SKM 2023 – scientific programme

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CPP: Fachverband Chemische Physik und Polymerphysik

CPP 2: Modeling and Simulation of Soft Matter I

CPP 2.8: Talk

Monday, March 27, 2023, 11:45–12:00, MER 02

Simulation-based inference of single-molecule force-spectroscopy — •Roberto Covino^1,2, Lars Dingeldein¹, and Pilar Cossio³ — ¹Frankfurt Institute for Advanced Studies, 60438 Frankfurt am Main, Germany — ²International Max Planck Research School on Cellular Biophysics, 60438 Frankfurt am Main, Germany — ³Center for Computational Mathematics and Biology, Flatiron Institute, 10010 New York, United States

Single-molecule force spectroscopy (smFS) is a powerful approach to studying molecular self-organization. However, the coupling of the molecule with the ever-present experimental device introduces artifacts, that complicates the interpretation of these experiments. Performing statistical inference to learn hidden molecular properties is challenging because these measurements produce non-Markovian time series, and even minimal models lead to intractable likelihoods. To overcome these challenges, we developed a computational framework built on novel statistical methods called simulation-based inference (SBI). SBI enabled us to directly estimate the Bayesian posterior, and extract reduced quantitative models from smFS, by encoding a mechanistic model into a simulator in combination with probabilistic deep learning. Using synthetic data, we could systematically disentangle the measurement of hidden molecular properties from experimental artifacts. The integration of physical models with machine-learning density estimation is general, transparent, easy to use, and broadly applicable to other types of biophysical experiments.