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

Effective Dynamic Models of Metabolic Networks

Michael Vilkhovoy, Mason Minot, Jeffrey D. Varner
doi: https://doi.org/10.1101/047316
Michael Vilkhovoy
*School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mason Minot
*School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850 USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jeffrey D. Varner
*School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14850 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
  • Preview PDF
Loading

Abstract

Mathematical models of biochemical networks are useful tools to understand and ultimately predict how cells utilize nutrients to produce valuable products. Hybrid cybernetic models in combination with elementary modes (HCM) is a tool to model cellular metabolism. However, HCM is limited to reduced metabolic networks because of the computational burden of calculating elementary modes. In this study, we developed the hybrid cybernetic modeling with flux balance analysis or HCM-FBA technique which uses flux balance solutions instead of elementary modes to dynamically model metabolism. We show HCM-FBA has comparable performance to HCM for a proof of concept metabolic network and for a reduced anaerobic E. coli network. Next, HCM-FBA was applied to a larger metabolic network of aerobic E. coli metabolism which was infeasible for HCM (29 FBA modes versus more than 153,000 elementary modes). Global sensitivity analysis further reduced the number of FBA modes required to describe the aerobic E. coli data, while maintaining model fit. Thus, HCM-FBA is a promising alternative to HCM for large networks where the generation of elementary modes is infeasible.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted August 31, 2016.
Download PDF
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.
Effective Dynamic Models of Metabolic 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
Effective Dynamic Models of Metabolic Networks
Michael Vilkhovoy, Mason Minot, Jeffrey D. Varner
bioRxiv 047316; doi: https://doi.org/10.1101/047316
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Effective Dynamic Models of Metabolic Networks
Michael Vilkhovoy, Mason Minot, Jeffrey D. Varner
bioRxiv 047316; doi: https://doi.org/10.1101/047316

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

  • Systems Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (4089)
  • Biochemistry (8772)
  • Bioengineering (6487)
  • Bioinformatics (23356)
  • Biophysics (11756)
  • Cancer Biology (9154)
  • Cell Biology (13256)
  • Clinical Trials (138)
  • Developmental Biology (7417)
  • Ecology (11376)
  • Epidemiology (2066)
  • Evolutionary Biology (15093)
  • Genetics (10403)
  • Genomics (14014)
  • Immunology (9126)
  • Microbiology (22070)
  • Molecular Biology (8783)
  • Neuroscience (47393)
  • Paleontology (350)
  • Pathology (1421)
  • Pharmacology and Toxicology (2482)
  • Physiology (3705)
  • Plant Biology (8054)
  • Scientific Communication and Education (1433)
  • Synthetic Biology (2211)
  • Systems Biology (6017)
  • Zoology (1250)