Evolution of stem cell populations: Mechanistic mathematical modelling vs single cell data
Stem cells in adult tissues generate cells needed for plasticity, growth, and repair and play a critical role in the evolution of cancer. The proper system performance requires an ongoing capacity of stem cells for self-renewal and differentiation, called stemness, which must be robustly regulated at the cell population cell. The system usually exhibits great heterogeneity at the single cell level that evolves in time and space. It is so far not understood if and how this heterogeneity contributes to the system’s control. In this talk I will discuss different mathematical approaches to modelling and analysis of stem cell transitions. Inferring the information on the control of stem cell dynamics from single cell data requires combining statistical data analysis with mechanistic models of stem cell self-renewal and differentiation. A new class of structured population models allows describing evolution of cell distributions in the feature space detected by single cell omics that can be defined in terms of nonnegative Radon measures with bounded Lipschitz distance, extending the notion of Wasserstein distance to measures with growth or decline. The theoretical concepts and modelling challenges will be discussed on examples of hematopoietic and neural stem cell systems.