[{"volume":"2016-July","ec_funded":1,"publication_status":"published","file":[{"access_level":"local","relation":"main_file","content_type":"application/pdf","file_id":"5203","checksum":"7219432b43defc62a0d45f48d4ce6a19","creator":"system","date_updated":"2020-07-14T12:44:43Z","file_size":539166,"date_created":"2018-12-12T10:16:17Z","file_name":"IST-2017-810-v1+1_root.pdf"}],"language":[{"iso":"eng"}],"scopus_import":1,"month":"07","abstract":[{"lang":"eng","text":"In recent years, several biomolecular systems have been shown to be scale-invariant (SI), i.e. to show the same output dynamics when exposed to geometrically scaled input signals (u → pu, p > 0) after pre-adaptation to accordingly scaled constant inputs. In this article, we show that SI systems-as well as systems invariant with respect to other input transformations-can realize nonlinear differential operators: when excited by inputs obeying functional forms characteristic for a given class of invariant systems, the systems' outputs converge to constant values directly quantifying the speed of the input."}],"oa_version":"Preprint","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:43Z","date_updated":"2021-01-12T06:49:51Z","ddc":["003","621"],"type":"conference","conference":{"name":"ACC: American Control Conference","end_date":"2016-07-08","location":"Boston, MA, USA","start_date":"2016-07-06"},"status":"public","pubrep_id":"810","_id":"1320","doi":"10.1109/ACC.2016.7526722","date_published":"2016-07-28T00:00:00Z","date_created":"2018-12-11T11:51:21Z","has_accepted_license":"1","year":"2016","day":"28","quality_controlled":"1","publisher":"IEEE","acknowledgement":"The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n° [291734]. Work supported in part by grants AFOSR FA9550-14-1-0060 and NIH 1R01GM100473.","publist_id":"5950","author":[{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","first_name":"Moritz","last_name":"Lang","full_name":"Lang, Moritz"},{"last_name":"Sontag","full_name":"Sontag, Eduardo","first_name":"Eduardo"}],"title":"Scale-invariant systems realize nonlinear differential operators","citation":{"mla":"Lang, Moritz, and Eduardo Sontag. Scale-Invariant Systems Realize Nonlinear Differential Operators. Vol. 2016–July, 7526722, IEEE, 2016, doi:10.1109/ACC.2016.7526722.","short":"M. Lang, E. Sontag, in:, IEEE, 2016.","ieee":"M. Lang and E. Sontag, “Scale-invariant systems realize nonlinear differential operators,” presented at the ACC: American Control Conference, Boston, MA, USA, 2016, vol. 2016–July.","ama":"Lang M, Sontag E. Scale-invariant systems realize nonlinear differential operators. In: Vol 2016-July. IEEE; 2016. doi:10.1109/ACC.2016.7526722","apa":"Lang, M., & Sontag, E. (2016). Scale-invariant systems realize nonlinear differential operators (Vol. 2016–July). Presented at the ACC: American Control Conference, Boston, MA, USA: IEEE. https://doi.org/10.1109/ACC.2016.7526722","chicago":"Lang, Moritz, and Eduardo Sontag. “Scale-Invariant Systems Realize Nonlinear Differential Operators,” Vol. 2016–July. IEEE, 2016. https://doi.org/10.1109/ACC.2016.7526722.","ista":"Lang M, Sontag E. 2016. Scale-invariant systems realize nonlinear differential operators. ACC: American Control Conference vol. 2016–July, 7526722."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"article_number":"7526722"},{"type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"status":"public","pubrep_id":"662","_id":"1332","file_date_updated":"2020-07-14T12:44:44Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:49:57Z","ddc":["570","579"],"scopus_import":1,"month":"01","intvolume":" 7","abstract":[{"text":"Antibiotic-sensitive and -resistant bacteria coexist in natural environments with low, if detectable, antibiotic concentrations. Except possibly around localized antibiotic sources, where resistance can provide a strong advantage, bacterial fitness is dominated by stresses unaffected by resistance to the antibiotic. How do such mixed and heterogeneous conditions influence the selective advantage or disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels of tetracyclines potentiate selection for or against tetracycline resistance around localized sources of almost any toxin or stress. Furthermore, certain stresses generate alternating rings of selection for and against resistance around a localized source of the antibiotic. In these conditions, localized antibiotic sources, even at high strengths, can actually produce a net selection against resistance to the antibiotic. Our results show that interactions between the effects of an antibiotic and other stresses in inhomogeneous environments can generate pervasive, complex patterns of selection both for and against antibiotic resistance.","lang":"eng"}],"oa_version":"Published Version","volume":7,"license":"https://creativecommons.org/licenses/by/4.0/","publication_status":"published","file":[{"file_name":"IST-2016-662-v1+1_ncomms10333.pdf","date_created":"2018-12-12T10:13:52Z","file_size":1844107,"date_updated":"2020-07-14T12:44:44Z","creator":"system","file_id":"5039","checksum":"ef147bcbb8bd37e9079cf3ce06f5815d","content_type":"application/pdf","relation":"main_file","access_level":"open_access"}],"language":[{"iso":"eng"}],"article_number":"10333","author":[{"first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187"},{"first_name":"Adam","last_name":"Palmer","full_name":"Palmer, Adam"},{"first_name":"Idan","full_name":"Yelin, Idan","last_name":"Yelin"},{"first_name":"Roy","last_name":"Kishony","full_name":"Kishony, Roy"}],"publist_id":"5936","title":"Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments","citation":{"ista":"Chait RP, Palmer A, Yelin I, Kishony R. 2016. Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. 7, 10333.","chicago":"Chait, Remy P, Adam Palmer, Idan Yelin, and Roy Kishony. “Pervasive Selection for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.” Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms10333.","apa":"Chait, R. P., Palmer, A., Yelin, I., & Kishony, R. (2016). Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms10333","ama":"Chait RP, Palmer A, Yelin I, Kishony R. Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. 2016;7. doi:10.1038/ncomms10333","ieee":"R. P. Chait, A. Palmer, I. Yelin, and R. Kishony, “Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments,” Nature Communications, vol. 7. Nature Publishing Group, 2016.","short":"R.P. Chait, A. Palmer, I. Yelin, R. Kishony, Nature Communications 7 (2016).","mla":"Chait, Remy P., et al. “Pervasive Selection for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.” Nature Communications, vol. 7, 10333, Nature Publishing Group, 2016, doi:10.1038/ncomms10333."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","publisher":"Nature Publishing Group","oa":1,"acknowledgement":"This work was partially supported by US National Institutes of Health grant R01-GM081617, Israeli Centers of Research Excellence I-CORE Program ISF Grant No. 152/11, and the European Research Council FP7 ERC Grant 281891.","date_published":"2016-01-20T00:00:00Z","doi":"10.1038/ncomms10333","date_created":"2018-12-11T11:51:25Z","has_accepted_license":"1","year":"2016","day":"20","publication":"Nature Communications"},{"page":"1147 - 1151","date_created":"2018-12-11T11:51:29Z","doi":"10.1126/science.aag0822","volume":353,"date_published":"2016-09-09T00:00:00Z","issue":"6304","publication_status":"published","year":"2016","language":[{"iso":"eng"}],"publication":"Science","day":"09","oa":1,"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/"}],"publisher":"American Association for the Advancement of Science","scopus_import":1,"quality_controlled":"1","intvolume":" 353","month":"09","abstract":[{"lang":"eng","text":"A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front.While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front,we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate provides a versatile platformfor studying microbial adaption and directly visualizing evolutionary dynamics."}],"oa_version":"Preprint","author":[{"last_name":"Baym","full_name":"Baym, Michael","first_name":"Michael"},{"full_name":"Lieberman, Tami","last_name":"Lieberman","first_name":"Tami"},{"last_name":"Kelsic","full_name":"Kelsic, Eric","first_name":"Eric"},{"last_name":"Chait","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P"},{"first_name":"Rotem","last_name":"Gross","full_name":"Gross, Rotem"},{"full_name":"Yelin, Idan","last_name":"Yelin","first_name":"Idan"},{"first_name":"Roy","last_name":"Kishony","full_name":"Kishony, Roy"}],"publist_id":"5911","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"title":"Spatiotemporal microbial evolution on antibiotic landscapes","citation":{"chicago":"Baym, Michael, Tami Lieberman, Eric Kelsic, Remy P Chait, Rotem Gross, Idan Yelin, and Roy Kishony. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.” Science. American Association for the Advancement of Science, 2016. https://doi.org/10.1126/science.aag0822.","ista":"Baym M, Lieberman T, Kelsic E, Chait RP, Gross R, Yelin I, Kishony R. 2016. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 353(6304), 1147–1151.","mla":"Baym, Michael, et al. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.” Science, vol. 353, no. 6304, American Association for the Advancement of Science, 2016, pp. 1147–51, doi:10.1126/science.aag0822.","apa":"Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., & Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.aag0822","ama":"Baym M, Lieberman T, Kelsic E, et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 2016;353(6304):1147-1151. doi:10.1126/science.aag0822","short":"M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony, Science 353 (2016) 1147–1151.","ieee":"M. Baym et al., “Spatiotemporal microbial evolution on antibiotic landscapes,” Science, vol. 353, no. 6304. American Association for the Advancement of Science, pp. 1147–1151, 2016."},"date_updated":"2021-01-12T06:50:01Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","type":"journal_article","status":"public","_id":"1342"},{"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"article_number":"036005","title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","publist_id":"5815","author":[{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele","last_name":"De Martino","orcid":"0000-0002-5214-4706","full_name":"De Martino, Daniele"},{"full_name":"Capuani, Fabrizio","last_name":"Capuani","first_name":"Fabrizio"},{"first_name":"Andrea","last_name":"De Martino","full_name":"De Martino, Andrea"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"De Martino, D., Capuani, F., & De Martino, A. (2016). Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/3/036005","ama":"De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005","ieee":"D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.","short":"D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).","mla":"De Martino, Daniele, et al. “Growth against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.” Physical Biology, vol. 13, no. 3, 036005, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/3/036005.","ista":"De Martino D, Capuani F, De Martino A. 2016. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 13(3), 036005.","chicago":"De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Growth against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.” Physical Biology. IOP Publishing Ltd., 2016. https://doi.org/10.1088/1478-3975/13/3/036005."},"oa":1,"quality_controlled":"1","publisher":"IOP Publishing Ltd.","acknowledgement":"The research leading to these results has received funding from the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038.","date_created":"2018-12-11T11:51:46Z","date_published":"2016-05-27T00:00:00Z","doi":"10.1088/1478-3975/13/3/036005","publication":"Physical Biology","day":"27","year":"2016","status":"public","type":"journal_article","_id":"1394","department":[{"_id":"GaTk"}],"date_updated":"2021-01-12T06:50:23Z","intvolume":" 13","month":"05","main_file_link":[{"url":"https://arxiv.org/abs/1601.03243","open_access":"1"}],"scopus_import":1,"oa_version":"Preprint","abstract":[{"lang":"eng","text":"The solution space of genome-scale models of cellular metabolism provides a map between physically\r\nviable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the\r\ncorresponding growth rates. By sampling the solution space of E. coliʼs metabolic network, we show\r\nthat empirical growth rate distributions recently obtained in experiments at single-cell resolution can\r\nbe explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the\r\nhigher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of\r\na large bacterial population that captures this trade-off. The scaling relationships observed in\r\nexperiments encode, in such frameworks, for the same distance from the maximum achievable growth\r\nrate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being\r\ngrounded on genome-scale metabolic network reconstructions, these results allow for multiple\r\nimplications and extensions in spite of the underlying conceptual simplicity."}],"ec_funded":1,"issue":"3","volume":13,"language":[{"iso":"eng"}],"publication_status":"published"},{"quality_controlled":"1","publisher":"Genetics Society of America","oa":1,"year":"2016","day":"06","publication":"Genetics","page":"1523 - 1548","doi":"10.1534/genetics.115.184127","date_published":"2016-04-06T00:00:00Z","date_created":"2018-12-11T11:51:55Z","project":[{"name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"name":"Information processing and computation in fish groups","grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425"}],"citation":{"mla":"Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016, pp. 1523–48, doi:10.1534/genetics.115.184127.","ama":"Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127","apa":"Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation for the dynamics of quantitative traits. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.115.184127","short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.","ieee":"K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of America, pp. 1523–1548, 2016.","chicago":"Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of America, 2016. https://doi.org/10.1534/genetics.115.184127.","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 1523–1548."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'ová, Katarína","orcid":"0000-0002-7214-0171","last_name":"Bod'ová"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton"}],"publist_id":"5787","article_processing_charge":"No","external_id":{"arxiv":["1510.08344"]},"title":"A general approximation for the dynamics of quantitative traits","abstract":[{"lang":"eng","text":"Selection, mutation, and random drift affect the dynamics of allele frequencies and consequently of quantitative traits. While the macroscopic dynamics of quantitative traits can be measured, the underlying allele frequencies are typically unobserved. Can we understand how the macroscopic observables evolve without following these microscopic processes? This problem has been studied previously by analogy with statistical mechanics: the allele frequency distribution at each time point is approximated by the stationary form, which maximizes entropy. We explore the limitations of this method when mutation is small (4Nμ < 1) so that populations are typically close to fixation, and we extend the theory in this regime to account for changes in mutation strength. We consider a single diallelic locus either under directional selection or with overdominance and then generalize to multiple unlinked biallelic loci with unequal effects. We find that the maximum-entropy approximation is remarkably accurate, even when mutation and selection change rapidly. "}],"oa_version":"Preprint","scopus_import":"1","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1510.08344"}],"month":"04","intvolume":" 202","publication_status":"published","language":[{"iso":"eng"}],"issue":"4","volume":202,"ec_funded":1,"_id":"1420","type":"journal_article","status":"public","date_updated":"2022-08-01T10:49:55Z","department":[{"_id":"GaTk"},{"_id":"NiBa"}]}]