Studying the Effects of Noise in Spatial Cell Population Dynamics

Cells must navigate a variety of fluctuating environmental conditions in order to survive. Their ability to do so is affected by both genetic and epigenetic features, which, together with inherent noise in gene expression, result in a distribution of cell states amongst genetically identical cells. Previous studies have explored the role of gene expression on proliferation and vice versa. However, a majority of studies assume the population to be well-mixed and ignore the role of space in population dynamics. Crowding, migration, local interactions, etc. are key features of spatial population dynamics and have important implications for the growth and migration of cells. Thus, space introduces an additional complexity in the feedback between the gene regulation and the growth rate of the population. We develop spatial agent-based models, where the cells can divide and migrate, and, as a control, non-spatial models as well. The feedback between gene expression and population dynamics is introduced through dilution of gene products at division and cell state, influencing growth rates. We model the gene expression dynamics through stochastic differential equations and use the Gillespie algorithm to simulate the stochastic update of the population. We explore the importance of noise in cell division and gene expression on population dynamics in the spatial context. Our framework enables a quantitative comparison of how selection operates in spatially confined vs. unconfined biological contexts.