View Program - 2022 Population, Evolutionary, and Quantitative Genetics Conference

Through innovations in both imaging techniques and the ability to process these images at scale, high-resolution imaging is transforming the field of molecular biology, yet its power has yet to be fully utilized for asking questions in evolutionary biology. Just as demographic surveys can reveal more or less densely populated areas where, for example, a contagious disease may spread at different rates, these imaging datasets can help us quantify cellular and molecular patterns of spatial variation and understand how this variation affects rates of evolution, by impeding or accelerating the spread of new variants through the population. What are spatial topologies that act to amplify the selective advantage of new mutations in the population, versus structures that dampen the force of selection and slow down rates of evolution?

While classic models in population genetic theory have been extraordinarily important for producing initial testable predictions about the role of space and structure in evolution, most previous modeling approaches represent spatial structure as a small number of distinct patches, symmetrically connected by migration corridors. This makes these models analytically tractable, but it also makes their predictions hard to use for the wealth of these emerging spatially-resolved datasets with large amounts of spatial heterogeneity and complex patterns of cellular co-localization and interaction. The challenge is that studying more complex spatial topologies and deriving intuitive analytic results with predictive power is a much harder mathematical problem. Solving this problem lies in finding the right spatial geometric representations that can both capture the complexity of spatial structure in emerging datasets, but also allow for mathematical modeling tractability, in other words finding the mathematical representations that inform on the spatial characteristics driving functional, evolutionary design.

I will discuss how we can build a general theory of evolutionary dynamics for populations with complex spatial structure. Using tools from network theory and algebraic topology, I will present how we can derive the relevant selective parameters as a function of the statistical properties of the population spatial structure. I will also present several relevant applications of our theory, including recent work where we have used recent microscopy datasets to build the cellular spatial networks of the stem cell niches of the bone marrow and to ask whether the spatial arrangement of these cellular collectives acts to amplify or suppress the spread of variants in the cellular population.

From pattern to function: eco-evolutionary representations of complex spatial structure for the new era of spatial biology