title: Hysteresis can grant fitness in stochastically varying environment.
abstract: Although the existence of multiple stable phenotypes of living organisms enables random switching between phenotypes as well as non-random history dependent switching called hysteresis, only random switching has been considered in prior experimental and theoretical models of adaptation to variable environments. This work considers the possibility that hysteresis may also evolve together with random phenotype switching to maximize population growth. In addition to allowing the possibility that switching rates between different phenotypes may depend not only on a continuous environmental input variable, but also on the phenotype itself, the present work considers an opportunity cost of the switching events. This opportunity cost arises as a result of a lag phase experimentally observed after phenotype switching and stochastic behavior of the environmental input. It is shown that stochastic environmental variation results in maximal asymptotic growth rate when organisms display hysteresis for sufficiently slowly varying environmental input. At the same time, sinusoidal input does not cause evolution of memory suggesting that the connection between the lag phase, stochastic environmental variation and evolution of hysteresis is a result of a stochastic resonance type phenomenon.
authors: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, United States of America.
authors: Gary
authors: G
authors: Friedman
authors: Department of Applied Mathematics, University College Cork, Cork, Ireland.
authors: Stephen
authors: S
authors: McCarthy
authors: Department of Applied Mathematics, University College Cork, Cork, Ireland; Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, United States of America.
authors: Dmitrii
authors: D
authors: Rachinskii
mesh_terms: D000222:Adaptation, Physiological; D000465:Algorithms; D005075:Biological Evolution; D004777:Environment; D008954:Models, Biological; D008962:Models, Theoretical; D010641:Phenotype