[{"pubrep_id":"810","oa_version":"Preprint","file":[{"file_name":"IST-2017-810-v1+1_root.pdf","access_level":"local","creator":"system","file_size":539166,"content_type":"application/pdf","file_id":"5203","relation":"main_file","date_updated":"2020-07-14T12:44:43Z","date_created":"2018-12-12T10:16:17Z","checksum":"7219432b43defc62a0d45f48d4ce6a19"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1320","title":"Scale-invariant systems realize nonlinear differential operators","ddc":["003","621"],"status":"public","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."}],"type":"conference","date_published":"2016-07-28T00:00:00Z","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.","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.","ama":"Lang M, Sontag E. Scale-invariant systems realize nonlinear differential operators. In: Vol 2016-July. IEEE; 2016. doi: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.","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","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."},"day":"28","has_accepted_license":"1","scopus_import":1,"author":[{"full_name":"Lang, Moritz","first_name":"Moritz","last_name":"Lang","id":"29E0800A-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Sontag, Eduardo","last_name":"Sontag","first_name":"Eduardo"}],"date_updated":"2021-01-12T06:49:51Z","date_created":"2018-12-11T11:51:21Z","volume":"2016-July","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.","year":"2016","publication_status":"published","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"IEEE","file_date_updated":"2020-07-14T12:44:43Z","publist_id":"5950","ec_funded":1,"article_number":"7526722","conference":{"name":"ACC: American Control Conference","start_date":"2016-07-06","location":"Boston, MA, USA","end_date":"2016-07-08"},"doi":"10.1109/ACC.2016.7526722","language":[{"iso":"eng"}],"quality_controlled":"1","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"month":"07"},{"language":[{"iso":"eng"}],"doi":"10.1038/ncomms10333","quality_controlled":"1","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"},"month":"01","date_created":"2018-12-11T11:51:25Z","date_updated":"2021-01-12T06:49:57Z","volume":7,"author":[{"full_name":"Chait, Remy P","first_name":"Remy P","last_name":"Chait","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0876-3187"},{"full_name":"Palmer, Adam","first_name":"Adam","last_name":"Palmer"},{"full_name":"Yelin, Idan","first_name":"Idan","last_name":"Yelin"},{"last_name":"Kishony","first_name":"Roy","full_name":"Kishony, Roy"}],"publication_status":"published","publisher":"Nature Publishing Group","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"year":"2016","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.","file_date_updated":"2020-07-14T12:44:44Z","publist_id":"5936","article_number":"10333","date_published":"2016-01-20T00:00:00Z","publication":"Nature Communications","citation":{"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","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.","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.","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","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.","short":"R.P. Chait, A. Palmer, I. Yelin, R. Kishony, Nature Communications 7 (2016).","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."},"day":"20","has_accepted_license":"1","scopus_import":1,"oa_version":"Published Version","file":[{"relation":"main_file","file_id":"5039","date_created":"2018-12-12T10:13:52Z","date_updated":"2020-07-14T12:44:44Z","checksum":"ef147bcbb8bd37e9079cf3ce06f5815d","file_name":"IST-2016-662-v1+1_ncomms10333.pdf","access_level":"open_access","content_type":"application/pdf","file_size":1844107,"creator":"system"}],"pubrep_id":"662","status":"public","ddc":["570","579"],"title":"Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments","intvolume":" 7","_id":"1332","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","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"}],"type":"journal_article"},{"scopus_import":1,"month":"09","day":"09","page":"1147 - 1151","quality_controlled":"1","oa":1,"main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/","open_access":"1"}],"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.","short":"M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony, Science 353 (2016) 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.","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.","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","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.","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"},"publication":"Science","language":[{"iso":"eng"}],"doi":"10.1126/science.aag0822","date_published":"2016-09-09T00:00:00Z","type":"journal_article","publist_id":"5911","issue":"6304","abstract":[{"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.","lang":"eng"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"American Association for the Advancement of Science","intvolume":" 353","publication_status":"published","title":"Spatiotemporal microbial evolution on antibiotic landscapes","status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1342","year":"2016","oa_version":"Preprint","volume":353,"date_updated":"2021-01-12T06:50:01Z","date_created":"2018-12-11T11:51:29Z","author":[{"full_name":"Baym, Michael","first_name":"Michael","last_name":"Baym"},{"first_name":"Tami","last_name":"Lieberman","full_name":"Lieberman, Tami"},{"last_name":"Kelsic","first_name":"Eric","full_name":"Kelsic, Eric"},{"orcid":"0000-0003-0876-3187","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","first_name":"Remy P","full_name":"Chait, Remy P"},{"full_name":"Gross, Rotem","last_name":"Gross","first_name":"Rotem"},{"full_name":"Yelin, Idan","last_name":"Yelin","first_name":"Idan"},{"first_name":"Roy","last_name":"Kishony","full_name":"Kishony, Roy"}]},{"article_number":"036005","ec_funded":1,"publist_id":"5815","department":[{"_id":"GaTk"}],"publisher":"IOP Publishing Ltd.","publication_status":"published","year":"2016","acknowledgement":"The research leading to these results has received funding from the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038.","volume":13,"date_updated":"2021-01-12T06:50:23Z","date_created":"2018-12-11T11:51:46Z","author":[{"last_name":"De Martino","first_name":"Daniele","orcid":"0000-0002-5214-4706","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele"},{"last_name":"Capuani","first_name":"Fabrizio","full_name":"Capuani, Fabrizio"},{"full_name":"De Martino, Andrea","last_name":"De Martino","first_name":"Andrea"}],"month":"05","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"quality_controlled":"1","oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/1601.03243","open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.1088/1478-3975/13/3/036005","type":"journal_article","issue":"3","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."}],"intvolume":" 13","title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","status":"public","_id":"1394","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","scopus_import":1,"day":"27","citation":{"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.","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","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.","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","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.","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."},"publication":"Physical Biology","date_published":"2016-05-27T00:00:00Z"},{"publist_id":"5787","ec_funded":1,"year":"2016","publication_status":"published","publisher":"Genetics Society of America","department":[{"_id":"GaTk"},{"_id":"NiBa"}],"author":[{"last_name":"Bod'ová","first_name":"Katarína","orcid":"0000-0002-7214-0171","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'ová, Katarína"},{"full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Barton, Nicholas H","last_name":"Barton","first_name":"Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2022-08-01T10:49:55Z","date_created":"2018-12-11T11:51:55Z","volume":202,"month":"04","oa":1,"external_id":{"arxiv":["1510.08344"]},"main_file_link":[{"url":"http://arxiv.org/abs/1510.08344","open_access":"1"}],"quality_controlled":"1","project":[{"call_identifier":"FP7","name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152","_id":"25B07788-B435-11E9-9278-68D0E5697425"},{"_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012","name":"Information processing and computation in fish groups"}],"doi":"10.1534/genetics.115.184127","language":[{"iso":"eng"}],"type":"journal_article","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. "}],"issue":"4","_id":"1420","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","title":"A general approximation for the dynamics of quantitative traits","intvolume":" 202","oa_version":"Preprint","scopus_import":"1","day":"06","article_processing_charge":"No","publication":"Genetics","citation":{"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.","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","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 1523–1548.","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","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.","short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.","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."},"page":"1523 - 1548","date_published":"2016-04-06T00:00:00Z"},{"oa_version":"Preprint","_id":"1485","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis","status":"public","intvolume":" 13","abstract":[{"text":"In this article the notion of metabolic turnover is revisited in the light of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo methods we perform an exact sampling of the enzymatic fluxes in a genome scale metabolic network of E. Coli in stationary growth conditions from which we infer the metabolites turnover times. However the latter are inferred from net fluxes, and we argue that this approximation is not valid for enzymes working nearby thermodynamic equilibrium. We recalculate turnover times from total fluxes by performing an energy balance analysis of the network and recurring to the fluctuation theorem. We find in many cases values one of order of magnitude lower, implying a faster picture of intermediate metabolism.","lang":"eng"}],"issue":"1","type":"journal_article","date_published":"2016-01-29T00:00:00Z","publication":"Physical Biology","citation":{"apa":"De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/1/016003","ieee":"D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP Publishing Ltd., 2016.","ista":"De Martino D. 2016. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 13(1), 016003.","ama":"De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003","chicago":"De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of E. Coli from the Energy Balance Analysis.” Physical Biology. IOP Publishing Ltd., 2016. https://doi.org/10.1088/1478-3975/13/1/016003.","short":"D. De Martino, Physical Biology 13 (2016).","mla":"De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of E. Coli from the Energy Balance Analysis.” Physical Biology, vol. 13, no. 1, 016003, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/1/016003."},"day":"29","scopus_import":1,"author":[{"first_name":"Daniele","last_name":"De Martino","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5214-4706","full_name":"De Martino, Daniele"}],"date_updated":"2021-01-12T06:51:04Z","date_created":"2018-12-11T11:52:18Z","volume":13,"year":"2016","publication_status":"published","publisher":"IOP Publishing Ltd.","department":[{"_id":"GaTk"}],"publist_id":"5702","ec_funded":1,"article_number":"016003","doi":"10.1088/1478-3975/13/1/016003","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1505.04613"}],"oa":1,"quality_controlled":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"month":"01"},{"month":"11","project":[{"name":"Quantitative Reactive Modeling","call_identifier":"FP7","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425"},{"grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425","name":"Rigorous Systems Engineering","call_identifier":"FWF"},{"name":"The Wittgenstein Prize","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","language":[{"iso":"eng"}],"doi":"10.1016/j.biosystems.2016.07.005","ec_funded":1,"publist_id":"6210","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"Elsevier","publication_status":"published","year":"2016","acknowledgement":"This work is based on the CMSB 2015 paper “Adaptive moment closure for parameter inference of biochemical reaction networks” (Bogomolov et al., 2015). The work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS1), by the European Research Council (ERC) under grant 267989 (QUAREM) and by the Austrian Science Fund (FWF) under grants S11402-N23 (RiSE) and Z211-N23 (Wittgenstein Award). J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734.","volume":149,"date_created":"2018-12-11T11:50:24Z","date_updated":"2023-02-23T10:08:46Z","related_material":{"record":[{"relation":"earlier_version","status":"public","id":"1658"}]},"author":[{"first_name":"Christian","last_name":"Schilling","full_name":"Schilling, Christian"},{"full_name":"Bogomolov, Sergiy","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0686-0365","first_name":"Sergiy","last_name":"Bogomolov"},{"full_name":"Henzinger, Thomas A","orcid":"0000−0002−2985−7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","first_name":"Thomas A"},{"full_name":"Podelski, Andreas","first_name":"Andreas","last_name":"Podelski"},{"full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","first_name":"Jakob"}],"scopus_import":1,"day":"01","page":"15 - 25","citation":{"short":"C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25.","mla":"Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems, vol. 149, Elsevier, 2016, pp. 15–25, doi:10.1016/j.biosystems.2016.07.005.","chicago":"Schilling, Christian, Sergiy Bogomolov, Thomas A Henzinger, Andreas Podelski, and Jakob Ruess. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems. Elsevier, 2016. https://doi.org/10.1016/j.biosystems.2016.07.005.","ama":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 2016;149:15-25. doi:10.1016/j.biosystems.2016.07.005","apa":"Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., & Ruess, J. (2016). Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. Elsevier. https://doi.org/10.1016/j.biosystems.2016.07.005","ieee":"C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive moment closure for parameter inference of biochemical reaction networks,” Biosystems, vol. 149. Elsevier, pp. 15–25, 2016.","ista":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. 2016. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 149, 15–25."},"publication":"Biosystems","date_published":"2016-11-01T00:00:00Z","type":"journal_article","abstract":[{"text":"Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd","lang":"eng"}],"intvolume":" 149","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","status":"public","_id":"1148","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"None"},{"abstract":[{"text":"With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with.","lang":"eng"}],"type":"conference","oa_version":"Published Version","file":[{"relation":"main_file","file_id":"8096","checksum":"cff63e7a4b8ac466ba51a9c84153a940","date_updated":"2020-07-14T12:48:09Z","date_created":"2020-07-06T12:59:09Z","access_level":"open_access","file_name":"2016_ProcALIFE_Martius.pdf","content_type":"application/pdf","file_size":678670,"creator":"cziletti"}],"ddc":["610"],"status":"public","title":"Self-organized control of an tendon driven arm by differential extrinsic plasticity","intvolume":" 28","_id":"8094","user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","day":"01","article_processing_charge":"No","has_accepted_license":"1","scopus_import":1,"date_published":"2016-09-01T00:00:00Z","page":"142-143","publication":"Proceedings of the Artificial Life Conference 2016","citation":{"chicago":"Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In Proceedings of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.","short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 142–143.","mla":"Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” Proceedings of the Artificial Life Conference 2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.","ieee":"G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control of an tendon driven arm by differential extrinsic plasticity,” in Proceedings of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp. 142–143.","apa":"Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In Proceedings of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029","ista":"Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of an tendon driven arm by differential extrinsic plasticity. Proceedings of the Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems vol. 28, 142–143.","ama":"Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029"},"file_date_updated":"2020-07-14T12:48:09Z","ec_funded":1,"date_updated":"2021-01-12T08:16:53Z","date_created":"2020-07-05T22:00:47Z","volume":28,"author":[{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","first_name":"Georg S"},{"last_name":"Hostettler","first_name":"Rafael","full_name":"Hostettler, Rafael"},{"first_name":"Alois","last_name":"Knoll","full_name":"Knoll, Alois"},{"full_name":"Der, Ralf","last_name":"Der","first_name":"Ralf"}],"publication_status":"published","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"publisher":"MIT Press","year":"2016","month":"09","publication_identifier":{"isbn":["9780262339360"]},"language":[{"iso":"eng"}],"conference":{"end_date":"2016-07-08","start_date":"2016-07-04","location":"Cancun, Mexico","name":"ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems"},"doi":"10.7551/978-0-262-33936-0-ch029","quality_controlled":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"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"},"oa":1},{"file":[{"checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","date_created":"2019-01-25T10:35:00Z","date_updated":"2020-07-14T12:44:38Z","file_id":"5884","relation":"main_file","creator":"kschuh","file_size":4492021,"content_type":"application/pdf","access_level":"open_access","file_name":"2016_PLOS_Prentice.pdf"}],"oa_version":"Published Version","intvolume":" 12","ddc":["570"],"title":"Error-robust modes of the retinal population code","status":"public","_id":"1197","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","issue":"11","abstract":[{"lang":"eng","text":"Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina."}],"type":"journal_article","date_published":"2016-11-17T00:00:00Z","citation":{"ama":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes of the retinal population code. PLoS Computational Biology. 2016;12(11). doi:10.1371/journal.pcbi.1005148","ista":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855.","ieee":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust modes of the retinal population code,” PLoS Computational Biology, vol. 12, no. 11. Public Library of Science, 2016.","apa":"Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M. (2016). Error-robust modes of the retinal population code. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005148","mla":"Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of Science, 2016, doi:10.1371/journal.pcbi.1005148.","short":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational Biology 12 (2016).","chicago":"Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148."},"publication":"PLoS Computational Biology","has_accepted_license":"1","day":"17","scopus_import":1,"volume":12,"date_created":"2018-12-11T11:50:40Z","date_updated":"2023-02-23T14:05:40Z","related_material":{"record":[{"relation":"research_data","status":"public","id":"9709"}]},"author":[{"full_name":"Prentice, Jason","first_name":"Jason","last_name":"Prentice"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"full_name":"Ioffe, Mark","last_name":"Ioffe","first_name":"Mark"},{"last_name":"Loback","first_name":"Adrianna","full_name":"Loback, Adrianna"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper"},{"last_name":"Berry","first_name":"Michael","full_name":"Berry, Michael"}],"department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","publication_status":"published","acknowledgement":"JSP was supported by a C.V. Starr Fellowship from the Starr Foundation (http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation (https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler for helpful comments on the manuscript.","year":"2016","publist_id":"6153","file_date_updated":"2020-07-14T12:44:38Z","article_number":"e1005855","language":[{"iso":"eng"}],"doi":"10.1371/journal.pcbi.1005148","project":[{"call_identifier":"FWF","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26"}],"quality_controlled":"1","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"},"month":"11"},{"publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Neural Information Processing Systems","year":"2016","acknowledgement":"DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1 (JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement no. 291734 (CS)","date_updated":"2021-01-12T08:22:08Z","date_created":"2018-12-11T11:49:21Z","volume":29,"author":[{"full_name":"Monk, Travis","last_name":"Monk","first_name":"Travis"},{"full_name":"Savin, Cristina","last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jörg","last_name":"Lücke","full_name":"Lücke, Jörg"}],"ec_funded":1,"publist_id":"6469","quality_controlled":"1","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"language":[{"iso":"eng"}],"conference":{"location":"Barcelona, Spaine","start_date":"2016-12-05","end_date":"2016-12-10","name":"NIPS: Neural Information Processing Systems"},"month":"01","title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","status":"public","intvolume":" 29","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"948","oa_version":"None","alternative_title":["Advances in Neural Information Processing Systems"],"type":"conference","abstract":[{"lang":"eng","text":"Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations."}],"page":"4285 - 4293","citation":{"ama":"Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems; 2016:4285-4293.","ieee":"T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.","apa":"Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.","ista":"Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 4285–4293.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293.","mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93.","chicago":"Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems, 2016."},"date_published":"2016-01-01T00:00:00Z","scopus_import":1,"day":"01"}]