and time scales of nanometers and milliseconds. This wide range of length and time scales makes these systems notoriously difficult to simulate. While mean-field rate equations cannot describe such processes, the mesoscopic Green's Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. The recently developed multiscale Molecular Dynamics Green's Function Reaction Dynamics (MD-GFRD) approach combines GFRD for simulating the system at the mesocopic scale where particles are far apart, with microscopic Molecular (or Brownian) Dynamics, for simulating the system at the microscopic scale where reactants are in close proximity. The association and dissociation of particles are treated with rare event path sampling techniques. I will illustrate the efficiency of this method for patchy particle systems. Replacing the microscopic regime with a Markov State Model avoids the microscopic regime completely. The MSM is then pre-computed using advanced path-sampling techniques such as multistate transition interface sampling. I illustrate this approach on patchy particle systems that show multiple modes of binding. MD-GFRD is generic, and can be used to efficiently simulate reaction-diffusion systems at the particle level, including the orientational dynamics, opening up the possibility for large-scale simulations of e.g. protein signaling networks.
Hybrid stochastic simplifications for multiscale gene networks
Directory of Open Access Journals (Sweden)
Debussche Arnaud
2009-09-01
Full Text Available Abstract Background Stochastic simulation of gene networks by Markov processes has important applications in molecular biology. The complexity of exact simulation algorithms scales with the number of discrete jumps to be performed. Approximate schemes reduce the computational time by reducing the number of simulated discrete events. Also, answering important questions about the relation between network topology and intrinsic noise generation and propagation should be based on general mathematical results. These general results are difficult to obtain for exact models. Results We propose a unified framework for hybrid simplifications of Markov models of multiscale stochastic gene networks dynamics. We discuss several possible hybrid simplifications, and provide algorithms to obtain them from pure jump processes. In hybrid simplifications, some components are discrete and evolve by jumps, while other components are continuous. Hybrid simplifications are obtained by partial Kramers-Moyal expansion 123 which is equivalent to the application of the central limit theorem to a sub-model. By averaging and variable aggregation we drastically reduce simulation time and eliminate non-critical reactions. Hybrid and averaged simplifications can be used for more effective simulation algorithms and for obtaining general design principles relating noise to topology and time scales. The simplified models reproduce with good accuracy the stochastic properties of the gene networks, including waiting times in intermittence phenomena, fluctuation amplitudes and stationary distributions. The methods are illustrated on several gene network examples. Conclusion Hybrid simplifications can be used for onion-like (multi-layered approaches to multi-scale biochemical systems, in which various descriptions are used at various scales. Sets of discrete and continuous variables are treated with different methods and are coupled together in a physically justified approach.
DEFF Research Database (Denmark)
Alistar, Mirela; Pop, Paul
2015-01-01
that each biochemical operation in an application is characterized by a worst-case execution time (wcet). However, during the execution of the application, due to variability and randomness in biochemical reactions, operations may finish earlier than their wcetswcets, resulting in unexploited slack...... in the schedule. In this paper, we first propose an online synthesis strategy that re-synthesizes the application at runtime when operations experience variability in their execution time, exploiting thus the slack to obtain shorter application completion times. We also propose a quasi-static synthesis strategy...... approaches have been proposed for the synthesis of digital microfluidic biochips, which, starting from a biochemical application and a given biochip architecture, determine the allocation, resource binding, scheduling, placement and routing of the operations in the application. Researchers have assumed...
International Nuclear Information System (INIS)
Hansen, O.
1983-01-01
A brief review is presented of the experimental and theoretical situation regarding transfer reactions and inelastic scattering. In the first category there is little (very little) precision data for heavy projectiles and consequently almost no experience with quantitative theoretical analysis. For the inelastic scattering the rather extensive data strongly supports the coupled channels models with collective formfactors. At the most back angles, at intensities about 10 -5 of Rutherford scattering, a second, compound-like mechanism becomes dominant. The description of the interplay of these two opposite mechanisms provides a new challenge for our understanding
Biochemical and Molecular Characterization of Plant-Parasitic Nematodes
Directory of Open Access Journals (Sweden)
I.M. de O. Abrantes
2004-08-01
Full Text Available Nematologists need correct species identification to carry out research, teaching, extension and other activities. Therefore, nematode taxonomy must be pursued diligently at all levels. The identification of plant-parasitic nematodes is not always easy and that of some species is especially difficult. Most of the information that nematologists use when characterizing and identifying specimens is based on morphological and morphometrical characters. Although these characters are of primary importance, in the last three decades they have been supplemented by biochemical/ molecular characters. Biochemical approaches include the separation of proteins (general proteins and isozymes by one-dimensional gel electrophoresis, isoelectric focusing, two-dimensional gel electrophoresis, and sodium dodecyl sulphate-capillary gel electrophoresis. Serology has also been found effective in the identification and quantification of nematodes, monoclonal antibodies being a more useful immunological tool than polyclonal antibodies. Identification based on the direct examination of DNA is potentially a more powerful method to characterize inter- and intra-specific variability. The development of techniques such as the polymerase chain reaction, restriction fragment length polymorphism, randomly amplified polymorphic DNA, and amplified fragment length polymorphism has increased the accuracy and speed of nematode characterization/identification. Progress continues to be made and more and more nematologists are using molecular techniques for diagnostic purposes and to assess genetic variation.
A graphical user interface for a method to infer kinetics and network architecture (MIKANA).
Mourão, Márcio A; Srividhya, Jeyaraman; McSharry, Patrick E; Crampin, Edmund J; Schnell, Santiago
2011-01-01
One of the main challenges in the biomedical sciences is the determination of reaction mechanisms that constitute a biochemical pathway. During the last decades, advances have been made in building complex diagrams showing the static interactions of proteins. The challenge for systems biologists is to build realistic models of the dynamical behavior of reactants, intermediates and products. For this purpose, several methods have been recently proposed to deduce the reaction mechanisms or to estimate the kinetic parameters of the elementary reactions that constitute the pathway. One such method is MIKANA: Method to Infer Kinetics And Network Architecture. MIKANA is a computational method to infer both reaction mechanisms and estimate the kinetic parameters of biochemical pathways from time course data. To make it available to the scientific community, we developed a Graphical User Interface (GUI) for MIKANA. Among other features, the GUI validates and processes an input time course data, displays the inferred reactions, generates the differential equations for the chemical species in the pathway and plots the prediction curves on top of the input time course data. We also added a new feature to MIKANA that allows the user to exclude a priori known reactions from the inferred mechanism. This addition improves the performance of the method. In this article, we illustrate the GUI for MIKANA with three examples: an irreversible Michaelis-Menten reaction mechanism; the interaction map of chemical species of the muscle glycolytic pathway; and the glycolytic pathway of Lactococcus lactis. We also describe the code and methods in sufficient detail to allow researchers to further develop the code or reproduce the experiments described. The code for MIKANA is open source, free for academic and non-academic use and is available for download (Information S1).
International Nuclear Information System (INIS)
Polettini, Matteo; Esposito, Massimiliano
2014-01-01
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
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.
Improved methods for the mathematically controlled comparison of biochemical systems
Directory of Open Access Journals (Sweden)
Schwacke John H
2004-06-01
Full Text Available Abstract The method of mathematically controlled comparison provides a structured approach for the comparison of alternative biochemical pathways with respect to selected functional effectiveness measures. Under this approach, alternative implementations of a biochemical pathway are modeled mathematically, forced to be equivalent through the application of selected constraints, and compared with respect to selected functional effectiveness measures. While the method has been applied successfully in a variety of studies, we offer recommendations for improvements to the method that (1 relax requirements for definition of constraints sufficient to remove all degrees of freedom in forming the equivalent alternative, (2 facilitate generalization of the results thus avoiding the need to condition those findings on the selected constraints, and (3 provide additional insights into the effect of selected constraints on the functional effectiveness measures. We present improvements to the method and related statistical models, apply the method to a previously conducted comparison of network regulation in the immune system, and compare our results to those previously reported.
International Nuclear Information System (INIS)
Corner, J.; Richardson, K.; Fenton, N.
1990-01-01
Nuclear reactions' marks a new development in the study of television as an agency of public policy debate. During the Eighties, nuclear energy became a major international issue. The disasters at Three-mile Island and Chernobyl created a global anxiety about its risks and a new sensitivity to it among politicians and journalists. This book is a case-study into documentary depictions of nuclear energy in television and video programmes and into the interpretations and responses of viewers drawn from many different occupational groupings. How are the complex and specialist arguments about benefit, risk and proof conveyed through the different conventions of commentary, interview and film sequence? What symbolic associations does the visual language of television bring to portrayals of the issue? And how do viewers make sense of various and conflicting accounts, connecting what they see and hear on the screen with their pre-existing knowledge, experience and 'civic' expectations. The authors examine some of the contrasting forms and themes which have been used by programme makers to explain and persuade, and then give a sustained analysis of the nature and sources of viewers' own accounts. 'Nuclear Reactions' inquires into the public meanings surrounding energy and the environment, spelling out in its conclusion some of the implications for future media treatments of this issue. It is also a key contribution to the international literature on 'television knowledge' and the processes of active viewing. (author)
International Nuclear Information System (INIS)
Kato, Kiyoshi; Kimura, Masaaki; Furutachi, Naoya; Makinaga, Ayano; Togashi, Tomoaki; Otuka, Naohiko
2010-01-01
The activities of the Japan Nuclear Reaction Data Centre is explained. The main task of the centre is data compilation of Japanese nuclear reaction data in collaboration of the International Network of Nuclear Reaction Data Centres. As one of recent activities, preparation of a new database (NRDF/A) and evaluation of astronuclear reaction data are reported. Collaboration in the nuclear data activities among Asian countries is proposed.
Polyphenol Oxidases in Crops: Biochemical, Physiological and Genetic Aspects
Directory of Open Access Journals (Sweden)
Francesca Taranto
2017-02-01
Full Text Available Enzymatic browning is a colour reaction occurring in plants, including cereals, fruit and horticultural crops, due to oxidation during postharvest processing and storage. This has a negative impact on the colour, flavour, nutritional properties and shelf life of food products. Browning is usually caused by polyphenol oxidases (PPOs, following cell damage caused by senescence, wounding and the attack of pests and pathogens. Several studies indicated that PPOs play a role in plant immunity, and emerging evidence suggested that PPOs might also be involved in other physiological processes. Genomic investigations ultimately led to the isolation of PPO homologs in several crops, which will be possibly characterized at the functional level in the near future. Here, focusing on the botanic families of Poaceae and Solanaceae, we provide an overview on available scientific literature on PPOs, resulting in useful information on biochemical, physiological and genetic aspects.
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....... Here we provide a detailed biochemical study of the activity of all five classes of SSs in barley endosperm. Each enzyme was produced recombinantly in E. coli and the properties and modes of action in vitro were studied in isolation from other SSs and other substrate modifying activities. Our results...... define the mode of action of each SS class in unprecedented detail; we analyze their substrate selection, temperature dependence and stability, substrate affinity and temporal abundance during barley development. Our results are at variance with some generally accepted ideas about starch biosynthesis...
Biochemical Removal of HAP Precursors From Coal
International Nuclear Information System (INIS)
Olson, G.; Tucker, L.; Richards, J.
1997-07-01
This project addresses DOE's interest in advanced concepts for controlling emissions of air toxics from coal-fired utility boilers. We are determining the feasibility of developing a biochemical process for the precombustion removal of substantial percentages of 13 inorganic hazardous air pollutant (HAP) precursors from coal. These HAP precursors are Sb, As, Be, Cd, Cr, Cl, Co, F, Pb, Hg, Mn, Ni, and Se. Although rapid physical coal cleaning is done routinely in preparation plants, biochemical processes for removal of HAP precursors from coal potentially offer advantages of deeper cleaning, more specificity, and less coal loss. Compared to chemical processes for coal cleaning, biochemical processes potentially offer lower costs and milder process conditions. Pyrite oxidizing bacteria, most notably Thiobacillusferrooxidans, are being evaluated in this project for their ability to remove HAP precursors from U.S. coals
Biochemical Removal of HAP Precursors From Coal
Energy Technology Data Exchange (ETDEWEB)
Olson, G.; Tucker, L.; Richards, J.
1997-07-01
This project addresses DOE`s interest in advanced concepts for controlling emissions of air toxics from coal-fired utility boilers. We are determining the feasibility of developing a biochemical process for the precombustion removal of substantial percentages of 13 inorganic hazardous air pollutant (HAP) precursors from coal. These HAP precursors are Sb, As, Be, Cd, Cr, Cl, Co, F, Pb, Hg, Mn, Ni, and Se. Although rapid physical coal cleaning is done routinely in preparation plants, biochemical processes for removal of HAP precursors from coal potentially offer advantages of deeper cleaning, more specificity, and less coal loss. Compared to chemical processes for coal cleaning, biochemical processes potentially offer lower costs and milder process conditions. Pyrite oxidizing bacteria, most notably Thiobacillusferrooxidans, are being evaluated in this project for their ability to remove HAP precursors from U.S. coals.
International Nuclear Information System (INIS)
Plante, Ianik; Devroye, Luc
2015-01-01
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.
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.
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.
Spallation reactions; Reactions de spallation
Energy Technology Data Exchange (ETDEWEB)
Cugon, J.
1996-12-31
Spallation reactions dominate the interactions of hadrons with nuclei in the GeV range (from {approx} 0.1 to {approx} 10 GeV). They correspond to a sometimes important ejection of light particles leaving most of the time a residue of mass commensurate with the target mass. The main features of the experimental data are briefly reviewed. The most successful theoretical model, namely the intranuclear cascade + evaporation model, is presented. Its physical content, results and possible improvements are critically discussed. Alternative approaches are shortly reviewed. (author). 84 refs.
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.
A brief review of advances in complex networks of nuclear science and technology field
International Nuclear Information System (INIS)
Fang Jinqing
2010-01-01
A brief review of advances in complex networks of nuclear science and technology field at home and is given and summarized. These complex networks include: nuclear energy weapon network, network centric warfare, beam transport networks, continuum percolation evolving network associated with nuclear reactions, global nuclear power station network, (nuclear) chemistry reaction networks, radiological monitoring and anti-nuclear terror networks, and so on. Some challenge issues and development prospects of network science are pointed out finally. (authors)
Autonomous bio-chemical decontaminator (ABCD) against weapons of mass destruction
Hyacinthe, Berg P.
2006-05-01
The proliferation of weapons of mass destruction (WMD) and the use of such elements pose an eminent asymmetric threat with disastrous consequences to the national security of any nation. In particular, the use of biochemical warfare agents against civilians and unprotected troops in international conflicts or by terrorists against civilians is considered as a very peculiar threat. Accordingly, taking a quarantine-before-inhalation approach to biochemical warfare, the author introduces the notion of autonomous biochemical decontamination against WMD. In the unfortunate event of a biochemical attack, the apparatus proposed herein is intended to automatically detect, identify, and more importantly neutralize a biochemical threat. Along with warnings concerning a cyber-WMD nexus, various sections cover discussions on human senses and computer sensors, corroborating evidence related to detection and neutralization of chemical toxins, and cyber-assisted olfaction in stand alone, peer-to-peer, and network settings. In essence, the apparatus can be used in aviation and mass transit security to initiate mass decontamination by dispersing a decontaminant aerosol or to protect the public water supply against a potential bioterrorist attack. Future effort may involve a system-on-chip (SoC) embodiment of this apparatus that allows a safer environment for the emerging phenomenon of cyber-assisted olfaction and morph cell phones into ubiquitous sensors/decontaminators. Although this paper covers mechanisms and protocols to avail a neutralizing substance, further research will need to explore the substance's various pharmacological profiles and potential side effects.
Saetchnikov, Vladimir A.; Tcherniavskaia, Elina A.; Saetchnikov, Anton V.; Schweiger, Gustav; Ostendorf, Andreas
2014-05-01
Experimental data on detection and identification of variety of biochemical agents, such as proteins, microelements, antibiotic of different generation etc. in both single and multi component solutions under varied in wide range concentration analyzed on the light scattering parameters of whispering gallery mode optical resonance based sensor are represented. Multiplexing on parameters and components has been realized using developed fluidic sensor cell with fixed in adhesive layer dielectric microspheres and data processing. Biochemical component identification has been performed by developed network analysis techniques. Developed approach is demonstrated to be applicable both for single agent and for multi component biochemical analysis. Novel technique based on optical resonance on microring structures, plasmon resonance and identification tools has been developed. To improve a sensitivity of microring structures microspheres fixed by adhesive had been treated previously by gold nanoparticle solution. Another technique used thin film gold layers deposited on the substrate below adhesive. Both biomolecule and nanoparticle injections caused considerable changes of optical resonance spectra. Plasmonic gold layers under optimized thickness also improve parameters of optical resonance spectra. Biochemical component identification has been also performed by developed network analysis techniques both for single and for multi component solution. So advantages of plasmon enhancing optical microcavity resonance with multiparameter identification tools is used for development of a new platform for ultra sensitive label-free biomedical sensor.
[Pulmonary reaction after furazidin (Furagin). Case report].
Zielonka, T M; Demkow, U; Kuś, J
1997-05-01
For the first time in Poland we present the case of pulmonary reaction to furazidin which is by chemical structure closely related to nitrofurantoin. 63 years old woman presented generalized symptoms of acute hypersensitivity reaction induced by furazidin as well as features of chronic pulmonary fibrosis. After few months of treatment with this drug patients complained of weight loss, dyspnea on effort, non-productive cough, chills and fever. Radiological and functional evaluation of respiratory system confirmed features of lung fibrosis. Drug provocation test was positive. In vitro furazidin in low concentrations stimulated proliferation of patient's lymphocytes. After cessation of treatment we have observed rapid improvement of clinical, radiological, biochemical and functional parameters.
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
Biochemical characterization of Tunisian grapevine varieties
Directory of Open Access Journals (Sweden)
Ferjani Ben Abdallah
1998-03-01
The study of GPI, PGM, AAT and peroxydase isozyme banding patterns in combination with berry colour has led to establish a classification of the 61 autochton varieties into 37 groups including 26 varieties definitely differentiated through the results of this biochemical study.
Survey of Biochemical Education in Japanese Universities.
Kagawa, Yasuo
1995-01-01
Reports findings of questionnaires sent to faculty in charge of biochemical education in medical schools and other programs from dentistry to agriculture. Total class hours have declined since 1984. New trends include bioethics and computer-assisted learning. Tables show trends in lecture hours, lecture content, laboratory hours, core subject…
Haematological and biochemical responses of starter broiler ...
African Journals Online (AJOL)
A study was conducted to investigate the haematological and biochemical responses of starter broiler chickens fed copper and probiotics supplemented diets. A total of 180-day old Marshal broiler chicks were randomly allotted to six treatment groups of 30 birds each. The treatments were divided into three replicates of ten ...
Biochemical applications of FT-IR spectroscopy
Pistorius, A.M.A.
1996-01-01
This thesis describes the use of (FT-)IR spectroscopy in general biochemical research. In chapter 3, IR spectroscopy is used in the quantitation of residual detergent after reconstitution of an integral membrane protein in a pre-defined lipid matrix. This chapter discusses the choice of the
Some hematological and biochemical parameters in smokeless ...
African Journals Online (AJOL)
AJB SERVER
2007-01-04
Jan 4, 2007 ... The effect of Jharda powder (smokeless tobacco) on some hematological and biochemical parameters in consumers was investigated. Hematological parameters including hemoglobin content and white blood cell and leukocyte counts were higher in jharda powder consumers, while monocytes and.
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.
Some hematological and biochemical parameters in smokeless ...
African Journals Online (AJOL)
The effect of Jharda powder (smokeless tobacco) on some hematological and biochemical parameters in consumers was investigated. Hematological parameters including hemoglobin content and white blood cell and leukocyte counts were higher in jharda powder consumers, while monocytes and basophiles counts were ...
Haematological And Biochemical Effects Of Sulphadimidine In ...
African Journals Online (AJOL)
Haematological and biochemical efects of sulphadmidine were studied in Nigerian mongrel dogs. Five Nigerian mongrel dogs of either sex weighing between 7 and 12 kg were used for the study. The pretreatment blood and serum samples were collected and the weight of animals taken before the administraton of 100 ...
Biochemical and secondary metabolites changes under moisture ...
African Journals Online (AJOL)
The study showed the importance of carbohydrate and nitrogen cycle related metabolites in mediating tolerance in cassava by affecting their phenotypic expression in the plant. Keywords: Hydrothermal stress, bio-chemicals, pigments, secondary metabolites, cassava. African Journal of Biotechnology, Vol 13(31) 3173-3186 ...
Biochemical and serological characterization of Escherichia coli ...
African Journals Online (AJOL)
This study was designed to determine the isolation rate, serotypes and biochemical profiles of E. coli from colibacillosis and dead-in-shell embryos in Zaria, Northern-Nigeria. The isolation rate of E. coli from hatcheries studied were 4.67% and 7.50% from farms of Simtu Agricultural Company and National Animal Production ...
Biochemical and Kinetic Characterization of Geranylgeraniol 18 ...
African Journals Online (AJOL)
This enzyme and its gene are an attractive target for development of plaunotol production and its detailed biochemical properties need to be understood. Recently, even though the gene (CYP97C27) coding for GGOH 18-hydroxylase has been identified, cloned, and expressed in Escherichia coli system, the enzyme activity ...
Evaluation of Haematological and Biochemical Parameters of ...
African Journals Online (AJOL)
ADOWIE PERE
Evaluation of Haematological and Biochemical Parameters of Juvenile Oreochromis niloticus after Exposure to Water Soluble Fractions of ... niloticus were evaluated. After a preliminary determination of the 96 h-LC50 of ... evaporation, dissolution, emulsion, photolysis and biodegradation which generate a water soluble.
Metabonomics and medicine: the Biochemical Oracle.
Mitchell, Steve; Holmes, Elaine; Carmichael, Paul
2002-10-01
Occasionally, a new idea emerges that has the potential to revolutionize an entire field of scientific endeavour. It is now within our grasp to be able to detect subtle perturbations within the phenomenally complex biochemical matrix of living organisms. The discipline of metabonomics promises an all-encompassing approach to understanding total, yet fundamental, changes occurring in disease processes, drug toxicity and cell function.
Discordant results between biochemical and molecular transthyretin
Indian Academy of Sciences (India)
Discordant results between biochemical and molecular transthyretin assays: lessons learned from a unique testing algorithm at the Mayo Clinic. Honey V. Reddi Brittany C. Thomas Kurt S. Willkomm Matthew J. Ferber Kandelaria M. Rumilla Kimiyo M. Raymond John F. O'Brien W. Edward Highsmith. Research Note Volume ...
Biochemical and microstructural characteristics of meat samples ...
African Journals Online (AJOL)
This study was conducted to compare the efficiency of different plant proteases for changing biochemical and microstructural characteristics in muscle foods. The meat samples from chicken, giant catfish, pork and beef were treated with four types of proteolytic enzymes: Calotropis procera latex proteases, papaya latex ...
Clinical, Biochemical, and Molecular Spectrum of Hyperargininemia Due to Arginase I Deficiency
Scaglia, Fernando; Lee, Brendan
2006-01-01
The urea cycle consists of six consecutive enzymatic reactions that convert waste nitrogen into urea. Urea cycle disorders are a group of inborn errors of hepatic metabolism that often result in life threatening hyperammonemia and hyperglutaminemia. Deficiencies of all of the enzymes of the cycle have been described and although each specific disorder results in the accumulation of different precursors, hyperammonemia and hyperglutaminemia are common biochemical hallmarks of these disorders. ...
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
International Nuclear Information System (INIS)
Balogh, Brian.
1991-01-01
Chain Reaction is a work of recent American political history. It seeks to explain how and why America came to depend so heavily on its experts after World War II, how those experts translated that authority into political clout, and why that authority and political discretion declined in the 1970s. The author's research into the internal memoranda of the Atomic Energy Commission substantiates his argument in historical detail. It was not the ravages of American anti-intellectualism, as so many scholars have argued, that brought the experts back down to earth. Rather, their decline can be traced to the very roots of their success after World War II. The need to over-state anticipated results in order to garner public support, incessant professional and bureaucratic specialization, and the sheer proliferation of expertise pushed arcane and insulated debates between experts into public forums at the same time that a broad cross section of political participants found it easier to gain access to their own expertise. These tendencies ultimately undermined the political influence of all experts. (author)
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.
The interplay of intrinsic and extrinsic bounded noises in biomolecular networks.
Caravagna, Giulio; Mauri, Giancarlo; d'Onofrio, Alberto
2013-01-01
After being considered as a nuisance to be filtered out, it became recently clear that biochemical noise plays a complex role, often fully functional, for a biomolecular network. The influence of intrinsic and extrinsic noises on biomolecular networks has intensively been investigated in last ten years, though contributions on the co-presence of both are sparse. Extrinsic noise is usually modeled as an unbounded white or colored gaussian stochastic process, even though realistic stochastic perturbations are clearly bounded. In this paper we consider Gillespie-like stochastic models of nonlinear networks, i.e. the intrinsic noise, where the model jump rates are affected by colored bounded extrinsic noises synthesized by a suitable biochemical state-dependent Langevin system. These systems are described by a master equation, and a simulation algorithm to analyze them is derived. This new modeling paradigm should enlarge the class of systems amenable at modeling. We investigated the influence of both amplitude and autocorrelation time of a extrinsic Sine-Wiener noise on: (i) the Michaelis-Menten approximation of noisy enzymatic reactions, which we show to be applicable also in co-presence of both intrinsic and extrinsic noise, (ii) a model of enzymatic futile cycle and (iii) a genetic toggle switch. In (ii) and (iii) we show that the presence of a bounded extrinsic noise induces qualitative modifications in the probability densities of the involved chemicals, where new modes emerge, thus suggesting the possible functional role of bounded noises.
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...... was extensively validated against published flux data, and flux distribution values were found to correlate well between simulations and experiments. The split pathway of the lysine synthesis pathway of C. glutamicum was investigated, and it was found that the direct dehydrogenase variant gave a higher lysine...... yield than the alternative succinyl pathway at high lysine production rates. The NADPH demand of the network was not found to be critical for lysine production until lysine yields exceeded 55% (mmol lysine (mmol glucose)(-1)). The model was validated during growth on the organic acids acetate...
Geometric universality of currents in an open network of interacting particles
International Nuclear Information System (INIS)
Sinitsyn, Nikolai A.; Chernyak, Vladimir Y.; Chertkov, Michael
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.
A technique is presented for finding the least squares estimates for the ultimate biochemical oxygen demand (BOD) and rate coefficient for the BOD reaction without resorting to complicated computer algorithms or subjective graphical methods. This may be used in stream water quali...
Combining molecular dynamics with mesoscopic Green’s function reaction dynamics simulations
Energy Technology Data Exchange (ETDEWEB)
Vijaykumar, Adithya, E-mail: vijaykumar@amolf.nl [FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam (Netherlands); van ’t Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam (Netherlands); Bolhuis, Peter G. [van ’t Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, 1090 GD Amsterdam (Netherlands); Rein ten Wolde, Pieter, E-mail: p.t.wolde@amolf.nl [FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam (Netherlands)
2015-12-07
In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. We propose a novel approach that combines GFRD for simulating the system at the mesoscopic scale where particles are far apart, with a microscopic technique such as Langevin dynamics or Molecular Dynamics (MD), for simulating the system at the microscopic scale where reactants are in close proximity. This scheme defines the regions where the particles are close together and simulated with high microscopic resolution and those where they are far apart and simulated with lower mesoscopic resolution, adaptively on the fly. The new multi-scale scheme, called MD-GFRD, is generic and can be used to efficiently simulate reaction-diffusion systems at the particle level.
Combining molecular dynamics with mesoscopic Green’s function reaction dynamics simulations
International Nuclear Information System (INIS)
Vijaykumar, Adithya; Bolhuis, Peter G.; Rein ten Wolde, Pieter
2015-01-01
In many reaction-diffusion processes, ranging from biochemical networks, catalysis, to complex self-assembly, the spatial distribution of the reactants and the stochastic character of their interactions are crucial for the macroscopic behavior. The recently developed mesoscopic Green’s Function Reaction Dynamics (GFRD) method enables efficient simulation at the particle level provided the microscopic dynamics can be integrated out. Yet, many processes exhibit non-trivial microscopic dynamics that can qualitatively change the macroscopic behavior, calling for an atomistic, microscopic description. We propose a novel approach that combines GFRD for simulating the system at the mesoscopic scale where particles are far apart, with a microscopic technique such as Langevin dynamics or Molecular Dynamics (MD), for simulating the system at the microscopic scale where reactants are in close proximity. This scheme defines the regions where the particles are close together and simulated with high microscopic resolution and those where they are far apart and simulated with lower mesoscopic resolution, adaptively on the fly. The new multi-scale scheme, called MD-GFRD, is generic and can be used to efficiently simulate reaction-diffusion systems at the particle level
Flow Type Bio-Chemical Calorimeter with Micro Differential Thermopile Sensor.
Saito, Masataka; Nakabeppu, Osamu
2015-04-01
Bio-chemical calorimeters with a MEMS (Micro-Electro-Mechanical Systems) thermopile sensor have been studied for monitoring detailed processes of the biochemical reactions of a minute sample with a high temporal resolution. The bio-calorimeters are generally divided into a batch-type and a flow-type. We developed a highly sensitive batch-type calorimeter which can detect a 100 nW level thermal reaction. However it shows a long settling time of 2 hours because of the heat capacity of a whole calorimeter. Thus, the flow-type calorimeters in passive and active mode have been studied for measuring the thermal reactions in an early stage after starting an analysis. The flow-type calorimeter consists of the MEMS differential thermopile sensor, a pair of micro channel reactor in a PDMS (polydimethylsiloxane) sheet in a three-fold thermostat chamber. The calorimeter in the passive mode was tested with dilution reactions of ethanol to water and NaCl aqueous solution to water. It was shown that the calorimeter detects exo- and endothermic reaction over 250 nW at solution flow rate of 0.05 ~ 1 µl/min with a settling time of about 4 minutes. In the active mode, a response test was conducted by using heat removal by water flow from the reactor channel. The active calorimetry enhances the response time about three to four times faster.
Model based design of biochemical micro-reactors
Directory of Open Access Journals (Sweden)
Tobias eElbinger
2016-02-01
Full Text Available Mathematical modelling of biochemical pathways is an important resource in Synthetic Biology, as the predictive power of simulating synthetic pathways represents an important step in the design of synthetic metabolons. In this paper, we are concerned with the mathematical modeling, simulation and optimization of metabolic processes in biochemical micro-reactors able to carry out enzymatic reactions and to exchange metabolites with their surrounding medium. The results of the reported modeling approach are incorporated in the design of the first micro-reactor prototypes that are under construction. These microreactors consist of compartments separated by membranes carrying specific transporters for the input of substrates and export of products. Inside the compartments multi-enzyme complexes assembled on nano-beads by peptide adapters are used to carry out metabolic reactions.The spatially resolved mathematical model describing the ongoing processes consists of a system of diffusion equations together with boundary and initial conditions. The boundary conditions model the exchange of metabolites with the neighboring compartments and the reactions at the surface of the nano-beads carrying the multi-enzyme complexes. Efficient and accurate approaches for numerical simulation of the mathematical model and for optimal design of the micro-reactor are developed. As a proof-of-concept scenario, a synthetic pathway for the conversion of sucrose to glucose-6-phosphate (G6P was chosen. In this context, the mathematical model is employed to compute the spatio-temporal distributions of the metabolite concentrations, as well as application relevant quantities like the outflow rate of G6P. These computations are performed for different scenarios, where the number of beads as well as their loading capacity are varied. The computed metabolite distributions show spatial patterns which differ for different experimental arrangements. Furthermore, the total output
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
Quell, Jan D; Römisch-Margl, Werner; Colombo, Marco; Krumsiek, Jan; Evans, Anne M; Mohney, Robert; Salomaa, Veikko; de Faire, Ulf; Groop, Leif C; Agakov, Felix; Looker, Helen C; McKeigue, Paul; Colhoun, Helen M; Kastenmüller, Gabi
2017-12-15
Identification of metabolites in non-targeted metabolomics continues to be a bottleneck in metabolomics studies in large human cohorts. Unidentified metabolites frequently emerge in the results of association studies linking metabolite levels to, for example, clinical phenotypes. For further analyses these unknown metabolites must be identified. Current approaches utilize chemical information, such as spectral details and fragmentation characteristics to determine components of unknown metabolites. Here, we propose a systems biology model exploiting the internal correlation structure of metabolite levels in combination with existing biochemical and genetic information to characterize properties of unknown molecules. Levels of 758 metabolites (439 known, 319 unknown) in human blood samples of 2279 subjects were measured using a non-targeted metabolomics platform (LC-MS and GC-MS). We reconstructed the structure of biochemical pathways that are imprinted in these metabolomics data by building an empirical network model based on 1040 significant partial correlations between metabolites. We further added associations of these metabolites to 134 genes from genome-wide association studies as well as reactions and functional relations to genes from the public database Recon 2 to the network model. From the local neighborhood in the network, we were able to predict the pathway annotation of 180 unknown metabolites. Furthermore, we classified 100 pairs of known and unknown and 45 pairs of unknown metabolites to 21 types of reactions based on their mass differences. As a proof of concept, we then looked further into the special case of predicted dehydrogenation reactions leading us to the selection of 39 candidate molecules for 5 unknown metabolites. Finally, we could verify 2 of those candidates by applying LC-MS analyses of commercially available candidate substances. The formerly unknown metabolites X-13891 and X-13069 were shown to be 2-dodecendioic acid and 9
LeishCyc: a biochemical pathways database for Leishmania major
Directory of Open Access Journals (Sweden)
Doyle Maria A
2009-06-01
Full Text Available Abstract Background Leishmania spp. are sandfly transmitted protozoan parasites that cause a spectrum of diseases in more than 12 million people worldwide. Much research is now focusing on how these parasites adapt to the distinct nutrient environments they encounter in the digestive tract of the sandfly vector and the phagolysosome compartment of mammalian macrophages. While data mining and annotation of the genomes of three Leishmania species has provided an initial inventory of predicted metabolic components and associated pathways, resources for integrating this information into metabolic networks and incorporating data from transcript, protein, and metabolite profiling studies is currently lacking. The development of a reliable, expertly curated, and widely available model of Leishmania metabolic networks is required to facilitate systems analysis, as well as discovery and prioritization of new drug targets for this important human pathogen. Description The LeishCyc database was initially built from the genome sequence of Leishmania major (v5.2, based on the annotation published by the Wellcome Trust Sanger Institute. LeishCyc was manually curated to remove errors, correct automated predictions, and add information from the literature. The ongoing curation is based on public sources, literature searches, and our own experimental and bioinformatics studies. In a number of instances we have improved on the original genome annotation, and, in some ambiguous cases, collected relevant information from the literature in order to help clarify gene or protein annotation in the future. All genes in LeishCyc are linked to the corresponding entry in GeneDB (Wellcome Trust Sanger Institute. Conclusion The LeishCyc database describes Leishmania major genes, gene products, metabolites, their relationships and biochemical organization into metabolic pathways. LeishCyc provides a systematic approach to organizing the evolving information about Leishmania
Biochemical and toxicological studies of aqueous extract of ...
African Journals Online (AJOL)
Biochemical and toxicological studies of aqueous extract of Syzigium ... tract diseases and also used as food spices), on some biochemical indices, such as ... liver functions and blood parameters were studied in adult albino rats of both sexes.