ArticleBMC bioinformatics2017
A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed.
- Designing function-specific minimal microbiomes from large microbial communities.NPJ systems biology and applications · 2024Article
- A structural property for reduction of biochemical networks.Scientific reports · 2021Article
- The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli.BMC bioinformatics · 2021Article
- Boosting the extraction of elementary flux modes in genome-scale metabolic networks using the linear programming approach.Bioinformatics (Oxford, England) · 2020Article
- Network reduction methods for genome-scale metabolic models.Cellular and molecular life sciences : CMLS · 2020Review
- Metabolic kinetic modeling provides insight into complex biological questions, but hurdles remain.Current opinion in biotechnology · 2019Review
- Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions.PLoS computational biology · 2019Article
- Improving the EFMs quality by augmenting their representativeness in LP methods.BMC systems biology · 2018Article
- Modeling the multi-scale mechanisms of macromolecular resource allocation.Current opinion in microbiology · 2018Review
- Grid-based computational methods for the design of constraint-based parsimonious chemical reaction networks to simulate metabolite production: GridProd.BMC bioinformatics · 2018Article
- A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models.Metabolites · 2017Article
- redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models.PLoS computational biology · 2017Article
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2 authors.
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Abstract
backgroundConstraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement.
resultsHere we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways.
conclusionsApplying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.
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