Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

A dynamical low-rank approach to solve the chemical master equation for biological reaction networks

View ORCID ProfileMartina Prugger, Lukas Einkemmer, Carlos F. Lopez
doi: https://doi.org/10.1101/2022.05.04.490585
Martina Prugger
1Department of Biochemistry, University of Innsbruck, Innsbruck, Tyrol, Austria
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Martina Prugger
Lukas Einkemmer
3Department of Mathematics, University of Innsbruck, Innsbruck, Tyrol, Austria
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Carlos F. Lopez
2Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN, USA
4Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Solving the chemical master equation is an indispensable tool in understanding the behavior of biological and chemical systems. In particular, it is increasingly recognized that commonly used ODE models are not able to capture the stochastic nature of many cellular processes. Solving the chemical master equation directly, however, suffers from the curse of dimensionality. That is, both memory and computational effort scale exponentially in the number of species. In this paper we propose a dynamical low-rank approach that enables the simulation of large biological networks. The approach is guided by partitioning the network into biological relevant subsets and thus avoids the use of single species basis functions that are known to give inaccurate results for biological systems. We use the proposed method to gain insight into the nature of asynchronous vs. synchronous updating in Boolean models and successfully simulate a 41 species apoptosis model on a standard desktop workstation.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted May 04, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
A dynamical low-rank approach to solve the chemical master equation for biological reaction networks
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
A dynamical low-rank approach to solve the chemical master equation for biological reaction networks
Martina Prugger, Lukas Einkemmer, Carlos F. Lopez
bioRxiv 2022.05.04.490585; doi: https://doi.org/10.1101/2022.05.04.490585
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
A dynamical low-rank approach to solve the chemical master equation for biological reaction networks
Martina Prugger, Lukas Einkemmer, Carlos F. Lopez
bioRxiv 2022.05.04.490585; doi: https://doi.org/10.1101/2022.05.04.490585

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4246)
  • Biochemistry (9173)
  • Bioengineering (6806)
  • Bioinformatics (24064)
  • Biophysics (12158)
  • Cancer Biology (9565)
  • Cell Biology (13825)
  • Clinical Trials (138)
  • Developmental Biology (7660)
  • Ecology (11737)
  • Epidemiology (2066)
  • Evolutionary Biology (15544)
  • Genetics (10672)
  • Genomics (14362)
  • Immunology (9515)
  • Microbiology (22910)
  • Molecular Biology (9131)
  • Neuroscience (49156)
  • Paleontology (358)
  • Pathology (1487)
  • Pharmacology and Toxicology (2584)
  • Physiology (3851)
  • Plant Biology (8351)
  • Scientific Communication and Education (1473)
  • Synthetic Biology (2301)
  • Systems Biology (6206)
  • Zoology (1303)