{"entity": "researcher", "timestamp": "2026-08-20T22:07:35.462Z", "family": "Chen", "given": "Yu", "initials": "Y", "orcid": "0000-0003-3326-9068", "affiliations": ["Department of Life Sciences, Chalmers University of Technology, Gothenburg, Sweden.", "Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/4d5b3f948bb84c05a28b5feeb3738128.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/4d5b3f948bb84c05a28b5feeb3738128"}}, "publications": [{"entity": "publication", "iuid": "fa8f0b2e9e654bd1a0728a072024b326", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/fa8f0b2e9e654bd1a0728a072024b326.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/fa8f0b2e9e654bd1a0728a072024b326"}}, "title": "Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.", "authors": [{"family": "Chen", "given": "Yu", "initials": "Y", "orcid": "0000-0003-3326-9068", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4d5b3f948bb84c05a28b5feeb3738128.json"}}, {"family": "Gustafsson", "given": "Johan", "initials": "J", "orcid": "0000-0001-5072-2659", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/39eef1abb23c4ecab986fe0646830f07.json"}}, {"family": "Tafur Rangel", "given": "Albert", "initials": "A", "orcid": "0000-0002-9428-183X", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/6bb75f8d7aa64fdda5093249081d4bb2.json"}}, {"family": "Anton", "given": "Mihail", "initials": "M", "orcid": "0000-0002-7753-9042", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/733c89e6b386409392abf45608738222.json"}}, {"family": "Domenzain", "given": "Iv\u00e1n", "initials": "I", "orcid": "0000-0002-5322-2040", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/588d2eed593448689fd63a7e656b5615.json"}}, {"family": "Kittikunapong", "given": "Cheewin", "initials": "C"}, {"family": "Li", "given": "Feiran", "initials": "F", "orcid": "0000-0001-9155-5260", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/638ad58b8b28497a83955cea3c7dee17.json"}}, {"family": "Yuan", "given": "Le", "initials": "L", "orcid": "0000-0003-3317-9011", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fb344bfa13e345bd8632a85d937f09df.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J"}, {"family": "Kerkhoven", "given": "Eduard J", "initials": "EJ", "orcid": "0000-0002-3593-5792", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/182c8731b6744479864cc75ced13d229.json"}}], "type": "journal article", "published": "2024-03-00", "journal": {"title": "Nat Protoc", "issn": "1750-2799", "volume": "19", "issue": "3", "pages": "629-667", "issn-l": null}, "abstract": "Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.", "doi": "10.1038/s41596-023-00931-7", "pmid": "38238583", "labels": [], "xrefs": [{"db": "pii", "key": "10.1038/s41596-023-00931-7"}], "notes": [], "created": "2026-08-20T09:03:59.144Z", "modified": "2026-08-20T09:03:59.381Z"}, {"entity": "publication", "iuid": "51db658804ca4eb2a4f054d3c835eb58", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/51db658804ca4eb2a4f054d3c835eb58.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/51db658804ca4eb2a4f054d3c835eb58"}}, "title": "Yeast metabolic innovations emerged via expanded metabolic network and gene positive selection.", "authors": [{"family": "Lu", "given": "Hongzhong", "initials": "H"}, {"family": "Li", "given": "Feiran", "initials": "F", "orcid": "0000-0001-9155-5260", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/638ad58b8b28497a83955cea3c7dee17.json"}}, {"family": "Yuan", "given": "Le", "initials": "L", "orcid": "0000-0003-3317-9011", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/fb344bfa13e345bd8632a85d937f09df.json"}}, {"family": "Domenzain", "given": "Iv\u00e1n", "initials": "I"}, {"family": "Yu", "given": "Rosemary", "initials": "R", "orcid": "0000-0001-9901-4055", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/b720cb77fa854c4e8942646d5f46ea4b.json"}}, {"family": "Wang", "given": "Hao", "initials": "H", "orcid": "0000-0001-7475-0136", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/77429d26b3884b328ef827033ff1e7b6.json"}}, {"family": "Li", "given": "Gang", "initials": "G", "orcid": "0000-0001-6778-2842", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/915d0247b03e4403aebdbec47a071798.json"}}, {"family": "Chen", "given": "Yu", "initials": "Y", "orcid": "0000-0003-3326-9068", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/4d5b3f948bb84c05a28b5feeb3738128.json"}}, {"family": "Ji", "given": "Boyang", "initials": "B", "orcid": "0000-0002-7269-4342", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/9d7490eea1834751b0909f45dc80a18e.json"}}, {"family": "Kerkhoven", "given": "Eduard J", "initials": "EJ", "orcid": "0000-0002-3593-5792", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/182c8731b6744479864cc75ced13d229.json"}}, {"family": "Nielsen", "given": "Jens", "initials": "J", "orcid": "0000-0002-9955-6003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/33f2b49a39ed4e54ba77dfe397ed3087.json"}}], "type": "journal article", "published": "2021-10-00", "journal": {"title": "Mol Syst Biol", "issn": "1744-4292", "volume": "17", "issue": "10", "pages": "e10427", "issn-l": "1744-4292"}, "abstract": "Yeasts are known to have versatile metabolic traits, while how these metabolic traits have evolved has not been elucidated systematically. We performed integrative evolution analysis to investigate how genomic evolution determines trait generation by reconstructing genome-scale metabolic models (GEMs) for 332 yeasts. These GEMs could comprehensively characterize trait diversity and predict enzyme functionality, thereby signifying that sequence-level evolution has shaped reaction networks towards new metabolic functions. Strikingly, using GEMs, we can mechanistically map different evolutionary events, e.g. horizontal gene transfer and gene duplication, onto relevant subpathways to explain metabolic plasticity. This demonstrates that gene family expansion and enzyme promiscuity are prominent mechanisms for metabolic trait gains, while GEM simulations reveal that additional factors, such as gene loss from distant pathways, contribute to trait losses. Furthermore, our analysis could pinpoint to specific genes and pathways that have been under positive selection and relevant for the formulation of complex metabolic traits, i.e. thermotolerance and the Crabtree effect. Our findings illustrate how multidimensional evolution in both metabolic network structure and individual enzymes drives phenotypic variations.", "doi": "10.15252/msb.202110427", "pmid": "34676984", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC8532513"}], "notes": [], "created": "2026-08-20T12:51:57.255Z", "modified": "2026-08-20T12:51:57.435Z"}]}