[{"article_processing_charge":"Yes (in subscription journal)","has_accepted_license":"1","day":"26","scopus_import":1,"date_published":"2017-04-26T00:00:00Z","page":"393 - 403","citation":{"ama":"Mitosch K, Rieckh G, Bollenbach MT. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. 2017;4(4):393-403. doi:10.1016/j.cels.2017.03.001","ista":"Mitosch K, Rieckh G, Bollenbach MT. 2017. Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. 4(4), 393–403.","ieee":"K. Mitosch, G. Rieckh, and M. T. Bollenbach, “Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment,” Cell Systems, vol. 4, no. 4. Cell Press, pp. 393–403, 2017.","apa":"Mitosch, K., Rieckh, G., & Bollenbach, M. T. (2017). Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment. Cell Systems. Cell Press. https://doi.org/10.1016/j.cels.2017.03.001","mla":"Mitosch, Karin, et al. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” Cell Systems, vol. 4, no. 4, Cell Press, 2017, pp. 393–403, doi:10.1016/j.cels.2017.03.001.","short":"K. Mitosch, G. Rieckh, M.T. Bollenbach, Cell Systems 4 (2017) 393–403.","chicago":"Mitosch, Karin, Georg Rieckh, and Mark Tobias Bollenbach. “Noisy Response to Antibiotic Stress Predicts Subsequent Single Cell Survival in an Acidic Environment.” Cell Systems. Cell Press, 2017. https://doi.org/10.1016/j.cels.2017.03.001."},"publication":"Cell Systems","issue":"4","abstract":[{"lang":"eng","text":"Antibiotics elicit drastic changes in microbial gene expression, including the induction of stress response genes. While certain stress responses are known to “cross-protect” bacteria from other stressors, it is unclear whether cellular responses to antibiotics have a similar protective role. By measuring the genome-wide transcriptional response dynamics of Escherichia coli to four antibiotics, we found that trimethoprim induces a rapid acid stress response that protects bacteria from subsequent exposure to acid. Combining microfluidics with time-lapse imaging to monitor survival and acid stress response in single cells revealed that the noisy expression of the acid resistance operon gadBC correlates with single-cell survival. Cells with higher gadBC expression following trimethoprim maintain higher intracellular pH and survive the acid stress longer. The seemingly random single-cell survival under acid stress can therefore be predicted from gadBC expression and rationalized in terms of GadB/C molecular function. Overall, we provide a roadmap for identifying the molecular mechanisms of single-cell cross-protection between antibiotics and other stressors."}],"type":"journal_article","file":[{"access_level":"open_access","file_name":"IST-2017-901-v1+1_1-s2.0-S2405471217300868-main.pdf","file_size":2438660,"content_type":"application/pdf","creator":"system","relation":"main_file","file_id":"5041","checksum":"04ff20011c3d9a601c514aa999a5fe1a","date_updated":"2020-07-14T12:47:35Z","date_created":"2018-12-12T10:13:54Z"}],"oa_version":"Published Version","pubrep_id":"901","intvolume":" 4","status":"public","title":"Noisy response to antibiotic stress predicts subsequent single cell survival in an acidic environment","ddc":["576","610"],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"666","publication_identifier":{"issn":["24054712"]},"month":"04","language":[{"iso":"eng"}],"doi":"10.1016/j.cels.2017.03.001","project":[{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Optimality principles in responses to antibiotics"},{"name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","grant_number":"P27201-B22","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425","name":"Revealing the fundamental limits of cell growth"}],"quality_controlled":"1","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"oa":1,"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","publist_id":"7061","ec_funded":1,"file_date_updated":"2020-07-14T12:47:35Z","volume":4,"date_updated":"2023-09-07T12:00:25Z","date_created":"2018-12-11T11:47:48Z","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"818"}]},"author":[{"first_name":"Karin","last_name":"Mitosch","id":"39B66846-F248-11E8-B48F-1D18A9856A87","full_name":"Mitosch, Karin"},{"full_name":"Rieckh, Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","last_name":"Rieckh","first_name":"Georg"},{"full_name":"Bollenbach, Tobias","last_name":"Bollenbach","first_name":"Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"publisher":"Cell Press","department":[{"_id":"ToBo"},{"_id":"GaTk"}],"publication_status":"published","year":"2017"},{"publication_identifier":{"issn":["00278424"]},"month":"10","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5635929/","open_access":"1"}],"external_id":{"pmid":["28923953"],"isi":["000412130500061"]},"oa":1,"project":[{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","name":"Optimality principles in responses to antibiotics","call_identifier":"FP7"},{"_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF"}],"isi":1,"quality_controlled":"1","doi":"10.1073/pnas.1713372114","language":[{"iso":"eng"}],"publist_id":"6827","ec_funded":1,"pmid":1,"year":"2017","publisher":"National Academy of Sciences","department":[{"_id":"ToBo"}],"publication_status":"published","author":[{"full_name":"De Vos, Marjon","id":"3111FFAC-F248-11E8-B48F-1D18A9856A87","last_name":"De Vos","first_name":"Marjon"},{"last_name":"Zagórski","first_name":"Marcin P","orcid":"0000-0001-7896-7762","id":"343DA0DC-F248-11E8-B48F-1D18A9856A87","full_name":"Zagórski, Marcin P"},{"last_name":"Mcnally","first_name":"Alan","full_name":"Mcnally, Alan"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","last_name":"Bollenbach","first_name":"Mark Tobias","full_name":"Bollenbach, Mark Tobias"}],"volume":114,"date_updated":"2023-09-26T16:18:48Z","date_created":"2018-12-11T11:48:41Z","scopus_import":"1","article_processing_charge":"No","day":"03","citation":{"short":"M. de Vos, M.P. Zagórski, A. Mcnally, M.T. Bollenbach, PNAS 114 (2017) 10666–10671.","mla":"de Vos, Marjon, et al. “Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance in Polymicrobial Infections.” PNAS, vol. 114, no. 40, National Academy of Sciences, 2017, pp. 10666–71, doi:10.1073/pnas.1713372114.","chicago":"Vos, Marjon de, Marcin P Zagórski, Alan Mcnally, and Mark Tobias Bollenbach. “Interaction Networks, Ecological Stability, and Collective Antibiotic Tolerance in Polymicrobial Infections.” PNAS. National Academy of Sciences, 2017. https://doi.org/10.1073/pnas.1713372114.","ama":"de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. 2017;114(40):10666-10671. doi:10.1073/pnas.1713372114","ieee":"M. de Vos, M. P. Zagórski, A. Mcnally, and M. T. Bollenbach, “Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections,” PNAS, vol. 114, no. 40. National Academy of Sciences, pp. 10666–10671, 2017.","apa":"de Vos, M., Zagórski, M. P., Mcnally, A., & Bollenbach, M. T. (2017). Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1713372114","ista":"de Vos M, Zagórski MP, Mcnally A, Bollenbach MT. 2017. Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections. PNAS. 114(40), 10666–10671."},"publication":"PNAS","page":"10666 - 10671","date_published":"2017-10-03T00:00:00Z","type":"journal_article","issue":"40","abstract":[{"text":"Polymicrobial infections constitute small ecosystems that accommodate several bacterial species. Commonly, these bacteria are investigated in isolation. However, it is unknown to what extent the isolates interact and whether their interactions alter bacterial growth and ecosystem resilience in the presence and absence of antibiotics. We quantified the complete ecological interaction network for 72 bacterial isolates collected from 23 individuals diagnosed with polymicrobial urinary tract infections and found that most interactions cluster based on evolutionary relatedness. Statistical network analysis revealed that competitive and cooperative reciprocal interactions are enriched in the global network, while cooperative interactions are depleted in the individual host community networks. A population dynamics model parameterized by our measurements suggests that interactions restrict community stability, explaining the observed species diversity of these communities. We further show that the clinical isolates frequently protect each other from clinically relevant antibiotics. Together, these results highlight that ecological interactions are crucial for the growth and survival of bacteria in polymicrobial infection communities and affect their assembly and resilience. ","lang":"eng"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"822","intvolume":" 114","status":"public","title":"Interaction networks, ecological stability, and collective antibiotic tolerance in polymicrobial infections","oa_version":"Submitted Version"},{"issue":"3","abstract":[{"text":"RNA Polymerase II pauses and backtracks during transcription, with many consequences for gene expression and cellular physiology. Here, we show that the energy required to melt double-stranded nucleic acids in the transcription bubble predicts pausing in Saccharomyces cerevisiae far more accurately than nucleosome roadblocks do. In addition, the same energy difference also determines when the RNA polymerase backtracks instead of continuing to move forward. This data-driven model corroborates—in a genome wide and quantitative manner—previous evidence that sequence-dependent thermodynamic features of nucleic acids influence both transcriptional pausing and backtracking.","lang":"eng"}],"type":"journal_article","pubrep_id":"800","oa_version":"Published Version","file":[{"creator":"system","content_type":"application/pdf","file_size":3429381,"file_name":"IST-2017-800-v1+1_journal.pone.0174066.pdf","access_level":"open_access","date_updated":"2018-12-12T10:09:47Z","date_created":"2018-12-12T10:09:47Z","file_id":"4772","relation":"main_file"}],"_id":"1029","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","intvolume":" 12","status":"public","ddc":["570"],"title":"Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast","has_accepted_license":"1","article_processing_charge":"Yes","day":"16","scopus_import":"1","date_published":"2017-03-16T00:00:00Z","citation":{"ama":"Lukacisin M, Landon M, Jajoo R. Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast. PLoS One. 2017;12(3). doi:10.1371/journal.pone.0174066","ista":"Lukacisin M, Landon M, Jajoo R. 2017. Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast. PLoS One. 12(3), e0174066.","apa":"Lukacisin, M., Landon, M., & Jajoo, R. (2017). Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0174066","ieee":"M. Lukacisin, M. Landon, and R. Jajoo, “Sequence-specific thermodynamic properties of nucleic acids influence both transcriptional pausing and backtracking in yeast,” PLoS One, vol. 12, no. 3. Public Library of Science, 2017.","mla":"Lukacisin, Martin, et al. “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” PLoS One, vol. 12, no. 3, e0174066, Public Library of Science, 2017, doi:10.1371/journal.pone.0174066.","short":"M. Lukacisin, M. Landon, R. Jajoo, PLoS One 12 (2017).","chicago":"Lukacisin, Martin, Matthieu Landon, and Rishi Jajoo. “Sequence-Specific Thermodynamic Properties of Nucleic Acids Influence Both Transcriptional Pausing and Backtracking in Yeast.” PLoS One. Public Library of Science, 2017. https://doi.org/10.1371/journal.pone.0174066."},"publication":"PLoS One","publist_id":"6361","file_date_updated":"2018-12-12T10:09:47Z","license":"https://creativecommons.org/licenses/by/4.0/","article_number":"e0174066","related_material":{"record":[{"status":"public","relation":"popular_science","id":"5556"},{"status":"public","relation":"dissertation_contains","id":"6392"}]},"author":[{"first_name":"Martin","last_name":"Lukacisin","id":"298FFE8C-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6549-4177","full_name":"Lukacisin, Martin"},{"full_name":"Landon, Matthieu","last_name":"Landon","first_name":"Matthieu"},{"first_name":"Rishi","last_name":"Jajoo","full_name":"Jajoo, Rishi"}],"volume":12,"date_updated":"2024-03-28T23:30:04Z","date_created":"2018-12-11T11:49:46Z","year":"2017","publisher":"Public Library of Science","department":[{"_id":"ToBo"}],"publication_status":"published","publication_identifier":{"issn":["19326203"]},"month":"03","doi":"10.1371/journal.pone.0174066","language":[{"iso":"eng"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"external_id":{"isi":["000396318300121"]},"oa":1,"isi":1,"quality_controlled":"1"},{"publication":"PLoS Computational Biology","citation":{"chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005609.","mla":"Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” PLoS Computational Biology, vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005609.","short":"M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017).","ista":"Lukacisinova M, Novak S, Paixao T. 2017. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 13(7), e1005609.","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes,” PLoS Computational Biology, vol. 13, no. 7. Public Library of Science, 2017.","apa":"Lukacisinova, M., Novak, S., & Paixao, T. (2017). Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005609","ama":"Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. PLoS Computational Biology. 2017;13(7). doi:10.1371/journal.pcbi.1005609"},"article_type":"original","date_published":"2017-07-18T00:00:00Z","scopus_import":1,"day":"18","has_accepted_license":"1","_id":"696","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes","status":"public","ddc":["576"],"intvolume":" 13","pubrep_id":"894","file":[{"relation":"main_file","file_id":"5117","date_created":"2018-12-12T10:15:01Z","date_updated":"2020-07-14T12:47:46Z","checksum":"9143c290fa6458ed2563bff4b295554a","file_name":"IST-2017-894-v1+1_journal.pcbi.1005609.pdf","access_level":"open_access","content_type":"application/pdf","file_size":3775716,"creator":"system"}],"oa_version":"Published Version","type":"journal_article","abstract":[{"lang":"eng","text":"Mutator strains are expected to evolve when the availability and effect of beneficial mutations are high enough to counteract the disadvantage from deleterious mutations that will inevitably accumulate. As the population becomes more adapted to its environment, both availability and effect of beneficial mutations necessarily decrease and mutation rates are predicted to decrease. It has been shown that certain molecular mechanisms can lead to increased mutation rates when the organism finds itself in a stressful environment. While this may be a correlated response to other functions, it could also be an adaptive mechanism, raising mutation rates only when it is most advantageous. Here, we use a mathematical model to investigate the plausibility of the adaptive hypothesis. We show that such a mechanism can be mantained if the population is subjected to diverse stresses. By simulating various antibiotic treatment schemes, we find that combination treatments can reduce the effectiveness of second-order selection on stress-induced mutagenesis. We discuss the implications of our results to strategies of antibiotic therapy."}],"issue":"7","oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"quality_controlled":"1","project":[{"name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","call_identifier":"FP7","grant_number":"618091","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"}],"doi":"10.1371/journal.pcbi.1005609","language":[{"iso":"eng"}],"month":"07","publication_identifier":{"issn":["1553734X"]},"year":"2017","publication_status":"published","publisher":"Public Library of Science","department":[{"_id":"ToBo"},{"_id":"NiBa"},{"_id":"CaGu"}],"author":[{"id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004","first_name":"Marta","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta"},{"id":"461468AE-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-824X","first_name":"Sebastian","last_name":"Novak","full_name":"Novak, Sebastian"},{"last_name":"Paixao","first_name":"Tiago","orcid":"0000-0003-2361-3953","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","full_name":"Paixao, Tiago"}],"related_material":{"record":[{"relation":"research_data","status":"public","id":"9849"},{"id":"9850","status":"public","relation":"research_data"},{"status":"public","relation":"research_data","id":"9851"},{"id":"9852","status":"public","relation":"research_data"},{"relation":"dissertation_contains","status":"public","id":"6263"}]},"date_created":"2018-12-11T11:47:58Z","date_updated":"2024-03-28T23:30:28Z","volume":13,"article_number":"e1005609","file_date_updated":"2020-07-14T12:47:46Z","ec_funded":1,"publist_id":"7004"},{"file_date_updated":"2019-01-18T09:57:57Z","publist_id":"6364","ec_funded":1,"author":[{"first_name":"Marta","last_name":"Lukacisinova","id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004","full_name":"Lukacisinova, Marta"},{"full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"6263"}]},"date_created":"2018-12-11T11:49:45Z","date_updated":"2024-03-28T23:30:29Z","volume":46,"year":"2017","publication_status":"published","department":[{"_id":"ToBo"}],"publisher":"Elsevier","month":"08","doi":"10.1016/j.copbio.2017.02.013","language":[{"iso":"eng"}],"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"oa":1,"external_id":{"isi":["000408077400015"]},"isi":1,"quality_controlled":"1","project":[{"name":"Revealing the mechanisms underlying drug interactions","call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","grant_number":"P27201-B22"},{"grant_number":"303507","_id":"25E83C2C-B435-11E9-9278-68D0E5697425","name":"Optimality principles in responses to antibiotics","call_identifier":"FP7"},{"_id":"25EB3A80-B435-11E9-9278-68D0E5697425","grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth"}],"abstract":[{"lang":"eng","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."}],"type":"journal_article","pubrep_id":"801","file":[{"file_size":858338,"content_type":"application/pdf","creator":"dernst","access_level":"open_access","file_name":"2017_CurrentOpinion_Lukaciinova.pdf","success":1,"date_updated":"2019-01-18T09:57:57Z","date_created":"2019-01-18T09:57:57Z","relation":"main_file","file_id":"5846"}],"oa_version":"Published Version","_id":"1027","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","ddc":["570"],"status":"public","title":"Toward a quantitative understanding of antibiotic resistance evolution","intvolume":" 46","day":"01","has_accepted_license":"1","article_processing_charge":"Yes (in subscription journal)","scopus_import":"1","date_published":"2017-08-01T00:00:00Z","publication":"Current Opinion in Biotechnology","citation":{"short":"M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017) 90–97.","mla":"Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology, vol. 46, Elsevier, 2017, pp. 90–97, doi:10.1016/j.copbio.2017.02.013.","chicago":"Lukacisinova, Marta, and Mark Tobias Bollenbach. “Toward a Quantitative Understanding of Antibiotic Resistance Evolution.” Current Opinion in Biotechnology. Elsevier, 2017. https://doi.org/10.1016/j.copbio.2017.02.013.","ama":"Lukacisinova M, Bollenbach MT. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 2017;46:90-97. doi:10.1016/j.copbio.2017.02.013","ieee":"M. Lukacisinova and M. T. Bollenbach, “Toward a quantitative understanding of antibiotic resistance evolution,” Current Opinion in Biotechnology, vol. 46. Elsevier, pp. 90–97, 2017.","apa":"Lukacisinova, M., & Bollenbach, M. T. (2017). Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. Elsevier. https://doi.org/10.1016/j.copbio.2017.02.013","ista":"Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97."},"article_type":"original","page":"90 - 97"}]