{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-growth-rate-from-gene-expression","title":"Predicting Growth Rate from Gene Expression","arxiv_id":"1901.05010","date":"2019-01-15","proceeding":null,"authors":[],"abstract":"Growth rate is one of the most important and most complex phenotypic\ncharacteristics of unicellular microorganisms, which determines the genetic\nmutations that dominate at the population level, and ultimately whether the\npopulation will survive. Translating changes at the genetic level to their\ngrowth rate consequences remains a subject of intense interest, since such a\nmapping could rationally direct experiments to optimize antibiotic efficacy or\nbioreactor productivity. In this paper, we directly map transcriptional\nprofiles to growth rates by gathering published gene-expression data from\nEscherichia coli and Saccharomyces cerevisiae with corresponding growth-rate\nmeasurements. Using a machine-learning technique called k-nearest-neighbors\nregression, we build a model which predicts growth rate from gene expression.\nBy exploiting the correlated nature of gene expression and sparsifying the\nmodel, we capture 81% of the variance in growth rate of the E. coli dataset\nwhile reducing the number of features from over 4,000 to nine. In S.\ncerevisiae, we account for 89% of the variance in growth rate while reducing\nfrom over 5,500 dimensions to 18. Such a model provides a basis for selecting\nsuccessful strategies from among the combinatorial number of experimental\npossibilities when attempting to optimize complex phenotypic traits like growth\nrate.","url_abs":"http://arxiv.org/abs/1901.05010v1","url_pdf":"http://arxiv.org/pdf/1901.05010v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-growth-rate-from-gene-expression","repo_url":"https://github.com/twytock/MI-POGUE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}