Optimal speed and heterogeneity of adaptation in fluctuating environments

Cells need to constantly respond and adapt to changes, based on signals from their environment. Much of this response is governed through so-called signal transduction cascades, molecular pathways that translate a signal into a required action, which often includes a change in the expression of specific genes. In this project, we will investigate the optimal speed and heterogeneity of phenotypic adaptation in fluctuating or other types of stochastic environments. We will do this by developing evolutionary models to determine optimal environmental responses for two model systems: the response to fluctuating sugar availability in yeast (such as shifts from glucose to maltose and vice-versa) and the response to good or bad growth conditions in E. coli, which results in the switching of cells to a normal growing state or a dormant antibiotic-tolerant persister cell state. In both cases, what we wish to determine is how heterogeneously and how fast cells should switch between these alternative phenotypic states, given the predictability of their environment. We will both use evolutionary systems biology models, whereby the cells’ phenotypic response are based on a full gene regulatory network, and the optimal parameters of this gene regulatory network are determined for any given type of environment, as well as more abstract adaptive dynamics types of models.