Title:
From single cells to microbial consortia and back: stochastic chemical kinetics coupled to population dynamics
Abstract:
At the single-cell level, biochemical processes are inherently stochastic. Such processes are typically studied using models based on stochastic chemical kinetics, governed by a chemical master equation (CME). The CME describes the time evolution of the probability distribution over system states and has been a tremendously helpful tool in shedding light on the functioning of cellular processes. However, both in nature and the majority of lab experiments, single cells are not living in isolation but are part of a growing population or community. In such contexts, stochasticity at the single-cell scale leads to population heterogeneity and cells may be subject to population processes, such as selection, that drive the population distribution away from the probability distribution of the single-cell process. Here, I will present our work on augmenting the CME to construct models that capture coupled dynamics of single-cell and population processes. I will then show that the theory of these models can be used to explain experimental results on plasmid copy number fluctuations and population growth in media that selects against cells that have lost the plasmid. Finally, I will present an optogenetic recombination system that allows one to partition yeast populations into different cell types via external application of blue light to cells. I will show how this system allows one to create and dynamically control simple artificial yeast consortia and demonstrate how augmented CME models are useful to predict emerging population dynamics from a specification of the single-cell recombination system, in particular when the system components are expressed from plasmids.