{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T12:13:11Z","timestamp":1784117591656,"version":"3.55.0"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1009589","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000}}],"reference-count":53,"publisher":"Public Library of Science (PLoS)","issue":"11","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001711","name":"schweizerischer nationalfonds zur f\u00f6rderung der wissenschaftlichen forschung","doi-asserted-by":"publisher","award":["176279"],"award-info":[{"award-number":["176279"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F\u00f6rderung der Wissenschaftlichen Forschung","doi-asserted-by":"crossref","award":["163390"],"award-info":[{"award-number":["163390"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    Genome-scale metabolic models (GEMs) provide a powerful framework for simulating the entire set of biochemical reactions in a cell using a constraint-based modeling strategy called flux balance analysis (FBA). FBA relies on an assumed metabolic objective for generating metabolic fluxes using GEMs. But, the most appropriate metabolic objective is not always obvious for a given condition and is likely context-specific, which often complicate the estimation of metabolic flux alterations between conditions. Here, we propose a new method, called \u0394FBA (deltaFBA), that integrates differential gene expression data to evaluate directly metabolic flux differences between two conditions. Notably, \u0394FBA does not require specifying the cellular objective. Rather, \u0394FBA seeks to maximize the consistency and minimize inconsistency between the predicted flux differences and differential gene expression. We showcased the performance of \u0394FBA through several case studies involving the prediction of metabolic alterations caused by genetic and environmental perturbations in\n                    <jats:italic>Escherichia coli<\/jats:italic>\n                    and caused by Type-2 diabetes in human muscle. Importantly, in comparison to existing methods, \u0394FBA gives a more accurate prediction of flux differences.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1009589","type":"journal-article","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T14:55:31Z","timestamp":1636556131000},"page":"e1009589","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":34,"title":["\u0394FBA\u2014Predicting metabolic flux alterations using genome-scale metabolic models and differential transcriptomic data"],"prefix":"10.1371","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6059-5403","authenticated-orcid":true,"given":"Sudharshan","family":"Ravi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6480-7976","authenticated-orcid":true,"given":"Rudiyanto","family":"Gunawan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"pcbi.1009589.ref001","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1186\/s12859-019-2685-9","article-title":"Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer","volume":"20","author":"I Granata","year":"2019","journal-title":"BMC Bioinformatics"},{"key":"pcbi.1009589.ref002","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1002\/msb.145122","article-title":"Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling","volume":"10","author":"R Agren","year":"2014","journal-title":"Mol Syst Biol"},{"key":"pcbi.1009589.ref003","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ymben.2016.10.022","article-title":"Genome scale metabolic modeling of cancer","author":"A Nilsson","year":"2017","journal-title":"Metabolic Engineering. Academic Press Inc"},{"key":"pcbi.1009589.ref004","doi-asserted-by":"crossref","first-page":"237","DOI":"10.3389\/fphys.2013.00237","article-title":"The evolution of genome-scale models of cancer metabolism","volume":"4","author":"NE Lewis","year":"2013","journal-title":"Front Physiol"},{"key":"pcbi.1009589.ref005","doi-asserted-by":"crossref","first-page":"413","DOI":"10.3389\/fphys.2015.00413","article-title":"Applications of genome-scale metabolic models in biotechnology and systems medicine","volume":"6","author":"C Zhang","year":"2016","journal-title":"Front Physiol"},{"key":"pcbi.1009589.ref006","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/s13059-019-1730-3","article-title":"Current status and applications of genome-scale metabolic models","volume":"20","author":"C Gu","year":"2019","journal-title":"Genome Biol"},{"key":"pcbi.1009589.ref007","article-title":"Applications of genome-scale metabolic reconstructions","author":"MA Oberhardt","year":"2009","journal-title":"Molecular Systems Biology"},{"key":"pcbi.1009589.ref008","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/B978-0-12-385118-5.00024-4","article-title":"A practical guide to genome-scale metabolic models and their analysis","author":"F Santos","year":"2011","journal-title":"Methods in Enzymology. Academic Press Inc"},{"key":"pcbi.1009589.ref009","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1038\/nrg3643","article-title":"Constraint-based models predict metabolic and associated cellular functions","volume":"15","author":"A Bordbar","year":"2014","journal-title":"Nat Rev Genet"},{"key":"pcbi.1009589.ref010","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1038\/nrmicro2737","article-title":"Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods","author":"NE Lewis","year":"2012","journal-title":"Nature Reviews Microbiology. Nature Publishing Group"},{"key":"pcbi.1009589.ref011","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/nbt.1614","article-title":"What is flux balance analysis?","volume":"28","author":"JD Orth","year":"2010","journal-title":"Nat Biotechnol"},{"key":"pcbi.1009589.ref012","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.copbio.2008.08.007","article-title":"Towards systems metabolic engineering of microorganisms for amino acid production","author":"JH Park","year":"2008","journal-title":"Current Opinion in Biotechnology"},{"key":"pcbi.1009589.ref013","doi-asserted-by":"crossref","first-page":"612","DOI":"10.1002\/biot.200700240","article-title":"Strategies for systems-level metabolic engineering","author":"TY Kim","year":"2008","journal-title":"Biotechnology Journal"},{"key":"pcbi.1009589.ref014","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1128\/MMBR.00025-07","article-title":"Progress in Metabolic Engineering of Saccharomyces cerevisiae","volume":"72","author":"E Nevoigt","year":"2008","journal-title":"Microbiol Mol Biol Rev"},{"key":"pcbi.1009589.ref015","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1039\/B712395G","article-title":"Metabolic flux analysis and metabolic engineering of microorganisms","volume":"4","author":"HU Kim","year":"2008","journal-title":"Mol Biosyst"},{"key":"pcbi.1009589.ref016","doi-asserted-by":"crossref","first-page":"831","DOI":"10.15252\/msb.20156157","article-title":"Do genome-scale models need exact solvers or clearer standards?","volume":"11","author":"A Ebrahim","year":"2015","journal-title":"Mol Syst Biol"},{"key":"pcbi.1009589.ref017","doi-asserted-by":"crossref","first-page":"e1006867","DOI":"10.1371\/journal.pcbi.1006867","article-title":"Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions","volume":"15","author":"A Richelle","year":"2019","journal-title":"PLOS Comput Biol"},{"key":"pcbi.1009589.ref018","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1039\/C2MB25453K","article-title":"Analysis of omics data with genome-scale models of metabolism","author":"DR Hyduke","year":"2013","journal-title":"Molecular BioSystems. NIH Public Access"},{"key":"pcbi.1009589.ref019","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1038\/msb.2010.47","article-title":"Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models","volume":"6","author":"NE Lewis","year":"2010","journal-title":"Mol Syst Biol"},{"key":"pcbi.1009589.ref020","doi-asserted-by":"crossref","first-page":"e1000082","DOI":"10.1371\/journal.pcbi.1000082","article-title":"Context-specific metabolic networks are consistent with experiments","volume":"4","author":"SA Becker","year":"2008","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1009589.ref021","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1093\/bioinformatics\/btq602","article-title":"iMAT: an integrative metabolic analysis tool","volume":"26","author":"H Zur","year":"2010","journal-title":"Bioinformatics"},{"key":"pcbi.1009589.ref022","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1093\/bioinformatics\/btq702","article-title":"Functional integration of a metabolic network model and expression data without arbitrary thresholding","volume":"27","author":"PA Jensen","year":"2011","journal-title":"Bioinformatics"},{"key":"pcbi.1009589.ref023","doi-asserted-by":"crossref","first-page":"e1000489","DOI":"10.1371\/journal.pcbi.1000489","article-title":"Interpreting Expression Data with Metabolic Flux Models: Predicting Mycobacterium tuberculosis Mycolic Acid Production","volume":"5","author":"C Colijn","year":"2009","journal-title":"PLOS Comput Biol"},{"key":"pcbi.1009589.ref024","first-page":"1","article-title":"Improving metabolic flux predictions using absolute gene expression data","volume":"6","author":"D Lee","year":"2012","journal-title":"BMC Syst Biol"},{"key":"pcbi.1009589.ref025","first-page":"1","article-title":"RELATCH: relative optimality in metabolic networks explains robust metabolic and regulatory responses to perturbations","volume":"13","author":"J Kim","year":"2012","journal-title":"Genome Biol"},{"key":"pcbi.1009589.ref026","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1752-0509-6-150","article-title":"Genome-level transcription data of Yersinia pestis analyzed with a New metabolic constraint-based approach","volume":"6","author":"A Navid","year":"2012","journal-title":"BMC Syst Biol"},{"key":"pcbi.1009589.ref027","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":"D Machado","year":"2014","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1009589.ref028","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1038\/msb.2013.52","article-title":"Genome-scale models of metabolism and gene expression extend and refine growth phenotype prediction","volume":"9","author":"EJ O\u2019Brien","year":"2013","journal-title":"Mol Syst Biol"},{"key":"pcbi.1009589.ref029","doi-asserted-by":"crossref","first-page":"935","DOI":"10.15252\/msb.20167411","article-title":"Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints","volume":"13","author":"BJ S\u00e1nchez","year":"2017","journal-title":"Mol Syst Biol"},{"key":"pcbi.1009589.ref030","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1038\/s41467-019-13818-7","article-title":"The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models","volume":"11","author":"P Salvy","year":"2020","journal-title":"Nat Commun"},{"key":"pcbi.1009589.ref031","doi-asserted-by":"crossref","first-page":"e1007036","DOI":"10.1371\/journal.pcbi.1007036","article-title":"Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models","volume":"15","author":"V Pandey","year":"2019","journal-title":"PLoS Comput Biol"},{"key":"pcbi.1009589.ref032","doi-asserted-by":"crossref","first-page":"2418","DOI":"10.1039\/C7MB00462A","article-title":"A computational method using differential gene expression to predict altered metabolism of multicellular organisms","volume":"13","author":"L Zhu","year":"2017","journal-title":"Mol Biosyst"},{"key":"pcbi.1009589.ref033","doi-asserted-by":"crossref","first-page":"514","DOI":"10.1093\/bioinformatics\/btz584","article-title":"MOOMIN\u2014Mathematical explOration of \u2018Omics data on a MetabolIc Network","volume":"36","author":"T Pusa","year":"2020","journal-title":"Bioinformatics"},{"key":"pcbi.1009589.ref034","doi-asserted-by":"crossref","first-page":"639","DOI":"10.1038\/s41596-018-0098-2","article-title":"Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0","volume":"14","author":"L Heirendt","year":"2019","journal-title":"Nat Protoc"},{"key":"pcbi.1009589.ref035","volume":"316","author":"N Ishii","year":"2007","journal-title":"Multiple high-throughput analyses monitor the response of E. coli to perturbations"},{"key":"pcbi.1009589.ref036","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.cels.2015.09.008","article-title":"Pseudo-transition Analysis Identifies the Key Regulators of Dynamic Metabolic Adaptations from Steady-State Data","volume":"1","author":"L Gerosa","year":"2015","journal-title":"Cell Syst"},{"key":"pcbi.1009589.ref037","doi-asserted-by":"crossref","first-page":"921","DOI":"10.1016\/j.celrep.2015.04.010","article-title":"Proteome- and Transcriptome-Driven Reconstruction of the Human Myocyte Metabolic Network and Its Use for Identification of Markers for Diabetes","volume":"11","author":"L V\u00e4remo","year":"2015","journal-title":"Cell Rep"},{"key":"pcbi.1009589.ref038","doi-asserted-by":"crossref","unstructured":"Griva I, Nash S (Stephen G., Sofer A. Linear and nonlinear optimization. 2009; 742.","DOI":"10.1137\/1.9780898717730"},{"key":"pcbi.1009589.ref039","doi-asserted-by":"crossref","first-page":"15112","DOI":"10.1073\/pnas.232349399","article-title":"Analysis of optimality in natural and perturbed metabolic networks","volume":"99","author":"D Segr\u00e8","year":"2002","journal-title":"Proc Natl Acad Sci U S A"},{"key":"pcbi.1009589.ref040","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1016\/j.cels.2017.01.010","article-title":"A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models Article A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models","volume":"4","author":"S Opdam","year":"2017","journal-title":"Cell Syst"},{"key":"pcbi.1009589.ref041","doi-asserted-by":"crossref","first-page":"e1007185","DOI":"10.1371\/journal.pcbi.1007185","article-title":"Assessing key decisions for transcriptomic data integration in biochemical networks","volume":"15","author":"A Richelle","year":"2019","journal-title":"PLOS Comput Biol"},{"key":"pcbi.1009589.ref042","doi-asserted-by":"crossref","first-page":"e47","DOI":"10.1093\/nar\/gkv007","article-title":"Limma powers differential expression analyses for RNA-sequencing and microarray studies","volume":"43","author":"ME Ritchie","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"pcbi.1009589.ref043","doi-asserted-by":"crossref","first-page":"3261","DOI":"10.1210\/jc.2011-3454","article-title":"Physical activity is the key determinant of skeletal muscle mitochondrial function in type 2 diabetes","volume":"97","author":"FHJ Van Tienen","year":"2012","journal-title":"J Clin Endocrinol Metab"},{"key":"pcbi.1009589.ref044","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1172\/JCI41940","article-title":"Increased SRF transcriptional activity in human and mouse skeletal muscle is a signature of insulin resistance","volume":"121","author":"W Jin","year":"2011","journal-title":"J Clin Invest"},{"key":"pcbi.1009589.ref045","doi-asserted-by":"crossref","first-page":"926","DOI":"10.1016\/j.molmet.2016.08.001","article-title":"Defects in muscle branched-chain amino acid oxidation contribute to impaired lipid metabolism","volume":"5","author":"C Lerin","year":"2016","journal-title":"Mol Metab"},{"key":"pcbi.1009589.ref046","first-page":"389","article-title":"The role of AMP kinase in diabetes","author":"P Misra","year":"2007","journal-title":"Indian Journal of Medical Research. Indian J Med Res"},{"key":"pcbi.1009589.ref047","article-title":"Oxidative stress and calcium dysregulation by palmitate in type 2 diabetes","author":"LD Ly","year":"2017","journal-title":"Exp Mol Med"},{"key":"pcbi.1009589.ref048","article-title":"Palmitate induces a pro-inflammatory response in human pancreatic islets that mimics CCL2 expression by beta cells in type 2 diabetes","author":"M Igoillo-Esteve","year":"2010","journal-title":"Diabetologia"},{"key":"pcbi.1009589.ref049","article-title":"Arachidonic acid in health and disease with focus on hypertension and diabetes mellitus: A review","author":"UN Das","year":"2018","journal-title":"Journal of Advanced Research"},{"key":"pcbi.1009589.ref050","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.copbio.2014.12.016","article-title":"Next-generation genome-scale models for metabolic engineering","author":"ZA King","year":"2015","journal-title":"Current Opinion in Biotechnology. Elsevier Ltd"},{"key":"pcbi.1009589.ref051","first-page":"971","article-title":"Using genome-scale models to predict biological capabilities","author":"EJ O\u2019Brien","year":"2015","journal-title":"Cell. Cell Press"},{"key":"pcbi.1009589.ref052","article-title":"Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics","author":"F Xu","year":"2013","journal-title":"J Clin Endocrinol Metab"},{"key":"pcbi.1009589.ref053","article-title":"The relationship between phospholipids and insulin resistance: From clinical to experimental studies","author":"W Chang","year":"2019","journal-title":"Journal of Cellular and Molecular Medicine"}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1009589","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,11,22]],"date-time":"2021-11-22T00:00:00Z","timestamp":1637539200000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1009589","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,12]],"date-time":"2023-11-12T08:17:47Z","timestamp":1699777067000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1009589"}},"subtitle":[],"editor":[{"given":"Joerg","family":"Stelling","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2021,11,10]]},"references-count":53,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11,10]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1009589","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2021.01.18.427188","asserted-by":"object"}]},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,10]]}}}