Bartosz’s work on discovering biological oscillator models from data now out in iScience

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. Sparse Identification of Nonlinear Dynamics (SINDy) is a method that uses data to uncover the underlying equations of dynamical systems. It works by applying a technique that emphasizes simplicity, using the fact that the complex behavior of physical systems can often be explained by just a few key elements, especially with the right data and coordinate system. Bartosz has thoroughly explored how this approach can be adapted for studying biological oscillators. His insights have led to practical guidelines, which we’ve successfully applied to glycolytic oscillation data. Congratulations to Bartosz for this great work, now published in iScience!