Reconstruction of nonequilibrium dynamics from coarse-grained time-series data using graph neural network

Dr. Gili Bisker (Biomedical Engineering), Dr. Dan Raviv (Electrical Engineering)

Living systems operate far from thermal equilibrium, where molecular motors and enzymatic activity utilize chemical fuel in order to execute their biological function.

 

Some of the free-energy budget provided by the fuel molecules is translated into useful work, whereas the rest is dissipated as heat accompanied by an increase of the entropy of the environment. Inferring the underlying dynamics and quantifying the entropy production of such complex systems is a major challenge owing to the myriad degree of freedom.

 

We develop data-driven approaches for inferring the underlying nonequilibrium dynamics, given a limited set of observables, aiming to gain a better understanding of complex systems driven far-from-equilibrium.

 

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