Characterizing evolutionary models driving stem cell and somatic tissue development

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Little is understood about the evolutionary factors that drive stem cell behaviour. The research program of my laboratory draws from classic evolutionary theory and population genetics to develop novel population genetic models to study the impact of mutation on an individual's somatic and stem cell evolution. Previously, we have used large population genomic datasets, either generated or in the public domain, to estimate the parameters impacting mutation accumulation such as mutation rate, models of selection as well as rates of selection acting on individual stem cell populations. These will be used to develop statistical models of human stem cell evolution. We will consider the interaction effects of different mutation classes arising in clonal mitotic systems. These models will enable similar parameterization of key factors associated with somatic mutation accumulation in different human tissues and allow for a more robust assessment of the net selective environment contribution to stem cell evolution through capturing the interaction of both beneficial and deleterious selection acting on somatic mutations. ******Referring to the full host of organs and tissues involved in the production of cellular blood components, the hematopoietic system is one of the most highly regenerative and best characterised systems. The hematopoietic system is derived from a population of hematopoietic stem cells (HSCs) which are responsible for the production of one trillion cells per day. Fundamentally, HSCs are characterised by stemness; a quality which refers to their ability to 1) self-renew, 2) asymmetrically divide to produce multiple cells, 3) quiesce, and 4) regenerate tissue when transplanted. Owing to their unique ability to self-renew and maintain the mature blood cell pool, HSC populations have to be highly regulated and reductions in regulation are observed with aging and recently have been associated with adverse outcomes.******A better understanding of the evolutionary context underscoring stem cell dynamics will provide a deeper framework for understanding development and aging in a stem cell context but with application across different tissues. The statistical methods employed to develop evolutionary models will have utility across multiple model organisms and will fundamentally improve our ability to study temporal growth and development dynamics. **