ArticleMetabolic engineering communications2014
Expanding Metabolic Engineering Algorithms Using Feasible Space and Shadow Price Constraint Modules.
Article in Metabolic engineering communications, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Minimal cut sets in metabolic networks: from conceptual foundations to applications in metabolic engineering and biomedicine.Briefings in bioinformatics · 2025Review
- Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine.Metabolites · 2023Review
- Network analysis of toxin production in Clostridioides difficile identifies key metabolic dependencies.PLoS computational biology · 2023Article
- Synthetic and systems biology for microbial production of commodity chemicals.NPJ systems biology and applications · 2016Review
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Authors and funding
2 authors.
Funding
Abstract
While numerous computational methods have been developed that use genome-scale models to propose mutants for the purpose of metabolic engineering, they generally compare mutants based on a single criteria (e.g., production rate at a mutant's maximum growth rate). As such, these approaches remain limited in their ability to include multiple complex engineering constraints. To address this shortcoming, we have developed feasible space and shadow price constraint (FaceCon and ShadowCon) modules that can be added to existing mixed integer linear adaptive evolution metabolic engineering algorithms, such as OptKnock and OptORF. These modules allow strain designs to be identified amongst a set of multiple metabolic engineering algorithm solutions that are capable of high chemical production while also satisfying additional design criteria. We describe the various module implementations and their potential applications to the field of metabolic engineering. We then incorporated these modules into the OptORF metabolic engineering algorithm. Using an
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