gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35642935", "kind": "science-record", "revision": 3, "content_hash": "sha256:5544b04aea7ebd87b343b9ff01d307a4e55670ccdd475ce28160394cb5c00e9a", "created_at": "2026-11-01T18:14:33Z", "updated_at": "2026-11-01T21:27:59Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "gcFront: a tool for determining a Pareto front of growth-coupled cell factory designs.", "doi": "10.1093/bioinformatics/btac376", "pmid": "35642935", "authors": ["Laurence Legon", "Christophe Corre", "Declan G Bates", "Ahmad A Mannan"], "journal": "Bioinformatics (Oxford, England)", "year": 2022, "abstract": "MOTIVATION: A widely applicable strategy to create cell factories is to knockout (KO) genes or reactions to redirect cell metabolism so that chemical synthesis is made obligatory when the cell grows at its maximum rate. Synthesis is thus growth-coupled, and the stronger the coupling the more deleterious any impediments in synthesis are to cell growth, making high producer phenotypes evolutionarily robust. Additionally, we desire that these strains grow and synthesize at high rates. Genome-scale metabolic models can be used to explore and identify KOs that growth-couple synthesis, but these are rare in an immense design space, making the search difficult and slow.\nRESULTS: To address this multi-objective optimization problem, we developed a software tool named gcFront-using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength. Moreover, our measure of coupling strength facilitates the search so that gcFront not only finds a growth-coupled design in minutes but also outputs many alternative Pareto optimal designs from a single run-granting users flexibility in selecting designs to take to the lab.\nAVAILABILITY AND IMPLEMENTATION: gcFront, with documentation and a workable tutorial, is freely available at GitHub: https://github.com/lLegon/gcFront and archived at Zenodo, DOI: 10.5281/zenodo.5557755.\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "To address this multi-objective optimization problem, we developed a software tool named gcFront—using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35642935", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "To address this multi-objective optimization problem, we developed a software tool named gcFront—using a genetic algorithm it explores KOs that maximize cell growth, product synthesis and coupling strength."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data underlying this article are available in the article and its online Supplementary Information and Supplementary Data files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35642935", "locator": "sections[6] \"Data availability\" paragraph 2", "quote": "The data underlying this article are available in the article and its online Supplementary Information and Supplementary Data files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using a genetic algorithm with the Escherichia coli iML1515 genome-scale metabolic model, gcFront found its first growth-coupled design in 38% less time than gcOpt for succinate synthesis and 98% less time than RobustKnock for tyrosine synthesis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35642935", "locator": "sections[3] \"3 Comparative performance assessment\" paragraph 2", "quote": "gcFront found the first gc-design in 38% less time than gcOpt for succinate synthesis, 98% less time than RobustKnock for tyrosine synthesis, and orders of magnitude less time than the other methods and products (Fig. 1a, Supplementary Data)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using a genetic algorithm with the Escherichia coli iML1515 genome-scale metabolic model, gcFront found its first growth-coupled design in 38% less time than gcOpt for succinate synthesis and 98% less time than RobustKnock for tyrosine synthesis.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:35642935"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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