2024/10/21 by Lars Dingeldein, Pilar Cossio, Dingeldein, Lars +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · #Advanced Fluorescence Microscopy Techniques #Biomolecules (q-bio.BM) #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Physical sciences #Microfluidic and Capillary Electrophoresis Applications
paper · pdf · doi:10.48550/arxiv.2410.15896
openalex publication_date 2024/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Single-molecule experiments are a unique tool to characterize the structural dynamics of biomolecules. However, reconstructing molecular details from noisy single-molecule data is challenging. Simulation-based inference (SBI) integrates statistical inference, physics-based simulators, and machine learning and is emerging as a powerful framework for analysing complex experimental data. Recent advances in deep learning have accelerated the development of new SBI methods, enabling the application of Bayesian inference to an ever-increasing number of scientific problems. Here, we review the nascent application of SBI to the analysis of single-molecule experiments. We introduce parametric Bayesian inference and discuss its limitations. We then overview emerging deep-learning-based SBI methods to perform Bayesian inference for complex models encoded in computer simulators. We illustrate the first applications of SBI to single-molecule force-spectroscopy and cryo-electron microscopy experiments. SBI allows us to leverage powerful computer algorithms modeling complex biomolecular phenomena to connect scientific models and experiments in a principled way.