{"day":"21","ec_funded":1,"year":"2017","publisher":"American Physical Society","date_published":"2017-12-21T00:00:00Z","publist_id":"7266","language":[{"iso":"eng"}],"intvolume":"        96","title":"Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes","scopus_import":"1","quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/1707.00320","open_access":"1"}],"oa_version":"Submitted Version","issue":"6","article_processing_charge":"No","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:47:06Z","month":"12","citation":{"apa":"De Martino, D. (2017). Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. <i>Physical Review E</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">https://doi.org/10.1103/PhysRevE.96.060401</a>","chicago":"De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” <i>Physical Review E</i>. American Physical Society, 2017. <a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">https://doi.org/10.1103/PhysRevE.96.060401</a>.","ista":"De Martino D. 2017. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. 96(6), 060401.","ieee":"D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” <i>Physical Review E</i>, vol. 96, no. 6. American Physical Society, 2017.","mla":"De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” <i>Physical Review E</i>, vol. 96, no. 6, 060401, American Physical Society, 2017, doi:<a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">10.1103/PhysRevE.96.060401</a>.","ama":"De Martino D. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. <i>Physical Review E</i>. 2017;96(6). doi:<a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">10.1103/PhysRevE.96.060401</a>","short":"D. De Martino, Physical Review E 96 (2017)."},"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"status":"public","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","type":"journal_article","volume":96,"date_updated":"2023-10-10T13:29:38Z","publication_identifier":{"issn":["2470-0045"]},"alternative_title":["Rapid Communications"],"author":[{"orcid":"0000-0002-5214-4706","full_name":"De Martino, Daniele","first_name":"Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","last_name":"De Martino"}],"doi":"10.1103/PhysRevE.96.060401","_id":"548","abstract":[{"text":"In this work maximum entropy distributions in the space of steady states of metabolic networks are considered upon constraining the first and second moments of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux distributions, emerges upon considering control on the average growth (optimization) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of Escherichia coli where it quantifies the metabolic activity of slow growing phenotypes and it provides a quantitative map with metabolic fluxes, opening the possibility to detect coexistence from flux data. A preliminary analysis on data for E. coli cultures in standard conditions shows degeneracy for the inferred parameters that extend in the coexistence region.","lang":"eng"}],"article_number":"060401","publication":"Physical Review E","publication_status":"published"}