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Quantifying Spatiotemporal Parameters of Cellular Exocytosis in Micropatterned Cells

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Numerical Approach to Spatial Deterministic-Stochastic Models Arising in Cell Biology.

James C Schaff¹, Fei Gao¹, Ye Li¹

¹Richard D. Berlin Center for Cell Analysis and Modeling, Department of Cell Biology, University of Connecticut Health Center, Farmington, Connecticut, United States of America.

Plos Computational Biology

|December 14, 2016

Summary

This summary is machine-generated.

This study introduces a new hybrid solver for reaction-diffusion systems, combining deterministic and stochastic methods for efficient spatial simulations. This approach aids in modeling complex biological processes with varying stochasticity.

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Area of Science:

Computational Biology
Biophysics
Mathematical Biology

Background:

Hybrid deterministic-stochastic methods offer efficiency for models with mixed stochasticity levels.
General-purpose hybrid solvers for spatially resolved reaction-diffusion systems are scarce.

Purpose of the Study:

To describe the fundamentals of a general-purpose spatial hybrid method.
To provide a validated computational tool for complex biological simulations.

Main Methods:

Integrating a deterministic partial differential equation solver with a particle-based stochastic simulator (Smoldyn).
Generating spatially inhomogeneous hybrid system realizations.
Validating the algorithm using a calcium 'sparks' model.

Main Results:

The hybrid solver successfully generates realizations of spatially inhomogeneous systems.
The method was rigorously validated on a calcium 'sparks' model.
Application to a cell polarity model demonstrated its utility.

Conclusions:

The developed spatial hybrid method is a general-purpose tool for reaction-diffusion systems.
This approach facilitates the study of biological phenomena with disparate stochasticity.
The method is compatible with biologist-friendly frameworks like Virtual Cell.