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PLoS One. 2015 Jul 23;10(7):e0132397. doi: 10.1371/journal.pone.0132397. eCollection 2015.

Noise-Driven Phenotypic Heterogeneity with Finite Correlation Time in Clonal Populations.

Author information

1
Committee on Genetics, Genomics, and Systems Biology, University of Chicago, Chicago, IL, United States of America.
2
Ben May Department for Cancer Research, University of Chicago, Chicago, IL, United States of America; Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, United States of America.
3
Departments of Statistics, Ecology and Evolution, Molecular Genetics and Cell Biology, University of Chicago, Chicago, IL, United States of America.
4
Ben May Department for Cancer Research, University of Chicago, Chicago, IL, United States of America.
5
School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom.

Abstract

There has been increasing awareness in the wider biological community of the role of clonal phenotypic heterogeneity in playing key roles in phenomena such as cellular bet-hedging and decision making, as in the case of the phage-λ lysis/lysogeny and B. Subtilis competence/vegetative pathways. Here, we report on the effect of stochasticity in growth rate, cellular memory/intermittency, and its relation to phenotypic heterogeneity. We first present a linear stochastic differential model with finite auto-correlation time, where a randomly fluctuating growth rate with a negative average is shown to result in exponential growth for sufficiently large fluctuations in growth rate. We then present a non-linear stochastic self-regulation model where the loss of coherent self-regulation and an increase in noise can induce a shift from bounded to unbounded growth. An important consequence of these models is that while the average change in phenotype may not differ for various parameter sets, the variance of the resulting distributions may considerably change. This demonstrates the necessity of understanding the influence of variance and heterogeneity within seemingly identical clonal populations, while providing a mechanism for varying functional consequences of such heterogeneity. Our results highlight the importance of a paradigm shift from a deterministic to a probabilistic view of clonality in understanding selection as an optimization problem on noise-driven processes, resulting in a wide range of biological implications, from robustness to environmental stress to the development of drug resistance.

PMID:
26203903
PMCID:
PMC4512695
DOI:
10.1371/journal.pone.0132397
[Indexed for MEDLINE]
Free PMC Article

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