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ΔFBA-Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data
Sudharshan Ravi1,2, Rudiyanto Gunawan1
1Department of Chemical and Biological Engineering, University at Buffalo-SUNY, Buffalo, New York, United States of America.
Plos Computational Biology
|November 10, 2021
Summary
A new method, deltaFBA, uses gene expression data to directly predict metabolic flux differences between conditions without needing a predefined cellular objective. This approach improves accuracy in understanding metabolic alterations in various biological systems.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Computational Biology
Background:
- Genome-scale metabolic models (GEMs) and flux balance analysis (FBA) simulate cellular metabolism.
- FBA requires a defined metabolic objective, which is often context-specific and difficult to determine.
- Estimating metabolic flux changes between conditions using FBA can be complicated by the choice of objective.
Purpose of the Study:
- To introduce deltaFBA, a novel method for directly evaluating metabolic flux differences between two conditions.
- To integrate differential gene expression data into metabolic modeling without requiring a predefined cellular objective.
- To improve the accuracy of predicting metabolic alterations.
Main Methods:
- Developed deltaFBA, a method that maximizes consistency between predicted flux differences and differential gene expression.
- Applied deltaFBA to analyze metabolic alterations in Escherichia coli under genetic and environmental perturbations.
- Evaluated deltaFBA's performance in predicting metabolic changes associated with Type-2 diabetes in human muscle.
Main Results:
- DeltaFBA successfully predicts metabolic flux differences by integrating gene expression data.
- The method does not require specifying a cellular objective, simplifying the analysis.
- Case studies demonstrated deltaFBA's effectiveness in diverse biological contexts.
Conclusions:
- DeltaFBA offers a more accurate approach for predicting metabolic flux alterations compared to existing methods.
- The integration of gene expression data provides a powerful way to infer condition-specific metabolic changes.
- DeltaFBA advances the application of GEMs for understanding complex biological states and perturbations.
