Simulation-based inference of single-molecule force spectroscopy.

Abstract

Single-molecule force spectroscopy (smFS) is a powerful approach to studying the microscopic mechanisms of molecular self-organization in biological cells and complex materials. However, extracting quantitative reduced models from these experiments is still challenging because coupling the molecule with the ever-present experimental measuring device introduces artifacts that blur the measurements. Performing statistical inference within the Bayesian framework is an attractive way to extract quantitative models from smFS experiments. But even minimalistic models lead to mathematically intractable likelihoods---ubiquitous in the computational sciences---that hinder standard approaches based on likelihood optimization. We develop a computational framework that performs Bayesian inference to extract reduced models based on diffusive dynamics on a free energy landscape. We build on a new generation of likelihood-free inference methods called simulation-based inference (SBI). By encoding a parametric mechanistic model into a simulator in combination with probabilistic deep learning, SBI enables us to directly estimate the Bayesian posterior, avoiding any likelihood calculation. Using synthetic data, we show how we can systematically disentangle the measurement of hidden molecular properties from experimental artifacts. By integrating physics-based parametric models with machine learning density estimation, SBI enables accurate Bayesian inference for models with an intractable likelihood. It paves the way for more complex and realistic models that would otherwise be ruled out due to their mathematical intractability. SBI is general, conceptually transparent, easy to use, and broadly applicable to other types of biophysical experiments.