Data-driven discovery of biological oscillator models – Lendert Gelens

Oscillatory dynamics are a found everywhere in living systems, underlying processes such as metabolic regulation, cell division, and embryonic development. Identifying the mechanisms that generate these rhythms is challenging due to nonlinear interactions, multiple time scales, and limited access to all relevant variables. Data-driven approaches offer a promising route to infer dynamical models directly from time-series data. In this talk, I will discuss our work on data-driven discovery of models for (bio)chemical oscillators. In particular, I will present CLINE, a neural-network–based framework that infers key geometric features of phase space, such as nullclines, from oscillatory data and uses this information to construct low-dimensional dynamical models.