Bayesian Approach to MSD-Based Analysis of Particle Motion in Live Cells
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Author(s)
Monnier, Nilah
Guo, Syuan-Ming
Mori, Masashi
He, Jun
Lenart, Peter
Bathe, Mark
Date Issued
August 2012
Journal
Biophysical Journal
Publisher
Elsevier
Citation
Monnier, Nilah, Syuan-Ming Guo, Masashi Mori, Jun He, Peter Lenart, and Mark Bathe. “Bayesian Approach to MSD-Based Analysis of Particle Motion in Live Cells.” Biophysical Journal 103, no. 3 (August 2012): 616–626. © 2012 Biophysical Society
Version
Final published version
Abstract
Quantitative tracking of particle motion using live-cell imaging is a powerful approach to understanding the mechanism of transport of biological molecules, organelles, and cells. However, inferring complex stochastic motion models from single-particle trajectories in an objective manner is nontrivial due to noise from sampling limitations and biological heterogeneity. Here, we present a systematic Bayesian approach to multiple-hypothesis testing of a general set of competing motion models based on particle mean-square displacements that automatically classifies particle motion, properly accounting for sampling limitations and correlated noise while appropriately penalizing model complexity according to Occam's Razor to avoid over-fitting. We test the procedure rigorously using simulated trajectories for which the underlying physical process is known, demonstrating that it chooses the simplest physical model that explains the observed data. Further, we show that computed model probabilities provide a reliability test for the downstream biological interpretation of associated parameter values. We subsequently illustrate the broad utility of the approach by applying it to disparate biological systems including experimental particle trajectories from chromosomes, kinetochores, and membrane receptors undergoing a variety of complex motions. This automated and objective Bayesian framework easily scales to large numbers of particle trajectories, making it ideal for classifying the complex motion of large numbers of single molecules and cells from high-throughput screens, as well as single-cell-, tissue-, and organism-level studies.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Department of Chemistry
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1016/j.bpj.2012.06.029
