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From trajectories to models: data-driven approaches to decipher the dynamics of living systems

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SPLW01 - Building a bridge between non-equilibrium statistical physics and biology

Stochastic differential equations are often used to model the dynamics of living systems, from Brownian motion at the molecular scale to the dynamics of cells and animals. How does one learn such models from experimental data? This task faces multiple challenges, from information-theoretical limitations – data is scarce, noisy and incomplete – to practical considerations. I will present a recent and ongoing effort to develop new methods to reconstruct such stochastic dynamical models from experimental data, with a focus on robustness and data efficiency. Early applications of these methods include quantifying the dynamics and interactions of confined migrating cells. These methods provide a means to quantify complex behavior and unfold the underlying mechanisms of an apparently erratic trajectory.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Lecture Theatre 1 Scott Polar Research Institute, Museum Store LR5, Baker Building, CUED.

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