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An exact arithmetic toolbox for a consistent and reproducible structural analysis of metabolic network models
Leonid Chindelevitch1, Jason Trigg2, Aviv Regev3
11] Mathematics Department, Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, Massachusetts 02139, USA [2] Broad Institute, 7 Cambridge Center, Cambridge, Massachusetts 02142, USA.
Nature Communications
|October 8, 2014
Summary
Constraint-based metabolic models are crucial for whole-genome metabolism studies. Using exact arithmetic with the MONGOOSE toolbox reveals issues in biomass reactions and improves model analysis.
Area of Science:
- Systems biology
- Metabolic modeling
- Computational biology
Background:
- Constraint-based models are essential for genome-scale metabolic studies.
- Flux balance analysis (FBA) is a common analysis method for these models.
- Inconsistencies in FBA results across different software highlight a need for improved analytical methods.
Purpose of the Study:
- To introduce MONGOOSE, a novel toolbox for analyzing constraint-based metabolic models using exact arithmetic.
- To address software-dependent variations in FBA results by employing exact arithmetic.
- To systematically investigate the structural properties and limitations of metabolic network models.
Main Methods:
- Development of the MONGOOSE toolbox for exact arithmetic analysis of metabolic models.
- Application of MONGOOSE to analyze 98 existing metabolic network models.
- Proposal and application of a principled approach for unblocking biomass reactions.
Main Results:
- Identified that the biomass reaction is blocked in nearly half of the analyzed metabolic models.
- Demonstrated that exact arithmetic resolves inconsistencies in FBA results.
- Developed methods for identifying essential/synthetic lethal reactions and minimal media based on structural insights.
Conclusions:
- MONGOOSE provides a robust framework for analyzing metabolic models in exact arithmetic.
- A significant proportion of existing metabolic models contain unblocked biomass reactions, indicating potential issues.
- Structural analysis of constraint-based models offers deeper insights into their capabilities and limitations.
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