Phenomenological models of large complex populations of cells: from microbes to tissues

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In this proposal, I will extend my work on modeling population dynamics of cells in rapidly evolving microbial populations and differentiating/regenerating tissues to develop new strategies aiming at uncovering "statistical" rules governing collective phenomena in large populations of cells. I will use adaptive immunity in bacteria and cellular reprogramming as primary systems of interest, but the methods developed will be more generally applicable. In the context of microbial populations, I will model collective dynamics of adaptive immunity in bacterial cells that may individually not be efficient in avoiding viruses but collectively the population does better than its parts. This work will build upon my previous work on collective features of adaptive immunity (PNAS 2018) and the physiological constraints that limit individual cell's ability to avoid viruses (PNAS 2020), with a broader focus on how viruses affect microbial diversity and their fate. This project will be grounded by present experimental collaborations with Devaki Bhaya at Stanford University. In the context of tissues, I will focus on cellular reprogramming, which is a phenomenon where mature, specialized cells can be reprogrammed to an immature state capable of developing into all tissues of the body. Here I will track and analyze changes in gene expression in individual cells as they reprogram to identify key genetic regulators of the reprogramming process. This work stems from the large variability we recently discovered (Science 2019) in reprogramming ability amongst genetically identical cells. We demonstrated that a small fraction of "elite" cells contributes thousand-fold or more to the immature cell population than do most non-elite cells. This work will focus on the "path statistics" of elite versus non-elite cells as they traverse the high dimensional gene-expression space from a mature to an immature state. The single cell transcriptomics data for this project is being generated with Jeff Wrana's group at the Lunenfeld-Tanenbaum Research Institute in Toronto. My long-term ambition is to uncover the statistical rules that govern the structure-function relationship in large population of cells. Here, I will focus on extending my work on developing phenomenological models for large "strongly interacting" heterogeneous population of cells that effectively behave as a collective. Overall, my work will provide a deeper understanding of design principles that allow large collection of cells to perform specific functions from tissues in multicellular organisms, to understanding how complex microbial communities respond and adapt to environmental changes.