A Bayesian framework for the detection of diffusive heterogeneity

Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of...

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Bibliographic Details
Published in:PloS one 2020-05, Vol.15 (5), p.e0221841-e0221841
Main Authors: Cass, Julie A, Williams, C David, Theriot, Julie
Format: Article
Language:English
Subjects:
Bayesian analysis
Cell physiology
Cell size
Computer and Information Sciences
Cytological research
Design of experiments
Diffusion
Diffusion coefficient
Diffusion processes
Diffusivity
Engineering and Technology
Experimental design
Heterogeneity
Methods
Microscopy
Normal distribution
Organelles
Particle trajectories
Physical Sciences
Protein transport
Proteins
Random variables
Research and Analysis Methods
Stochastic processes
Stochasticity
Substrates
Uncertainty analysis
Viscosity
Citations: Items that this one cites
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Description
Summary:Cells are crowded and spatially heterogeneous, complicating the transport of organelles, proteins and other substrates. One aspect of this complex physical environment, the mobility of passively transported substrates, can be quantitatively characterized by the diffusion coefficient: a descriptor of how rapidly substrates will diffuse in the cell, dependent on their size and effective local viscosity. The spatial dependence of diffusivity is challenging to quantitatively characterize, because temporally and spatially finite observations offer limited information about a spatially varying stochastic process. We present a Bayesian framework that estimates diffusion coefficients from single particle trajectories, and predicts our ability to distinguish differences in diffusion coefficient estimates, conditional on how much they differ and the amount of data collected. This framework is packaged into a public software repository, including a tutorial Jupyter notebook demonstrating implementation of our method for diffusivity estimation, analysis of sources of uncertainty estimation, and visualization of all results. This estimation and uncertainty analysis allows our framework to be used as a guide in experimental design of diffusivity assays.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0221841