{"quality_controlled":"1","department":[{"_id":"ToBo"}],"doi":"10.1016/j.copbio.2017.02.013","volume":46,"page":"90 - 97","type":"journal_article","article_processing_charge":"Yes (in subscription journal)","pubrep_id":"801","isi":1,"file_date_updated":"2019-01-18T09:57:57Z","oa":1,"date_updated":"2024-05-06T22:30:27Z","date_created":"2018-12-11T11:49:45Z","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","external_id":{"isi":["000408077400015"]},"publisher":"Elsevier","ec_funded":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","short":"CC BY-NC-ND (4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png"},"abstract":[{"text":"The rising prevalence of antibiotic resistant bacteria is an increasingly serious public health challenge. To address this problem, recent work ranging from clinical studies to theoretical modeling has provided valuable insights into the mechanisms of resistance, its emergence and spread, and ways to counteract it. A deeper understanding of the underlying dynamics of resistance evolution will require a combination of experimental and theoretical expertise from different disciplines and new technology for studying evolution in the laboratory. Here, we review recent advances in the quantitative understanding of the mechanisms and evolution of antibiotic resistance. We focus on key theoretical concepts and new technology that enables well-controlled experiments. We further highlight key challenges that can be met in the near future to ultimately develop effective strategies for combating resistance.","lang":"eng"}],"citation":{"ieee":"M. Lukacisinova and M. T. Bollenbach, “Toward a quantitative understanding of antibiotic resistance evolution,” <i>Current Opinion in Biotechnology</i>, vol. 46. Elsevier, pp. 90–97, 2017.","short":"M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017) 90–97.","apa":"Lukacisinova, M., &#38; Bollenbach, M. T. (2017). Toward a quantitative understanding of antibiotic resistance evolution. <i>Current Opinion in Biotechnology</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">https://doi.org/10.1016/j.copbio.2017.02.013</a>","chicago":"Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” <i>Current Opinion in Biotechnology</i>. Elsevier, 2017. <a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">https://doi.org/10.1016/j.copbio.2017.02.013</a>.","ista":"Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97.","mla":"Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” <i>Current Opinion in Biotechnology</i>, vol. 46, Elsevier, 2017, pp. 90–97, doi:<a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">10.1016/j.copbio.2017.02.013</a>.","ama":"Lukacisinova M, Bollenbach MT. Toward a quantitative understanding of antibiotic resistance evolution. <i>Current Opinion in Biotechnology</i>. 2017;46:90-97. doi:<a href=\"https://doi.org/10.1016/j.copbio.2017.02.013\">10.1016/j.copbio.2017.02.013</a>"},"related_material":{"record":[{"relation":"dissertation_contains","id":"6263","status":"public"}]},"oa_version":"Published Version","date_published":"2017-08-01T00:00:00Z","title":"Toward a quantitative understanding of antibiotic resistance evolution","author":[{"id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004","first_name":"Marta","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta"},{"last_name":"Bollenbach","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias"}],"year":"2017","month":"08","language":[{"iso":"eng"}],"file":[{"access_level":"open_access","success":1,"date_created":"2019-01-18T09:57:57Z","file_name":"2017_CurrentOpinion_Lukaciinova.pdf","content_type":"application/pdf","file_size":858338,"file_id":"5846","creator":"dernst","date_updated":"2019-01-18T09:57:57Z","relation":"main_file"}],"ddc":["570"],"_id":"1027","publist_id":"6364","scopus_import":"1","day":"01","has_accepted_license":"1","publication":"Current Opinion in Biotechnology","project":[{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","name":"Revealing the mechanisms underlying drug interactions","grant_number":"P27201-B22","call_identifier":"FWF"},{"call_identifier":"FP7","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","grant_number":"303507","name":"Optimality principles in responses to antibiotics"},{"name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"publication_status":"published","status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","article_type":"original","intvolume":"        46"}