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A robust and efficient method for estimating enzyme complex abundance and metabolic flux from expression data
Brandon E Barker1, Narayanan Sadagopan2, Yiping Wang3
1Center for Advanced Computing, Cornell University, 534 Rhodes Hall, Ithaca, NY, USA.
Computational Biology and Chemistry
|September 19, 2015
Summary
We developed FALCON, a new method using metabolic networks and expression data to estimate metabolic fluxes. This approach improves predictive capabilities and understanding of metabolism by quantifying enzyme complex abundance.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Computational Biology
Background:
- Constraint-based modeling integrates diverse experimental data to enhance metabolic understanding.
- Existing methods using gene/protein intensities often create tissue-specific models, limiting reaction scope and flux estimation objectivity.
Purpose of the Study:
- To develop a novel method, FALCON, for estimating metabolic fluxes using metabolic network reconstructions and expression data.
- To introduce an algorithm for accurate quantification of enzyme complex abundance, crucial for flux estimation.
Main Methods:
- Flux Assignment with LAD (Least Absolute Deviation) convex objectives and Normalization (FALCON) method.
- Algorithm for quantifying enzyme complex abundance, handling multiple isoforms and large rules.
- Integration with COBRA Toolbox in MATLAB.
Main Results:
- FALCON demonstrates capability to work with large models and offers improved run-time performance.
- Enhanced analysis of enzyme complex formation and improved correlation with experimentally measured fluxes.
- Successful implementation in MATLAB with accessible source code.
Conclusions:
- FALCON provides a robust framework for estimating metabolic fluxes by integrating network reconstructions with expression data.
- The method enhances predictive accuracy and understanding of metabolic systems.
- The developed algorithms offer significant improvements in performance and handling complex biological data.
