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Journal articles on the topic 'Stochastic Simulation of Chemical Reactions'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Zenyuk, Dmitry Alexeyevich. "Stochastic simulation of chemical reactions in subdiffusion medium." Computer Research and Modeling 13, no. 1 (2021): 87–104. http://dx.doi.org/10.20537/2076-7633-2021-13-1-87-104.

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2

Cai, Xiaodong. "Exact stochastic simulation of coupled chemical reactions with delays." Journal of Chemical Physics 126, no. 12 (2007): 124108. http://dx.doi.org/10.1063/1.2710253.

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3

Nicolau, Dan V., and Kevin Burrage. "Stochastic simulation of chemical reactions in spatially complex media." Computers & Mathematics with Applications 55, no. 5 (2008): 1007–18. http://dx.doi.org/10.1016/j.camwa.2006.12.085.

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4

Zhou, Wen, Xinjun Peng, Zhenglou Yan, and Yifei Wang. "Accelerated stochastic simulation algorithm for coupled chemical reactions with delays." Computational Biology and Chemistry 32, no. 4 (2008): 240–42. http://dx.doi.org/10.1016/j.compbiolchem.2008.03.007.

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5

Vereecken, Luc, Guido Huyberechts, and Jozef Peeters. "Stochastic simulation of chemically activated unimolecular reactions." Journal of Chemical Physics 106, no. 16 (1997): 6564–73. http://dx.doi.org/10.1063/1.473656.

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6

Thanh, Vo Hong. "RSSALib: a library for stochastic simulation of complex biochemical reactions." Bioinformatics 36, no. 18 (2020): 4825–26. http://dx.doi.org/10.1093/bioinformatics/btaa602.

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Abstract Motivation Stochastic chemical kinetics is an essential mathematical framework for investigating the dynamics of biological processes, especially when stochasticity plays a vital role in their development. Simulation is often the only option for the analysis of many practical models due to their analytical intractability. Results We present in this article, the simulation library RSSALib, implementing our recently developed rejection-based stochastic simulation algorithm (RSSA) and a wide range of its improvements, to accelerate the simulation and analysis of biochemical reactions. RS

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7

Cai, Xiaodong, and Zhouyi Xu. "K-leap method for accelerating stochastic simulation of coupled chemical reactions." Journal of Chemical Physics 126, no. 7 (2007): 074102. http://dx.doi.org/10.1063/1.2436869.

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8

Xu, Yuting, and Yueheng Lan. "The N-leap method for stochastic simulation of coupled chemical reactions." Journal of Chemical Physics 137, no. 20 (2012): 204103. http://dx.doi.org/10.1063/1.4767343.

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9

LI, QIAN SHU, and RUI ZHU. "STOCHASTIC SIMULATION OF CHEMICAL CHUA SYSTEM." International Journal of Bifurcation and Chaos 14, no. 03 (2004): 1053–57. http://dx.doi.org/10.1142/s0218127404009582.

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A master equation for a scheme of chemical Chua system has been simulated stochastically. Two pairs of reaction simulations, one pair corresponding to deterministic chaos and the other pair to limit cycle, are carried out. The initial conditions differ with only one molecule for the former, while one hundred molecules for the latter. The rapid separation of time traces is shown only in the former case. This marked difference of dynamic behavior between the two cases might reveal the intrinsic random nature of deterministic chemical chaos, i.e. initial-condition sensitivity based on random coll

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10

MOREAU, M., O. BÉNICHOU, C. LOVERDO, and R. VOITURIEZ. "STOCHASTIC SEARCH PROCESSES AND CHEMICAL REACTIVITY IN HETEROGENEOUS MEDIA." International Journal of Bifurcation and Chaos 19, no. 10 (2009): 3519–24. http://dx.doi.org/10.1142/s0218127409024955.

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Intermittent search processes alternate between two different stochastic motions in order to reach a given target. If the faster motion has a lower probability to detect the target, a question arises concerning the efficiency of both processes, and it may be possible to minimize the search time by a convenient choice of the parameters. This argument has been used to interpret observations in molecular biology or to explain the behavior of animals when searching for food. It can also have interesting consequences for the kinetics of reactions in heterogeneous media. In particular, the reaction

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11

Haseltine, Eric L., and James B. Rawlings. "Approximate simulation of coupled fast and slow reactions for stochastic chemical kinetics." Journal of Chemical Physics 117, no. 15 (2002): 6959–69. http://dx.doi.org/10.1063/1.1505860.

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12

Andrews, Steven S., and Dennis Bray. "Stochastic simulation of chemical reactions with spatial resolution and single molecule detail." Physical Biology 1, no. 3 (2004): 137–51. http://dx.doi.org/10.1088/1478-3967/1/3/001.

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13

Salis, Howard, and Yiannis Kaznessis. "Accurate hybrid stochastic simulation of a system of coupled chemical or biochemical reactions." Journal of Chemical Physics 122, no. 5 (2005): 054103. http://dx.doi.org/10.1063/1.1835951.

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14

Cai, Xiaodong, and Ji Wen. "Efficient exact and K-skip methods for stochastic simulation of coupled chemical reactions." Journal of Chemical Physics 131, no. 6 (2009): 064108. http://dx.doi.org/10.1063/1.3204422.

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15

Zhou, Wen, Xin-jun Peng, Xiang Liu, Zheng-lou Yan, and Yi-fei Wang. "“Final all possible steps” approach for accelerating stochastic simulation of coupled chemical reactions." Applied Mathematics and Mechanics 29, no. 3 (2008): 379–87. http://dx.doi.org/10.1007/s10483-008-0309-x.

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16

TEWARI, SHIVENDRA G. "STOCHASTIC SIMULATION OF A DIMER SODIUM PUMP." Journal of Biological Systems 19, no. 04 (2011): 551–60. http://dx.doi.org/10.1142/s0218339011003920.

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Sodium pump is known to play an important role in almost all organs of our human body like heart, kidney, liver, brain, etc. A number of mechanisms for sodium pumping have been proposed till date, with Albers–Post Model being most widely used. Recently, Clarke proposed a two-gear dimer sodium pump model to replace the classical Albers–Post Model. This dimer model has two gears of sodium pumping depending upon the available adenosine triphosphate (ATP) concentrations. The mathematical model governing the two gears of sodium pumping overestimated the total fluorescence change of sodium pump labe

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17

Vlysidis, Michail, and Yiannis Kaznessis. "On Differences between Deterministic and Stochastic Models of Chemical Reactions: Schlögl Solved with ZI-Closure." Entropy 20, no. 9 (2018): 678. http://dx.doi.org/10.3390/e20090678.

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Deterministic and stochastic models of chemical reaction kinetics can give starkly different results when the deterministic model exhibits more than one stable solution. For example, in the stochastic Schlögl model, the bimodal stationary probability distribution collapses to a unimodal distribution when the system size increases, even for kinetic constant values that result in two distinct stable solutions in the deterministic Schlögl model. Using zero-information (ZI) closure scheme, an algorithm for solving chemical master equations, we compute stationary probability distributions for varyi

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18

Stutz, Timothy C., Alfonso Landeros, Jason Xu, Janet S. Sinsheimer, Mary Sehl, and Kenneth Lange. "Stochastic simulation algorithms for Interacting Particle Systems." PLOS ONE 16, no. 3 (2021): e0247046. http://dx.doi.org/10.1371/journal.pone.0247046.

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Interacting Particle Systems (IPSs) are used to model spatio-temporal stochastic systems in many disparate areas of science. We design an algorithmic framework that reduces IPS simulation to simulation of well-mixed Chemical Reaction Networks (CRNs). This framework minimizes the number of associated reaction channels and decouples the computational cost of the simulations from the size of the lattice. Decoupling allows our software to make use of a wide class of techniques typically reserved for well-mixed CRNs. We implement the direct stochastic simulation algorithm in the open source program

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19

Leier, Andre, and Tatiana T. Marquez-Lago. "Delay chemical master equation: direct and closed-form solutions." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471, no. 2179 (2015): 20150049. http://dx.doi.org/10.1098/rspa.2015.0049.

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The stochastic simulation algorithm (SSA) describes the time evolution of a discrete nonlinear Markov process. This stochastic process has a probability density function that is the solution of a differential equation, commonly known as the chemical master equation (CME) or forward-Kolmogorov equation. In the same way that the CME gives rise to the SSA, and trajectories of the latter are exact with respect to the former, trajectories obtained from a delay SSA are exact representations of the underlying delay CME (DCME). However, in contrast to the CME, no closed-form solutions have so far been

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20

Klingbeil, Guido, Radek Erban, Mike Giles, and Philip K. Maini. "Fat versus Thin Threading Approach on GPUs: Application to Stochastic Simulation of Chemical Reactions." IEEE Transactions on Parallel and Distributed Systems 23, no. 2 (2012): 280–87. http://dx.doi.org/10.1109/tpds.2011.157.

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21

Koumoutsakos, Petros, and Justin Feigelman. "Multiscale stochastic simulations of chemical reactions with regulated scale separation." Journal of Computational Physics 244 (July 2013): 290–97. http://dx.doi.org/10.1016/j.jcp.2012.11.030.

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22

Xing, Fei, Yi Ping Yao, Zhi Wen Jiang, and Bing Wang. "Fine-Grained Parallel and Distributed Spatial Stochastic Simulation of Biological Reactions." Advanced Materials Research 345 (September 2011): 104–12. http://dx.doi.org/10.4028/www.scientific.net/amr.345.104.

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To date, discrete event stochastic simulations of large scale biological reaction systems are extremely compute-intensive and time-consuming. Besides, it has been widely accepted that spatial factor plays a critical role in the dynamics of most biological reaction systems. The NSM (the Next Sub-Volume Method), a spatial variation of the Gillespie’s stochastic simulation algorithm (SSA), has been proposed for spatially stochastic simulation of those systems. While being able to explore high degree of parallelism in systems, NSM is inherently sequential, which still suffers from the problem of l

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23

Grimminger, J., and W. Schmickler. "Stochastic simulations of electrochemical electron transfer reactions." Journal of Applied Electrochemistry 36, no. 11 (2006): 1231–35. http://dx.doi.org/10.1007/s10800-006-9176-1.

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24

Macnamara, Shev, Sergio Blanes, and Arieh Iserles. "Simulation of bimolecular reactions: Numerical challenges with the graph Laplacian." ANZIAM Journal 61 (June 16, 2020): C59—C74. http://dx.doi.org/10.21914/anziamj.v61i0.15169.

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An important framework for modelling and simulation of chemical reactions is a Markov process sometimes known as a master equation. Explicit solutions of master equations are rare; in general the explicit solution of the governing master equation for a bimolecular reaction remains an open question. We show that a solution is possible in special cases. One method of solution is diagonalization. The crucial class of matrices that describe this family of models are non-symmetric graph Laplacians. We illustrate how standard numerical algorithms for finding eigenvalues fail for the non-symmetric gr

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25

Macedo de Souza, Danilo, Monique Pereira Santana Matos, Nemesio Matos Oliveira-Neto, and Rodrigo Veiga Tenório de Albuquerque. "An Analysis of Pseudo-First-Order Behavior in Bimolecular Chemical Reactions Via Stochastic Computational Simulation." Revista Virtual de Química 12, no. 3 (2020): 598–607. http://dx.doi.org/10.21577/1984-6835.20200047.

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26

Thomas, Philipp, and Vahid Shahrezaei. "Coordination of gene expression noise with cell size: analytical results for agent-based models of growing cell populations." Journal of The Royal Society Interface 18, no. 178 (2021): 20210274. http://dx.doi.org/10.1098/rsif.2021.0274.

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The chemical master equation and the Gillespie algorithm are widely used to model the reaction kinetics inside living cells. It is thereby assumed that cell growth and division can be modelled through effective dilution reactions and extrinsic noise sources. We here re-examine these paradigms through developing an analytical agent-based framework of growing and dividing cells accompanied by an exact simulation algorithm, which allows us to quantify the dynamics of virtually any intracellular reaction network affected by stochastic cell size control and division noise. We find that the solution

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27

GAVEAU, B., and M. MOREAU. "RESONANCE EFFECTS FOR CHEMICAL REACTIVITY IN COMPLEX MEDIA." International Journal of Bifurcation and Chaos 04, no. 05 (1994): 1297–309. http://dx.doi.org/10.1142/s0218127494000988.

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We study the effect of the stochastic time variations of the environment on the reaction rate constant of an elementary chemical reaction. The reaction is modelled by a one-dimensional stochastic process, which can belong to a very broad class. The short-range fluctuating interactions between the reacting complex and the solvent molecules are simulated by dichotomous barriers obeying a Poisson process, which can hinder the process. Then we relate the overall reaction constant to the reaction constant of the pure reactive process, in the absence of the barriers. We give exact results in the cas

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28

Meng, X. Flora, Ania-Ariadna Baetica, Vipul Singhal, and Richard M. Murray. "Recursively constructing analytic expressions for equilibrium distributions of stochastic biochemical reaction networks." Journal of The Royal Society Interface 14, no. 130 (2017): 20170157. http://dx.doi.org/10.1098/rsif.2017.0157.

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Noise is often indispensable to key cellular activities, such as gene expression, necessitating the use of stochastic models to capture its dynamics. The chemical master equation (CME) is a commonly used stochastic model of Kolmogorov forward equations that describe how the probability distribution of a chemically reacting system varies with time. Finding analytic solutions to the CME can have benefits, such as expediting simulations of multiscale biochemical reaction networks and aiding the design of distributional responses. However, analytic solutions are rarely known. A recent method of co

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29

Wang, Bing, Bonan Hou, Fei Xing, and Yiping Yao. "Abstract Next Subvolume Method: A logical process-based approach for spatial stochastic simulation of chemical reactions." Computational Biology and Chemistry 35, no. 3 (2011): 193–98. http://dx.doi.org/10.1016/j.compbiolchem.2011.05.001.

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30

Andrecut, M., and S. A. Kauffman. "Noise in Genetic Toggle Switch Models." Journal of Integrative Bioinformatics 3, no. 1 (2006): 63–77. http://dx.doi.org/10.1515/jib-2006-23.

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Summary In this paper we study the intrinsic noise effect on the switching behavior of a simple genetic circuit corresponding to the genetic toggle switch model. The numerical results obtained from a noisy mean-field model are compared to those obtained from the stochastic Gillespie simulation of the corresponding system of chemical reactions. Our results show that by using a two step reaction approach for modeling the transcription and translation processes one can make the system to lock in one of the steady states for exponentially long times.

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31

Matrisciano, Andrea, Tim Franken, Laura Catalina Gonzales Mestre, Anders Borg, and Fabian Mauss. "Development of a Computationally Efficient Tabulated Chemistry Solver for Internal Combustion Engine Optimization Using Stochastic Reactor Models." Applied Sciences 10, no. 24 (2020): 8979. http://dx.doi.org/10.3390/app10248979.

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The use of chemical kinetic mechanisms in computer aided engineering tools for internal combustion engine simulations is of high importance for studying and predicting pollutant formation of conventional and alternative fuels. However, usage of complex reaction schemes is accompanied by high computational cost in 0-D, 1-D and 3-D computational fluid dynamics frameworks. The present work aims to address this challenge and allow broader deployment of detailed chemistry-based simulations, such as in multi-objective engine optimization campaigns. A fast-running tabulated chemistry solver coupled t

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32

Wang, S., M. Chen, L. T. Watson, and Y. Cao. "Efficient implementation of the hybrid method for stochastic simulation of biochemical systems." Journal of Micromechanics and Molecular Physics 02, no. 02 (2017): 1750006. http://dx.doi.org/10.1142/s2424913017500060.

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Stochastic effect in cellular systems has been an important topic in systems biology. Stochastic modeling and simulation methods are important tools to study stochastic effect. Given the low efficiency of stochastic simulation algorithms, the hybrid method, which combines an ordinary differential equation (ODE) system with a stochastic chemically reacting system, shows its unique advantages in the modeling and simulation of biochemical systems. The efficiency of the hybrid method is usually limited by reactions in the stochastic subsystem, which are modeled and simulated using Gillespie’s fram

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33

Alfonso, L., G. B. Raga, and D. Baumgardner. "Monte Carlo simulations of two-component drop growth by stochastic coalescence." Atmospheric Chemistry and Physics Discussions 8, no. 2 (2008): 7289–313. http://dx.doi.org/10.5194/acpd-8-7289-2008.

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Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method.~The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by comparing the numerical with the analytical solutions found by Lushnikov (1975). Very good agreement was observed between the Monte Carlo simulations a

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34

Ilie, Silvana, and Monjur Morshed. "Adaptive Time-Stepping Using Control Theory for the Chemical Langevin Equation." Journal of Applied Mathematics 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/567275.

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Stochastic modeling of biochemical systems has been the subject of intense research in recent years due to the large number of important applications of these systems. A critical stochastic model of well-stirred biochemical systems in the regime of relatively large molecular numbers, far from the thermodynamic limit, is the chemical Langevin equation. This model is represented as a system of stochastic differential equations, with multiplicative and noncommutative noise. Often biochemical systems in applications evolve on multiple time-scales; examples include slow transcription and fast dimer

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35

Vázquez, Saulo, Xose Otero, and Emilio Martinez-Nunez. "A Trajectory-Based Method to Explore Reaction Mechanisms." Molecules 23, no. 12 (2018): 3156. http://dx.doi.org/10.3390/molecules23123156.

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The tsscds method, recently developed in our group, discovers chemical reaction mechanisms with minimal human intervention. It employs accelerated molecular dynamics, spectral graph theory, statistical rate theory and stochastic simulations to uncover chemical reaction paths and to solve the kinetics at the experimental conditions. In the present review, its application to solve mechanistic/kinetics problems in different research areas will be presented. Examples will be given of reactions involved in photodissociation dynamics, mass spectrometry, combustion chemistry and organometallic cataly

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36

CARETA, A., F. SAGUÉS, and J. M. SANCHO. "DYNAMICS OF REACTION-DIFFUSION INTERFACES UNDER STOCHASTIC CONVECTION: PRELIMINARY RESULTS." International Journal of Bifurcation and Chaos 04, no. 05 (1994): 1329–31. http://dx.doi.org/10.1142/s0218127494001015.

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Some preliminary results to illustrate the effect of turbulent convection on the dynamics of physicochemical systems incorporating reaction, diffusion and convection of chemical species are given. The whole approach rests on the use of stochastic differential equations with spatiotemporal correlated noise. In particular, it is shown how the propagation velocity of a chemically reacting front can be enhanced due to the fluid motion.

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37

Warne, David J., Ruth E. Baker, and Matthew J. Simpson. "Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art." Journal of The Royal Society Interface 16, no. 151 (2019): 20180943. http://dx.doi.org/10.1098/rsif.2018.0943.

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Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour

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38

Carvalho, Matheus Dias, Jorge David Alguiar Beliido, Antonio Marcos de Oliveira Siqueira, and Júlio Cesar Costa Campos. "Stochastic modeling of microstructure of homopolymers and copolymers in batch reactor." Research, Society and Development 9, no. 2 (2020): e04921930. http://dx.doi.org/10.33448/rsd-v9i2.1930.

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Find the microstructure of the product generated in a reaction of polymerization is desirable from a material science standpoint, due to the association between the microstructure and the physical properties. For the science of this fact, this paper aims to use stochastic modeling to obtain the microstructure and key information from a set of polymer chains generated during a reaction. From this data, the present article contributes to the minimization of experimental expenses, besides the saving of time, since no experiments are necessary to discover the characteristics of the polymer obtaine

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39

Wieder, Nicolas, Rainer H. A. Fink, and Frederic von Wegner. "Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics." Journal of Biomedicine and Biotechnology 2011 (2011): 1–5. http://dx.doi.org/10.1155/2011/572492.

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In simulations of chemical systems, the main task is to find an exact or approximate solution of thechemical master equation(CME) that satisfies certain constraints with respect to computation time and accuracy. WhileBrownian motionsimulations of single molecules are often too time consuming to represent the mesoscopic level, the classicalGillespie algorithmis a stochastically exact algorithm that provides satisfying results in the representation of calcium microdomains.Gillespie's algorithmcan be approximated via thetau-leapmethod and thechemical Langevin equation(CLE). Both methods lead to a

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40

Taranto, Aldo, and Shahjahan Khan. "Hidden Geometry of Bidirectional Grid-Constrained Stochastic Processes." Journal of Probability and Statistics 2021 (May 25, 2021): 1–13. http://dx.doi.org/10.1155/2021/9944543.

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Bidirectional Grid Constrained (BGC) stochastic processes (BGCSPs) are constrained Itô diffusions with the property that the further they drift away from the origin, the more the resistance to movement in that direction they undergo. The underlying characteristics of the BGC parameter Ψ X t , t are investigated by examining its geometric properties. The most appropriate convex form for Ψ , that is, the parabolic cylinder is identified after extensive simulation of various possible forms. The formula for the resulting hidden reflective barrier(s) is determined by comparing it with the simpler O

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41

Wagner, Vincent, and Nicole Erika Radde. "SiCaSMA: An Alternative Stochastic Description via Concatenation of Markov Processes for a Class of Catalytic Systems." Mathematics 9, no. 10 (2021): 1074. http://dx.doi.org/10.3390/math9101074.

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The Chemical Master Equation is a standard approach to model biochemical reaction networks. It consists of a system of linear differential equations, in which each state corresponds to a possible configuration of the reaction system, and the solution describes a time-dependent probability distribution over all configurations. The Stochastic Simulation Algorithm (SSA) is a method to simulate sample paths from this stochastic process. Both approaches are only applicable for small systems, characterized by few reactions and small numbers of molecules. For larger systems, the CME is computationall

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42

Cohen, D. M., and R. N. Bergman. "Prediction of positional isotopomers of the citric acid cycle: the syntactic approach." American Journal of Physiology-Endocrinology and Metabolism 266, no. 3 (1994): E341—E350. http://dx.doi.org/10.1152/ajpendo.1994.266.3.e341.

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We propose a syntactic approach to modeling of biochemical fluxes that combines a rule-based description of atomic transfer in chemical reactions with a structurally oriented, stochastic model of chemical reaction kinetics. This approach avoids the use of differential equations to describe the production and disappearance of each molecule. The computer simulation predicts the changes over time in the abundance of each positional isotopomer of every metabolic intermediate in the citric acid cycle of heart cells, subsequent to administration of [2-13C]acetate (including natural abundance of 13C)

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43

Singh, Abhyudai, and João P. Hespanha. "Stochastic hybrid systems for studying biochemical processes." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 368, no. 1930 (2010): 4995–5011. http://dx.doi.org/10.1098/rsta.2010.0211.

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Many protein and mRNA species occur at low molecular counts within cells, and hence are subject to large stochastic fluctuations in copy numbers over time. Development of computationally tractable frameworks for modelling stochastic fluctuations in population counts is essential to understand how noise at the cellular level affects biological function and phenotype. We show that stochastic hybrid systems (SHSs) provide a convenient framework for modelling the time evolution of population counts of different chemical species involved in a set of biochemical reactions. We illustrate recently dev

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44

Alfonso, L., G. B. Raga, and D. Baumgardner. "Monte Carlo simulations of two-component drop growth by stochastic coalescence." Atmospheric Chemistry and Physics 9, no. 4 (2009): 1241–51. http://dx.doi.org/10.5194/acp-9-1241-2009.

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Abstract. The evolution of two-dimensional drop distributions is simulated in this study using a Monte Carlo method. The stochastic algorithm of Gillespie (1976) for chemical reactions in the formulation proposed by Laurenzi et al. (2002) was used to simulate the kinetic behavior of the drop population. Within this framework, species are defined as droplets of specific size and aerosol composition. The performance of the algorithm was checked by a comparison with the analytical solutions found by Lushnikov (1975) and Golovin (1963) and with finite difference solutions of the two-component kine

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45

Jiang, Xiao, Kun Zhou, Ming Xiao, Ke Sun, and Yu Wang. "Stochastic Simulation of Soot Formation Evolution in Counterflow Diffusion Flames." Journal of Nanotechnology 2018 (2018): 1–8. http://dx.doi.org/10.1155/2018/9479582.

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Soot generally refers to carbonaceous particles formed during incomplete combustion of hydrocarbon fuels. A typical simulation of soot formation and evolution contains two parts: gas chemical kinetics, which models the chemical reaction from hydrocarbon fuels to soot precursors, that is, polycyclic aromatic hydrocarbons or PAHs, and soot dynamics, which models the soot formation from PAHs and evolution due to gas-soot and soot-soot interactions. In this study, two detailed gas kinetic mechanisms (ABF and KM2) have been compared during the simulation (using the solver Chemkin II) of ethylene co

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46

Maigaard, P., F. Mauss, and M. Kraft. "Homogeneous Charge Compression Ignition Engine: A Simulation Study on the Effects of Inhomogeneities." Journal of Engineering for Gas Turbines and Power 125, no. 2 (2003): 466–71. http://dx.doi.org/10.1115/1.1563240.

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A stochastic model for the HCCI engine is presented. The model is based on the PaSPFR-IEM model and accounts for inhomogeneities in the combustion chamber while including a detailed chemical model for natural gas combustion, consisting of 53 chemical species and 590 elementary chemical reactions. The model is able to take any type of inhomogeneities in the initial gas composition into account, such as inhomogeneities in the temperature field, in the air-fuel ratio or in the concentration of the recirculated exhaust gas. With this model the effect of temperature differences caused by the therma

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47

FRICKE, THOMAS, and DIETMAR WENDT. "THE MARKOFF AUTOMATON: A NEW ALGORITHM FOR SIMULATING THE TIME-EVOLUTION OF LARGE STOCHASTIC DYNAMIC SYSTEMS." International Journal of Modern Physics C 06, no. 02 (1995): 277–306. http://dx.doi.org/10.1142/s0129183195000216.

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We describe a new algorithm for simulating complex Markoff processes. We have used a reaction-cell method in order to simulate arbitrary reactions. It can be used for any kind of RDS on arbitrary topologies, including fractal dimensions or configurations not being related to any spatial geometry. The events within a single cell are managed by an event handler which has been implemented independently of the system studied. The method is exact on the Markoff level including the correct treatment of finite numbers of molecules. To demonstrate its properties, we apply it on a very simple reaction-

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LI, QIAN SHU, and RUI ZHU. "MESOSCOPIC DESCRIPTION OF CHEMICAL SUPERCRITICAL HOPF BIFURCATION." International Journal of Bifurcation and Chaos 14, no. 07 (2004): 2393–97. http://dx.doi.org/10.1142/s0218127404010643.

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The mesoscopic dynamic behavior of the Oregonator model of the Belousov–Zhabotinsky chemical reaction is investigated as the model system experiences a supercritical Hopf bifurcation from focus to limit cycle oscillation. The study is performed by stochastically simulating the corresponding chemical master equation. Comparing the mesoscopic dynamic results with those obtained by the macroscopic dynamics, we find in the mesoscopic description a new type of oscillating state, in which large-amplitude oscillations and small-amplitude oscillations appear randomly alternately. This new state comes

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Lunz, Davin, Gregory Batt, Jakob Ruess, and J. Frédéric Bonnans. "Beyond the chemical master equation: Stochastic chemical kinetics coupled with auxiliary processes." PLOS Computational Biology 17, no. 7 (2021): e1009214. http://dx.doi.org/10.1371/journal.pcbi.1009214.

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The chemical master equation and its continuum approximations are indispensable tools in the modeling of chemical reaction networks. These are routinely used to capture complex nonlinear phenomena such as multimodality as well as transient events such as first-passage times, that accurately characterise a plethora of biological and chemical processes. However, some mechanisms, such as heterogeneous cellular growth or phenotypic selection at the population level, cannot be represented by the master equation and thus have been tackled separately. In this work, we propose a unifying framework tha

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XIE, ZHI, and DON KULASIRI. "ON EXPLORING EFFECTS OF MOLECULAR NOISE IN A SIMPLE VIRAL INFECTION MODEL." International Journal of Biomathematics 03, no. 01 (2010): 1–19. http://dx.doi.org/10.1142/s1793524510000891.

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Intrinsic and extrinsic noises are all believed to be important in the development and function of many living organisms. In this study, we investigate the sources of the intrinsic noise and the influence of the extrinsic noise on an intracellular viral infection system. The contribution of the intrinsic noise from each reaction is measured by means of a special form of stochastic differential equations (SDEs), chemical Langevin equation. The intrinsic noise of the system is a linear sum of the noise in each of the reactions. The intrinsic noise mainly arises from the degradation of mRNA and t

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