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An Accelerated Method for Simulating Population Dynamics

Communications in Computational Physics 2013年第7期14卷 461-476页

作者： daniel a.charlebois Mads Kærn Department of Physics University of Ottawa150 Louis PasteurOttawaOntario K1N 6N5Canada Ottawa Institute of Systems Biology University of Ottawa451 Symth RoadOttawaOntario K1H 8M5Canada Department of Cellular andMolecular Medicine University of Ottawa451 Symth RoadOttawaOntario K1H 8M5Canada

We present an accelerated method for stochastically simulating the dynamics of heterogeneous cell *** algorithm combines a Monte Carlo approach for simulating the biochemical kinetics in single cells with a constant-number Monte Carlo method for simulating the reproductive fitness and the statistical characteristics of growing cell *** benchmark accuracy and performance,we compare simulation results with those generated from a previously validated population dynamics *** comparison demonstrates that the accelerated method accurately simulates population dynamics with significant reductions in runtime under commonly invoked steady-state and symmetric cell division *** the increasing complexity of cell population models,the method is an important addition to the arsenal of existing algorithms for simulating cellular and population dynamics that enables efficient,coarse-grained exploration of parameter space.

关键词： Accelerated stochastic simulation algorithm constant-number Monte Carlo gene expression population dynamics and fitness

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An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics

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Communications in Computational Physics 2011年第1期9卷 89-112页

作者： daniel a.charlebois Jukka Intosalmi Dawn Fraser Mads Kærn Department of Physics University of Ottawa150 Louis PasteurOttawaOntarioK1N 6N5Canada Ottawa Institute of Systems Biology University of Ottawa451 Symth RoadOttawaOntarioK1H 8M5Canada Department of Mathematics Tampere University of TechnologyP.O.Box 55333101 TampereFinland Department of Signal Processing Tampere University of TechnologyP.O.Box 55333101 TampereFinland Department of Cellular and Molecular Medicine University of Ottawa451 Symth RoadOttawaOntarioK1H 8M5Canada.

We present an algorithm for the stochastic simulation of gene expression and heterogeneous population *** algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell *** benchmark performance,we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios,including steadystate and time-dependent gene expression,and the effects on population heterogeneity of cell growth,division,and DNA *** comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene *** also use the algorithm to model gene expression dynamics within"bet-hedging"cell populations during their adaption to environmental *** simulations indicate that the algorithm provides a framework suitable for simulating and analyzing realistic models of heterogeneous population dynamics combining molecular-level stochastic reaction kinetics,relevant physiological details and phenotypic variability.

关键词： Constant-number Monte Carlo stochastic simulation algorithm gene expression heterogeneous population dynamics

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