An Accelerated Method for Simulating Population Dynamics
Daniel A. Charlebois 1*, Mads Kaern 21 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.
2 Department of Physics, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario K1N 6N5, Canada; Ottawa Institute of Systems Biology and Department of Cellular and Molecular Medicine, University of Ottawa, 451 Symth Road, Ottawa, Ontario K1H 8M5, Canada.
Received 13 June 2012; Accepted (in revised version) 12 October 2012
Available online 4 January 2013
We present an accelerated method for stochastically simulating the dynamics of heterogeneous cell populations. The 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 populations. To benchmark accuracy and performance, we compare simulation results with those generated from a previously validated population dynamics algorithm. The comparison demonstrates that the accelerated method accurately simulates population dynamics with significant reductions in runtime under commonly invoked steady-state and symmetric cell division assumptions. Considering 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.
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PACS: 87.10.Mn, 87.10.Rt, 87.16.Yc, 87.17.Ee
Key words: Accelerated stochastic simulation algorithm, constant-number Monte Carlo, gene expression, population dynamics and fitness.
Email: firstname.lastname@example.org (D. A. Charlebois), email@example.com (M. Kaern)