{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T02:51:45Z","timestamp":1761101505109,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031255984"},{"type":"electronic","value":"9783031255991"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-25599-1_43","type":"book-chapter","created":{"date-parts":[[2023,3,8]],"date-time":"2023-03-08T04:32:27Z","timestamp":1678249947000},"page":"596-612","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Inferring Pathological Metabolic Patterns in Breast Cancer Tissue from Genome-Scale Models"],"prefix":"10.1007","author":[{"given":"Matteo N.","family":"Amaradio","sequence":"first","affiliation":[]},{"given":"Giorgio","family":"Jansen","sequence":"additional","affiliation":[]},{"given":"Varun","family":"Ojha","sequence":"additional","affiliation":[]},{"given":"Jole","family":"Costanza","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Di Fatta","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Nicosia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,9]]},"reference":[{"issue":"7","key":"43_CR1","doi-asserted-by":"publisher","first-page":"1890","DOI":"10.1002\/bit.28103","volume":"119","author":"MN Amaradio","year":"2022","unstructured":"Amaradio, M.N., Ojha, V., Jansen, G., Gulisano, M., Costanza, J., Nicosia, G.: Pareto optimal metabolic engineering for the growth-coupled overproduction of sustainable chemicals. Biotechnol. Bioeng. 119(7), 1890\u20131902 (2022)","journal-title":"Biotechnol. Bioeng."},{"issue":"10","key":"43_CR2","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1039\/c3mb25558a","volume":"9","author":"C Angione","year":"2013","unstructured":"Angione, C., Costanza, J., Carapezza, G., Li\u00f3, P., Nicosia, G.: A design automation framework for computational bioenergetics in biological networks. Mol. BioSyst. 9(10), 2554\u20132564 (2013)","journal-title":"Mol. BioSyst."},{"key":"43_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.intimp.2020.106535","volume":"84","author":"K Barzaman","year":"2020","unstructured":"Barzaman, K., et al.: Breast cancer: biology, biomarkers, and treatments. Int. Immunopharmacol. 84, 106535 (2020)","journal-title":"Int. Immunopharmacol."},{"issue":"4","key":"43_CR4","first-page":"432","volume":"12","author":"T Biondi","year":"2006","unstructured":"Biondi, T., Ciccazzo, A., Cutello, V., D\u2019Antona, S., Nicosia, G., Spinella, S.: Multi-objective evolutionary algorithms and pattern search methods for circuit design problems. J. Univers. Comput. Sci. 12(4), 432\u2013449 (2006)","journal-title":"J. Univers. Comput. Sci."},{"issue":"9","key":"43_CR5","doi-asserted-by":"publisher","first-page":"2345","DOI":"10.3390\/cells10092345","volume":"10","author":"FJ Chou","year":"2021","unstructured":"Chou, F.J., Liu, Y., Lang, F., Yang, C.: D-2-hydroxyglutarate in glioma biology. Cells 10(9), 2345 (2021)","journal-title":"Cells"},{"key":"43_CR6","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"949","DOI":"10.1007\/11881070_125","volume-title":"ICNC 2006","author":"V Cutello","year":"2006","unstructured":"Cutello, V., Lee, D., Leone, S., Nicosia, G., Pavone, M.: Clonal selection algorithm with dynamic population size for bimodal search spaces. In: Jiao, L., Wang, L., Gao, X.-b, Liu, J., Wu, F. (eds.) ICNC 2006. LNCS, vol. 4221, pp. 949\u2013958. Springer, Heidelberg (2006). https:\/\/doi.org\/10.1007\/11881070_125"},{"key":"43_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1007\/978-3-642-14547-6_12","volume-title":"ICARIS 2010","author":"V Cutello","year":"2010","unstructured":"Cutello, V., Nicosia, G., Pavone, M., Stracquadanio, G.: An information-theoretic approach for clonal selection algorithms. In: Hart, E., McEwan, C., Timmis, J., Hone, A. (eds.) ICARIS 2010. LNCS, vol. 6209, pp. 144\u2013157. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-14547-6_12"},{"key":"43_CR8","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.semcdb.2019.04.013","volume":"98","author":"E DallaPozza","year":"2020","unstructured":"DallaPozza, E., et al.: Regulation of succinate dehydrogenase and role of succinate in cancer. Semin. Cell Dev. Biol. 98, 4\u201314 (2020)","journal-title":"Semin. Cell Dev. Biol."},{"issue":"13","key":"43_CR9","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1089\/ars.2019.7902","volume":"33","author":"P Je\u017eek","year":"2020","unstructured":"Je\u017eek, P.: 2-Hydroxyglutarate in cancer cells. Antioxid. Redox Signal. 33(13), 903\u2013926 (2020)","journal-title":"Antioxid. Redox Signal."},{"key":"43_CR10","doi-asserted-by":"crossref","unstructured":"Katsura, C., Ogunmwonyi, I., Kankam, H.K., Saha, S.: Breast cancer: presentation, investigation, and management. Br. J. Hosp. Med. (London, England) 83(2), 1\u20137 (2005)","DOI":"10.12968\/hmed.2021.0459"},{"key":"43_CR11","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.copbio.2014.12.020","volume":"34","author":"MA Keller","year":"2015","unstructured":"Keller, M.A., Piedrafita, G., Ralser, M.: The widespread role of non-enzymatic reactions in cellular metabolism. Curr. Opin. Biotechnol. 34, 153\u2013161 (2015)","journal-title":"Curr. Opin. Biotechnol."},{"issue":"D1","key":"43_CR12","doi-asserted-by":"publisher","first-page":"D515","DOI":"10.1093\/nar\/gkv1049","volume":"44","author":"ZA King","year":"2016","unstructured":"King, Z.A., et al.: BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44(D1), D515\u2013D522 (2016)","journal-title":"Nucleic Acids Res."},{"issue":"2","key":"43_CR13","doi-asserted-by":"publisher","first-page":"20190021","DOI":"10.1515\/jib-2019-0021","volume":"16","author":"M Hucka","year":"2019","unstructured":"Hucka, M., et al.: The systems biology markup language (SBML): language specification for level 3 version 2 Core release 2. J. Integr. Bioinform. 16(2), 20190021 (2019)","journal-title":"J. Integr. Bioinform."},{"issue":"12","key":"43_CR14","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1002\/bies.201500093","volume":"37","author":"Y Liu","year":"2015","unstructured":"Liu, Y., et al.: Targeting tumor suppressor genes for cancer therapy. BioEssays: News Rev. Mol. Cell. Dev. Biol. 37(12), 1277\u20131286 (2015)","journal-title":"BioEssays: News Rev. Mol. Cell. Dev. Biol."},{"key":"43_CR15","doi-asserted-by":"publisher","first-page":"122","DOI":"10.1016\/j.cbpa.2020.06.012","volume":"57","author":"S Liu","year":"2020","unstructured":"Liu, S., Cadoux-Hudson, T., Schofield, C.J.: Isocitrate dehydrogenase variants in cancer - cellular consequences and therapeutic opportunities. Curr. Opin. Chem. Biol. 57, 122\u2013134 (2020)","journal-title":"Curr. Opin. Chem. Biol."},{"key":"43_CR16","doi-asserted-by":"crossref","unstructured":"Mishra, P., Ambs, S.: Metabolic signatures of human breast cancer. Mol. Cell. Oncol. 2(3), e992217 (2015)","DOI":"10.4161\/23723556.2014.992217"},{"issue":"9","key":"43_CR17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1003837","volume":"10","author":"H Nam","year":"2014","unstructured":"Nam, H., et al.: A systems approach to predict oncometabolites via context-specific genome-scale metabolic networks. PLoS Comput. Biol. 10(9), e1003837 (2014)","journal-title":"PLoS Comput. Biol."},{"key":"43_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1007\/978-3-540-74126-8_17","volume-title":"WABI 2007","author":"G Nicosia","year":"2007","unstructured":"Nicosia, G., Stracquadanio, G.: Generalized pattern search and mesh adaptive direct search algorithms for protein structure prediction. In: Giancarlo, R., Hannenhalli, S. (eds.) WABI 2007. LNCS, vol. 4645, pp. 183\u2013193. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-74126-8_17"},{"issue":"D1","key":"43_CR19","first-page":"D402","volume":"48","author":"CJ Norsigian","year":"2020","unstructured":"Norsigian, C.J., et al.: BiGG models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree. Nucleic Acids Res. 48(D1), D402\u2013D406 (2020)","journal-title":"Nucleic Acids Res."},{"key":"43_CR20","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1038\/nbt.1614","volume":"28","author":"J Orth","year":"2010","unstructured":"Orth, J., Thiele, I., Palsson, B.: What is flux balance analysis? Nat. Biotechnol. 28, 245\u2013248 (2010)","journal-title":"Nat. Biotechnol."},{"issue":"4","key":"43_CR21","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1109\/TBCAS.2015.2467214","volume":"9","author":"A Patan\u00e8","year":"2015","unstructured":"Patan\u00e8, A., Santoro, A., Costanza, J., Carapezza, G., Nicosia, G.: Pareto optimal design for synthetic biology. IEEE Trans. Biomed. Circuits Syst. 9(4), 555\u2013571 (2015)","journal-title":"IEEE Trans. Biomed. Circuits Syst."},{"issue":"1-2","key":"43_CR22","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s10479-018-2865-4","volume":"276","author":"A Patan\u00e9","year":"2018","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. 276(1\u20132), 211\u2013227 (2018). https:\/\/doi.org\/10.1007\/s10479-018-2865-4","journal-title":"Ann. Oper. Res."},{"issue":"4","key":"43_CR23","doi-asserted-by":"publisher","first-page":"310","DOI":"10.1177\/0883073820962931","volume":"36","author":"M Peetsold","year":"2021","unstructured":"Peetsold, M., et al.: fumarase deficiency: a case with a new pathogenic mutation and a review of the literature. J. Child Neurol. 36(4), 310\u2013323 (2021)","journal-title":"J. Child Neurol."},{"key":"43_CR24","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.copbio.2019.11.007","volume":"64","author":"P Rana","year":"2020","unstructured":"Rana, P., Berry, C., Ghosh, P., Fong, S.S.: Recent advances on constraint-based models by integrating machine learning. Curr. Opin. Biotechnol. 64, 85\u201391 (2020)","journal-title":"Curr. Opin. Biotechnol."},{"issue":"1","key":"43_CR25","doi-asserted-by":"publisher","first-page":"20326","DOI":"10.1038\/s41598-021-99617-x","volume":"11","author":"MR Sharifi","year":"2021","unstructured":"Sharifi, M.R., Akbarifard, S., Qaderi, K., Madadi, M.R.: A new optimization algorithm to solve multi-objective problems. Sci. Rep. 11(1), 20326 (2021)","journal-title":"Sci. Rep."},{"key":"43_CR26","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.semcdb.2019.05.002","volume":"98","author":"C Schmidt","year":"2020","unstructured":"Schmidt, C., Sciacovelli, M., Frezza, C.: Fumarate hydratase in cancer: a multifaceted tumor suppressor. Semin. Cell Dev. Biol. 98, 15\u201325 (2020)","journal-title":"Semin. Cell Dev. Biol."},{"issue":"4","key":"43_CR27","first-page":"1","volume":"13","author":"R Umeton","year":"2012","unstructured":"Umeton, R., Nicosia, G., Dewey, C.F.: OREMPdb: a semantic dictionary of computational pathway models. BMC Bioinform. 13(4), 1\u20139 (2012)","journal-title":"BMC Bioinform."},{"key":"43_CR28","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.ymben.2021.01.008","volume":"64","author":"RP Van Rosmalen","year":"2021","unstructured":"Van Rosmalen, R.P., Smith, R.W., Martins Dos Santos, V.A.P., Fleck, C., Suarez-Diez, M.: Model reduction of genome-scale metabolic models as a basis for targeted kinetic models. Metab. Eng. 64, 74\u201384 (2021)","journal-title":"Metab. Eng."},{"key":"43_CR29","doi-asserted-by":"crossref","unstructured":"Vander Heiden, M.G.: Targeting cancer metabolism: a therapeutic window opens. Nat. Rev. Drug Discov. 10(9), 671\u2013684 (2011)","DOI":"10.1038\/nrd3504"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-25599-1_43","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,5]],"date-time":"2023-04-05T17:17:54Z","timestamp":1680715074000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-25599-1_43"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031255984","9783031255991"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-25599-1_43","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"9 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Certosa di Pontignano","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"lod2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2022.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"226","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"85","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.5","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}