Bayesian Inference of Bond Parameters from Single-Filament Interactions
Abstract
Abstract We map interaction forces between two vimentin filaments (cytoskeletal components crucial for cell mechanics) using optical tweezers, while controlling the relative velocity. We introduce a powerful Bayesian inference framework to learn bond parameters directly from force trajectories. The information gained about the bond parameters is maximized by an optimal relative velocity and further by distributing measurements across multiple velocities. Our Bayesian framework is broadly applicable to a large range of biomolecular interactions and force spectroscopy techniques.
Fields of Study & Subjects
Force Microscopy Techniques and Applications
Skin and Cellular Biology Research
Nuclear Structure and Function
Publication details
- Journal / source
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Publication date
- 2 June 2026
- Language
- EN
- Document type
- Preprint