Much of complex biology results from interactions among a large number of individually simpler elements. Behavior of large collections of cells from microbes to stem cells are no different. Nonetheless, the population dynamics of heterogeneous populations is only now beginning to attract the attention it deserves because we have only just, within the last decade or so developed experimental tools for tracking heterogeneous populations. In this talk I will describe how we are using theoretical ideas inspired from statistical physics to understand behavior of such heterogeneous populations, focusing on three examples. In first, I will present a coarse-grained model of blood regeneration, which provides a framework to understand large variations (~3 orders of magnitude) among contributions from individual stem cells without active competition. In contrast, the second describes how competition plays a central role in understanding dynamics of reprogramming population of somatic cells. Finally, I will show that all, rather than a small population of, cancer cells can evade drug response by entering a reversible drug-tolerant state.
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