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Updated: Mar 7, 2026

Experimental Methodology for Estimation of Local Heat Fluxes and Burning Rates in Steady Laminar Boundary Layer Diffusion Flames
Published on: June 1, 2016
How important is thermodynamics for identifying elementary flux modes?
Sabine Peres1,2, Mario Jolicœur3, Cécile Moulin1
1LRI, Université Paris-Sud, CNRS, Université Paris-Saclay, 91405 Orsay, France.
We developed a method to compute thermodynamically feasible elementary flux modes (tEFMs) using equilibrium constants. This approach refines elementary flux mode (EFM) analysis by identifying biochemically relevant pathways in metabolic networks.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Biochemical Pathway Analysis
Background:
- Elementary flux modes (EFMs) are fundamental building blocks of metabolic networks.
- Traditional EFM computation often relies on simplifying assumptions about reaction reversibility.
- Incorporating thermodynamic constraints can improve the biological relevance of EFM analysis.
Purpose of the Study:
- To present a novel method for computing thermodynamically feasible elementary flux modes (tEFMs).
- To evaluate the impact of equilibrium constants on EFM enumeration compared to binary reaction distinctions.
- To assess the biological relevance of tEFMs in established metabolic models.
Main Methods:
- Computation of tEFMs utilizing equilibrium constants, obviating the need for internal metabolite concentrations.
- Comparison of the tEFM method against a binary approach (reversible/irreversible reactions).
- Application of the tEFM method to monosaccharide and central carbon metabolism models of Chinese hamster ovary cells.
Main Results:
- Using equilibrium constants reduced the number of EFMs by half when all reactions were reversible.
- The tEFM method successfully identified and excluded biochemically irrelevant EFMs.
- Results from applied models showed good agreement with biochemical observations.
Conclusions:
- Thermodynamic feasibility constraints enhance the biological relevance of EFM analysis.
- The tEFM method provides a more accurate representation of metabolic capabilities.
- This approach refines metabolic network modeling and interpretation.
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