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Diffusion analysis of single particle trajectories in a Bayesian nonparametrics framework.

Rebeca Cardim Falcao1, Daniel Coombs1,2

  • 1Department of Mathematics and Institute of Applied Mathematics, University of British Columbia, 1984 Mathematics Road, Vancouver, BC, V6T 1Z2, Canada.

Physical Biology
|December 21, 2019
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Summary

This study introduces an infinite Hidden Markov Model (iHMM) to automatically determine the number of diffusive states in single particle tracking (SPT) data. This method improves the analysis of biomolecular motion without pre-defined state assumptions.

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Area of Science:

  • Biophysics
  • Molecular Biology
  • Immunology

Background:

  • Single particle tracking (SPT) analyzes subcellular dynamics using fluorescently labeled molecules.
  • Brownian diffusion models are common for biomolecule motion, but may not capture complex behaviors.
  • Existing Hidden Markov Models (HMM) require a pre-specified number of diffusive states, potentially biasing results.

Purpose of the Study:

  • To develop a method for simultaneously inferring the number of diffusive states and motion parameters from SPT data.
  • To address limitations of a priori assumptions in HMM for analyzing molecular dynamics.
  • To enhance the analysis of complex biomolecular trajectories.

Main Methods:

  • Utilized an infinite Hidden Markov Model (iHMM) within the Bayesian nonparametric framework.
  • Extended iHMM concepts from molecular biophysics to the SPT context.
  • Implemented an additional constraint to improve computational efficiency and convergence speed.

Main Results:

  • Successfully inferred the number of diffusive states and motion parameters concurrently.
  • Demonstrated iHMM performance on simulated SPT data.
  • Applied the iHMM to a real-world dataset of B cell receptor motion on B cell plasma membranes.

Conclusions:

  • The proposed iHMM method offers a powerful, data-driven approach to analyze molecular diffusion in SPT.
  • This technique overcomes the need for pre-defined state numbers, providing more accurate insights into spatiotemporal dynamics.
  • The iHMM is a valuable tool for studying complex biological systems, such as immune cell signaling.