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Comparison of pathway analysis and constraint-based methods for cell factory design
Vítor Vieira1, Paulo Maia2, Miguel Rocha3
1Centro de Engenharia Biológica, Universidade do Minho, Braga, Portugal.
BMC Bioinformatics
|June 22, 2019
Summary
Computational strain optimization methods (CSOMs) like evolutionary algorithms and minimal cut sets (MCS) identify strategies for compound overproduction. MCSs reveal novel mechanisms for succinate production in yeast, complementing EA strategies.
Area of Science:
- Metabolic Engineering
- Synthetic Biology
- Computational Biology
Background:
- Computational strain optimization methods (CSOMs) are crucial for designing metabolic cell factories for compound overproduction.
- Minimal cut sets (MCS) offer an alternative to simulation-based CSOMs by identifying intervention strategies without optimality bias.
- Discrepancies in problem formulation hinder direct comparison between different CSOM approaches.
Purpose of the Study:
- To critically compare the performance, robustness, and predicted phenotypes of strategies generated by evolutionary algorithms (EA) and MCS.
- To analyze the structure and size of strategies derived from EA and MCS for metabolic engineering applications.
- To generalize problem formulations for MCS enumeration in growth-coupled product synthesis.
Main Methods:
- Developed a pipeline for enumerating strategies using EA and MCS.
- Applied filtering and flux analysis to predicted mutants for succinic acid production optimization in Saccharomyces cerevisiae.
- Generalized problem formulations for MCS enumeration in the context of growth-coupled synthesis.
Main Results:
- EA strategies offered a balance between growth rates and succinic acid overproduction.
- Constrained MCS identified diverse phenotypes with varying degrees of growth-coupling.
- MCS revealed the significance of the gamma-aminobutyric acid shunt and cofactor pool manipulation for growth-coupled succinate production.
Conclusions:
- Both EA and MCS are valuable for identifying growth-coupled mutants in metabolic engineering.
- MCS, despite limitations, uncover novel compound overproduction mechanisms.
- Integrating outputs from both EA and MCS provides a comprehensive view of potential in vivo strategies.
Keywords:
Computational strain designEvolutionary algorithmsGenome-scale metabolic modelsGrowth-coupled product synthesisMetabolic pathway analysisMinimal cut setsMore Related Videos
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