{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T04:10:58Z","timestamp":1772165458635,"version":"3.50.1"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T00:00:00Z","timestamp":1582502400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T00:00:00Z","timestamp":1582502400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and\/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Production of ethanol by the widely used cell factory\n                      <jats:italic>Saccharomyces cerevisiae<\/jats:italic>\n                      was adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did an in vivo validation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      The multi-objective programming framework we developed, called\n                      <jats:sc>Momo<\/jats:sc>\n                      , is open-source and uses\n                      <jats:sc>PolySCIP<\/jats:sc>\n                      (Available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/polyscip.zib.de\/\">http:\/\/polyscip.zib.de\/<\/jats:ext-link>\n                      ). as underlying multi-objective solver.\n                      <jats:sc>Momo<\/jats:sc>\n                      is available at\n                      <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"http:\/\/momo-sysbio.gforge.inria.fr\">http:\/\/momo-sysbio.gforge.inria.fr<\/jats:ext-link>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-020-3377-1","type":"journal-article","created":{"date-parts":[[2020,2,24]],"date-time":"2020-02-24T11:09:09Z","timestamp":1582542549000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["MOMO - multi-objective metabolic mixed integer optimization: application to yeast strain engineering"],"prefix":"10.1186","volume":"21","author":[{"given":"Ricardo","family":"Andrade","sequence":"first","affiliation":[]},{"given":"Mahdi","family":"Doostmohammadi","sequence":"additional","affiliation":[]},{"given":"Jo\u00e3o L.","family":"Santos","sequence":"additional","affiliation":[]},{"given":"Marie-France","family":"Sagot","sequence":"additional","affiliation":[]},{"given":"Nuno P.","family":"Mira","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1954-5487","authenticated-orcid":false,"given":"Susana","family":"Vinga","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,2,24]]},"reference":[{"issue":"2","key":"3377_CR1","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1109\/TCBB.2007.070203","volume":"4","author":"J Handl","year":"2007","unstructured":"Handl J, Kell DB, Knowles J. Multiobjective optimization in bioinformatics and computational biology. IEEE\/ACM Trans Comput Biol Bioinforma. 2007; 4(2):279\u2013292. https:\/\/doi.org\/10.1109\/tcbb.2007.070203.","journal-title":"IEEE\/ACM Trans Comput Biol Bioinforma"},{"key":"3377_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.jbiotec.2016.01.005","volume":"222","author":"AF Villaverde","year":"2016","unstructured":"Villaverde AF, Bongard SP, Mauch K, Balsa-Canto E, Banga JR. Metabolic engineering with multi-objective optimization of kinetic models. J Biotechnol. 2016; 222:1\u20138.","journal-title":"J Biotechnol"},{"issue":"1","key":"3377_CR3","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/1752-0509-5-145","volume":"5","author":"W-H Wu","year":"2011","unstructured":"Wu W-H, Wang F-S, Chang MS. Multi-objective optimization of enzyme manipulations in metabolic networks considering resilience effects. BMC Syst Biol. 2011; 5(1):145.","journal-title":"BMC Syst Biol"},{"issue":"1","key":"3377_CR4","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s10479-018-2865-4","volume":"276","author":"A Patan\u00e9","year":"2019","unstructured":"Patan\u00e9 A, Jansen G, Conca P, Carapezza G, Costanza J, Nicosia G. Multi-objective optimization of genome-scale metabolic models: the case of ethanol production. Ann Oper Res. 2019; 276(1):211\u2013227.","journal-title":"Ann Oper Res"},{"issue":"6","key":"3377_CR5","doi-asserted-by":"publisher","first-page":"647","DOI":"10.1002\/bit.10803","volume":"84","author":"AP Burgard","year":"2003","unstructured":"Burgard AP, Pharkya P, Maranas CD. Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng. 2003; 84(6):647\u201357.","journal-title":"Biotechnol Bioeng"},{"key":"3377_CR6","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1016\/j.compchemeng.2014.06.007","volume":"72","author":"A Chowdhury","year":"2015","unstructured":"Chowdhury A, Zomorrodi AR, Maranas CD. Bilevel optimization techniques in computational strain design. Comput Chem Eng. 2015; 72:363\u201372.","journal-title":"Comput Chem Eng"},{"issue":"4","key":"3377_CR7","doi-asserted-by":"publisher","first-page":"536","DOI":"10.1093\/bioinformatics\/btp704","volume":"26","author":"N Tepper","year":"2009","unstructured":"Tepper N, Shlomi T. Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways. Bioinformatics. 2009; 26(4):536\u201343.","journal-title":"Bioinformatics"},{"issue":"1","key":"3377_CR8","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1186\/s12918-017-0515-0","volume":"11","author":"A Hartmann","year":"2017","unstructured":"Hartmann A, Vila-Santa A, Kallscheuer N, Vogt M, Julien-Laferri\u00e8re A, Sagot MF, et al.OptPipe - a pipeline for optimizing metabolic engineering targets. BMC Syst Biol. 2017; 11(1):143. https:\/\/doi.org\/10.1186\/s12918-017-0515-0.","journal-title":"BMC Syst Biol"},{"issue":"1","key":"3377_CR9","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1128\/MMBR.00014-15","volume":"80","author":"P Maia","year":"2016","unstructured":"Maia P, Rocha M, Rocha I. In silico constraint-based strain optimization methods: the quest for optimal cell factories. Microbiol Mol Biol Rev. 2016; 80(1):45\u201367.","journal-title":"Microbiol Mol Biol Rev"},{"issue":"2","key":"3377_CR10","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.ymben.2010.12.004","volume":"13","author":"O H\u00e4dicke","year":"2011","unstructured":"H\u00e4dicke O, Klamt S. Computing complex metabolic intervention strategies using constrained minimal cut sets. Metab Eng. 2011; 13(2):204\u2013213. https:\/\/doi.org\/10.1016\/j.ymben.2010.12.004.","journal-title":"Metab Eng"},{"key":"3377_CR11","doi-asserted-by":"publisher","unstructured":"Maia P, Rocha I, Ferreira EC, Rocha M. Evaluating evolutionary multiobjective algorithms for the in silico optimization of mutant strains. In: Oct 2008 in 2008 8th IEEE International Conference on BioInformatics and BioEngineering. IEEE: 2008. https:\/\/doi.org\/10.1109\/bibe.2008.4696733.","DOI":"10.1109\/bibe.2008.4696733"},{"issue":"23","key":"3377_CR12","doi-asserted-by":"publisher","first-page":"3097","DOI":"10.1093\/bioinformatics\/bts590","volume":"28","author":"J Costanza","year":"2012","unstructured":"Costanza J, Carapezza G, Angione C, Li\u00f3 P, Nicosia G. Robust design of microbial strains. Bioinformatics. 2012; 28(23):3097\u2013104.","journal-title":"Bioinformatics"},{"issue":"6","key":"3377_CR13","doi-asserted-by":"publisher","first-page":"817","DOI":"10.1016\/j.biotechadv.2010.07.001","volume":"28","author":"SI Mussatto","year":"2010","unstructured":"Mussatto SI, Dragone G, Guimar\u00e3es PM, Silva JPA, Carneiro LM, Roberto IC, et al.Technological trends, global market, and challenges of bio-ethanol production. Biotechnol Adv. 2010; 28(6):817\u2013830.","journal-title":"Biotechnol Adv"},{"issue":"5","key":"3377_CR14","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1080\/13873950600723442","volume":"12","author":"OH Send\u00edn","year":"2006","unstructured":"Send\u00edn OH, Vera J, Torres NV, Banga JR. Model based optimization of biochemical systems using multiple objectives: a comparison of several solution strategies. Math Comput Model Dyn Syst. 2006; 12(5):469\u201387.","journal-title":"Math Comput Model Dyn Syst"},{"issue":"3","key":"3377_CR15","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1002\/bit.10676","volume":"83","author":"J Vera","year":"2003","unstructured":"Vera J, De Atauri P, Cascante M, Torres NV. Multicriteria optimization of biochemical systems by linear programming: application to production of ethanol by Saccharomyces cerevisiae. Biotechnol Bioeng. 2003; 83(3):335\u201343.","journal-title":"Biotechnol Bioeng"},{"issue":"10","key":"3377_CR16","doi-asserted-by":"publisher","first-page":"5528","DOI":"10.1073\/pnas.97.10.5528","volume":"97","author":"J Edwards","year":"2000","unstructured":"Edwards J, Palsson B. The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities. Proc Nat Acad Sci. 2000; 97(10):5528\u201333.","journal-title":"Proc Nat Acad Sci"},{"issue":"1","key":"3377_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1471-2105-1-1","volume":"1","author":"JS Edwards","year":"2000","unstructured":"Edwards JS, Palsson BO. Metabolic flux balance analysis and the in silico analysis of Escherichia coli K-12 gene deletions. BMC Bioinforma. 2000; 1(1):1.","journal-title":"BMC Bioinforma"},{"issue":"3","key":"3377_CR18","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"JD Orth","year":"2010","unstructured":"Orth JD, Thiele I, Palsson B\u00d8. What is flux balance analysis?. Nat Biotechnol. 2010; 28(3):245\u20138.","journal-title":"Nat Biotechnol"},{"issue":"3","key":"3377_CR19","doi-asserted-by":"publisher","first-page":"R695","DOI":"10.1152\/ajpregu.2001.280.3.R695","volume":"280","author":"R Ramakrishna","year":"2001","unstructured":"Ramakrishna R, Edwards JS, McCulloch A, Palsson BO. Flux-balance analysis of mitochondrial energy metabolism: consequences of systemic stoichiometric constraints. Am J Physiol Regul Integr Comp Physiol. 2001; 280(3):R695\u2013704.","journal-title":"Am J Physiol Regul Integr Comp Physiol"},{"issue":"4","key":"3377_CR20","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1093\/bib\/bbp011","volume":"10","author":"K Raman","year":"2009","unstructured":"Raman K, Chandra N. Flux balance analysis of biological systems: applications and challenges. Brief Bioinforma. 2009; 10(4):435\u2013449.","journal-title":"Brief Bioinforma"},{"issue":"10","key":"3377_CR21","doi-asserted-by":"publisher","first-page":"994","DOI":"10.1038\/nbt1094-994","volume":"12","author":"A Varma","year":"1994","unstructured":"Varma A, Palsson BO. Metabolic Flux Balancing: Basic Concepts, Scientific and Practical Use. Bio\/technology. 1994; 12(10):994\u20138.","journal-title":"Bio\/technology"},{"issue":"10","key":"3377_CR22","doi-asserted-by":"publisher","first-page":"3724","DOI":"10.1128\/AEM.60.10.3724-3731.1994","volume":"60","author":"A Varma","year":"1994","unstructured":"Varma A, Palsson BO. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl Environ Microbiol. 1994; 60(10):3724\u201331.","journal-title":"Appl Environ Microbiol"},{"key":"3377_CR23","unstructured":"Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, et al.Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0. ArXiv e-prints arXiv:171004038. 2017."},{"issue":"23","key":"3377_CR24","doi-asserted-by":"publisher","first-page":"15112","DOI":"10.1073\/pnas.232349399","volume":"99","author":"D Segr\u00e8","year":"2002","unstructured":"Segr\u00e8 D, Vitkup D, Church GM. Analysis of optimality in natural and perturbed metabolic networks. Proc Nat Acad Sci. 2002; 99(23):15112\u20137.","journal-title":"Proc Nat Acad Sci"},{"issue":"1","key":"3377_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12532-008-0001-1","volume":"1","author":"T Achterberg","year":"2009","unstructured":"Achterberg T. SCIP: solving constraint integer programs. Math Program Comput. 2009; 1(1):1\u201341.","journal-title":"Math Program Comput"},{"key":"3377_CR26","doi-asserted-by":"crossref","unstructured":"Bornd\u00f6rfer R, Schenker S, Skutella M, Strunk T. PolySCIP In: Greuel GM, Koch T, Paule P, Sommese A, editors. Mathematical Software \u2013 ICMS 2016, 5th International Conference, vol. 9725. Berlin: 2016. p. 259\u2013264.","DOI":"10.1007\/978-3-319-42432-3_32"},{"issue":"1","key":"3377_CR27","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/1475-2859-11-98","volume":"11","author":"Miguel C Teixeira","year":"2012","unstructured":"Teixeira MC, Godinho CP, Cabrito TR, et al., Increased expression of the yeast multidrug resistance ABC transporter Pdr18 leads to increased ethanol tolerance and ethanol production in high gravity alcoholic fermentation. Microb Cell Fact. 2012; 11:98. https:\/\/doi.org\/10.1186\/1475-2859-11-98.","journal-title":"Microbial Cell Factories"},{"issue":"1","key":"3377_CR28","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1186\/1752-0509-6-55","volume":"6","author":"BD Heavner","year":"2012","unstructured":"Heavner BD, Smallbone K, Barker B, Mendes P, Walker LP. Yeast 5 \u2013 an expanded reconstruction of the Saccharomyces cerevisiae metabolic network. BMC Syst Biol. 2012; 6(1):55. https:\/\/doi.org\/10.1186\/1752-0509-6-55.","journal-title":"BMC Syst Biol"},{"issue":"3","key":"3377_CR29","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1128\/MMBR.00025-07","volume":"72","author":"E Nevoigt","year":"2008","unstructured":"Nevoigt E. Progress in metabolic engineering of Saccharomyces cerevisiae. Microbiol Mol Biol Rev. 2008; 72(3):379\u2013412.","journal-title":"Microbiol Mol Biol Rev"},{"issue":"1","key":"3377_CR30","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1186\/s13068-017-0791-3","volume":"10","author":"I Papapetridis","year":"2017","unstructured":"Papapetridis I, van Dijk M, van Maris AJA, Pronk JT. Metabolic engineering strategies for optimizing acetate reduction, ethanol yield and osmotolerance in Saccharomyces cerevisiae. Biotechnol Biofuels. 2017; 10(1):107. https:\/\/doi.org\/10.1186\/s13068-017-0791-3.","journal-title":"Biotechnol Biofuels"},{"issue":"2","key":"3377_CR31","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1016\/j.ymben.2005.09.007","volume":"8","author":"C Bro","year":"2006","unstructured":"Bro C, Regenberg B, F\u00f6rster J, Nielsen J. In silico aided metabolic engineering of Saccharomyces cerevisiae for improved bioethanol production. Metab Eng. 2006; 8(2):102\u2013111. https:\/\/doi.org\/10.1016\/j.ymben.2005.09.00.","journal-title":"Metab Eng"},{"issue":"1","key":"3377_CR32","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1186\/1471-2105-6-308","volume":"6","author":"KR Patil","year":"2005","unstructured":"Patil KR, Rocha I, F\u00f6rster J, Nielsen J. Evolutionary programming as a platform for in silico metabolic engineering. BMC bioinforma. 2005; 6(1):308.","journal-title":"BMC bioinforma"},{"issue":"21","key":"3377_CR33","doi-asserted-by":"publisher","first-page":"7695","DOI":"10.1073\/pnas.0406346102","volume":"102","author":"T Shlomi","year":"2005","unstructured":"Shlomi T, Berkman O, Ruppin E. Regulatory on\/off minimization of metabolic flux changes after genetic perturbations. Proc Nat Acad Sci. 2005; 102(21):7695\u20137700. https:\/\/doi.org\/10.1073\/pnas.0406346102.","journal-title":"Proc Nat Acad Sci"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3377-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-020-3377-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-020-3377-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,2,22]],"date-time":"2021-02-22T19:09:33Z","timestamp":1614020973000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-020-3377-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,24]]},"references-count":33,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["3377"],"URL":"https:\/\/doi.org\/10.1186\/s12859-020-3377-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/476689","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,24]]},"assertion":[{"value":"28 August 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 February 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"SV and M-FS are members of the Editorial Board of BMC Bioinformatics. The remaining authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"69"}}