Abstract:
Biological processes are complex and involve many components whose interactions are stochastic and only partially characterized. Instead of ignoring or guessing unknown details our work focuses on analyzing classes of complex processes that share some features but are left to vary arbitrarily in all unknown features. This allows us to derive impossibility constraints that can guide the design of synthetic biology applications and help us understand the operating principles of cellular processes. Additionally, I will discuss how invariants for classes of incompletely specified systems can be exploited to infer rate functions and causal interactions in complex systems from observing stochastic fluctuations of a small subset of components. Such approaches have the potential to turn quantitatively intractable complex systems into a sequence of solvable inference problems.

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