Volume 14, Issue 2
An Accelerated Method for Simulating Population Dynamics

Daniel A. Charlebois & Mads Kærn

Commun. Comput. Phys., 14 (2013), pp. 461-476.

Published online: 2014-08

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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-stateandsymmetric 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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@Article{CiCP-14-461, author = {}, title = {An Accelerated Method for Simulating Population Dynamics}, journal = {Communications in Computational Physics}, year = {2014}, volume = {14}, number = {2}, pages = {461--476}, 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-stateandsymmetric 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.


}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.130612.121012a}, url = {http://global-sci.org/intro/article_detail/cicp/7169.html} }
TY - JOUR T1 - An Accelerated Method for Simulating Population Dynamics JO - Communications in Computational Physics VL - 2 SP - 461 EP - 476 PY - 2014 DA - 2014/08 SN - 14 DO - http://dor.org/10.4208/cicp.130612.121012a UR - https://global-sci.org/intro/article_detail/cicp/7169.html KW - AB -

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-stateandsymmetric 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.


Daniel A. Charlebois & Mads Kærn. (2020). An Accelerated Method for Simulating Population Dynamics. Communications in Computational Physics. 14 (2). 461-476. doi:10.4208/cicp.130612.121012a
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