Inferring maps of forces inside cell membrane microdomains

Abstract

Mapping of the forces on biomolecules in cell membranes has spurred the development of effective labels, e.g. organic fluorophores and nanoparticles, to track trajectories of single biomolecules. Standard methods use particular statistics, namely the mean square displacement, to analyze the underlying dynamics. Here, we introduce general inference methods to fully exploit information in the experimental trajectories, providing sharp estimates of the forces and the diffusion coefficients in membrane microdomains. Rapid and reliable convergence of the inference scheme is demonstrated on trajectories generated numerically. The method is then applied to infer forces and potentials acting on the receptor of the $\epsilon$-toxin labeled by lanthanide-ion nanoparticles. Our scheme is applicable to any labeled biomolecule and results show show its general relevance for membrane compartmentation.

arXiv comment6 pages, 4 figures

References in corpus (2)

• Probing microscopic origins of confined subdiffusion by first-passage observables
• Quantification and Correction of Systematic Errors Due to Detector Time-Averaging in Single-Molecule Tracking Experiments

Cited by in corpus (15)

• Mapping the energy and diffusion landscapes of membrane proteins at the cell surface using high-density single-molecule imaging and Bayesian inference: application to the multi-scale dynamics of glycine receptors in the neuronal membrane
• First Exit Times of Harmonically Trapped Particles: A Didactic Review
• Inference in particle tracking experiments by passing messages between images
• Bayesian Decision Tree for the Classification of the Mode of Motion in Single-Molecule Trajectories
• Inferring Latent States and Refining Force Estimates via Hierarchical Dirichlet Process Modeling in Single Particle Tracking Experiments
• Bayesian inference with information content model check for Langevin equations
• Inference of Markov models from trajectories via Large Deviations at Level 2.5 with applications to random walks in disordered media
• Bayesian field theoretic reconstruction of bond potential and bond mobility in single molecule force spectroscopy
• Maximum likelihood estimates of diffusion coefficients from single-particle tracking experiments
• Robust Hypothesis Tests for Detecting Statistical Evidence of 2D and 3D Interactions in Single-Molecule Measurements
• Reversible reactions controlled by surface diffusion on a sphere
• Learning the nonlinear interactions from particle trajectories
• Correcting for Bias of Molecular Confinement Parameters Induced by Small Time Series Sample Sizes in Single-Molecule Trajectories Containing Measurement Noise
• Errors in energy landscapes measured with particle tracking
• Pitfalls in the quantitative analysis of random walks and the mapping of single-molecule dynamics at the cellular scale