{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T12:07:30Z","timestamp":1788955650003,"version":"build-2803163510"},"reference-count":106,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,2,11]],"date-time":"2023-02-11T00:00:00Z","timestamp":1676073600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["iScience"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1016\/j.isci.2023.106201","type":"journal-article","created":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T18:05:58Z","timestamp":1676484358000},"page":"106201","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":15,"title":["Phenotype-specific estimation of metabolic fluxes using gene expression data"],"prefix":"10.1016","volume":"26","author":[{"given":"Nicol\u00e1s","family":"Gonz\u00e1lez-Arru\u00e9","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Isidora","family":"Inostroza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ra\u00fal","family":"Conejeros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1809-3922","authenticated-orcid":false,"given":"Marcelo","family":"Rivas-Astroza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.isci.2023.106201_bib1","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1242\/dev.091777","article-title":"Stem cell metabolism in tissue development and aging","volume":"140","author":"Shyh-Chang","year":"2013","journal-title":"Development"},{"key":"10.1016\/j.isci.2023.106201_bib2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s41232-020-00117-8","article-title":"Metabolic re-modeling during somatic cell reprogramming to induced pluripotent stem cells: involvement of hypoxia-inducible factor 1","volume":"40","author":"Ishida","year":"2020","journal-title":"Inflamm. Regen."},{"key":"10.1016\/j.isci.2023.106201_bib3","doi-asserted-by":"crossref","first-page":"958","DOI":"10.1038\/s42255-020-0266-x","article-title":"Cellular metabolic reprogramming controls sugar appetite in Drosophila","volume":"2","author":"Carvalho-Santos","year":"2020","journal-title":"Nat. Metab."},{"key":"10.1016\/j.isci.2023.106201_bib4","doi-asserted-by":"crossref","first-page":"11299","DOI":"10.1073\/pnas.1406401111","article-title":"Improving fatty acids production by engineering dynamic pathway regulation and metabolic control","volume":"111","author":"Xu","year":"2014","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.isci.2023.106201_bib5","doi-asserted-by":"crossref","first-page":"1688","DOI":"10.1038\/s41467-017-01695-x","article-title":"Combinatorial metabolic engineering using an orthogonal tri-functional CRISPR system","volume":"8","author":"Lian","year":"2017","journal-title":"Nat. Commun."},{"key":"10.1016\/j.isci.2023.106201_bib6","doi-asserted-by":"crossref","first-page":"3751","DOI":"10.1038\/s41467-019-11793-7","article-title":"Programmable biomolecular switches for rewiring flux in Escherichia coli","volume":"10","author":"Gao","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.isci.2023.106201_bib7","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1038\/s41587-021-01195-w","article-title":"Carbon-negative production of acetone and isopropanol by gas fermentation at industrial pilot scale","volume":"40","author":"Liew","year":"2022","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib8","doi-asserted-by":"crossref","first-page":"e1600200","DOI":"10.1126\/sciadv.1600200","article-title":"Fundamentals of cancer metabolism","volume":"2","author":"DeBerardinis","year":"2016","journal-title":"Sci. Adv."},{"key":"10.1016\/j.isci.2023.106201_bib9","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.cmet.2015.12.006","article-title":"The emerging hallmarks of cancer metabolism","volume":"23","author":"Pavlova","year":"2016","journal-title":"Cell Metabol."},{"key":"10.1016\/j.isci.2023.106201_bib10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-10189-x","article-title":"Diabetes causes marked inhibition of mitochondrial metabolism in pancreatic \u03b2-cells","volume":"10","author":"Haythorne","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.isci.2023.106201_bib11","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1016\/j.cell.2012.02.032","article-title":"Cellular metabolism and disease: what do metabolic outliers teach us?","volume":"148","author":"DeBerardinis","year":"2012","journal-title":"Cell"},{"key":"10.1016\/j.isci.2023.106201_bib12","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.ymben.2003.12.002","article-title":"Integration of gene expression data into genome-scale metabolic models","volume":"6","author":"\u00c5kesson","year":"2004","journal-title":"Metab. Eng."},{"key":"10.1016\/j.isci.2023.106201_bib13","doi-asserted-by":"crossref","first-page":"10000822","DOI":"10.1371\/journal.pcbi.1000082","article-title":"Context-specific metabolic networks are consistent with experiments","volume":"4","author":"Becker","year":"2008","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib14","doi-asserted-by":"crossref","first-page":"10004899","DOI":"10.1371\/journal.pcbi.1000489","article-title":"Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production","volume":"5","author":"Colijn","year":"2009","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib15","doi-asserted-by":"crossref","first-page":"10026888","DOI":"10.1371\/journal.pcbi.1002688","article-title":"Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia","volume":"8","author":"Fang","year":"2012","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib16","doi-asserted-by":"crossref","first-page":"3140","DOI":"10.1093\/bioinformatics\/btq602","article-title":"iMAT: an integrative metabolic analysis tool","volume":"26","author":"Zur","year":"2010","journal-title":"Bioinformatics"},{"key":"10.1016\/j.isci.2023.106201_bib17","doi-asserted-by":"crossref","first-page":"e1002518","DOI":"10.1371\/journal.pcbi.1002518","article-title":"Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT","volume":"8","author":"Agren","year":"2012","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib18","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":"Agren","year":"2014","journal-title":"Mol. Syst. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib19","doi-asserted-by":"crossref","first-page":"e0157101","DOI":"10.1371\/journal.pone.0157101","article-title":"E-Flux2 and SPOT: validated methods for inferring intracellular metabolic flux distributions from transcriptomic data","volume":"11","author":"Kim","year":"2016","journal-title":"PLoS One"},{"key":"10.1016\/j.isci.2023.106201_bib20","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1203\/00006450-199506000-00001","article-title":"Translational control of gene expression","volume":"37","author":"Jansen","year":"1995","journal-title":"Pediatr. Res."},{"key":"10.1016\/j.isci.2023.106201_bib21","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/S0014-5793(01)02613-8","article-title":"Transcriptome meets metabolome: hierarchical and metabolic regulation of the glycolytic pathway","volume":"500","author":"ter Kuile","year":"2001","journal-title":"FEBS Lett."},{"key":"10.1016\/j.isci.2023.106201_bib22","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1007\/s00253-002-0949-0","article-title":"Integration of the information from gene expression and metabolic fluxes for the analysis of the regulatory mechanisms in Synechocystis","volume":"58","author":"Yang","year":"2002","journal-title":"Appl. Microbiol. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib23","doi-asserted-by":"crossref","first-page":"101396","DOI":"10.1016\/j.molmet.2021.101396","article-title":"Toward modeling metabolic state from single-cell transcriptomics","volume":"57","author":"Hrovatin","year":"2021","journal-title":"Mol. Metabol."},{"key":"10.1016\/j.isci.2023.106201_bib24","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":"10.1016\/j.isci.2023.106201_bib25","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","volume":"4","author":"Opdam","year":"2017","journal-title":"Cell Syst."},{"key":"10.1016\/j.isci.2023.106201_bib26","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.3389\/fmicb.2018.01690","article-title":"A review of dynamic modeling approaches and their application in computational strain optimization for metabolic engineering","volume":"9","author":"Kim","year":"2018","journal-title":"Front. Microbiol."},{"key":"10.1016\/j.isci.2023.106201_bib27","doi-asserted-by":"crossref","first-page":"614832","DOI":"10.3389\/fcell.2020.614832","article-title":"Computation of single cell metabolite distributions using mixture models","volume":"8","author":"Tonn","year":"2020","journal-title":"Front. Cell Dev. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.3390\/metabo10080303","article-title":"Metabolic modelling as a framework for metabolomics data integration and analysis","volume":"10","author":"Volkova","year":"2020","journal-title":"Metabolites"},{"key":"10.1016\/j.isci.2023.106201_bib29","doi-asserted-by":"crossref","first-page":"322","DOI":"10.3390\/pr9020322","article-title":"Modelling cell metabolism: a review on constraint based steady-state and kinetic approaches","volume":"9","author":"Yasemi","year":"2021","journal-title":"Processes"},{"key":"10.1016\/j.isci.2023.106201_bib30","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.copbio.2003.08.001","article-title":"Advances in flux balance analysis","volume":"14","author":"Kauffman","year":"2003","journal-title":"Curr. Opin. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib31","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":"10.1016\/j.isci.2023.106201_bib32","first-page":"1218","article-title":"Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling","volume":"19","author":"Vijayakumar","year":"2018","journal-title":"Briefings Bioinf."},{"key":"10.1016\/j.isci.2023.106201_bib33","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1007\/s11831-018-9290-3","article-title":"Mathematical framework behind the reconstruction and analysis of genome scale metabolic models","volume":"26","author":"Pinzon","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"10.1016\/j.isci.2023.106201_bib34","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s11306-012-0462-0","article-title":"MetaboLights: towards a new COSMOS of metabolomics data management","volume":"8","author":"Steinbeck","year":"2012","journal-title":"Metabolomics"},{"key":"10.1016\/j.isci.2023.106201_bib35","doi-asserted-by":"crossref","first-page":"D991","DOI":"10.1093\/nar\/gks1193","article-title":"NCBI geo: archive for functional genomics data sets\u2014update","volume":"41","author":"Barrett","year":"2013","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.isci.2023.106201_bib36","doi-asserted-by":"crossref","first-page":"D463","DOI":"10.1093\/nar\/gkv1042","article-title":"Metabolomics Workbench: an international repository for metabolomics data and metadata, metabolite standards, protocols, tutorials and training, and analysis tools","volume":"44","author":"Sud","year":"2016","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.isci.2023.106201_bib37","doi-asserted-by":"crossref","first-page":"D711","DOI":"10.1093\/nar\/gky964","article-title":"ArrayExpress update\u00a0\u2013 from bulk to single-cell expression data","volume":"47","author":"Athar","year":"2019","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.isci.2023.106201_bib38","doi-asserted-by":"crossref","first-page":"D442","DOI":"10.1093\/nar\/gky1106","article-title":"The PRIDE database and related tools and resources in 2019: improving support for quantification data","volume":"47","author":"Perez-Riverol","year":"2018","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.isci.2023.106201_bib39","first-page":"D77","article-title":"Expression Atlas update: from tissues to single cells","volume":"48","author":"Papatheodorou","year":"2020","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.isci.2023.106201_bib40","doi-asserted-by":"crossref","first-page":"eaay5947","DOI":"10.1126\/science.aay5947","article-title":"An atlas of the protein-coding genes in the human, pig, and mouse brain","volume":"367","author":"Sj\u00f6stedt","year":"2020","journal-title":"Science"},{"key":"10.1016\/j.isci.2023.106201_bib41","doi-asserted-by":"crossref","first-page":"eabh2169","DOI":"10.1126\/sciadv.abh2169","article-title":"A single\u2013cell type transcriptomics map of human tissues","volume":"7","author":"Karlsson","year":"2021","journal-title":"Sci. Adv."},{"key":"10.1016\/j.isci.2023.106201_bib42","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.molcel.2015.05.004","article-title":"High-throughput sequencing technologies","volume":"58","author":"Reuter","year":"2015","journal-title":"Mol. Cell"},{"key":"10.1016\/j.isci.2023.106201_bib43","doi-asserted-by":"crossref","first-page":"e27041","DOI":"10.7554\/eLife.27041","article-title":"Science forum: the human cell atlas","volume":"6","author":"Regev","year":"2017","journal-title":"Elife"},{"key":"10.1016\/j.isci.2023.106201_bib44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystems.2016.06.001","article-title":"Advances in the integration of transcriptional regulatory information into genome-scale metabolic models","volume":"147","author":"Vivek-Ananth","year":"2016","journal-title":"Biosystems"},{"key":"10.1016\/j.isci.2023.106201_bib45","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1038\/nbt.1487","article-title":"Network based prediction of human tissue-specific metabolism","volume":"26","author":"Shlomi","year":"2008","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib46","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0043006","article-title":"Predictive potential of flux balance analysis of Saccharomyces cerevisiae using as optimization function combinations of cell compartmental objectives","volume":"7","author":"Garc\u00eda S\u00e1nchez","year":"2012","journal-title":"PLoS One"},{"key":"10.1016\/j.isci.2023.106201_bib47","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1038\/84379","article-title":"In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data","volume":"19","author":"Edwards","year":"2001","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib48","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1038\/nature01149","article-title":"Escherichia coli K-12 undergoes adaptive evolution to achieve in\u00a0silico predicted optimal growth","volume":"420","author":"Ibarra","year":"2002","journal-title":"Nature"},{"key":"10.1016\/j.isci.2023.106201_bib49","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1186\/1471-2105-9-43","article-title":"Predicting biological system objectives de novo from internal state measurements","volume":"9","author":"Gianchandani","year":"2008","journal-title":"BMC Bioinf."},{"key":"10.1016\/j.isci.2023.106201_bib50","doi-asserted-by":"crossref","first-page":"1690","DOI":"10.1007\/s11306-015-0819-2","article-title":"Biomass composition: the \u201celephant in the room\u201d of metabolic modelling","volume":"11","author":"Dikicioglu","year":"2015","journal-title":"Metabolomics"},{"key":"10.1016\/j.isci.2023.106201_bib51","doi-asserted-by":"crossref","first-page":"8304260","DOI":"10.1155\/2019\/8304260","article-title":"Human systems biology and metabolic modelling: a review\u2014from disease metabolism to precision medicine","volume":"2019","author":"Angione","year":"2019","journal-title":"BioMed Res. Int."},{"key":"10.1016\/j.isci.2023.106201_bib52","doi-asserted-by":"crossref","first-page":"e0243067","DOI":"10.1371\/journal.pone.0243067","article-title":"Metabolic flux configuration determination using information entropy","volume":"15","author":"Rivas-Astroza","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.isci.2023.106201_bib53","doi-asserted-by":"crossref","first-page":"19033","DOI":"10.1073\/pnas.0609152103","article-title":"Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns","volume":"103","author":"Lezon","year":"2006","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.isci.2023.106201_bib54","doi-asserted-by":"crossref","first-page":"036005","DOI":"10.1088\/1478-3975\/13\/3\/036005","article-title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","volume":"13","author":"Martino","year":"2016","journal-title":"Phys. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib55","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1038\/s41467-018-05417-9","article-title":"Statistical mechanics for metabolic networks during steady state growth","volume":"9","author":"De Martino","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.isci.2023.106201_bib56","doi-asserted-by":"crossref","first-page":"e1006823","DOI":"10.1371\/journal.pcbi.1006823","article-title":"Maximum entropy and population heterogeneity in continuous cell cultures","volume":"1","author":"Fernandez-de-Cossio-Diaz","year":"2019","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib57","doi-asserted-by":"crossref","first-page":"2395","DOI":"10.1007\/s00285-020-01499-6","article-title":"Dynamic metabolic resource allocation based on the maximum entropy principle","volume":"80","author":"Tourigny","year":"2020","journal-title":"J.\u00a0Math. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib58","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jtbi.2011.09.029","article-title":"A variational principle for computing nonequilibrium fluxes and potentials in genome-scale biochemical networks","volume":"292","author":"Fleming","year":"2012","journal-title":"J.\u00a0Theor. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib59","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon","year":"1948","journal-title":"Bell System Technical Journal"},{"key":"10.1016\/j.isci.2023.106201_bib60","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1103\/PhysRev.106.620","article-title":"Information theory and statistical mechanics","volume":"106","author":"Jaynes","year":"1957","journal-title":"Phys. Rev."},{"key":"10.1016\/j.isci.2023.106201_bib61","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1103\/PhysRev.108.171","article-title":"Information theory and statistical mechanics. II","volume":"108","author":"Jaynes","year":"1957","journal-title":"Phys. Rev."},{"key":"10.1016\/j.isci.2023.106201_bib62","first-page":"275","article-title":"Bias plus variance decomposition for zero-one loss functions","volume":"96","author":"Kohavi","year":"1996","journal-title":"ICML"},{"key":"10.1016\/j.isci.2023.106201_bib63","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.3390\/e17041971","article-title":"On the relationship between the second fundamental theorem of the mechanical theory of heat and probability calculations regarding the conditions for thermal equilibrium","volume":"17","author":"Sharp","year":"2015","journal-title":"Entropy"},{"key":"10.1016\/j.isci.2023.106201_bib64","doi-asserted-by":"crossref","first-page":"e0238689","DOI":"10.1371\/journal.pone.0238689","article-title":"Assessment of transcriptomic constraint based methods for central carbon flux inference","volume":"15","author":"Bhadra-Lobo","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.isci.2023.106201_bib65","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":"Ebrahim","year":"2015","journal-title":"Mol. Syst. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib66","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1529\/biophysj.106.093138","article-title":"Thermodynamics-based metabolic flux analysis","volume":"92","author":"Henry","year":"2007","journal-title":"Biophys. J."},{"key":"10.1016\/j.isci.2023.106201_bib67","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1016\/j.bpj.2010.12.3707","article-title":"Elimination of thermodynamically infeasible loops in steady-state metabolic models","volume":"100","author":"Schellenberger","year":"2011","journal-title":"Biophys. J."},{"key":"10.1016\/j.isci.2023.106201_bib68","doi-asserted-by":"crossref","first-page":"2159","DOI":"10.1093\/bioinformatics\/btv096","article-title":"CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions","volume":"31","author":"Desouki","year":"2015","journal-title":"Bioinformatics"},{"key":"10.1016\/j.isci.2023.106201_bib69","article-title":"Removing both internal and unrealistic energy-generating cycles in flux balance analysis","author":"Noor","year":"2018","journal-title":"arXiv"},{"key":"10.1016\/j.isci.2023.106201_bib70","doi-asserted-by":"crossref","first-page":"3765","DOI":"10.1073\/pnas.0306244101","article-title":"Universality and flexibility in gene expression from bacteria to human","volume":"101","author":"Ueda","year":"2004","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.isci.2023.106201_bib71","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1038\/msb.2011.65","article-title":"A comprehensive genome-scale reconstruction of Escherichia coli metabolism\u20142011","volume":"7","author":"Orth","year":"2011","journal-title":"Mol. Syst. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib72","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1186\/1752-0509-3-37","article-title":"Connecting extracellular metabolomic measurements to intracellular flux states in yeast","volume":"3","author":"Mo","year":"2009","journal-title":"BMC Syst. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib73","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/1475-2859-11-136","article-title":"Scheffersomyces stipitis: a com parative systems biology study with the Crabtree positive yeast Saccharomyces cerevisiae","volume":"11","author":"Papini","year":"2012","journal-title":"Microb. Cell Factories"},{"key":"10.1016\/j.isci.2023.106201_bib74","doi-asserted-by":"crossref","first-page":"16005","DOI":"10.1038\/npjsba.2016.5","article-title":"Regulation of amino-acid metabolism controls flux to lipid accumulation in Yarrowia lipolytica","volume":"2","author":"Kerkhoven","year":"2016","journal-title":"NPJ Syst. Biol. Appl."},{"key":"10.1016\/j.isci.2023.106201_bib75","doi-asserted-by":"crossref","first-page":"28791","DOI":"10.1074\/jbc.M703759200","article-title":"Genome scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data","volume":"282","author":"Oh","year":"2007","journal-title":"J.\u00a0Biol. Chem."},{"key":"10.1016\/j.isci.2023.106201_bib76","doi-asserted-by":"crossref","first-page":"688","DOI":"10.1371\/journal.pcbi.0010068","article-title":"The activity reaction core and plasticity of metabolic networks","volume":"1","author":"Almaas","year":"2005","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib77","doi-asserted-by":"crossref","first-page":"1136","DOI":"10.1128\/JB.182.4.1136-1143.2000","article-title":"The metabolic network of lactococcus lactis: distribution of 14C-labeled substrates between catabolic and anabolic pathways","volume":"182","author":"Nov\u00e1k","year":"2000","journal-title":"J.\u00a0Bacteriol."},{"key":"10.1016\/j.isci.2023.106201_bib78","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.copbio.2014.12.003","article-title":"How to measure metabolic fluxes: a taxonomic guide for (13)C fluxomics","volume":"34","author":"Niedenf\u00fchr","year":"2015","journal-title":"Curr. Opin. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib105","doi-asserted-by":"crossref","first-page":"e86587","DOI":"10.1371\/journal.pone.0086587","article-title":"optGpSampler : an improved tool for uniformly sampling the solution-space of genome-scale metabolic networks","volume":"9","author":"Megchelenbrink","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.isci.2023.106201_bib79","doi-asserted-by":"crossref","first-page":"646","DOI":"10.1016\/j.cell.2011.02.013","article-title":"Hallmarks of cancer: the next generation","volume":"144","author":"Hanahan","year":"2011","journal-title":"Cell"},{"key":"10.1016\/j.isci.2023.106201_bib80","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1038\/nrc3038","article-title":"Otto Warburg\u2019s contributions to current concepts of cancer metabolism","volume":"11","author":"Koppenol","year":"2011","journal-title":"Nat. Rev. Cancer"},{"key":"10.1016\/j.isci.2023.106201_bib81","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1016\/j.cell.2015.12.034","article-title":"Metabolic heterogeneity in human lung tumors","volume":"164","author":"Hensley","year":"2016","journal-title":"Cell"},{"key":"10.1016\/j.isci.2023.106201_bib82","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1016\/j.cmet.2018.07.020","article-title":"Isotope tracing of human clear cell renal cell carcinomas demonstrates suppressed glucose oxidation in\u00a0vivo","volume":"28","author":"Courtney","year":"2018","journal-title":"Cell Metabol."},{"key":"10.1016\/j.isci.2023.106201_bib83","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1007\/s12094-019-02187-8","article-title":"Emerging roles of aerobic glycolysis in breast cancer","volume":"22","author":"Wu","year":"2020","journal-title":"Clin. Transl. Oncol."},{"key":"10.1016\/j.isci.2023.106201_bib84","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1038\/s41571-019-0203-7","article-title":"Navigating metabolic pathways to enhance antitumour immunity and immunotherapy","volume":"16","author":"Li","year":"2019","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"10.1016\/j.isci.2023.106201_bib85","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1038\/nbt.4072","article-title":"Recon3D enables a three-dimensional view of gene variation in human metabolism","volume":"36","author":"Brunk","year":"2018","journal-title":"Nat. Biotechnol."},{"key":"10.1016\/j.isci.2023.106201_bib86","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1126\/science.123.3191.309","article-title":"On the origin of cancer cells","volume":"123","author":"Warburg","year":"1956","journal-title":"Science"},{"key":"10.1016\/j.isci.2023.106201_bib87","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1038\/nrg3185","article-title":"Insights into the regulation of protein abundance from proteomic and transcriptomic analyses","volume":"13","author":"Vogel","year":"2012","journal-title":"Nat. Rev. Genet."},{"key":"10.1016\/j.isci.2023.106201_bib88","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1038\/s43588-021-00074-3","article-title":"Dynamic flux balance analysis of whole-body metabolism for type 1 diabetes","volume":"1","author":"Ben Guebila","year":"2021","journal-title":"Nat. Comput. Sci."},{"key":"10.1016\/j.isci.2023.106201_bib89","doi-asserted-by":"crossref","first-page":"2717","DOI":"10.1002\/bit.26384","article-title":"Quantitative intracellular flux modeling and applications in biotherapeutic development and production using CHO cell cultures","volume":"114","author":"Huang","year":"2017","journal-title":"Biotechnol. Bioeng."},{"key":"10.1016\/j.isci.2023.106201_bib90","doi-asserted-by":"crossref","first-page":"2250010","DOI":"10.1142\/S021972002250010X","article-title":"Flux balance network expansion predicts stage-specific human peri_implantation embryo metabolism","volume":"20","author":"Dadashi","year":"2022","journal-title":"J.\u00a0Bioinf. Comput. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib91","doi-asserted-by":"crossref","first-page":"3401","DOI":"10.1073\/pnas.1514240113","article-title":"Global characterization of in vivo enzyme catalytic rates and their correspondence to in vitro kcat measurements","volume":"113","author":"Davidi","year":"2016","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.isci.2023.106201_bib92","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1214\/aoms\/1177729694","article-title":"On information and sufficiency","volume":"22","author":"Kullback","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"10.1016\/j.isci.2023.106201_bib93","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1093\/bioinformatics\/bts635","article-title":"STAR: ultrafast universal RNA-seq aligner","volume":"29","author":"Dobin","year":"2013","journal-title":"Bioinformatics"},{"key":"10.1016\/j.isci.2023.106201_bib94","doi-asserted-by":"crossref","first-page":"4697","DOI":"10.1158\/0008-5472.CAN-17-0395","article-title":"MAPK signaling and inflammation link melanoma phenotype switching to induction of CD73 during immunotherapy","volume":"77","author":"Reinhardt","year":"2017","journal-title":"Cancer Res."},{"key":"10.1016\/j.isci.2023.106201_bib95","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/1752-0509-7-74","article-title":"COBRApy: constraints based reconstruction and analysis for python","volume":"7","author":"Ebrahim","year":"2013","journal-title":"BMC Syst. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib96","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1007\/s10107-004-0559-y","article-title":"On the implementation of an interior-point filter line search algorithm for large-scale nonlinear programming","volume":"106","author":"W\u00e4chter","year":"2006","journal-title":"Math. Program."},{"key":"10.1016\/j.isci.2023.106201_bib97","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12532-018-0139-4","article-title":"CasADi \u2013 a software framework for nonlinear optimization and optimal control","volume":"11","author":"Andersson","year":"2019","journal-title":"Math. Program. Comput."},{"key":"10.1016\/j.isci.2023.106201_bib98","doi-asserted-by":"crossref","first-page":"10084","DOI":"10.1093\/nar\/gks804","article-title":"A comprehensive comparison of RNA-Seq-based transcriptome analysis from reads to differential gene expression and cross-comparison with microarrays: a case study in Saccharomyces cerevisiae","volume":"40","author":"Nookaew","year":"2012","journal-title":"Nucleic Acids Res."},{"key":"10.1016\/j.isci.2023.106201_bib99","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s12934-017-0690-0","article-title":"Substrates and oxygen dependent citric acid production by Yarrowia lipolytica: insights through transcriptome and fluxome analyses","volume":"16","author":"Sabra","year":"2017","journal-title":"Microb. Cell Factories"},{"key":"10.1016\/j.isci.2023.106201_bib100","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":"Gerosa","year":"2015","journal-title":"Cell Syst."},{"key":"10.1016\/j.isci.2023.106201_bib101","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1038\/msb.2013.66","article-title":"Transcriptional regulation is insufficient to explain substrate induced flux changes in Bacillus subtilis","volume":"9","author":"Chubukov","year":"2013","journal-title":"Mol. Syst. Biol."},{"key":"10.1016\/j.isci.2023.106201_bib102","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1126\/science.1206848","article-title":"Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis","volume":"335","author":"Nicolas","year":"2012","journal-title":"Science"},{"key":"10.1016\/j.isci.2023.106201_bib103","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s13059-019-1661-z","article-title":"Genome scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors","volume":"20","author":"Shen","year":"2019","journal-title":"Genome Biol."},{"key":"10.1016\/j.isci.2023.106201_bib104","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1186\/1754-6834-5-72","article-title":"A constraint-based model of Scheffersomyces stipitis for improved ethanol production","volume":"5","author":"Liu","year":"2012","journal-title":"Biotechnol. Biofuels"},{"key":"10.1016\/j.isci.2023.106201_bib106","series-title":"Python Reference Manual","author":"Van Rossum","year":"1995"}],"container-title":["iScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S258900422300278X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S258900422300278X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:27:31Z","timestamp":1740180451000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S258900422300278X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":106,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["S258900422300278X"],"URL":"https:\/\/doi.org\/10.1016\/j.isci.2023.106201","relation":{},"ISSN":["2589-0042"],"issn-type":[{"value":"2589-0042","type":"print"}],"subject":[],"published":{"date-parts":[[2023,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Phenotype-specific estimation of metabolic fluxes using gene expression data","name":"articletitle","label":"Article Title"},{"value":"iScience","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.isci.2023.106201","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 The Author(s).","name":"copyright","label":"Copyright"}],"article-number":"106201"}}