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MultiMetEval: comparative and multi-objective analysis of genome-scale metabolic models
Piotr Zakrzewski1, Marnix H Medema, Albert Gevorgyan
1Department of Microbial Physiology, University of Groningen, Groningen, The Netherlands.
Plos One
|December 29, 2012
Summary
New software, Multi-Metabolic Evaluator (MultiMetEval), enables comparative metabolic modeling and multi-objective analysis. It reveals that high natural product gene diversity doesn't guarantee overproduction and highlights inherent metabolic switches in actinobacteria.
Area of Science:
- Systems Biology
- Metabolic Engineering
- Computational Biology
Background:
- Genome-scale metabolic models (GEMs) are crucial for understanding cellular metabolism.
- High-throughput construction of GEMs necessitates advanced computational tools for comparative analysis.
- Analyzing multiple models under various cellular objectives requires specialized software.
Purpose of the Study:
- To introduce Multi-Metabolic Evaluator (MultiMetEval), a user-friendly software framework for comparative and multi-objective metabolic modeling.
- To facilitate efficient analysis of multiple GEMs and their Pareto fronts between cellular objectives.
- To uncover novel biological insights through comparative and multi-objective flux balance analysis.
Main Methods:
- Development of the MultiMetEval software framework, built upon SurreyFBA.
- Application of flux balance analysis (FBA) to collections of metabolic models.
- Implementation of multi-objective analysis to calculate Pareto fronts between biomass production and natural product biosynthesis.
- Utilized a dataset of 38 actinobacterial GEMs for analysis.
Main Results:
- Comparative FBA indicated that Streptomyces species with high secondary metabolite gene cluster diversity are not necessarily optimized for compound overproduction.
- Multi-objective analysis demonstrated that discrete metabolic switches in actinobacteria are intrinsic to their metabolic network architecture.
- Identified that comparative and multi-objective modeling yield insights unattainable through standard FBA.
Conclusions:
- MultiMetEval provides a powerful and accessible platform for biologists to perform complex metabolic modeling analyses.
- Comparative and multi-objective modeling are essential for deeper understanding of metabolic networks and cellular behavior.
- The study highlights the potential of computational approaches to reveal intricate relationships between genotype, metabolic capabilities, and phenotype.
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