title: Stochastic gene expression in switching environments.
abstract: Organisms are known to adapt to regularly varying environments. However, in most cases, the fluctuations of the environment are irregular and stochastic, alternating between favorable and unfavorable regimes, so that cells must cope with an uncertain future. A possible response is population diversification. We assume here that the cell population is divided into two groups, corresponding to two phenotypes, having distinct growth rates, and that cells can switch randomly their phenotypes. In static environments, the net growth rate is maximized when the population is homogeneously composed of cells having the largest growth rate. In random environments, growth rates fluctuate and observations reveal that sometimes heterogeneous populations have a larger net growth rate than homogeneous ones, a fact illustrated recently through Monte-Carlo simulations based on a birth and migration process in a random environment. We study this process mathematically by focusing on the proportion f(t) of cells having the largest growth rate at time t, and give explicitly the related steady state distribution pi. We also prove the convergence of empirical averages along trajectories to the first moment E(pi) (f), and provide efficient numerical methods for computing E(pi) (f).
authors: Section de Mathématiques, University of Geneva, Genève, Switzerland. Martin.Gander@math.unige.ch
authors: Martin J
authors: MJ
authors: Gander
authors: Christian
authors: C
authors: Mazza
authors: Hansklaus
authors: H
authors: Rummler
mesh_terms: D000222:Adaptation, Physiological / Q000235:genetics*; D000465:Algorithms; D002468:Cell Physiological Phenomena / Q000235:genetics; D049109:Cell Proliferation; D002477:Cells / Q000166:cytology; D003198:Computer Simulation; D005786:Gene Expression Regulation / Q000502:physiology*; D008390:Markov Chains; D008954:Models, Biological; D009010:Monte Carlo Method; D010641:Phenotype; D013269:Stochastic Processes