Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Nonlinear metabolic control analysis.

V Hatzimanikatis1

  • 1Computational Biology Group, Dupont Central Research & Development, Wilmington, DE 19880-0328, USA. vassily@innocent.com

Metabolic Engineering
|August 10, 2000
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

redLips: a comprehensive mechanistic model of the lipid metabolic network of yeast.

FEMS yeast research·2020
Same author

Molecular thermodynamics of metabolism: hydration quantities and the equation-of-state approach.

Physical chemistry chemical physics : PCCP·2016
Same author

Thermodynamics-based Metabolite Sensitivity Analysis in metabolic networks.

Metabolic engineering·2016
Same author

Molecular thermodynamics of metabolism: quantum thermochemical calculations for key metabolites.

Physical chemistry chemical physics : PCCP·2015
Same author

Exploration of trade-offs between steady-state and dynamic properties in signaling cycles.

Physical biology·2012
Same author

Dynamical analysis of gene networks requires both mRNA and protein expression information.

Metabolic engineering·2000
Same journal

Metabolic rewiring overcomes physiological constraints in Sphingobium lignivorans SYK-6 for valorization of industrial lignin streams.

Metabolic engineering·2026
Same journal

"Adaptively evolved chitin overproduction in Saccharomyces cerevisiae".

Metabolic engineering·2026
Same journal

Programmable and controllable sexual life cycle for improved evolution in Komegataella phaffii.

Metabolic engineering·2026
Same journal

Evolution-guided high yield production of potent Gα<sub>q/11</sub>-signalling inhibitors FR900359 and YM-254890.

Metabolic engineering·2026
Same journal

Engineering a microbial platform for the biosynthesis of anthranilic acid and its derivatives.

Metabolic engineering·2026
Same journal

Metabolic engineering strategies for producing decanoic acid and related oleochemicals: 1-decanol, 2-nonanone, and poly(3-hydroxydecanoate) in Escherichia coli.

Metabolic engineering·2026
See all related articles

This study introduces a new modeling framework to predict metabolic system responses to genetic changes, overcoming limitations of traditional metabolic control analysis (MCA) for broader applications in metabolic engineering.

Area of Science:

  • Systems biology
  • Metabolic engineering
  • Biochemical pathway analysis

Background:

  • Mathematical models are crucial for predicting metabolic system behavior and identifying targets for metabolic engineering.
  • Metabolic Control Analysis (MCA) provides quantitative indices but is limited to small parameter changes.

Purpose of the Study:

  • To develop a novel modeling framework for accurately describing metabolic responses across a wide range of parameter variations.
  • To enable more robust predictions for metabolic engineering strategies.

Main Methods:

  • The framework utilizes existing Metabolic Control Analysis (MCA) indices and reaction rates at a reference steady state.
  • It employs simplifying assumptions about reaction mechanisms, obviating the need for intracellular metabolite concentrations.

Related Experiment Videos

  • Validation was performed on three distinct elementary metabolic systems.

    • Main Results:

    • The new framework accurately predicts metabolic responses even with significant changes in metabolic parameters.
    • Demonstrated efficacy on unbranched pathways, interconvertible enzyme systems, and feedback-inhibited branched pathways.
    • Successfully predicted highly nonlinear responses.

    Conclusions:

    • The developed modeling framework extends the predictive capabilities of MCA beyond small perturbations.
    • This approach offers a powerful tool for metabolic engineering by providing accurate predictions for substantial parameter modifications.
    • The method is applicable without requiring knowledge of intracellular metabolite concentrations.