ArticleMetabolic engineering communications2019
OptCouple: Joint simulation of gene knockouts, insertions and medium modifications for prediction of growth-coupled strain designs.
Article in Metabolic engineering communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Nutrient control enables metabolic reconstruction ofbioRxiv : the preprint server for biology · 2026Article
- Balancing Cell Growth and Product Synthesis for Efficient Microbial Cell Factories.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- Metabolic growth-coupling strategies forMetabolic engineering communications · 2025Article
- Adapting OptCouple to Identify Strategies with Increased Product Yields in Community Cohorts ofMetabolites · 2025Article
- Finding Multiple Optimal Solutions to an Integer Linear Program by Random Perturbations of Its Objective Function.Algorithms · 2025Article
- Exploring Biomass Precursor Synthesis as a Determinant in Microbial Adaptation to Unadapted Carbon Sources with AdaptUC.Research (Washington, D.C.) · 2025Article
- CFSA: Comparative flux sampling analysis as a guide for strain design.Metabolic engineering communications · 2024Article
- OptEnvelope: A target point guided method for growth-coupled production using knockouts.PloS one · 2023Article
- StrainDesign: a comprehensive Python package for computational design of metabolic networks.Bioinformatics (Oxford, England) · 2022Article
- Predictive evolution of metabolic phenotypes using model-designed environments.Molecular systems biology · 2022Article
- Genome-scale metabolic network models: from first-generation to next-generation.Applied microbiology and biotechnology · 2022Review
- OptDesign: Identifying Optimum Design Strategies in Strain Engineering for Biochemical Production.ACS synthetic biology · 2022Article
- The future of self-selecting and stable fermentations.Journal of industrial microbiology & biotechnology · 2020Review
- Adaptive laboratory evolution for growth coupled microbial production.World journal of microbiology & biotechnology · 2020Review
- An extended and generalized framework for the calculation of metabolic intervention strategies based on minimal cut sets.PLoS computational biology · 2020Article
- NIHBA: a network interdiction approach for metabolic engineering design.Bioinformatics (Oxford, England) · 2020Article
- The era of big data: Genome-scale modelling meets machine learning.Computational and structural biotechnology journal · 2020Review
- The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology.Metabolic engineering · 2019Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Biological production of chemicals is an attractive alternative to petrochemical-based production, due to advantages in environmental impact and the spectrum of feasible targets. However, engineering microbial strains to overproduce a compound of interest can be a long, costly and painstaking process. If production can be coupled to cell growth it is possible to use adaptive laboratory evolution to increase the production rate. Strategies for coupling production to growth, however, are often not trivial to find. Here we present OptCouple, a constraint-based modeling algorithm to simultaneously identify combinations of gene knockouts, insertions and medium supplements that lead to growth-coupled production of a target compound. We validated the algorithm by showing that it can find novel strategies that are growth-coupled in silico for a compound that has not been coupled to growth previously, as well as reproduce known growth-coupled strain designs for two different target compounds. Furthermore, we used OptCouple to construct an alternative design with potential for higher production. We provide an efficient and easy-to-use implementation of the OptCouple algorithm in the cameo Python package for computational strain design.
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Registered trials
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