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, 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 introduce a multi-scale modeling framework that allows one to capture coupled stochastic single-cell and population process. I will show that the expected population distribution of such multi-scale models can be calculated by solving a modified version of the CME that is of the same dimensionality as the standard CME. I will then show how such models can be used to explain experimental data 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 and show how our modeling framework can be used to predict and control emerging dynamics of the population composition in response to time-varying light stimuli.

 

Bio:
Jakob Ruess received his PhD in 2015 from the Automatic Control Laboratory at ETH Zurich, Switzerland, where he worked under the supervision of John Lygeros on using moment equations of stochastic reaction networks for problems such as parameter inference and experimental design. He moved on to IST Austria for a postdoc where he worked together with Remy Chait, Gasper Tkacik and Calin Guet to realize the first study on optogenetic feedback control of gene expression dynamics inside single cells. Since 2016, he is a permanent researcher at the French National Institute for Research in Computer Science and Automation (Inria). In 2022, he received an ERC Starting Grant, entitled BridgingScales, which aims to study the dynamics of coupled stochastic single-cell and population processes.