ArticleBioinformatics (Oxford, England)2012
Fast stochastic algorithm for simulating evolutionary population dynamics.
Article in Bioinformatics (Oxford, England), 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Using mixture density networks to emulate a stochastic within-host model of Francisella tularensis infection.PLoS computational biology · 2023Article
- Implications of Noise on Neural Correlates of Consciousness: A Computational Analysis of Stochastic Systems of Mutually Connected Processes.Entropy (Basel, Switzerland) · 2021Article
- Acquired resistance to combined BET and CDK4/6 inhibition in triple-negative breast cancer.Nature communications · 2020Article
- Modeling cell population dynamics.In silico biology · 2019Article
- OncoSimulR: genetic simulation with arbitrary epistasis and mutator genes in asexual populations.Bioinformatics (Oxford, England) · 2017Article
- Suppression of Beneficial Mutations in Dynamic Microbial Populations.Physical review letters · 2017Article
- Identifying restrictions in the order of accumulation of mutations during tumor progression: effects of passengers, evolutionary models, and sampling.BMC bioinformatics · 2015Article
- A queueing approach to multi-site enzyme kinetics.Interface focus · 2014Article
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Authors and funding
3 authors.
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Abstract
motivationMany important aspects of evolutionary dynamics can only be addressed through simulations. However, accurate simulations of realistically large populations over long periods of time needed for evolution to proceed are computationally expensive. Mutants can be present in very small numbers and yet (if they are more fit than others) be the key part of the evolutionary process. This leads to significant stochasticity that needs to be accounted for. Different evolutionary events occur at very different time scales: mutations are typically much rarer than reproduction and deaths.
resultsWe introduce a new exact algorithm for fast fully stochastic simulations of evolutionary dynamics that include birth, death and mutation events. It produces a significant speedup compared to direct stochastic simulations in a typical case when the population size is large and the mutation rates are much smaller than birth and death rates. The algorithm performance is illustrated by several examples that include evolution on a smooth and rugged fitness landscape. We also show how this algorithm can be adapted for approximate simulations of more complex evolutionary problems and illustrate it by simulations of a stochastic competitive growth model.
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