Stochastic mathematical models are an essential tool for interpreting biological phenomena. This is particularly the case for finer-scale observations occurring at the level of single cells, where coarse-scale deterministic trends can obscure the inherently discrete nature of the reactions involved. As experimental techniques for obtaining single-cell transcriptomic data have improved, a fundamental question that often remains overlooked is whether or not a stochastic model can be confidently fit to the available data.
In this talk, we will use simple stochastic models to illustrate some of the limitations to inferring the underlying regulatory dynamics of a noisy system from experimental data. More specifically, we show that naïve inference from steady state distributions is insufficient for making definitive conclusions of the underlying dynamics of the typical distribution of cell states.
Dr Lucy Ham
Postdoctoral Research Fellow, The University of Melbourne
Dr. Lucy Ham received a Ph.D. in pure mathematics in 2017 at La Trobe University, under the mentorship of Professor Brian Davey and Associate Professor Tomasz Kowalski. Her Ph.D. research was in the mathematics underlying the theory of computing, with a focus on universal algebraic approaches to classifying the computational complexity of constraint problems.
Dr Ham joined the Theoretical Systems Biology Group at the University of Melbourne as a postdoctoral researcher in 2018. Her research interests are in the development of mathematical and statistical approaches for the analysis of complex biological processes. She has particular expertise in the creation of stochastic models for understanding both the development and regulation of cells.