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10:19
Measurement of Energy Metabolism in Explanted Retinal Tissue Using Extracellular Flux Analysis
Published on: January 7, 2019
Thermodynamics-based metabolic flux analysis.
Christopher S Henry1, Linda J Broadbelt, Vassily Hatzimanikatis
1Department of Chemical and Biological Engineering, McCormick School of Engineering and Applied Sciences, Northwestern University, Evanston, Illinois, USA.
Biophysical Journal
|December 19, 2006
Summary
A new thermodynamics-based metabolic flux analysis (TMFA) method generates feasible metabolic flux and metabolite activity profiles. This approach identifies thermodynamic bottlenecks and regulatory reaction candidates in genome-scale models like E. coli.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Biochemical Thermodynamics
Background:
- Metabolic Flux Analysis (MFA) traditionally uses mass balance constraints.
- Existing MFA methods may not account for thermodynamic feasibility.
- Understanding thermodynamic constraints is crucial for accurate metabolic modeling.
Purpose of the Study:
- Introduce Thermodynamics-based Metabolic Flux Analysis (TMFA) for genome-scale models.
- Incorporate linear thermodynamic constraints into MFA.
- Generate thermodynamically feasible flux and metabolite activity profiles.
Main Methods:
- Developed TMFA by adding linear thermodynamic constraints to standard MFA.
- Applied TMFA to a genome-scale metabolic model of Escherichia coli.
- Analyzed thermodynamically feasible ranges for reaction Gibbs free energy (Delta(r)G') and metabolite activities.
Main Results:
- TMFA successfully produced thermodynamically feasible flux distributions.
- Identified dihydroorotase as a potential thermodynamic bottleneck in E. coli.
- Numerous reactions with consistently negative Delta(r)G' were found, suggesting regulatory roles, particularly in biosynthesis pathways.
- Determined feasible ranges for key metabolite concentration ratios (ATP/ADP, NAD(P)/NAD(P)H, H+/H+), encompassing experimental values.
Conclusions:
- TMFA provides a more robust approach to metabolic modeling by ensuring thermodynamic feasibility.
- The method can identify critical thermodynamic bottlenecks and potential regulatory sites within metabolic networks.
- TMFA offers insights into cellular energy management and cofactor utilization.
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