Bartosz Prokop

Abstract Dynamical systems theory provides a mathematical framework for describing how interacting biological components evolve over time and space, from molecular oscillators to large-scale biological patterns. Such systems often involve nonlinear feedbacks, delays and multiscale interactions, making mechanistic model construction increasingly challenging as experimental measurements become richer and higher dimensional.

Abstract Periodic changes in the concentration or activity of different molecules regulate vital cellular processes such as cell division and circadian rhythms. Developing mathematical models is essential to better understand the mechanisms underlying these oscillations. Recent data-driven methods like SINDy have fundamentally changed model identification, yet their application to experimental biological data remains limited.

Abstract A large number of important dynamical biological processes, such as the early embryonic cell cycle, cardiac rhythms, or circadian rhythms, are dominated by periodic changes, also called oscillations. It has been a long-standing interest of scientists to understand the underlying mechanisms that describe and regulate this dynamic behavior, usually using classical model identification techniques.