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

Published online by Cambridge University Press:  20 August 2015

Daniel A. Charlebois*
Affiliation:
Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario, K1N 6N5, Canada Ottawa Institute of Systems Biology, University of Ottawa, 451 Symth Road, Ottawa, Ontario, K1H 8M5, Canada
Jukka Intosalmi
Affiliation:
Department of Mathematics, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland Department of Signal Processing, Tampere University of Technology, P.O. Box 553, 33101 Tampere, Finland
Dawn Fraser
Affiliation:
Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario, K1N 6N5, Canada Ottawa Institute of Systems Biology, University of Ottawa, 451 Symth Road, Ottawa, Ontario, K1H 8M5, Canada
Mads Kærn*
Affiliation:
Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario, K1N 6N5, Canada Ottawa Institute of Systems Biology, University of Ottawa, 451 Symth Road, Ottawa, Ontario, K1H 8M5, Canada Department of Cellular and Molecular Medicine, University of Ottawa, 451 Symth Road, Ottawa, Ontario, K1H 8M5, Canada
*
Corresponding author.Email:daniel.charlebois@uottawa.ca
Corresponding author.Email:mkaern@uottawa.ca
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Abstract

We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The 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 populations. To benchmark performance, we compare simulation results with steady-state and time-dependent analytical solutions for several scenarios, including steady-state and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within “bet-hedging” cell populations during their adaption to environmental stress. These 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.

Type
Research Article
Copyright
Copyright © Global Science Press Limited 2011

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References

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