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Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

A rigorous framework for multiscale simulation of stochastic cellular networks.

Michael W Chevalier¹, Hana El-Samad

¹Department of Biochemistry and Biophysics, California Institute for Quantitative Biosciences, University of California San Francisco, 1700, 4th Street, San Francisco, California 94143-2542, USA. michael.chevalier@ucsf.edu

The Journal of Chemical Physics

|August 14, 2009

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Biological noise causes cell variability. A new modified chemical master equation (CME) framework offers accurate approximations for stochastic simulation algorithm (SSA) challenges in complex biochemical networks.

Area of Science:

Biochemistry
Computational Biology
Systems Biology

Background:

Biological systems exhibit inherent noise and stochasticity due to molecular randomness.
This randomness causes cell-to-cell variability, even in genetically identical populations.
Stochastic biochemical networks are often modeled using the chemical master equation (CME), but it's analytically intractable for most cases.

Purpose of the Study:

To develop a novel computational framework for analyzing stochastic biochemical networks.
To address the computational limitations of the stochastic simulation algorithm (SSA) for systems with disparate timescales or molecular counts.
To provide a hierarchy of approximations for CME-based simulations.

Main Methods:

Developed a modified CME framework by partitioning reactions into restricted and unrestricted sets.

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Last Updated: Jun 21, 2026

Finite Element Modelling of a Cellular Electric Microenvironment

Finite Element Modelling of a Cellular Electric Microenvironment

Published on: May 18, 2021

Utilized this decomposition to generate a hierarchy of approximation algorithms.

Demonstrated that previously developed algorithms are limiting cases of this new formulation.

Main Results:

The modified CME framework provides a systematic way to derive approximations.
The proposed methods were applied to biologically relevant systems.
Accuracy and efficiency of the new methods were demonstrated through these applications.

Conclusions:

The modified CME framework offers a powerful and flexible approach to approximate solutions for stochastic biochemical systems.
This work unifies and extends existing approximation algorithms.
The methods enhance the efficiency and accuracy of simulating complex cellular networks.