Deriving simple models of complicated biological systems from noisy data: a few adventures in statistical inference

28 Mar 2014

Curtis Callan
Department of Physics
Princeton University


As a physicist, I am always looking for simple explanations of complicated phenomena. Pursuing this goal in biology has been something of a fool's errand in the past, but the ongoing explosion in the quantity and quality of the data that can be collected from biological systems is fundamentally changing the nature of this game. In particular, we can now take a "statistical inference" approach to understanding important cellular functions: i.e. we can attempt to use large amounts of (astutely generated) noisy data to make precise inferences about the microscopic stochastic machines that underlie these functions. In this talk, I will describe the results of applying this approach to two interesting problems in cellular biology: the regulation of bacterial gene transcription, and the generation of immune system diversity by genome editing. Looking beyond its performance in these two specific examples, I will speculate on the promise of this general approach in other areas of biology.

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