Understanding the population structure and fate of large rapidly evolving populations

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Much of complex biology results from interactions among a large number of individually simple elements. For example, the composition of the large interacting population of microbes that coexist in an animal's mouth and gut profoundly affect its physiology. Similarly, about 1011 new blood cells regenerate everyday from a much smaller yet a large population of epigenetically and developmentally diverse stem cell population. Thus understanding the general principles that govern the collective dynamics of large population of cells is vital for a deeper understanding of a variety of biological phenomena.***Much like thermodynamic laws that capture the average macroscopic behavior of large number of molecules, the goal of this program is to build coarse-grained models that capture the dynamics of key observable features of large populations in a few specific experimental and computational systems. Focusing on specific systems will allow us to develop these models in a systematic manner, in continuous validation by experiments. Specific systems considered in this work are: (1) collective dynamics of bacteria and its predatory viruses (phages), (2) role of intracellular selection in maintaining mitochondrial DNA fidelity, and (3) understand temporal stability of blood regeneration in animals.***Collective dynamics of large populations of cells is fueled by cell-to-cell variability resulting from a combination of genetic and epigenetic mechanisms. What variations spread in the population depends on a combination of physical processes (stochastic dynamics and spatiotemporal interactions among cells) and the effect of variation on cellular phenotype. The laws that describe the spread of variation are not well understood. This is because the dynamics is very nonlinear and stochastic. To make progress we utilize deep sequencing to directly track individual trajectories of genomic variations as they arise and spread (or go extinct) in the laboratory experiments. Armed with these trajectories we then develop a statistical theory for relevant macroscopic properties using the tools from Statistical/Computational Physics.***Trainees who will be funded through this proposal will receive multi-faceted training in statistical physics, bioinformatics, evolutionary dynamics, high-performance scientific computing, and systems biology, making them skilled candidates for either academia or biomedical industries. These skills will help meet the current and future demand for a highly qualified workforce in Canada.**