Speaker
Description
The challenge of developing predictive models for gene regulatory networks has motivated a multitude of approaches, ranging from the discrete to the continuous, the deterministic to the probabilistic. In many settings, however, available experimental data do not determine a unique model: time series or input-output data typically admit multiple network structures or dynamical rules that are equally consistent with the observations and are computationally indistinguishable. This ambiguity raises a fundamental question at the interface of biology and mathematics: how can one design experiments that more effectively discriminate among competing models?
In this talk, I will highlight algebraic, geometric, and combinatorial methods for modeling gene regulatory networks and focus on reducing model uncertainty through improved experimental design. The central idea is to use structural properties of model classes to identify data that are maximally informative for model selection. This perspective has led to new questions and results involving identifiability, algebraic characterization of candidate models, and the geometry of data.
