Daniel A. Charlebois

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We show that the effect of stress on the reproductive fitness of noisy cell populations can be modeled as a first-passage time problem, and demonstrate that even relatively short-lived fluctuations in gene expression can ensure the long-term survival of a drug-resistant population. We examine how this effect contributes to the development of drug-resistant(More)
UNLABELLED We present CellLine, a simulator of the dynamics of gene regulatory networks (GRN) in the cells of a lineage. From user-defined reactions and initial substance quantities, it generates cell lineages, i.e. genealogic pedigrees of cells related through mitotic division. Each cell's dynamics is driven by a delayed stochastic simulation algorithm(More)
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,(More)
The appearance of microarray technology led to the development of algorithms to infer the structure underlying the dynamics of gene regulatory networks (GRNs) from gene expression data. Yet, this technique is currently highly noisy, leading to the question of how inferable are GRNs from this data. To answer this question, we use realistic models of GRNs [1,(More)
The benefits of " bet-hedging " strategies have been assumed to be the main cause of phenotypic diversity in biological populations. However, in their recent work, Healey et al (2016) provide experimental support for negative frequency-dependent selection (NFDS) as an alternative driving force of diversity. NFDS favors rare phenotypes over common ones,(More)
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