{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:33:07Z","timestamp":1773797587181,"version":"3.50.1"},"reference-count":24,"publisher":"Oxford University Press (OUP)","issue":"14","license":[{"start":{"date-parts":[[2017,7,12]],"date-time":"2017-07-12T00:00:00Z","timestamp":1499817600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Intratumour heterogeneity poses many challenges to the treatment of cancer. Unfortunately, the transcriptional and metabolic information retrieved by currently available computational and experimental techniques portrays the average behaviour of intermixed and heterogeneous cell subpopulations within a given tumour. Emerging single-cell genomic analyses are nonetheless unable to characterize the interactions among cancer subpopulations. In this study, we propose popFBA, an extension to classic Flux Balance Analysis, to explore how metabolic heterogeneity and cooperation phenomena affect the overall growth of cancer cell populations.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We show how clones of a metabolic network of human central carbon metabolism, sharing the same stoichiometry and capacity constraints, may follow several different metabolic paths and cooperate to maximize the growth of the total population. We also introduce a method to explore the space of possible interactions, given some constraints on plasma supply of nutrients. We illustrate how alternative nutrients in plasma supply and\/or a dishomogeneous distribution of oxygen provision may affect the landscape of heterogeneous phenotypes. We finally provide a technique to identify the most proliferative cells within the heterogeneous population.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>the popFBA MATLAB function and the SBML model are available at https:\/\/github.com\/BIMIB-DISCo\/popFBA.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx251","type":"journal-article","created":{"date-parts":[[2017,4,20]],"date-time":"2017-04-20T07:52:13Z","timestamp":1492674733000},"page":"i311-i318","source":"Crossref","is-referenced-by-count":31,"title":["popFBA: tackling intratumour heterogeneity with Flux Balance Analysis"],"prefix":"10.1093","volume":"33","author":[{"given":"Chiara","family":"Damiani","sequence":"first","affiliation":[{"name":"SYSBIO Centre of Systems Biology, Milan, Italy"},{"name":"Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marzia","family":"Di Filippo","sequence":"additional","affiliation":[{"name":"SYSBIO Centre of Systems Biology, Milan, Italy"},{"name":"Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dario","family":"Pescini","sequence":"additional","affiliation":[{"name":"SYSBIO Centre of Systems Biology, Milan, Italy"},{"name":"Department of Statistics and Quantitative Methods, University Milano-Bicocca, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Davide","family":"Maspero","sequence":"additional","affiliation":[{"name":"Department of Biotechnology and Biosciences, University Milano-Bicocca, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riccardo","family":"Colombo","sequence":"additional","affiliation":[{"name":"SYSBIO Centre of Systems Biology, Milan, Italy"},{"name":"Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giancarlo","family":"Mauri","sequence":"additional","affiliation":[{"name":"SYSBIO Centre of Systems Biology, Milan, Italy"},{"name":"Department of Informatics, Systems and Communication, University Milano-Bicocca, Milan, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2017,7,12]]},"reference":[{"key":"2023051506494695100_btx251-B1","doi-asserted-by":"crossref","first-page":"e1000859.","DOI":"10.1371\/journal.pcbi.1000859","article-title":"Sampling the solution space in genome-scale metabolic networks reveals transcriptional regulation in key enzymes","volume":"6","author":"Bordel","year":"2010","journal-title":"PLoS Comput. Biol"},{"key":"2023051506494695100_btx251-B2","doi-asserted-by":"crossref","first-page":"338","DOI":"10.1038\/nature12625","article-title":"The causes and consequences of genetic heterogeneity in cancer evolution","volume":"501","author":"Burrell","year":"2013","journal-title":"Nature"},{"key":"2023051506494695100_btx251-B3","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1158\/2159-8290.CD-12-0345","article-title":"Cancer cell metabolism: one hallmark, many faces","volume":"2","author":"Cantor","year":"2012","journal-title":"Cancer Disc"},{"key":"2023051506494695100_btx251-B4","doi-asserted-by":"crossref","first-page":"1034","DOI":"10.3390\/metabo4041034","article-title":"Computational strategies for a system-level understanding of metabolism","volume":"4","author":"Cazzaniga","year":"2014","journal-title":"Metabolites"},{"key":"2023051506494695100_btx251-B5","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/s11047-014-9439-4","article-title":"An ensemble evolutionary constraint-based approach to understand the emergence of metabolic phenotypes","volume":"13","author":"Damiani","year":"2014","journal-title":"Nat. Comput"},{"key":"2023051506494695100_btx251-B6","doi-asserted-by":"crossref","first-page":"946","DOI":"10.3390\/metabo3040946","article-title":"Counting and correcting thermodynamically infeasible flux cycles in genome-scale metabolic networks","volume":"3","author":"De Martino","year":"2013","journal-title":"Metabolites"},{"key":"2023051506494695100_btx251-B7","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.compbiolchem.2016.03.002","article-title":"Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models","volume":"62","author":"Di Filippo","year":"2016","journal-title":"Comput. Biol. Chem"},{"key":"2023051506494695100_btx251-B8","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-57711-1_11","article-title":"Constraint-Based Modeling and Simulation of Cell Populations","author":"Di Filippo","year":"2017","journal-title":"Advances in Artificial Life, Evolutionary Computation, and Systems Chemistry"},{"key":"2023051506494695100_btx251-B9","doi-asserted-by":"crossref","first-page":"5130","DOI":"10.1158\/0008-5472.CAN-12-1949","article-title":"Reciprocal metabolic reprogramming through lactate shuttle coordinately influences tumor-stroma interplay","volume":"72","author":"Fiaschi","year":"2012","journal-title":"Cancer Res"},{"key":"2023051506494695100_btx251-B10","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.cell.2008.08.026","article-title":"Metabolic phenotyping in health and disease","volume":"134","author":"Holmes","year":"2008","journal-title":"Cell"},{"key":"2023051506494695100_btx251-B11","doi-asserted-by":"crossref","first-page":"e1003580.","DOI":"10.1371\/journal.pcbi.1003580","article-title":"Systematic evaluation of methods for integration of transcriptomic data into constraint-based models of metabolism","volume":"10","author":"Machado","year":"2014","journal-title":"PLoS Comput. Biol"},{"key":"2023051506494695100_btx251-B12","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.ymben.2003.09.002","article-title":"The effects of alternate optimal solutions in constraint-based genome-scale metabolic models","volume":"5","author":"Mahadevan","year":"2003","journal-title":"Metab. Eng"},{"key":"2023051506494695100_btx251-B13","doi-asserted-by":"crossref","first-page":"649.","DOI":"10.1038\/msb.2013.5","article-title":"Integration of clinical data with a genome-scale metabolic model of the human adipocyte","volume":"9","author":"Mardinoglu","year":"2013","journal-title":"Mol. Syst. Biol"},{"key":"2023051506494695100_btx251-B14","doi-asserted-by":"crossref","first-page":"2504","DOI":"10.4161\/cc.10.15.16585","article-title":"Cancer cells metabolically \u201cfertilize\u201d the tumor microenvironment with hydrogen peroxide, driving the Warburg effect: implications for PET imaging of human tumors","volume":"10","author":"Martinez-Outschoorn","year":"2011","journal-title":"Cell Cycle"},{"key":"2023051506494695100_btx251-B15","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/nrc3261","article-title":"Intra-tumour heterogeneity: a looking glass for cancer?","volume":"12","author":"Marusyk","year":"2012","journal-title":"Nat. Rev. Cancer"},{"key":"2023051506494695100_btx251-B16","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/nbt.1614","article-title":"What is flux balance analysis?","volume":"28","author":"Orth","year":"2010","journal-title":"Nat. Biotechnol"},{"key":"2023051506494695100_btx251-B17","doi-asserted-by":"crossref","first-page":"1797","DOI":"10.1101\/gr.2546004","article-title":"Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states","volume":"14","author":"Reed","year":"2004","journal-title":"Genome Res"},{"key":"2023051506494695100_btx251-B18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.semcancer.2014.04.003","article-title":"Modeling metabolism: a window toward a comprehensive interpretation of networks in cancer","volume":"30","author":"Resendis-Antonio","year":"2015","journal-title":"Semin. Cancer Biol"},{"key":"2023051506494695100_btx251-B19","doi-asserted-by":"crossref","first-page":"154.","DOI":"10.1186\/1471-2407-14-154","article-title":"Tumor-stroma metabolic relationship based on lactate shuttle can sustain prostate cancer progression","volume":"14","author":"Sanit\u00e0","year":"2014","journal-title":"BMC Cancer"},{"key":"2023051506494695100_btx251-B20","doi-asserted-by":"crossref","first-page":"5457","DOI":"10.1074\/jbc.R800048200","article-title":"Use of randomized sampling for analysis of metabolic networks","volume":"284","author":"Schellenberger","year":"2009","journal-title":"J. Biol. Chem"},{"key":"2023051506494695100_btx251-B21","doi-asserted-by":"crossref","first-page":"1290","DOI":"10.1038\/nprot.2011.308","article-title":"Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox v2.0","volume":"6","author":"Schellenberger","year":"2011","journal-title":"Nat. Protoc"},{"key":"2023051506494695100_btx251-B22","doi-asserted-by":"crossref","first-page":"1219","DOI":"10.1038\/aps.2015.92","article-title":"Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment","volume":"36","author":"Sun","year":"2015","journal-title":"Acta Pharmacol. Sin"},{"key":"2023051506494695100_btx251-B23","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.ccr.2012.02.014","article-title":"Metabolic reprogramming: a cancer hallmark even warburg did not anticipate","volume":"21","author":"Ward","year":"2012","journal-title":"Cancer Cell"},{"key":"2023051506494695100_btx251-B24","doi-asserted-by":"crossref","first-page":"1772","DOI":"10.4161\/cc.10.11.15659","article-title":"Evidence for a stromal-epithelial \u201clactate shuttle\u201d in human tumors: MCT4 is a marker of oxidative stress in cancer-associated fibroblasts","volume":"10","author":"Whitaker-Menezes","year":"2011","journal-title":"Cell Cycle"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/14\/i311\/50315000\/bioinformatics_33_14_i311.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/33\/14\/i311\/50315000\/bioinformatics_33_14_i311.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T06:50:27Z","timestamp":1684133427000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/33\/14\/i311\/3953966"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,12]]},"references-count":24,"journal-issue":{"issue":"14","published-print":{"date-parts":[[2017,7,15]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btx251","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2017,7,15]]},"published":{"date-parts":[[2017,7,12]]}}}