Optimal cellular adaptation in fluctuating metabolic microenvironments

Cellular metabolic adaptation defines how living systems navigate highly variable nutritional environments, from single cells in spatiotemporally varying environments to complex multicellular organisms anticipating the next nutrient-rich period. Given the relevance of metabolic adaptation to many chronic diseases, including diabetes and cancer, the precise nature by which adaptation occurs is of great interest and not fully understood. Here, we develop a stochastic model to characterize the decision-making employed by systems that optimally navigate fluctuating nutritional environments. In accounting for experimentally observed cellular memory, we show that adaptive populations capable of sensing their environments and estimating the nutrient state more effectively navigate fluctuating metabolic environments when compared to their passive counterparts. We demonstrate the benefit of larger metabolic memory on long-term growth rates for stationary environments, and we find cells capable of adjusting their memory can more efficiently grow in a changing metabolic landscape. Surprisingly, when comparing the growth rate of adaptive cells in constant environments to those in random environments, our model predicts that the later population does consistently worse, in agreement with recent empirical observations in bacterial systems. Our modeling framework is of general utility for studying phenotypic responses to signaling input schemes requiring systems to strike a tradeoff across multiple phenotypes and fluctuating environmental states. Such modeling can be applied to predict the response of adaptive systems to environmental alteration and therapeutically relevant interventions.