From single cells to populations and back: optogenetic control of microbial communities – Opt-MC

One of the core challenges in biology is to understand how behavior at the scale of populations and ecosystems emerges from biochemical processes inside single cells. Central to this problem is that intracellular processes are inherently stochastic and create variability in isogenic microbial populations. Emerging population dynamics are therefore difficult to predict with existing modeling tools as they depend on the complex interplay of stochastic single-cell and population processes such as selection or growth. In this project, we will develop mathematical methods for analysing and simulating models where intracellular stochastic chemical kinetics, governed by the chemical master equation, are coupled to population processes. We will use diffusion approximations coupled to parameter inference methods to ensure that models of real systems can be learned from experimental data. Furthermore, we will focus on solving optimal control problems and use our results to determine how cells need to be actuated to ensure that desired dynamics of microbial populations and communities emerge. To demonstrate practical importance of our methods, we will study a light-inducible artificial recombination system that can be used to partition yeast populations into two cell types with designable functionalities. Concretely, we will focus on a system in which yeast cells arrest growth upon recombination and that allows one to use light to dynamically regulate the population composition. Furthermore, we will develop a multiscale model of a system for light-inducible production of toxic proteins of biotechnological interest that impair cellular growth and control the population such that growth and protein production are optimally balanced and the total amount of protein produced by the population is maximized.