Abstract

The talk will discuss the use of stochastic models in the molecular biology of populations of cells. The starting point will be standard models of chemical reaction systems based on the Chemical Master Equation and its variants such as the moment equations; in our context the predictions of these equations will be interpreted as statistics of molecule counts across different cells in the population. We will first concentrate on state estimation based on single cell measurements, obtained for example by flow cytometry. We will also discuss how the same filtering method can be used “in silico”, to speed up stochastic simulation by filtering its results through the moment equations. We will then turn to system identification, in particular the estimation of extrinsic noise arising out of reaction rate variability. Finally, we will discuss control of the statistics of cell populations using external, macroscopic control signals, specifically light activated gene transcription. The theoretical discussion will be complemented by experimental results in yeast.