[{"article_processing_charge":"No","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"volume":11,"quality_controlled":"1","has_accepted_license":"1","citation":{"short":"M. Lukacisinova, B. Fernando, M.T. Bollenbach, Nature Communications 11 (2020).","ista":"Lukacisinova M, Fernando B, Bollenbach MT. 2020. Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. Nature Communications. 11, 3105.","mla":"Lukacisinova, Marta, et al. “Highly Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic Resistance.” <i>Nature Communications</i>, vol. 11, 3105, Springer Nature, 2020, doi:<a href=\"https://doi.org/10.1038/s41467-020-16932-z\">10.1038/s41467-020-16932-z</a>.","apa":"Lukacisinova, M., Fernando, B., &#38; Bollenbach, M. T. (2020). Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-020-16932-z\">https://doi.org/10.1038/s41467-020-16932-z</a>","ieee":"M. Lukacisinova, B. Fernando, and M. T. Bollenbach, “Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance,” <i>Nature Communications</i>, vol. 11. Springer Nature, 2020.","ama":"Lukacisinova M, Fernando B, Bollenbach MT. Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance. <i>Nature Communications</i>. 2020;11. doi:<a href=\"https://doi.org/10.1038/s41467-020-16932-z\">10.1038/s41467-020-16932-z</a>","chicago":"Lukacisinova, Marta, Booshini Fernando, and Mark Tobias Bollenbach. “Highly Parallel Lab Evolution Reveals That Epistasis Can Curb the Evolution of Antibiotic Resistance.” <i>Nature Communications</i>. Springer Nature, 2020. <a href=\"https://doi.org/10.1038/s41467-020-16932-z\">https://doi.org/10.1038/s41467-020-16932-z</a>."},"intvolume":"        11","publisher":"Springer Nature","month":"06","extern":"1","date_updated":"2025-04-15T08:09:37Z","oa":1,"scopus_import":"1","ddc":["570"],"file_date_updated":"2020-07-14T12:48:08Z","status":"public","_id":"8037","author":[{"full_name":"Lukacisinova, Marta","last_name":"Lukacisinova","first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004"},{"last_name":"Fernando","full_name":"Fernando, Booshini","first_name":"Booshini"},{"orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"day":"19","pmid":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Genetic perturbations that affect bacterial resistance to antibiotics have been characterized genome-wide, but how do such perturbations interact with subsequent evolutionary adaptation to the drug? Here, we show that strong epistasis between resistance mutations and systematically identified genes can be exploited to control spontaneous resistance evolution. We evolved hundreds of Escherichia coli K-12 mutant populations in parallel, using a robotic platform that tightly controls population size and selection pressure. We find a global diminishing-returns epistasis pattern: strains that are initially more sensitive generally undergo larger resistance gains. However, some gene deletion strains deviate from this general trend and curtail the evolvability of resistance, including deletions of genes for membrane transport, LPS biosynthesis, and chaperones. Deletions of efflux pump genes force evolution on inferior mutational paths, not explored in the wild type, and some of these essentially block resistance evolution. This effect is due to strong negative epistasis with resistance mutations. The identified genes and cellular functions provide potential targets for development of adjuvants that may block spontaneous resistance evolution when combined with antibiotics."}],"publication":"Nature Communications","file":[{"content_type":"application/pdf","date_created":"2020-06-30T09:58:50Z","file_size":1546491,"creator":"cziletti","date_updated":"2020-07-14T12:48:08Z","file_name":"2020_NatureComm_Lukacisinova.pdf","file_id":"8071","access_level":"open_access","relation":"main_file","checksum":"4f5f49d63add331d5eb8a2bae477b396"}],"external_id":{"isi":["000545685100002"],"pmid":["32561723"]},"date_published":"2020-06-19T00:00:00Z","doi":"10.1038/s41467-020-16932-z","article_number":"3105","year":"2020","publication_identifier":{"eissn":["20411723"]},"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","date_created":"2020-06-29T07:59:35Z","project":[{"call_identifier":"FWF","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions"},{"grant_number":"RGP0042/2013","name":"Revealing the fundamental limits of cell growth","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"title":"Highly parallel lab evolution reveals that epistasis can curb the evolution of antibiotic resistance","type":"journal_article","language":[{"iso":"eng"}],"isi":1,"publication_status":"published","article_type":"original"},{"publication_status":"published","type":"dissertation","title":"Genetic determinants of antibiotic resistance evolution","language":[{"iso":"eng"}],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","date_created":"2019-04-09T13:57:15Z","alternative_title":["ISTA Thesis"],"doi":"10.15479/AT:ISTA:th1072","publication_identifier":{"issn":["2663-337X"]},"year":"2018","date_published":"2018-12-28T00:00:00Z","file":[{"relation":"main_file","access_level":"open_access","embargo":"2020-01-25","file_id":"6264","file_name":"2018_Thesis_Lukacisinova.pdf","checksum":"fc60585c9eaad868ac007004ef130908","file_size":5656866,"content_type":"application/pdf","date_created":"2019-04-09T13:49:24Z","date_updated":"2021-02-11T11:17:17Z","creator":"dernst"},{"checksum":"264057ec0a92ab348cc83b41f021ba92","embargo_to":"open_access","access_level":"closed","relation":"source_file","file_id":"6265","file_name":"2018_Thesis_Lukacisinova_source.docx","date_updated":"2020-07-14T12:47:25Z","creator":"dernst","file_size":5168054,"content_type":"application/vnd.openxmlformats-officedocument.wordprocessingml.document","date_created":"2019-04-09T13:49:23Z"}],"abstract":[{"text":"Antibiotic  resistance  can  emerge  spontaneously  through  genomic  mutation  and  render treatment   ineffective.   To   counteract   this process, in   addition   to   the   discovery   and description of resistance mechanisms,a deeper understanding of resistanceevolvabilityand its  determinantsis  needed. To address  this challenge,  this  thesisuncoversnew  genetic determinants   of   resistance   evolvability   using   a   customized   robotic   setup, exploressystematic   ways   in   which   resistance   evolution   is   perturbed   due   to dose-responsecharacteristics  of  drugs and  mutation  rate  differences,and  mathematically  investigates the evolutionary fate of one specific type of evolvability modifier -a stress-induced mutagenesis allele.We  find  severalgenes  which  strongly  inhibit  or  potentiate  resistance  evolution.  In  order to identify   them,   we   first developedan   automated   high-throughput   feedback-controlled protocol whichkeeps the population size and selection pressure approximately constant for hundreds  of  cultures  by  dynamically  re-diluting  the  cultures  and  adjusting  the  antibiotic concentration.  We  implementedthis  protocol  on  a  customized  liquid  handling  robot  and propagated  100  different  gene  deletion  strains  of Escherichia  coliin  triplicate  for  over  100 generations  in  tetracycline  and  in  chloramphenicol,  and  comparedtheir  adaptation  rates.We  find  a  diminishing  returns  pattern,  where  initially  sensitive  strains  adapted  more compared to less sensitive ones.  Our data uncover that deletions of certain genes which do not  affect  mutation  rate,including  efflux  pump  components,  a  chaperone  and severalstructural  and regulatory  genes  can strongly  and  reproducibly  alterresistance  evolution. Sequencing   analysis of   evolved   populations   indicates   that   epistasis   with   resistance mutations  is  the  most  likelyexplanation. This  work  could  inspire  treatment  strategies  in which  targeted  inhibitors  of  evolvability  mechanisms  will  be  given  alongside  antibiotics  to slow down resistance evolution and extend theefficacy of antibiotics.We implemented  astochasticpopulation  genetics  model, toverifyways  in  which  general properties,  namely,  dose-response  characteristics  of  drugs  and  mutation  rates,  influence evolutionary  dynamics.  In  particular,  under  the  exposure  to  antibiotics  with  shallow  dose-response  curves,bacteria  have  narrower  distributions  of  fitness  effects  of  new  mutations. We  show  that in  silicothis  also  leads  to  slower  resistance  evolution.  We see and  confirm with experiments that increased mutation rates, apart from speeding up evolution, also leadto high reproducibility of phenotypic adaptation in a context of continually strong selection pressure.Knowledge  of  these  patterns  can  aid  in  predicting  the  dynamics  of  antibiotic resistance evolutionand adapting treatment schemes accordingly.Focusing on   a   previously   described   type   of   evolvability   modifier –a   stress-induced mutagenesis  allele –we  find  conditions  under  which  it  can  persist  in  a  population  under periodic  selectionakin  to  clinical  treatment. We  set  up  a  deterministic infinite  populationcontinuous  time  model  tracking  the  frequencies  of  a  mutator  and  resistance  allele  and evaluate  various  treatment  schemes  in  how  well  they  maintain  a stress-induced mutator allele. In particular,a high diversity  of stresses  is  crucial  for  the  persistence of the  mutator allele. This leads to a general trade-off where exactly those diversifying treatment schemes which  are  likely  to  decrease  levels  of  resistance  could  lead  to  stronger  selection  of  highly evolvable genotypes.In  the  long  run,  this  work  will  lead  to  a  deeper  understanding  of  the  genetic  and  cellular mechanisms involved in antibiotic resistance evolution and could inspire new strategies for slowing down its rate. ","lang":"eng"}],"oa_version":"Published Version","author":[{"last_name":"Lukacisinova","full_name":"Lukacisinova, Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","orcid":"0000-0002-2519-8004"}],"day":"28","status":"public","_id":"6263","corr_author":"1","degree_awarded":"PhD","ddc":["570","576","579"],"file_date_updated":"2021-02-11T11:17:17Z","department":[{"_id":"ToBo"}],"related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"1027"},{"id":"696","relation":"part_of_dissertation","status":"public"},{"status":"public","relation":"part_of_dissertation","id":"1619"}]},"oa":1,"date_updated":"2026-04-08T14:15:06Z","OA_place":"publisher","month":"12","publisher":"Institute of Science and Technology Austria","supervisor":[{"orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Tobias","last_name":"Bollenbach","first_name":"Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"has_accepted_license":"1","citation":{"chicago":"Lukacisinova, Marta. “Genetic Determinants of Antibiotic Resistance Evolution.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">https://doi.org/10.15479/AT:ISTA:th1072</a>.","ama":"Lukacisinova M. Genetic determinants of antibiotic resistance evolution. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">10.15479/AT:ISTA:th1072</a>","ieee":"M. Lukacisinova, “Genetic determinants of antibiotic resistance evolution,” Institute of Science and Technology Austria, 2018.","apa":"Lukacisinova, M. (2018). <i>Genetic determinants of antibiotic resistance evolution</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">https://doi.org/10.15479/AT:ISTA:th1072</a>","mla":"Lukacisinova, Marta. <i>Genetic Determinants of Antibiotic Resistance Evolution</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:th1072\">10.15479/AT:ISTA:th1072</a>.","short":"M. Lukacisinova, Genetic Determinants of Antibiotic Resistance Evolution, Institute of Science and Technology Austria, 2018.","ista":"Lukacisinova M. 2018. Genetic determinants of antibiotic resistance evolution. Institute of Science and Technology Austria."},"page":"91","acknowledged_ssus":[{"_id":"M-Shop"},{"_id":"LifeSc"}],"article_processing_charge":"No"},{"publication_status":"published","article_type":"original","project":[{"call_identifier":"FP7","grant_number":"618091","name":"Speed of Adaptation in Population Genetics and Evolutionary Computation","_id":"25B1EC9E-B435-11E9-9278-68D0E5697425"}],"title":"Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes","type":"journal_article","isi":1,"language":[{"iso":"eng"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2018-12-11T11:47:58Z","doi":"10.1371/journal.pcbi.1005609","article_number":"e1005609","year":"2017","publication_identifier":{"issn":["1553-734X"]},"publication":"PLoS Computational Biology","file":[{"date_updated":"2020-07-14T12:47:46Z","creator":"system","file_size":3775716,"content_type":"application/pdf","date_created":"2018-12-12T10:15:01Z","checksum":"9143c290fa6458ed2563bff4b295554a","access_level":"open_access","relation":"main_file","file_name":"IST-2017-894-v1+1_journal.pcbi.1005609.pdf","file_id":"5117"}],"external_id":{"isi":["000406619800014"]},"date_published":"2017-07-18T00:00:00Z","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."}],"oa_version":"Published Version","issue":"7","author":[{"orcid":"0000-0002-2519-8004","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta"},{"orcid":"0000-0002-2519-824X","id":"461468AE-F248-11E8-B48F-1D18A9856A87","first_name":"Sebastian","last_name":"Novak","full_name":"Novak, Sebastian"},{"last_name":"Paixao","full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago","orcid":"0000-0003-2361-3953"}],"day":"18","status":"public","_id":"696","corr_author":"1","scopus_import":"1","ddc":["576"],"file_date_updated":"2020-07-14T12:47:46Z","department":[{"_id":"ToBo"},{"_id":"NiBa"},{"_id":"CaGu"}],"publist_id":"7004","oa":1,"related_material":{"record":[{"status":"public","relation":"research_data","id":"9849"},{"id":"9850","status":"public","relation":"research_data"},{"id":"9851","status":"public","relation":"research_data"},{"id":"9852","relation":"research_data","status":"public"},{"id":"6263","relation":"dissertation_contains","status":"public"}]},"date_updated":"2026-06-21T22:31:05Z","month":"07","intvolume":"        13","publisher":"Public Library of Science","quality_controlled":"1","pubrep_id":"894","volume":13,"has_accepted_license":"1","citation":{"ama":"Lukacisinova M, Novak S, Paixao T. Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. <i>PLoS Computational Biology</i>. 2017;13(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">10.1371/journal.pcbi.1005609</a>","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” <i>PLoS Computational Biology</i>. Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">https://doi.org/10.1371/journal.pcbi.1005609</a>.","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes,” <i>PLoS Computational Biology</i>, vol. 13, no. 7. Public Library of Science, 2017.","apa":"Lukacisinova, M., Novak, S., &#38; Paixao, T. (2017). Stress induced mutagenesis: Stress diversity facilitates the persistence of mutator genes. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">https://doi.org/10.1371/journal.pcbi.1005609</a>","mla":"Lukacisinova, Marta, et al. “Stress Induced Mutagenesis: Stress Diversity Facilitates the Persistence of Mutator Genes.” <i>PLoS Computational Biology</i>, vol. 13, no. 7, e1005609, Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609\">10.1371/journal.pcbi.1005609</a>.","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.","short":"M. Lukacisinova, S. Novak, T. Paixao, PLoS Computational Biology 13 (2017)."},"article_processing_charge":"No","ec_funded":1,"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"}},{"file":[{"success":1,"date_created":"2019-01-18T09:57:57Z","content_type":"application/pdf","file_size":858338,"creator":"dernst","date_updated":"2019-01-18T09:57:57Z","file_name":"2017_CurrentOpinion_Lukaciinova.pdf","file_id":"5846","relation":"main_file","access_level":"open_access"}],"external_id":{"isi":["000408077400015"]},"date_published":"2017-08-01T00:00:00Z","publication":"Current Opinion in Biotechnology","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"}],"oa_version":"Published Version","day":"01","author":[{"id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004"},{"last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X"}],"article_type":"original","publication_status":"published","isi":1,"language":[{"iso":"eng"}],"project":[{"call_identifier":"FWF","grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425"},{"_id":"25E83C2C-B435-11E9-9278-68D0E5697425","name":"Optimality principles in responses to antibiotics","grant_number":"303507","call_identifier":"FP7"},{"name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"}],"title":"Toward a quantitative understanding of antibiotic resistance evolution","type":"journal_article","date_created":"2018-12-11T11:49:45Z","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","year":"2017","doi":"10.1016/j.copbio.2017.02.013","month":"08","publisher":"Elsevier","intvolume":"        46","has_accepted_license":"1","citation":{"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>.","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>","ista":"Lukacisinova M, Bollenbach MT. 2017. Toward a quantitative understanding of antibiotic resistance evolution. Current Opinion in Biotechnology. 46, 90–97.","short":"M. Lukacisinova, M.T. Bollenbach, Current Opinion in Biotechnology 46 (2017) 90–97.","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>","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>.","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."},"quality_controlled":"1","pubrep_id":"801","volume":46,"ec_funded":1,"article_processing_charge":"Yes (in subscription journal)","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","image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)"},"page":"90 - 97","_id":"1027","corr_author":"1","status":"public","ddc":["570"],"department":[{"_id":"ToBo"}],"file_date_updated":"2019-01-18T09:57:57Z","scopus_import":"1","oa":1,"related_material":{"record":[{"id":"6263","status":"public","relation":"dissertation_contains"}]},"publist_id":"6364","date_updated":"2026-06-21T22:31:05Z"},{"date_published":"2017-07-18T00:00:00Z","month":"07","publisher":"Public Library of Science","abstract":[{"lang":"eng","text":"This text provides additional information about the model, a derivation of the analytic results in Eq (4), and details about simulations of an additional parameter set."}],"citation":{"short":"M. Lukacisinova, S. Novak, T. Paixao, (2017).","ista":"Lukacisinova M, Novak S, Paixao T. 2017. Modelling and simulation details, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s001\">10.1371/journal.pcbi.1005609.s001</a>.","apa":"Lukacisinova, M., Novak, S., &#38; Paixao, T. (2017). Modelling and simulation details. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s001\">https://doi.org/10.1371/journal.pcbi.1005609.s001</a>","mla":"Lukacisinova, Marta, et al. <i>Modelling and Simulation Details</i>. Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s001\">10.1371/journal.pcbi.1005609.s001</a>.","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Modelling and simulation details.” Public Library of Science, 2017.","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Modelling and Simulation Details.” Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s001\">https://doi.org/10.1371/journal.pcbi.1005609.s001</a>.","ama":"Lukacisinova M, Novak S, Paixao T. Modelling and simulation details. 2017. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s001\">10.1371/journal.pcbi.1005609.s001</a>"},"oa_version":"Published Version","article_processing_charge":"No","day":"18","author":[{"first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","full_name":"Lukacisinova, Marta","last_name":"Lukacisinova","orcid":"0000-0002-2519-8004"},{"orcid":"0000-0002-2519-824X","last_name":"Novak","full_name":"Novak, Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87","first_name":"Sebastian"},{"full_name":"Paixao, Tiago","last_name":"Paixao","first_name":"Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2361-3953"}],"_id":"9849","status":"public","department":[{"_id":"ToBo"},{"_id":"NiBa"},{"_id":"CaGu"}],"type":"research_data_reference","title":"Modelling and simulation details","date_created":"2021-08-09T14:02:34Z","related_material":{"record":[{"id":"696","status":"public","relation":"used_in_publication"}]},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","year":"2017","doi":"10.1371/journal.pcbi.1005609.s001","date_updated":"2025-09-10T11:11:52Z"},{"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_created":"2021-08-09T14:05:24Z","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"696"}]},"doi":"10.1371/journal.pcbi.1005609.s002","date_updated":"2025-09-10T11:11:52Z","year":"2017","status":"public","_id":"9850","type":"research_data_reference","title":"Extensions of the model","department":[{"_id":"ToBo"},{"_id":"CaGu"},{"_id":"NiBa"}],"oa_version":"Published Version","citation":{"apa":"Lukacisinova, M., Novak, S., &#38; Paixao, T. (2017). Extensions of the model. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s002\">https://doi.org/10.1371/journal.pcbi.1005609.s002</a>","mla":"Lukacisinova, Marta, et al. <i>Extensions of the Model</i>. Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s002\">10.1371/journal.pcbi.1005609.s002</a>.","short":"M. Lukacisinova, S. Novak, T. Paixao, (2017).","ista":"Lukacisinova M, Novak S, Paixao T. 2017. Extensions of the model, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s002\">10.1371/journal.pcbi.1005609.s002</a>.","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Extensions of the Model.” Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s002\">https://doi.org/10.1371/journal.pcbi.1005609.s002</a>.","ama":"Lukacisinova M, Novak S, Paixao T. Extensions of the model. 2017. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s002\">10.1371/journal.pcbi.1005609.s002</a>","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Extensions of the model.” Public Library of Science, 2017."},"author":[{"full_name":"Lukacisinova, Marta","last_name":"Lukacisinova","first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004"},{"id":"461468AE-F248-11E8-B48F-1D18A9856A87","first_name":"Sebastian","last_name":"Novak","full_name":"Novak, Sebastian","orcid":"0000-0002-2519-824X"},{"orcid":"0000-0003-2361-3953","full_name":"Paixao, Tiago","last_name":"Paixao","first_name":"Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","day":"18","month":"07","date_published":"2017-07-18T00:00:00Z","publisher":"Public Library of Science","abstract":[{"lang":"eng","text":"In this text, we discuss how a cost of resistance and the possibility of lethal mutations impact our model."}]},{"citation":{"ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Heuristic prediction for multiple stresses.” Public Library of Science, 2017.","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Heuristic Prediction for Multiple Stresses.” Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s003\">https://doi.org/10.1371/journal.pcbi.1005609.s003</a>.","ama":"Lukacisinova M, Novak S, Paixao T. Heuristic prediction for multiple stresses. 2017. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s003\">10.1371/journal.pcbi.1005609.s003</a>","short":"M. Lukacisinova, S. Novak, T. Paixao, (2017).","ista":"Lukacisinova M, Novak S, Paixao T. 2017. Heuristic prediction for multiple stresses, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s003\">10.1371/journal.pcbi.1005609.s003</a>.","apa":"Lukacisinova, M., Novak, S., &#38; Paixao, T. (2017). Heuristic prediction for multiple stresses. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s003\">https://doi.org/10.1371/journal.pcbi.1005609.s003</a>","mla":"Lukacisinova, Marta, et al. <i>Heuristic Prediction for Multiple Stresses</i>. Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s003\">10.1371/journal.pcbi.1005609.s003</a>."},"oa_version":"Published Version","article_processing_charge":"No","day":"18","author":[{"orcid":"0000-0002-2519-8004","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta"},{"orcid":"0000-0002-2519-824X","first_name":"Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87","full_name":"Novak, Sebastian","last_name":"Novak"},{"orcid":"0000-0003-2361-3953","last_name":"Paixao","full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago"}],"date_published":"2017-07-18T00:00:00Z","month":"07","abstract":[{"lang":"eng","text":"Based on the intuitive derivation of the dynamics of SIM allele frequency pM in the main text, we present a heuristic prediction for the long-term SIM allele frequencies with χ > 1 stresses and compare it to numerical simulations."}],"publisher":"Public Library of Science","date_created":"2021-08-09T14:08:14Z","related_material":{"record":[{"id":"696","status":"public","relation":"used_in_publication"}]},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","year":"2017","date_updated":"2025-09-10T11:11:52Z","doi":"10.1371/journal.pcbi.1005609.s003","_id":"9851","status":"public","department":[{"_id":"ToBo"},{"_id":"CaGu"},{"_id":"NiBa"}],"title":"Heuristic prediction for multiple stresses","type":"research_data_reference"},{"publisher":"Public Library of Science","abstract":[{"lang":"eng","text":"We show how different combination strategies affect the fraction of individuals that are multi-resistant."}],"month":"07","date_published":"2017-07-18T00:00:00Z","author":[{"id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004"},{"orcid":"0000-0002-2519-824X","first_name":"Sebastian","id":"461468AE-F248-11E8-B48F-1D18A9856A87","full_name":"Novak, Sebastian","last_name":"Novak"},{"orcid":"0000-0003-2361-3953","last_name":"Paixao","full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago"}],"day":"18","article_processing_charge":"No","oa_version":"Published Version","citation":{"mla":"Lukacisinova, Marta, et al. <i>Resistance Frequencies for Different Combination Strategies</i>. Public Library of Science, 2017, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s004\">10.1371/journal.pcbi.1005609.s004</a>.","apa":"Lukacisinova, M., Novak, S., &#38; Paixao, T. (2017). Resistance frequencies for different combination strategies. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s004\">https://doi.org/10.1371/journal.pcbi.1005609.s004</a>","short":"M. Lukacisinova, S. Novak, T. Paixao, (2017).","ista":"Lukacisinova M, Novak S, Paixao T. 2017. Resistance frequencies for different combination strategies, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s004\">10.1371/journal.pcbi.1005609.s004</a>.","chicago":"Lukacisinova, Marta, Sebastian Novak, and Tiago Paixao. “Resistance Frequencies for Different Combination Strategies.” Public Library of Science, 2017. <a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s004\">https://doi.org/10.1371/journal.pcbi.1005609.s004</a>.","ama":"Lukacisinova M, Novak S, Paixao T. Resistance frequencies for different combination strategies. 2017. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1005609.s004\">10.1371/journal.pcbi.1005609.s004</a>","ieee":"M. Lukacisinova, S. Novak, and T. Paixao, “Resistance frequencies for different combination strategies.” Public Library of Science, 2017."},"title":"Resistance frequencies for different combination strategies","type":"research_data_reference","department":[{"_id":"ToBo"},{"_id":"CaGu"},{"_id":"NiBa"}],"status":"public","_id":"9852","doi":"10.1371/journal.pcbi.1005609.s004","date_updated":"2025-09-10T11:11:52Z","year":"2017","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","related_material":{"record":[{"id":"696","status":"public","relation":"used_in_publication"}]},"date_created":"2021-08-09T14:11:40Z"},{"date_updated":"2025-09-23T09:58:54Z","doi":"10.1371/journal.pbio.1002299.s001","year":"2015","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_created":"2021-07-23T11:53:50Z","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1619"}]},"type":"research_data_reference","title":"Excel file containing the raw data for all figures","department":[{"_id":"ToBo"}],"status":"public","_id":"9711","author":[{"id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume","last_name":"Chevereau","full_name":"Chevereau, Guillaume"},{"id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","last_name":"Lukacisinova","full_name":"Lukacisinova, Marta","orcid":"0000-0002-2519-8004"},{"first_name":"Tugce","last_name":"Batur","full_name":"Batur, Tugce"},{"first_name":"Aysegul","last_name":"Guvenek","full_name":"Guvenek, Aysegul"},{"first_name":"Dilay Hazal","full_name":"Ayhan, Dilay Hazal","last_name":"Ayhan"},{"last_name":"Toprak","full_name":"Toprak, Erdal","first_name":"Erdal"},{"orcid":"0000-0003-4398-476X","full_name":"Bollenbach, Mark Tobias","last_name":"Bollenbach","first_name":"Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","day":"18","oa_version":"Published Version","citation":{"ieee":"G. Chevereau <i>et al.</i>, “Excel file containing the raw data for all figures.” Public Library of Science, 2015.","ama":"Chevereau G, Lukacisinova M, Batur T, et al. Excel file containing the raw data for all figures. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299.s001\">10.1371/journal.pbio.1002299.s001</a>","chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Excel File Containing the Raw Data for All Figures.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pbio.1002299.s001\">https://doi.org/10.1371/journal.pbio.1002299.s001</a>.","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Excel file containing the raw data for all figures, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pbio.1002299.s001\">10.1371/journal.pbio.1002299.s001</a>.","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015).","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., &#38; Bollenbach, M. T. (2015). Excel file containing the raw data for all figures. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pbio.1002299.s001\">https://doi.org/10.1371/journal.pbio.1002299.s001</a>","mla":"Chevereau, Guillaume, et al. <i>Excel File Containing the Raw Data for All Figures</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299.s001\">10.1371/journal.pbio.1002299.s001</a>."},"publisher":"Public Library of Science","month":"11","date_published":"2015-11-18T00:00:00Z"},{"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1619"}]},"date_created":"2021-08-03T07:05:16Z","date_updated":"2025-09-23T09:58:54Z","doi":"10.1371/journal.pbio.1002299.s008","year":"2015","status":"public","_id":"9765","type":"research_data_reference","title":"Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs","department":[{"_id":"ToBo"}],"oa_version":"Published Version","citation":{"ieee":"G. Chevereau <i>et al.</i>, “Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs.” Public Library of Science, 2015.","ama":"Chevereau G, Lukacisinova M, Batur T, et al. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. 2015. doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299.s008\">10.1371/journal.pbio.1002299.s008</a>","chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Hazal Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs.” Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pbio.1002299.s008\">https://doi.org/10.1371/journal.pbio.1002299.s008</a>.","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan DH, Toprak E, Bollenbach MT. 2015. Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pbio.1002299.s008\">10.1371/journal.pbio.1002299.s008</a>.","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D.H. Ayhan, E. Toprak, M.T. Bollenbach, (2015).","mla":"Chevereau, Guillaume, et al. <i>Gene Ontology Enrichment Analysis for the Most Sensitive Gene Deletion Strains for All Drugs</i>. Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299.s008\">10.1371/journal.pbio.1002299.s008</a>.","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D. H., Toprak, E., &#38; Bollenbach, M. T. (2015). Gene ontology enrichment analysis for the most sensitive gene deletion strains for all drugs. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pbio.1002299.s008\">https://doi.org/10.1371/journal.pbio.1002299.s008</a>"},"author":[{"first_name":"Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","full_name":"Chevereau, Guillaume","last_name":"Chevereau"},{"full_name":"Lukacisinova, Marta","last_name":"Lukacisinova","first_name":"Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-2519-8004"},{"full_name":"Batur, Tugce","last_name":"Batur","first_name":"Tugce"},{"first_name":"Aysegul","full_name":"Guvenek, Aysegul","last_name":"Guvenek"},{"first_name":"Dilay Hazal","last_name":"Ayhan","full_name":"Ayhan, Dilay Hazal"},{"last_name":"Toprak","full_name":"Toprak, Erdal","first_name":"Erdal"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias"}],"day":"18","article_processing_charge":"No","month":"11","date_published":"2015-11-18T00:00:00Z","publisher":"Public Library of Science"},{"publication_status":"published","type":"journal_article","title":"Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene","project":[{"call_identifier":"FP7","name":"Polarity and subcellular dynamics in plants","grant_number":"282300","_id":"25716A02-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T11:52:26Z","acknowledgement":"This work was supported by ERC Independent Research grant (ERC-2011-StG-20101109-PSDP to JF). JM internship was supported by the grant “Action Austria – Slovakia”.\r\nData associated with the article are available under the terms of the Creative Commons Zero \"No rights reserved\" data waiver (CC0 1.0 Public domain dedication). \r\n\r\nData availability: \r\nF1000Research: Dataset 1. Dataset 1, 10.5256/f1000research.7143.d104552\r\n\r\nF1000Research: Dataset 2. Dataset 2, 10.5256/f1000research.7143.d104553\r\n\r\nF1000Research: Dataset 3. Dataset 3, 10.5256/f1000research.7143.d104554","doi":"10.12688/f1000research.7143.1","year":"2015","publication":"F1000 Research ","file":[{"checksum":"8beae5cbe988e1060265ae7de2ee8306","file_name":"IST-2016-497-v1+1_10.12688_f1000research.7143.1_20151102.pdf","file_id":"5198","access_level":"open_access","relation":"main_file","creator":"system","date_updated":"2020-07-14T12:44:59Z","content_type":"application/pdf","date_created":"2018-12-12T10:16:12Z","file_size":4414248}],"date_published":"2015-10-01T00:00:00Z","abstract":[{"lang":"eng","text":"The Auxin Binding Protein1 (ABP1) has been identified based on its ability to bind auxin with high affinity and studied for a long time as a prime candidate for the extracellular auxin receptor responsible for mediating in particular the fast non-transcriptional auxin responses. However, the contradiction between the embryo-lethal phenotypes of the originally described Arabidopsis T-DNA insertional knock-out alleles (abp1-1 and abp1-1s) and the wild type-like phenotypes of other recently described loss-of-function alleles (abp1-c1 and abp1-TD1) questions the biological importance of ABP1 and relevance of the previous genetic studies. Here we show that there is no hidden copy of the ABP1 gene in the Arabidopsis genome but the embryo-lethal phenotypes of abp1-1 and abp1-1s alleles are very similar to the knock-out phenotypes of the neighboring gene, BELAYA SMERT (BSM). Furthermore, the allelic complementation test between bsm and abp1 alleles shows that the embryo-lethality in the abp1-1 and abp1-1s alleles is caused by the off-target disruption of the BSM locus by the T-DNA insertions. This clarifies the controversy of different phenotypes among published abp1 knock-out alleles and asks for reflections on the developmental role of ABP1."}],"oa_version":"Published Version","author":[{"id":"483727CA-F248-11E8-B48F-1D18A9856A87","first_name":"Jaroslav","last_name":"Michalko","full_name":"Michalko, Jaroslav"},{"last_name":"Dravecka","full_name":"Dravecka, Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta","orcid":"0000-0002-2519-8004"},{"orcid":"0000-0003-4398-476X","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Tobias","last_name":"Bollenbach","full_name":"Bollenbach, Tobias"},{"first_name":"Jirí","id":"4159519E-F248-11E8-B48F-1D18A9856A87","full_name":"Friml, Jirí","last_name":"Friml","orcid":"0000-0002-8302-7596"}],"day":"01","status":"public","_id":"1509","corr_author":"1","scopus_import":"1","department":[{"_id":"JiFr"},{"_id":"ToBo"}],"file_date_updated":"2020-07-14T12:44:59Z","ddc":["570"],"publist_id":"5668","oa":1,"date_updated":"2025-04-15T07:48:03Z","month":"10","intvolume":"         4","publisher":"F1000 Research","quality_controlled":"1","volume":4,"pubrep_id":"497","citation":{"ieee":"J. Michalko, M. Lukacisinova, M. T. Bollenbach, and J. Friml, “Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene,” <i>F1000 Research </i>, vol. 4. F1000 Research, 2015.","ama":"Michalko J, Lukacisinova M, Bollenbach MT, Friml J. Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene. <i>F1000 Research </i>. 2015;4. doi:<a href=\"https://doi.org/10.12688/f1000research.7143.1\">10.12688/f1000research.7143.1</a>","chicago":"Michalko, Jaroslav, Marta Lukacisinova, Mark Tobias Bollenbach, and Jiří Friml. “Embryo-Lethal Phenotypes in Early Abp1 Mutants Are Due to Disruption of the Neighboring BSM Gene.” <i>F1000 Research </i>. F1000 Research, 2015. <a href=\"https://doi.org/10.12688/f1000research.7143.1\">https://doi.org/10.12688/f1000research.7143.1</a>.","ista":"Michalko J, Lukacisinova M, Bollenbach MT, Friml J. 2015. Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene. F1000 Research . 4.","short":"J. Michalko, M. Lukacisinova, M.T. Bollenbach, J. Friml, F1000 Research  4 (2015).","apa":"Michalko, J., Lukacisinova, M., Bollenbach, M. T., &#38; Friml, J. (2015). Embryo-lethal phenotypes in early abp1 mutants are due to disruption of the neighboring BSM gene. <i>F1000 Research </i>. F1000 Research. <a href=\"https://doi.org/10.12688/f1000research.7143.1\">https://doi.org/10.12688/f1000research.7143.1</a>","mla":"Michalko, Jaroslav, et al. “Embryo-Lethal Phenotypes in Early Abp1 Mutants Are Due to Disruption of the Neighboring BSM Gene.” <i>F1000 Research </i>, vol. 4, F1000 Research, 2015, doi:<a href=\"https://doi.org/10.12688/f1000research.7143.1\">10.12688/f1000research.7143.1</a>."},"has_accepted_license":"1","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"article_processing_charge":"No","ec_funded":1},{"author":[{"last_name":"Chevereau","full_name":"Chevereau, Guillaume","id":"424D78A0-F248-11E8-B48F-1D18A9856A87","first_name":"Guillaume"},{"orcid":"0000-0002-2519-8004","last_name":"Dravecka","full_name":"Dravecka, Marta","id":"4342E402-F248-11E8-B48F-1D18A9856A87","first_name":"Marta"},{"first_name":"Tugce","full_name":"Batur, Tugce","last_name":"Batur"},{"first_name":"Aysegul","full_name":"Guvenek, Aysegul","last_name":"Guvenek"},{"full_name":"Ayhan, Dilay","last_name":"Ayhan","first_name":"Dilay"},{"full_name":"Toprak, Erdal","last_name":"Toprak","first_name":"Erdal"},{"last_name":"Bollenbach","full_name":"Bollenbach, Mark Tobias","id":"3E6DB97A-F248-11E8-B48F-1D18A9856A87","first_name":"Mark Tobias","orcid":"0000-0003-4398-476X"}],"day":"18","oa_version":"Published Version","issue":"11","abstract":[{"lang":"eng","text":"The emergence of drug resistant pathogens is a serious public health problem. It is a long-standing goal to predict rates of resistance evolution and design optimal treatment strategies accordingly. To this end, it is crucial to reveal the underlying causes of drug-specific differences in the evolutionary dynamics leading to resistance. However, it remains largely unknown why the rates of resistance evolution via spontaneous mutations and the diversity of mutational paths vary substantially between drugs. Here we comprehensively quantify the distribution of fitness effects (DFE) of mutations, a key determinant of evolutionary dynamics, in the presence of eight antibiotics representing the main modes of action. Using precise high-throughput fitness measurements for genome-wide Escherichia coli gene deletion strains, we find that the width of the DFE varies dramatically between antibiotics and, contrary to conventional wisdom, for some drugs the DFE width is lower than in the absence of stress. We show that this previously underappreciated divergence in DFE width among antibiotics is largely caused by their distinct drug-specific dose-response characteristics. Unlike the DFE, the magnitude of the changes in tolerated drug concentration resulting from genome-wide mutations is similar for most drugs but exceptionally small for the antibiotic nitrofurantoin, i.e., mutations generally have considerably smaller resistance effects for nitrofurantoin than for other drugs. A population genetics model predicts that resistance evolution for drugs with this property is severely limited and confined to reproducible mutational paths. We tested this prediction in laboratory evolution experiments using the “morbidostat”, a device for evolving bacteria in well-controlled drug environments. Nitrofurantoin resistance indeed evolved extremely slowly via reproducible mutations—an almost paradoxical behavior since this drug causes DNA damage and increases the mutation rate. Overall, we identified novel quantitative characteristics of the evolutionary landscape that provide the conceptual foundation for predicting the dynamics of drug resistance evolution."}],"publication":"PLoS Biology","external_id":{"isi":["000365898900011"]},"file":[{"file_name":"IST-2016-468-v1+1_journal.pbio.1002299.pdf","file_id":"4723","access_level":"open_access","relation":"main_file","checksum":"0e82e3279f50b15c6c170c042627802b","date_created":"2018-12-12T10:09:00Z","content_type":"application/pdf","file_size":1387760,"creator":"system","date_updated":"2020-07-14T12:45:07Z"}],"date_published":"2015-11-18T00:00:00Z","doi":"10.1371/journal.pbio.1002299","year":"2015","article_number":"e1002299","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","date_created":"2018-12-11T11:53:04Z","type":"journal_article","title":"Quantifying the determinants of evolutionary dynamics leading to drug resistance","project":[{"name":"Revealing the fundamental limits of cell growth","grant_number":"RGP0042/2013","_id":"25EB3A80-B435-11E9-9278-68D0E5697425"},{"grant_number":"P27201-B22","name":"Revealing the mechanisms underlying drug interactions","_id":"25E9AF9E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"_id":"25E83C2C-B435-11E9-9278-68D0E5697425","name":"Optimality principles in responses to antibiotics","grant_number":"303507","call_identifier":"FP7"}],"language":[{"iso":"eng"}],"isi":1,"publication_status":"published","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","short":"CC BY (4.0)"},"article_processing_charge":"No","ec_funded":1,"volume":13,"pubrep_id":"468","quality_controlled":"1","citation":{"ama":"Chevereau G, Lukacisinova M, Batur T, et al. Quantifying the determinants of evolutionary dynamics leading to drug resistance. <i>PLoS Biology</i>. 2015;13(11). doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299\">10.1371/journal.pbio.1002299</a>","chicago":"Chevereau, Guillaume, Marta Lukacisinova, Tugce Batur, Aysegul Guvenek, Dilay Ayhan, Erdal Toprak, and Mark Tobias Bollenbach. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” <i>PLoS Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pbio.1002299\">https://doi.org/10.1371/journal.pbio.1002299</a>.","ieee":"G. Chevereau <i>et al.</i>, “Quantifying the determinants of evolutionary dynamics leading to drug resistance,” <i>PLoS Biology</i>, vol. 13, no. 11. Public Library of Science, 2015.","mla":"Chevereau, Guillaume, et al. “Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.” <i>PLoS Biology</i>, vol. 13, no. 11, e1002299, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pbio.1002299\">10.1371/journal.pbio.1002299</a>.","apa":"Chevereau, G., Lukacisinova, M., Batur, T., Guvenek, A., Ayhan, D., Toprak, E., &#38; Bollenbach, M. T. (2015). Quantifying the determinants of evolutionary dynamics leading to drug resistance. <i>PLoS Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pbio.1002299\">https://doi.org/10.1371/journal.pbio.1002299</a>","short":"G. Chevereau, M. Lukacisinova, T. Batur, A. Guvenek, D. Ayhan, E. Toprak, M.T. Bollenbach, PLoS Biology 13 (2015).","ista":"Chevereau G, Lukacisinova M, Batur T, Guvenek A, Ayhan D, Toprak E, Bollenbach MT. 2015. Quantifying the determinants of evolutionary dynamics leading to drug resistance. PLoS Biology. 13(11), e1002299."},"has_accepted_license":"1","intvolume":"        13","publisher":"Public Library of Science","month":"11","date_updated":"2026-06-21T22:31:05Z","publist_id":"5547","related_material":{"record":[{"relation":"research_data","status":"public","id":"9711"},{"id":"9765","status":"public","relation":"research_data"},{"relation":"dissertation_contains","status":"public","id":"6263"}]},"oa":1,"scopus_import":"1","department":[{"_id":"ToBo"}],"file_date_updated":"2020-07-14T12:45:07Z","ddc":["570"],"status":"public","_id":"1619","corr_author":"1"}]