English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  A local polynomial moment approximation for compartmentalized biochemical systems.

Bianucci, T., & Zechner, C. (2024). A local polynomial moment approximation for compartmentalized biochemical systems. Mathematical biosciences, 367: 109110. doi:10.1016/j.mbs.2023.109110.

Item is

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Bianucci, Tommaso, Author
Zechner, Christoph1, Author           
Affiliations:
1Max Planck Institute for Molecular Cell Biology and Genetics, Max Planck Society, ou_2340692              

Content

show
hide
Free keywords: -
 Abstract: Compartmentalized biochemical reactions are a ubiquitous building block of biological systems. The interplay between chemical and compartmental dynamics can drive rich and complex dynamical behaviors that are difficult to analyze mathematically - especially in the presence of stochasticity. We have recently proposed an effective moment equation approach to study the statistical properties of compartmentalized biochemical systems. So far, however, this approach is limited to polynomial rate laws and moreover, it relies on suitable moment closure approximations, which can be difficult to find in practice. In this work we propose a systematic method to derive closed moment dynamics for compartmentalized biochemical systems. We show that for the considered class of systems, the moment equations involve expectations over functions that factorize into two parts, one depending on the molecular content of the compartments and one depending on the compartment number distribution. Our method exploits this structure and approximates each function with suitable polynomial expansions, leading to a closed system of moment equations. We demonstrate the method using three systems inspired by cell populations and organelle networks and study its accuracy across different dynamical regimes.

Details

show
hide
Language(s):
 Dates: 2024-01-01
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1016/j.mbs.2023.109110
Other: cbg-8619
PMID: 38035996
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Mathematical biosciences
  Other : Math Biosci
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 367 Sequence Number: 109110 Start / End Page: - Identifier: -