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An Accelerated Method for Simulating Population Dynamics

Published online by Cambridge University Press:  03 June 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
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
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Abstract

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.

Type
Research Article
Copyright
Copyright © Global Science Press Limited 2013

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