ArticlePloS one2014
Design of optimally constructed metabolic networks of minimal functionality.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Finding Multiple Optimal Solutions to an Integer Linear Program by Random Perturbations of Its Objective Function.Algorithms · 2025Article
- FastKnock: an efficient next-generation approach to identify all knockout strategies for strain optimization.Microbial cell factories · 2024Article
- Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.BMC bioinformatics · 2017Article
- Efficient estimation of the maximum metabolic productivity of batch systems.Biotechnology for biofuels · 2017Article
- Which sets of elementary flux modes form thermodynamically feasible flux distributions?The FEBS journal · 2016Article
- Designing minimal microbial strains of desired functionality using a genetic algorithm.Algorithms for molecular biology : AMB · 2015Article
- Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine.Frontiers in physiology · 2015Review
- Tailoring strain construction strategies for muconic acid production inMetabolic engineering communications · 2014Article
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3 authors.
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Abstract
backgroundMetabolic engineering aims to design microorganisms that will generate a product of interest at high yield. Thus, a variety of in silico modeling strategies has been applied successfully, including the concepts of elementary flux modes (EFMs) and constrained minimal cut sets (cMCSs). The EFMs (minimal, steady state pathways through the system) can be calculated given a metabolic model. cMCSs are sets of reaction deletions in such a network that will allow desired pathways to survive and disable undesired ones (e.g., those with low product secretion or low growth rates). Grouping the modes into desired and undesired categories had to be done manually until now.
resultsAlthough the optimal solution for a given set of pathways will always be found with the currently available tools, manual selection may lead to a sub-optimal solution with respect to a metabolic engineering target. A small change in the selection of modes can reduce the number of necessary deletions while only slightly reducing production. Based on our recently introduced formulation of cut set calculations using binary linear programming, we suggest an algorithm that does not require manual selection of the desired pathways.
conclusionsWe demonstrated the principle of our algorithm with the help of a small toy network and applied it to a model of E. coli using different design objectives. Furthermore we validated our method by reproducing previously obtained results without requiring manual grouping of modes.
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