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Regulation of Metabolism

Regulation of Metabolism

Cellular needs and conditions vary from cell to cell and change within individual cells over time. For example, the required enzymes and energetic demands of stomach cells are different from those of fat storage cells, skin cells, blood cells, and nerve cells. Furthermore, a digestive cell works much harder to process and break down nutrients during the time that closely follows a meal compared with many hours after a meal. As these cellular demands and conditions vary, so do the amounts and...

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Related Experiment Video

Updated: May 13, 2026

Real Time Analysis of Metabolic Profile in Ex Vivo Mouse Intestinal Crypt Organoid Cultures

Published on: November 3, 2014

Whole-cell metabolic control analysis.

Frank J Bruggeman¹, Maaike Remeijer¹, Maarten Droste²

¹Systems Biology Lab, A-LIFE, AIMMS, VU University, Amsterdam, Netherlands.

|November 4, 2024

Summary

This summary is machine-generated.

Metabolic control analysis (MCA) now includes whole-cell metabolism and evolutionary growth-rate maximization. This approach predicts flux control coefficients and reveals elementary flux modes (EFMs) as optimal networks, with their number constrained by growth-limiting protein concentrations.

Keywords:

Elementary flux modes Enzyme kinetics Metabolic control analysis Whole cell models

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Last Updated: May 13, 2026

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Published on: November 3, 2014

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Published on: November 9, 2019

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Published on: March 1, 2022

Area of Science:

Systems Biology
Metabolic Engineering
Evolutionary Biology

Background:

Metabolic Control Analysis (MCA) traditionally focuses on enzyme activity.
Understanding cellular metabolism requires a whole-cell and evolutionary perspective.
Protein concentration optimization for growth-rate maximization is a key evolutionary driver.

Purpose of the Study:

Extend MCA to a whole-cell context considering evolutionary growth-rate maximization.
Predict flux control coefficients using proteomics and stoichiometric modeling.
Investigate the emergence and control properties of elementary flux modes (EFMs) in an optimal cellular state.

Main Methods:

Integrating proteomics data and stoichiometric modeling.
Treating protein concentrations as interdependent due to resource competition.
Analyzing elementary flux modes (EFMs) within an evolutionary optimization framework.

Main Results:

Elementary flux modes (EFMs) naturally emerge as optimal metabolic networks.
The number of expressed EFMs is determined by growth-limiting protein concentration constraints.
MCA results are modified by the expression of growth-unassociated proteins.
Flux control coefficients can be estimated from proteomics and ribosome-profiling data.

Conclusions:

MCA is extended to EFMs operating at an evolutionary optimum.
Proteomics data provides a whole-cell view of metabolic enzyme control on growth rate.
This framework can identify conserved, general principles of cellular metabolism across species.