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Super-resolved stochastic inference: learning the dynamics of soft biological matter

CORDIS · observation · Publication date unknown

from experimental trajectories. To this aim, I will build data-efficient tools to learn stochastic differential equations and discover physical models, employing methods from statistical physics and machine learning. The main focus of SuperStoc will be in resolving models with high precision from limited trajectories. To assess the efficiency of the methods I develop, I will design information-theoretical frameworks to quantify how much can be inferred from trajectories that are short, partial and noisy. The convergence of the resulting algorithms will be backed by mathematical proofs and numerical simulations in realistic conditions. I will apply these new tools to several key open biophysical problems where existing methods are failing: condensate-mediated interactions between genomic loci, cellular mechanosensing in confined environments, pattern formation in embryo development, and visual interaction between fish leading to collective motion. The resulting algorithms will be implemented into a software designed to be useful for the broad soft biological matter community. By pr

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recordType

award

status

SIGNED

region

EU

value

1477856

unit

EUR