Speaker
Description
Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular processes from experimental data, developing coarse-grained models to capture experimental observations, and leveraging those to target specific processes or features through external control remain significant challenges. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing these issues. We present preliminary results from experiments on fluctuating cell membranes and simulations of the self-assembly of branched actin networks, as well as kinesin-mediated cargo transport along intracellular tracks. These studies suggest potential avenues for non-equilibrium inference and control in experimental biophysical contexts, and highlights the complexities and limitations that still need to be addressed.
