The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform
Daniel T. Gillespie - One of the best experts on this subject based on the ideXlab platform.
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Perspective: Stochastic algorithms for Chemical Kinetics
The Journal of chemical physics, 2013Co-Authors: Daniel T. Gillespie, Andreas Hellander, Linda R. PetzoldAbstract:We outline our perspective on stochastic Chemical Kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional Chemical Kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with Chemical reactions inside the subvolumes.
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stochastic simulation of Chemical Kinetics
Annual Review of Physical Chemistry, 2007Co-Authors: Daniel T. GillespieAbstract:Stochastic Chemical Kinetics describes the time evolution of a wellstirred Chemically reacting system in a way that takes into account the fact that molecules come in whole numbers and exhibit some degree of randomness in their dynamical behavior. Researchers are increasingly using this approach to Chemical Kinetics in the analysis of cellular systems in biology, where the small molecular populations of only a few reactant species can lead to deviations from the predictions of the deterministic differential equations of classical Chemical Kinetics. After reviewing the supporting theory of stochastic Chemical Kinetics, I discuss some recent advances in methods for using that theory to make numerical simulations. These include improvements to the exact stochastic simulation algorithm (SSA) and the approximate explicit tau-leaping procedure, as well as the development of two approximate strategies for simulating systems that are dynamically stiff: implicit tau-leaping and the slow-scale SSA.
Linda R. Petzold - One of the best experts on this subject based on the ideXlab platform.
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Perspective: Stochastic algorithms for Chemical Kinetics
The Journal of chemical physics, 2013Co-Authors: Daniel T. Gillespie, Andreas Hellander, Linda R. PetzoldAbstract:We outline our perspective on stochastic Chemical Kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional Chemical Kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with Chemical reactions inside the subvolumes.
Paola Lecca - One of the best experts on this subject based on the ideXlab platform.
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Stochastic Chemical Kinetics
Biophysical Reviews, 2013Co-Authors: Paola LeccaAbstract:A review of the physical principles that are the ground of the stochastic formulation of Chemical Kinetics is presented along with a survey of the algorithms currently used to simulate it. This review covers the main literature of the last decade and focuses on the mathematical models describing the characteristics and the behavior of systems of Chemical reactions at the nano- and micro-scale. Advantages and limitations of the models are also discussed in the light of the more and more frequent use of these models and algorithms in modeling and simulating bioChemical and even biological processes.
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Deterministic Chemical Kinetics
Deterministic Versus Stochastic Modelling in Biochemistry and Systems Biology, 2013Co-Authors: Paola Lecca, Ian J. Laurenzi, Ferenc JordánAbstract:Abstract: The deterministic approach to Chemical Kinetics is used by Chemical and life scientists to characterize the time evolutions of Chemical reactions in large systems. In this chapter, we introduce key concepts including the Chemical rate equation, the Chemical rate constant, the material balance and the relationship between the “steady state” and Chemical equilibrium.
Yezdan Boz - One of the best experts on this subject based on the ideXlab platform.
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cooperative learning instruction for conceptual change in the concepts of Chemical Kinetics
Chemistry Education Research and Practice, 2012Co-Authors: Ozgecan Tastan Kirik, Yezdan BozAbstract:Learning is a social event and so the students need learning environments that enable them to work with their peers so that they can learn through their interactions. This study discusses the effectiveness of cooperative learning compared to traditional instruction in terms of students' motivation and understanding of Chemical Kinetics in a high school chemistry course. Participants were 110 eleventh grade students from two different schools. The researchers administered the Reaction Rate Concept Test to measure the students' understanding of Chemical Kinetics, the Science Process Skill Test to decide whether the groups were different in terms of their science process skills before instruction, and the Motivated Strategies for Learning Questionnaire to assess students' motivation for a chemistry course. Results of the experiment showed that compared to traditional instruction, cooperative learning enabled better understanding of the concepts of Chemical Kinetics and improved students' motivation to study chemistry for both schools.
Michael P. H. Stumpf - One of the best experts on this subject based on the ideXlab platform.
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Sensitivity, robustness, and identifiability in stochastic Chemical Kinetics models
Proceedings of the National Academy of Sciences of the United States of America, 2011Co-Authors: Michał Komorowski, Maria J. Costa, David A. Rand, Michael P. H. StumpfAbstract:We present a novel and simple method to numerically calculate Fisher information matrices for stochastic Chemical Kinetics models. The linear noise approximation is used to derive model equations and a likelihood function that leads to an efficient computational algorithm. Our approach reduces the problem of calculating the Fisher information matrix to solving a set of ordinary differential equations. This is the first method to compute Fisher information for stochastic Chemical Kinetics models without the need for Monte Carlo simulations. This methodology is then used to study sensitivity, robustness, and parameter identifiability in stochastic Chemical Kinetics models. We show that significant differences exist between stochastic and deterministic models as well as between stochastic models with time-series and time-point measurements. We demonstrate that these discrepancies arise from the variability in molecule numbers, correlations between species, and temporal correlations and show how this approach can be used in the analysis and design of experiments probing stochastic processes at the cellular level. The algorithm has been implemented as a Matlab package and is available from the authors upon request.