Dynamics and evolution of stochastic bistable gene networks with sensing in fluctuating environments

We study how cells can optimize fitness in variable environments by tuning the internal fluctuations of protein expression of a bistable genetic switch. We model cells as bistable toggle switches whose dynamics are governed by a delayed stochastic simulation algorithm. Each state of the toggle switch makes the cell more fit in one of two environmental conditions. Different noise levels in protein expression yield different fitness values for cells in an environment that randomly switches between the two conditions. We compare the behavior of two cell types, one that can sense the environmental condition and one that cannot. In fast changing environments both cell types evolve to be as noisy as possible while maintaining bistability of the toggle switch. In slowly changing environments, evolved nonsensing cells are less noisy while sensing cells evolve the same noise level as in fast changing environments. Sensing removes the need of genotypic changes to adapt to changes in the environment fluctuation rate, providing an evolutionary advantage in unpredictable environments.