[{"date_published":"2016-07-01T00:00:00Z","citation":{"ama":"Chalk MJ, Gutkin B, Denève S. Neural oscillations as a signature of efficient coding in the presence of synaptic delays. eLife. 2016;5(2016JULY). doi:10.7554/eLife.13824","ieee":"M. J. Chalk, B. Gutkin, and S. Denève, “Neural oscillations as a signature of efficient coding in the presence of synaptic delays,” eLife, vol. 5, no. 2016JULY. eLife Sciences Publications, 2016.","apa":"Chalk, M. J., Gutkin, B., & Denève, S. (2016). Neural oscillations as a signature of efficient coding in the presence of synaptic delays. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.13824","ista":"Chalk MJ, Gutkin B, Denève S. 2016. Neural oscillations as a signature of efficient coding in the presence of synaptic delays. eLife. 5(2016JULY), e13824.","short":"M.J. Chalk, B. Gutkin, S. Denève, ELife 5 (2016).","mla":"Chalk, Matthew J., et al. “Neural Oscillations as a Signature of Efficient Coding in the Presence of Synaptic Delays.” ELife, vol. 5, no. 2016JULY, e13824, eLife Sciences Publications, 2016, doi:10.7554/eLife.13824.","chicago":"Chalk, Matthew J, Boris Gutkin, and Sophie Denève. “Neural Oscillations as a Signature of Efficient Coding in the Presence of Synaptic Delays.” ELife. eLife Sciences Publications, 2016. https://doi.org/10.7554/eLife.13824."},"publication":"eLife","has_accepted_license":"1","day":"01","scopus_import":1,"pubrep_id":"700","oa_version":"Published Version","file":[{"file_id":"4874","relation":"main_file","date_updated":"2020-07-14T12:44:42Z","date_created":"2018-12-12T10:11:20Z","checksum":"dc52d967dc76174477bb258d84be2899","file_name":"IST-2016-700-v1+1_e13824-download.pdf","access_level":"open_access","creator":"system","file_size":2819055,"content_type":"application/pdf"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1266","intvolume":" 5","ddc":["571"],"title":"Neural oscillations as a signature of efficient coding in the presence of synaptic delays","status":"public","issue":"2016JULY","abstract":[{"lang":"eng","text":"Cortical networks exhibit ‘global oscillations’, in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a ‘prediction error’ while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code."}],"type":"journal_article","doi":"10.7554/eLife.13824","language":[{"iso":"eng"}],"oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"quality_controlled":"1","month":"07","author":[{"full_name":"Chalk, Matthew J","first_name":"Matthew J","last_name":"Chalk","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-7782-4436"},{"last_name":"Gutkin","first_name":"Boris","full_name":"Gutkin, Boris"},{"full_name":"Denève, Sophie","first_name":"Sophie","last_name":"Denève"}],"volume":5,"date_created":"2018-12-11T11:51:02Z","date_updated":"2021-01-12T06:49:30Z","acknowledgement":"Boris Gutkin acknowledges funding by the Russian Academic Excellence Project '5-100’.","year":"2016","department":[{"_id":"GaTk"}],"publisher":"eLife Sciences Publications","publication_status":"published","publist_id":"6056","file_date_updated":"2020-07-14T12:44:42Z","license":"https://creativecommons.org/licenses/by/4.0/","article_number":"e13824"},{"acknowledgement":"This work was supported in part by National Institute of Allergy and Infectious Diseases grant U54 AI057159, US National Institutes of Health grants R01 GM081617 (to R.K.) and GM086258 (to J.C.), European Research Council FP7 ERC grant 281891 (to R.K.) and a National Science Foundation Graduate Fellowship (to L.K.S.).\r\n","year":"2016","publication_status":"published","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"Nature Publishing Group","author":[{"full_name":"Stone, Laura","first_name":"Laura","last_name":"Stone"},{"last_name":"Baym","first_name":"Michael","full_name":"Baym, Michael"},{"full_name":"Lieberman, Tami","last_name":"Lieberman","first_name":"Tami"},{"full_name":"Chait, Remy P","last_name":"Chait","first_name":"Remy P","orcid":"0000-0003-0876-3187","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jon","last_name":"Clardy","full_name":"Clardy, Jon"},{"full_name":"Kishony, Roy","first_name":"Roy","last_name":"Kishony"}],"date_created":"2018-12-11T11:51:10Z","date_updated":"2021-01-12T06:49:39Z","volume":12,"publist_id":"6026","oa":1,"main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069154/","open_access":"1"}],"quality_controlled":"1","doi":"10.1038/nchembio.2176","language":[{"iso":"eng"}],"month":"11","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1290","status":"public","title":"Compounds that select against the tetracycline-resistance efflux pump","intvolume":" 12","oa_version":"Preprint","type":"journal_article","abstract":[{"lang":"eng","text":"We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline-resistance efflux pump in Escherichia coli and identified two hits, β-thujaplicin and disulfiram. Treating a tetracycline-resistant population with β-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline."}],"issue":"11","publication":"Nature Chemical Biology","citation":{"short":"L. Stone, M. Baym, T. Lieberman, R.P. Chait, J. Clardy, R. Kishony, Nature Chemical Biology 12 (2016) 902–904.","mla":"Stone, Laura, et al. “Compounds That Select against the Tetracycline-Resistance Efflux Pump.” Nature Chemical Biology, vol. 12, no. 11, Nature Publishing Group, 2016, pp. 902–04, doi:10.1038/nchembio.2176.","chicago":"Stone, Laura, Michael Baym, Tami Lieberman, Remy P Chait, Jon Clardy, and Roy Kishony. “Compounds That Select against the Tetracycline-Resistance Efflux Pump.” Nature Chemical Biology. Nature Publishing Group, 2016. https://doi.org/10.1038/nchembio.2176.","ama":"Stone L, Baym M, Lieberman T, Chait RP, Clardy J, Kishony R. Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. 2016;12(11):902-904. doi:10.1038/nchembio.2176","apa":"Stone, L., Baym, M., Lieberman, T., Chait, R. P., Clardy, J., & Kishony, R. (2016). Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. Nature Publishing Group. https://doi.org/10.1038/nchembio.2176","ieee":"L. Stone, M. Baym, T. Lieberman, R. P. Chait, J. Clardy, and R. Kishony, “Compounds that select against the tetracycline-resistance efflux pump,” Nature Chemical Biology, vol. 12, no. 11. Nature Publishing Group, pp. 902–904, 2016.","ista":"Stone L, Baym M, Lieberman T, Chait RP, Clardy J, Kishony R. 2016. Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. 12(11), 902–904."},"page":"902 - 904","date_published":"2016-11-01T00:00:00Z","scopus_import":1,"day":"01"},{"month":"07","quality_controlled":"1","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"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"}],"article_number":"7526722","file_date_updated":"2020-07-14T12:44:43Z","ec_funded":1,"publist_id":"5950","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","publisher":"IEEE","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"author":[{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","last_name":"Lang","first_name":"Moritz","full_name":"Lang, Moritz"},{"last_name":"Sontag","first_name":"Eduardo","full_name":"Sontag, Eduardo"}],"date_created":"2018-12-11T11:51:21Z","date_updated":"2021-01-12T06:49:51Z","volume":"2016-July","scopus_import":1,"day":"28","has_accepted_license":"1","citation":{"short":"M. Lang, E. Sontag, in:, IEEE, 2016.","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.","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","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.","ista":"Lang M, Sontag E. 2016. Scale-invariant systems realize nonlinear differential operators. ACC: American Control Conference vol. 2016–July, 7526722."},"date_published":"2016-07-28T00:00:00Z","type":"conference","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."}],"_id":"1320","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"Scale-invariant systems realize nonlinear differential operators","status":"public","ddc":["003","621"],"pubrep_id":"810","file":[{"access_level":"local","file_name":"IST-2017-810-v1+1_root.pdf","creator":"system","content_type":"application/pdf","file_size":539166,"file_id":"5203","relation":"main_file","checksum":"7219432b43defc62a0d45f48d4ce6a19","date_updated":"2020-07-14T12:44:43Z","date_created":"2018-12-12T10:16:17Z"}],"oa_version":"Preprint"},{"date_published":"2016-01-20T00:00:00Z","citation":{"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.","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"},"publication":"Nature Communications","has_accepted_license":"1","day":"20","scopus_import":1,"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","file_size":1844107,"content_type":"application/pdf","creator":"system"}],"oa_version":"Published Version","pubrep_id":"662","intvolume":" 7","status":"public","title":"Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments","ddc":["570","579"],"_id":"1332","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","abstract":[{"lang":"eng","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."}],"type":"journal_article","language":[{"iso":"eng"}],"doi":"10.1038/ncomms10333","quality_controlled":"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"},"oa":1,"month":"01","volume":7,"date_created":"2018-12-11T11:51:25Z","date_updated":"2021-01-12T06:49:57Z","author":[{"full_name":"Chait, Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-0876-3187","first_name":"Remy P","last_name":"Chait"},{"first_name":"Adam","last_name":"Palmer","full_name":"Palmer, Adam"},{"first_name":"Idan","last_name":"Yelin","full_name":"Yelin, Idan"},{"full_name":"Kishony, Roy","last_name":"Kishony","first_name":"Roy"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"Nature Publishing Group","publication_status":"published","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.","year":"2016","publist_id":"5936","file_date_updated":"2020-07-14T12:44:44Z","article_number":"10333"},{"main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/","open_access":"1"}],"oa":1,"citation":{"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","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.","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","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.","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.","short":"M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony, Science 353 (2016) 1147–1151.","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."},"publication":"Science","page":"1147 - 1151","quality_controlled":"1","date_published":"2016-09-09T00:00:00Z","doi":"10.1126/science.aag0822","language":[{"iso":"eng"}],"scopus_import":1,"month":"09","day":"09","_id":"1342","year":"2016","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"American Association for the Advancement of Science","intvolume":" 353","title":"Spatiotemporal microbial evolution on antibiotic landscapes","status":"public","publication_status":"published","author":[{"first_name":"Michael","last_name":"Baym","full_name":"Baym, Michael"},{"last_name":"Lieberman","first_name":"Tami","full_name":"Lieberman, Tami"},{"full_name":"Kelsic, Eric","first_name":"Eric","last_name":"Kelsic"},{"last_name":"Chait","first_name":"Remy P","orcid":"0000-0003-0876-3187","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","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"}],"volume":353,"oa_version":"Preprint","date_created":"2018-12-11T11:51:29Z","date_updated":"2021-01-12T06:50:01Z","type":"journal_article","publist_id":"5911","issue":"6304","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."}]},{"language":[{"iso":"eng"}],"doi":"10.1088/1478-3975/13/3/036005","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"quality_controlled":"1","oa":1,"main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1601.03243"}],"month":"05","volume":13,"date_created":"2018-12-11T11:51:46Z","date_updated":"2021-01-12T06:50:23Z","author":[{"first_name":"Daniele","last_name":"De Martino","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-5214-4706","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"}],"department":[{"_id":"GaTk"}],"publisher":"IOP Publishing Ltd.","publication_status":"published","acknowledgement":"The research leading to these results has received funding from the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038.","year":"2016","publist_id":"5815","ec_funded":1,"article_number":"036005","date_published":"2016-05-27T00:00:00Z","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","day":"27","scopus_import":1,"oa_version":"Preprint","intvolume":" 13","title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1394","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."}],"type":"journal_article"},{"publist_id":"5787","ec_funded":1,"publication_status":"published","publisher":"Genetics Society of America","department":[{"_id":"GaTk"},{"_id":"NiBa"}],"year":"2016","date_created":"2018-12-11T11:51:55Z","date_updated":"2022-08-01T10:49:55Z","volume":202,"author":[{"first_name":"Katarína","last_name":"Bod'ová","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7214-0171","full_name":"Bod'ová, Katarína"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper"},{"full_name":"Barton, Nicholas H","first_name":"Nicholas H","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240"}],"month":"04","quality_controlled":"1","project":[{"call_identifier":"FP7","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152"},{"name":"Information processing and computation in fish groups","_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012"}],"oa":1,"external_id":{"arxiv":["1510.08344"]},"main_file_link":[{"url":"http://arxiv.org/abs/1510.08344","open_access":"1"}],"language":[{"iso":"eng"}],"doi":"10.1534/genetics.115.184127","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","status":"public","title":"A general approximation for the dynamics of quantitative traits","intvolume":" 202","_id":"1420","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Preprint","scopus_import":"1","day":"06","article_processing_charge":"No","page":"1523 - 1548","publication":"Genetics","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.","short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.","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.","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","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 1523–1548.","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","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."},"date_published":"2016-04-06T00:00:00Z"},{"month":"01","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1505.04613"}],"oa":1,"project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"quality_controlled":"1","doi":"10.1088/1478-3975/13/1/016003","language":[{"iso":"eng"}],"article_number":"016003","publist_id":"5702","ec_funded":1,"year":"2016","department":[{"_id":"GaTk"}],"publisher":"IOP Publishing Ltd.","publication_status":"published","author":[{"last_name":"De Martino","first_name":"Daniele","orcid":"0000-0002-5214-4706","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele"}],"volume":13,"date_updated":"2021-01-12T06:51:04Z","date_created":"2018-12-11T11:52:18Z","scopus_import":1,"day":"29","citation":{"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.","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","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."},"publication":"Physical Biology","date_published":"2016-01-29T00:00:00Z","type":"journal_article","issue":"1","abstract":[{"lang":"eng","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."}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1485","intvolume":" 13","status":"public","title":"Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis","oa_version":"Preprint"},{"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"Elsevier","publication_status":"published","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.","year":"2016","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":[{"full_name":"Schilling, Christian","last_name":"Schilling","first_name":"Christian"},{"id":"369D9A44-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0686-0365","first_name":"Sergiy","last_name":"Bogomolov","full_name":"Bogomolov, Sergiy"},{"full_name":"Henzinger, Thomas A","first_name":"Thomas A","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","orcid":"0000−0002−2985−7724"},{"full_name":"Podelski, Andreas","last_name":"Podelski","first_name":"Andreas"},{"first_name":"Jakob","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob"}],"publist_id":"6210","ec_funded":1,"project":[{"grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","name":"Quantitative Reactive Modeling","call_identifier":"FP7"},{"call_identifier":"FWF","name":"Rigorous Systems Engineering","_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23"},{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","call_identifier":"FWF","name":"The Wittgenstein Prize"},{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"quality_controlled":"1","language":[{"iso":"eng"}],"doi":"10.1016/j.biosystems.2016.07.005","month":"11","intvolume":" 149","status":"public","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1148","oa_version":"None","type":"journal_article","abstract":[{"lang":"eng","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"}],"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","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.","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","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","scopus_import":1,"day":"01"},{"oa_version":"Published Version","file":[{"access_level":"open_access","file_name":"2016_ProcALIFE_Martius.pdf","creator":"cziletti","content_type":"application/pdf","file_size":678670,"file_id":"8096","relation":"main_file","checksum":"cff63e7a4b8ac466ba51a9c84153a940","date_created":"2020-07-06T12:59:09Z","date_updated":"2020-07-14T12:48:09Z"}],"intvolume":" 28","ddc":["610"],"status":"public","title":"Self-organized control of an tendon driven arm by differential extrinsic plasticity","_id":"8094","user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","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","date_published":"2016-09-01T00:00:00Z","page":"142-143","citation":{"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.","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.","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","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","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.","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."},"publication":"Proceedings of the Artificial Life Conference 2016","has_accepted_license":"1","article_processing_charge":"No","day":"01","scopus_import":1,"volume":28,"date_created":"2020-07-05T22:00:47Z","date_updated":"2021-01-12T08:16:53Z","author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","first_name":"Georg S","full_name":"Martius, Georg S"},{"last_name":"Hostettler","first_name":"Rafael","full_name":"Hostettler, Rafael"},{"first_name":"Alois","last_name":"Knoll","full_name":"Knoll, Alois"},{"first_name":"Ralf","last_name":"Der","full_name":"Der, Ralf"}],"publisher":"MIT Press","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"publication_status":"published","year":"2016","ec_funded":1,"file_date_updated":"2020-07-14T12:48:09Z","language":[{"iso":"eng"}],"doi":"10.7551/978-0-262-33936-0-ch029","conference":{"name":"ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems","start_date":"2016-07-04","location":"Cancun, Mexico","end_date":"2016-07-08"},"project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"quality_controlled":"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"},"oa":1,"publication_identifier":{"isbn":["9780262339360"]},"month":"09"},{"article_number":"e1005855","publist_id":"6153","file_date_updated":"2020-07-14T12:44:38Z","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","volume":12,"date_updated":"2023-02-23T14:05:40Z","date_created":"2018-12-11T11:50:40Z","related_material":{"record":[{"id":"9709","relation":"research_data","status":"public"}]},"author":[{"first_name":"Jason","last_name":"Prentice","full_name":"Prentice, Jason"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"full_name":"Ioffe, Mark","last_name":"Ioffe","first_name":"Mark"},{"last_name":"Loback","first_name":"Adrianna","full_name":"Loback, Adrianna"},{"first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"month":"11","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26"}],"quality_controlled":"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"},"oa":1,"language":[{"iso":"eng"}],"doi":"10.1371/journal.pcbi.1005148","type":"journal_article","issue":"11","abstract":[{"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.","lang":"eng"}],"intvolume":" 12","title":"Error-robust modes of the retinal population code","status":"public","ddc":["570"],"_id":"1197","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","file":[{"checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","date_updated":"2020-07-14T12:44:38Z","date_created":"2019-01-25T10:35:00Z","relation":"main_file","file_id":"5884","file_size":4492021,"content_type":"application/pdf","creator":"kschuh","access_level":"open_access","file_name":"2016_PLOS_Prentice.pdf"}],"oa_version":"Published Version","scopus_import":1,"has_accepted_license":"1","day":"17","citation":{"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.","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.","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","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."},"publication":"PLoS Computational Biology","date_published":"2016-11-17T00:00:00Z"},{"day":"01","scopus_import":1,"date_published":"2016-01-01T00:00:00Z","page":"4285 - 4293","citation":{"mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293.","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.","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.","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.","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."},"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."}],"alternative_title":["Advances in Neural Information Processing Systems"],"type":"conference","oa_version":"None","intvolume":" 29","status":"public","title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","_id":"948","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","month":"01","language":[{"iso":"eng"}],"conference":{"name":"NIPS: Neural Information Processing Systems","start_date":"2016-12-05","location":"Barcelona, Spaine","end_date":"2016-12-10"},"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"quality_controlled":"1","main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"publist_id":"6469","ec_funded":1,"volume":29,"date_created":"2018-12-11T11:49:21Z","date_updated":"2021-01-12T08:22:08Z","author":[{"full_name":"Monk, Travis","last_name":"Monk","first_name":"Travis"},{"full_name":"Savin, Cristina","first_name":"Cristina","last_name":"Savin","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jörg","last_name":"Lücke","full_name":"Lücke, Jörg"}],"department":[{"_id":"GaTk"}],"publisher":"Neural Information Processing Systems","publication_status":"published","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)"},{"department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","publication_status":"published","acknowledgement":"The authors would like to thank Thomas Sokolowski and Filipe Tostevin for helpful discussions. PH and UG were funded by the German Excellence Initiative via the program \"Nanosystems Initiative Munich\" (https://www.nano-initiative-munich.de) and the German Research Foundation via the SFB 1032 \"Nanoagents for Spatiotemporal Control of Molecular and Cellular Reactions\" (http://www.sfb1032.physik.uni-muenchen.de). GT was funded by the Austrian Science Fund (FWF P 28844) (http://www.fwf.ac.at).","year":"2016","volume":11,"date_created":"2018-12-11T11:51:03Z","date_updated":"2023-02-23T14:11:37Z","related_material":{"record":[{"id":"9869","relation":"research_data","status":"public"},{"status":"public","relation":"research_data","id":"9870"},{"relation":"research_data","status":"public","id":"9871"}]},"author":[{"first_name":"Patrick","last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick"},{"first_name":"Ulrich","last_name":"Gerland","full_name":"Gerland, Ulrich"},{"first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"article_number":"e0163628","publist_id":"6050","file_date_updated":"2020-07-14T12:44:42Z","project":[{"name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27"}],"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"},"language":[{"iso":"eng"}],"doi":"10.1371/journal.pone.0163628","month":"09","intvolume":" 11","status":"public","title":"Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information","ddc":["571"],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1270","file":[{"access_level":"open_access","file_name":"IST-2016-696-v1+1_journal.pone.0163628.PDF","creator":"system","file_size":4950415,"content_type":"application/pdf","file_id":"4837","relation":"main_file","checksum":"3d0d55d373096a033bd9cf79288c8586","date_updated":"2020-07-14T12:44:42Z","date_created":"2018-12-12T10:10:47Z"}],"oa_version":"Published Version","pubrep_id":"696","type":"journal_article","issue":"9","abstract":[{"text":"A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolpert's paradigmatic "French Flag" model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call "Counter" patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework.","lang":"eng"}],"citation":{"ama":"Hillenbrand P, Gerland U, Tkačik G. Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 2016;11(9). doi:10.1371/journal.pone.0163628","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information,” PLoS One, vol. 11, no. 9. Public Library of Science, 2016.","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 11(9), e0163628.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, PLoS One 11 (2016).","mla":"Hillenbrand, Patrick, et al. “Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information.” PLoS One, vol. 11, no. 9, e0163628, Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information.” PLoS One. Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628."},"publication":"PLoS One","date_published":"2016-09-27T00:00:00Z","scopus_import":1,"has_accepted_license":"1","day":"27"},{"author":[{"first_name":"Patrick","last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick"},{"full_name":"Gerland, Ulrich","last_name":"Gerland","first_name":"Ulrich"},{"full_name":"Tkačik, Gašper","first_name":"Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455"}],"related_material":{"record":[{"id":"1270","relation":"used_in_publication","status":"public"}]},"date_created":"2021-08-10T09:23:45Z","date_updated":"2023-02-21T16:56:40Z","oa_version":"Published Version","_id":"9870","year":"2016","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","status":"public","title":"Computation of positional information in an Ising model","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"abstract":[{"lang":"eng","text":"The effect of noise in the input field on an Ising model is approximated. Furthermore, methods to compute positional information in an Ising model by transfer matrices and Monte Carlo sampling are outlined."}],"type":"research_data_reference","date_published":"2016-09-27T00:00:00Z","doi":"10.1371/journal.pone.0163628.s002","citation":{"short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","mla":"Hillenbrand, Patrick, et al. Computation of Positional Information in an Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002.","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002.","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in an Ising model.” Public Library of Science, 2016.","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002."},"day":"27","month":"09","article_processing_charge":"No"},{"month":"09","day":"27","article_processing_charge":"No","date_published":"2016-09-27T00:00:00Z","doi":"10.1371/journal.pone.0163628.s001","citation":{"ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position, Public Library of Science, 10.1371/journal.pone.0163628.s001.","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of position.” Public Library of Science, 2016.","ama":"Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position. 2016. doi:10.1371/journal.pone.0163628.s001","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001.","mla":"Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016)."},"abstract":[{"lang":"eng","text":"A lower bound on the error of a positional estimator with limited positional information is derived."}],"type":"research_data_reference","date_created":"2021-08-10T08:53:48Z","date_updated":"2023-02-21T16:56:40Z","oa_version":"Published Version","author":[{"last_name":"Hillenbrand","first_name":"Patrick","full_name":"Hillenbrand, Patrick"},{"first_name":"Ulrich","last_name":"Gerland","full_name":"Gerland, Ulrich"},{"full_name":"Tkačik, Gašper","last_name":"Tkačik","first_name":"Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]},"title":"Error bound on an estimator of position","status":"public","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","year":"2016","_id":"9869","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf"},{"doi":"10.1371/journal.pone.0163628.s003","citation":{"ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in a discrete morphogen field. 2016. doi:10.1371/journal.pone.0163628.s003","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in a discrete morphogen field, Public Library of Science, 10.1371/journal.pone.0163628.s003.","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in a discrete morphogen field.” Public Library of Science, 2016.","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in a discrete morphogen field. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s003","mla":"Hillenbrand, Patrick, et al. Computation of Positional Information in a Discrete Morphogen Field. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s003.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in a Discrete Morphogen Field.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s003."},"article_processing_charge":"No","day":"27","month":"09","oa_version":"Published Version","date_created":"2021-08-10T09:27:35Z","date_updated":"2023-02-21T16:56:40Z","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]},"author":[{"full_name":"Hillenbrand, Patrick","first_name":"Patrick","last_name":"Hillenbrand"},{"full_name":"Gerland, Ulrich","last_name":"Gerland","first_name":"Ulrich"},{"first_name":"Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper"}],"department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","status":"public","title":"Computation of positional information in a discrete morphogen field","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","_id":"9871","year":"2016","abstract":[{"text":"The positional information in a discrete morphogen field with Gaussian noise is computed.","lang":"eng"}],"type":"research_data_reference"},{"day":"01","article_processing_charge":"No","has_accepted_license":"1","date_published":"2016-08-01T00:00:00Z","page":"114","citation":{"ama":"Rieckh G. Studying the complexities of transcriptional regulation. 2016.","ieee":"G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute of Science and Technology Austria, 2016.","apa":"Rieckh, G. (2016). Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","short":"G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute of Science and Technology Austria, 2016.","mla":"Rieckh, Georg. Studying the Complexities of Transcriptional Regulation. Institute of Science and Technology Austria, 2016.","chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016."},"abstract":[{"text":"The process of gene expression is central to the modern understanding of how cellular systems\r\nfunction. In this process, a special kind of regulatory proteins, called transcription factors,\r\nare important to determine how much protein is produced from a given gene. As biological\r\ninformation is transmitted from transcription factor concentration to mRNA levels to amounts of\r\nprotein, various sources of noise arise and pose limits to the fidelity of intracellular signaling.\r\nThis thesis concerns itself with several aspects of stochastic gene expression: (i) the mathematical\r\ndescription of complex promoters responsible for the stochastic production of biomolecules,\r\n(ii) fundamental limits to information processing the cell faces due to the interference from multiple\r\nfluctuating signals, (iii) how the presence of gene expression noise influences the evolution\r\nof regulatory sequences, (iv) and tools for the experimental study of origins and consequences\r\nof cell-cell heterogeneity, including an application to bacterial stress response systems.","lang":"eng"}],"alternative_title":["ISTA Thesis"],"type":"dissertation","oa_version":"Published Version","file":[{"checksum":"ec453918c3bf8e6f460fd1156ef7b493","date_updated":"2019-08-13T11:46:25Z","date_created":"2019-08-13T11:46:25Z","relation":"main_file","file_id":"6815","file_size":2614660,"content_type":"application/pdf","creator":"dernst","access_level":"closed","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf"},{"content_type":"application/pdf","file_size":6096178,"creator":"dernst","access_level":"open_access","file_name":"Thesis_Georg_Rieckh.pdf","checksum":"51ae398166370d18fd22478b6365c4da","success":1,"date_updated":"2020-09-21T11:30:40Z","date_created":"2020-09-21T11:30:40Z","relation":"main_file","file_id":"8542"}],"status":"public","title":"Studying the complexities of transcriptional regulation","ddc":["570"],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"1128","month":"08","publication_identifier":{"issn":["2663-337X"]},"degree_awarded":"PhD","supervisor":[{"full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"language":[{"iso":"eng"}],"oa":1,"file_date_updated":"2020-09-21T11:30:40Z","publist_id":"6232","date_created":"2018-12-11T11:50:18Z","date_updated":"2023-09-07T11:44:34Z","author":[{"first_name":"Georg","last_name":"Rieckh","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg"}],"publication_status":"published","publisher":"Institute of Science and Technology Austria","department":[{"_id":"GaTk"}],"year":"2016"},{"pubrep_id":"627","file":[{"date_created":"2018-12-12T10:12:01Z","date_updated":"2020-07-14T12:44:46Z","checksum":"fe3f3a1526d180b29fe691ab11435b78","relation":"main_file","file_id":"4919","file_size":861805,"content_type":"application/pdf","creator":"system","file_name":"IST-2016-627-v1+1_ncomms12307.pdf","access_level":"open_access"},{"checksum":"164864a1a675f3ad80e9917c27aba07f","date_created":"2018-12-12T10:12:02Z","date_updated":"2020-07-14T12:44:46Z","relation":"main_file","file_id":"4920","content_type":"application/pdf","file_size":1084703,"creator":"system","access_level":"open_access","file_name":"IST-2016-627-v1+2_ncomms12307-s1.pdf"}],"oa_version":"Published Version","_id":"1358","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","intvolume":" 7","status":"public","ddc":["576"],"title":"Intrinsic limits to gene regulation by global crosstalk","abstract":[{"lang":"eng","text":"Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements."}],"type":"journal_article","date_published":"2016-08-04T00:00:00Z","citation":{"chicago":"Friedlander, Tamar, Roshan Prizak, Calin C Guet, Nicholas H Barton, and Gašper Tkačik. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms12307.","mla":"Friedlander, Tamar, et al. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature Communications, vol. 7, 12307, Nature Publishing Group, 2016, doi:10.1038/ncomms12307.","short":"T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkačik, Nature Communications 7 (2016).","ista":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 7, 12307.","ieee":"T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton, and G. Tkačik, “Intrinsic limits to gene regulation by global crosstalk,” Nature Communications, vol. 7. Nature Publishing Group, 2016.","apa":"Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., & Tkačik, G. (2016). Intrinsic limits to gene regulation by global crosstalk. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms12307","ama":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 2016;7. doi:10.1038/ncomms12307"},"publication":"Nature Communications","has_accepted_license":"1","day":"04","scopus_import":1,"related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"6071"}]},"author":[{"full_name":"Friedlander, Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","last_name":"Friedlander","first_name":"Tamar"},{"full_name":"Prizak, Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87","first_name":"Roshan","last_name":"Prizak"},{"first_name":"Calin C","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052","full_name":"Guet, Calin C"},{"full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","first_name":"Nicholas H"},{"last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"}],"volume":7,"date_updated":"2023-09-07T12:53:49Z","date_created":"2018-12-11T11:51:34Z","year":"2016","department":[{"_id":"GaTk"},{"_id":"NiBa"},{"_id":"CaGu"}],"publisher":"Nature Publishing Group","publication_status":"published","publist_id":"5887","ec_funded":1,"file_date_updated":"2020-07-14T12:44:46Z","article_number":"12307","doi":"10.1038/ncomms12307","language":[{"iso":"eng"}],"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"},"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"},{"call_identifier":"FP7","name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152","_id":"25B07788-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FWF","name":"Biophysics of information processing in gene regulation","_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27"}],"quality_controlled":"1","month":"08"},{"type":"journal_article","abstract":[{"lang":"eng","text":"Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different initial conditions or to test how a system responds to external perturbations. For this analysis to be meaningful in real applications, however, it is of paramount importance to choose an appropriate model structure and to infer the model parameters from measured data. While many parameter inference methods are available for models based on deterministic ordinary differential equations, the same does not hold for more detailed individual-based models. Here we consider, in particular, stochastic models in which the time evolution of the species abundances is described by a continuous-time Markov chain. These models are governed by a master equation that is typically difficult to solve. Consequently, traditional inference methods that rely on iterative evaluation of parameter likelihoods are computationally intractable. The aim of this paper is to present recent advances in parameter inference for continuous-time Markov chain models, based on a moment closure approximation of the parameter likelihood, and to investigate how these results can help in understanding, and ultimately controlling, complex systems in ecology. Specifically, we illustrate through an agricultural pest case study how parameters of a stochastic individual-based model can be identified from measured data and how the resulting model can be used to solve an optimal control problem in a stochastic setting. In particular, we show how the matter of determining the optimal combination of two different pest control methods can be formulated as a chance constrained optimization problem where the control action is modeled as a state reset, leading to a hybrid system formulation."}],"_id":"10794","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","status":"public","ddc":["000","570"],"intvolume":" 3","oa_version":"Published Version","file":[{"content_type":"application/pdf","file_size":1371201,"creator":"dernst","file_name":"2015_FrontiersEnvironmScience_Parise.pdf","access_level":"open_access","date_updated":"2022-02-25T11:55:26Z","date_created":"2022-02-25T11:55:26Z","checksum":"26c222487564e1be02a11d688d6f769d","success":1,"relation":"main_file","file_id":"10795"}],"scopus_import":"1","keyword":["General Environmental Science"],"day":"10","article_processing_charge":"No","has_accepted_license":"1","publication":"Frontiers in Environmental Science","citation":{"ama":"Parise F, Lygeros J, Ruess J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. 2015;3. doi:10.3389/fenvs.2015.00042","ieee":"F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study,” Frontiers in Environmental Science, vol. 3. Frontiers, 2015.","apa":"Parise, F., Lygeros, J., & Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. Frontiers. https://doi.org/10.3389/fenvs.2015.00042","ista":"Parise F, Lygeros J, Ruess J. 2015. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. 3, 42.","short":"F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).","mla":"Parise, Francesca, et al. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” Frontiers in Environmental Science, vol. 3, 42, Frontiers, 2015, doi:10.3389/fenvs.2015.00042.","chicago":"Parise, Francesca, John Lygeros, and Jakob Ruess. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” Frontiers in Environmental Science. Frontiers, 2015. https://doi.org/10.3389/fenvs.2015.00042."},"article_type":"original","date_published":"2015-06-10T00:00:00Z","article_number":"42","file_date_updated":"2022-02-25T11:55:26Z","ec_funded":1,"acknowledgement":"The authors would like to acknowledge contributions from Baptiste Mottet who performed preliminary analysis regarding parameter inference for the considered case study in a student project (Mottet, 2014/2015).\r\nThe 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 No. [291734] and from SystemsX under the project SignalX.","year":"2015","publication_status":"published","publisher":"Frontiers","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"author":[{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"last_name":"Lygeros","first_name":"John","full_name":"Lygeros, John"},{"last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"}],"date_updated":"2022-02-25T11:59:23Z","date_created":"2022-02-25T11:42:25Z","volume":3,"month":"06","publication_identifier":{"issn":["2296-665X"]},"oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"quality_controlled":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"doi":"10.3389/fenvs.2015.00042","language":[{"iso":"eng"}]},{"article_number":"244103","ec_funded":1,"publist_id":"5632","file_date_updated":"2020-07-14T12:45:01Z","year":"2015","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"American Institute of Physics","publication_status":"published","author":[{"first_name":"Jakob","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob"}],"volume":143,"date_updated":"2021-01-12T06:51:28Z","date_created":"2018-12-11T11:52:36Z","month":"12","oa":1,"project":[{"_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989","call_identifier":"FP7","name":"Quantitative Reactive Modeling"},{"call_identifier":"FWF","name":"Rigorous Systems Engineering","_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23"},{"grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","call_identifier":"FWF"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"quality_controlled":"1","doi":"10.1063/1.4937937","language":[{"iso":"eng"}],"type":"journal_article","issue":"24","abstract":[{"lang":"eng","text":"Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space. "}],"_id":"1539","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 143","title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","ddc":["000"],"status":"public","pubrep_id":"593","oa_version":"Published Version","file":[{"relation":"main_file","file_id":"4641","date_updated":"2020-07-14T12:45:01Z","date_created":"2018-12-12T10:07:43Z","checksum":"838657118ae286463a2b7737319f35ce","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf","access_level":"open_access","content_type":"application/pdf","file_size":605355,"creator":"system"}],"scopus_import":1,"has_accepted_license":"1","day":"22","citation":{"apa":"Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. American Institute of Physics. https://doi.org/10.1063/1.4937937","ieee":"J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” Journal of Chemical Physics, vol. 143, no. 24. American Institute of Physics, 2015.","ista":"Ruess J. 2015. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 143(24), 244103.","ama":"Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 2015;143(24). doi:10.1063/1.4937937","chicago":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics. American Institute of Physics, 2015. https://doi.org/10.1063/1.4937937.","short":"J. Ruess, Journal of Chemical Physics 143 (2015).","mla":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:10.1063/1.4937937."},"publication":"Journal of Chemical Physics","date_published":"2015-12-22T00:00:00Z"},{"month":"06","language":[{"iso":"eng"}],"doi":"10.1073/pnas.1423947112","quality_controlled":"1","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"external_id":{"pmid":["26085136"]},"oa":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/"}],"publist_id":"5633","ec_funded":1,"date_created":"2018-12-11T11:52:36Z","date_updated":"2021-01-12T06:51:27Z","volume":112,"author":[{"last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"},{"full_name":"Parise, Francesca","first_name":"Francesca","last_name":"Parise"},{"first_name":"Andreas","last_name":"Milias Argeitis","full_name":"Milias Argeitis, Andreas"},{"full_name":"Khammash, Mustafa","last_name":"Khammash","first_name":"Mustafa"},{"full_name":"Lygeros, John","first_name":"John","last_name":"Lygeros"}],"publication_status":"published","publisher":"National Academy of Sciences","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"year":"2015","acknowledgement":"J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). ","pmid":1,"day":"30","scopus_import":1,"date_published":"2015-06-30T00:00:00Z","page":"8148 - 8153","publication":"PNAS","citation":{"ama":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112","ieee":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.","apa":"Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112","ista":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 112(26), 8148–8153.","short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","mla":"Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112.","chicago":"Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash, and John Lygeros. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1423947112."},"abstract":[{"text":"Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.","lang":"eng"}],"issue":"26","type":"journal_article","oa_version":"Submitted Version","title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","status":"public","intvolume":" 112","_id":"1538","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"day":"30","has_accepted_license":"1","scopus_import":1,"date_published":"2015-11-30T00:00:00Z","publication":"Frontiers in Computational Neuroscience","citation":{"chicago":"Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145.","mla":"Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.","short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).","ista":"Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 9(11), 145.","apa":"Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145","ieee":"M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” Frontiers in Computational Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.","ama":"Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 2015;9(11). doi:10.3389/fncom.2015.00145"},"issue":"11","type":"journal_article","oa_version":"Published Version","file":[{"checksum":"cea73b6d3ef1579f32da10b82f4de4fd","date_created":"2018-12-12T10:12:09Z","date_updated":"2020-07-14T12:45:02Z","relation":"main_file","file_id":"4927","content_type":"application/pdf","file_size":187038,"creator":"system","access_level":"open_access","file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf"}],"pubrep_id":"479","status":"public","ddc":["570"],"title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","intvolume":" 9","_id":"1564","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"11","language":[{"iso":"eng"}],"doi":"10.3389/fncom.2015.00145","quality_controlled":"1","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","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_date_updated":"2020-07-14T12:45:02Z","publist_id":"5607","ec_funded":1,"article_number":"145","date_updated":"2021-01-12T06:51:37Z","date_created":"2018-12-11T11:52:45Z","volume":9,"author":[{"full_name":"Gilson, Matthieu","last_name":"Gilson","first_name":"Matthieu"},{"full_name":"Savin, Cristina","last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Zenke","first_name":"Friedemann","full_name":"Zenke, Friedemann"}],"publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Frontiers Research Foundation","year":"2015"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1570","intvolume":" 112","title":"Novel plasticity rule can explain the development of sensorimotor intelligence","status":"public","oa_version":"Submitted Version","type":"journal_article","issue":"45","abstract":[{"text":"Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution.","lang":"eng"}],"citation":{"apa":"Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1508400112","ieee":"R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.","ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.","ama":"Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112","chicago":"Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1508400112.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","mla":"Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112."},"publication":"PNAS","page":"E6224 - E6232","date_published":"2015-11-10T00:00:00Z","scopus_import":1,"day":"10","pmid":1,"year":"2015","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"publisher":"National Academy of Sciences","publication_status":"published","author":[{"first_name":"Ralf","last_name":"Der","full_name":"Der, Ralf"},{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","first_name":"Georg S"}],"volume":112,"date_updated":"2021-01-12T06:51:40Z","date_created":"2018-12-11T11:52:47Z","publist_id":"5601","ec_funded":1,"external_id":{"pmid":["26504200"]},"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"oa":1,"project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme"}],"quality_controlled":"1","doi":"10.1073/pnas.1508400112","language":[{"iso":"eng"}],"month":"11"},{"publication_status":"published","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"Springer","year":"2015","date_created":"2018-12-11T11:53:18Z","date_updated":"2023-02-21T16:17:24Z","volume":9308,"author":[{"orcid":"0000-0002-0686-0365","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","last_name":"Bogomolov","first_name":"Sergiy","full_name":"Bogomolov, Sergiy"},{"full_name":"Henzinger, Thomas A","orcid":"0000−0002−2985−7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","first_name":"Thomas A"},{"first_name":"Andreas","last_name":"Podelski","full_name":"Podelski, Andreas"},{"first_name":"Jakob","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob"},{"last_name":"Schilling","first_name":"Christian","full_name":"Schilling, Christian"}],"related_material":{"record":[{"id":"1148","status":"public","relation":"later_version"}]},"ec_funded":1,"publist_id":"5492","quality_controlled":"1","project":[{"grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","name":"Quantitative Reactive Modeling","call_identifier":"FP7"},{"call_identifier":"FWF","name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"name":"Rigorous Systems Engineering","call_identifier":"FWF","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"conference":{"name":"CMSB: Computational Methods in Systems Biology","start_date":"2015-09-16","location":"Nantes, France","end_date":"2015-09-18"},"doi":"10.1007/978-3-319-23401-4_8","month":"09","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","status":"public","intvolume":" 9308","_id":"1658","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"None","alternative_title":["LNCS"],"type":"conference","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.","lang":"eng"}],"page":"77 - 89","citation":{"ama":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment closure for parameter inference of biochemical reaction networks. 2015;9308:77-89. doi:10.1007/978-3-319-23401-4_8","ista":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive moment closure for parameter inference of biochemical reaction networks. 9308, 77–89.","apa":"Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., & Schilling, C. (2015). Adaptive moment closure for parameter inference of biochemical reaction networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes, France: Springer. https://doi.org/10.1007/978-3-319-23401-4_8","ieee":"S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015.","mla":"Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8.","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","chicago":"Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8."},"date_published":"2015-09-01T00:00:00Z","series_title":"Lecture Notes in Computer Science","scopus_import":1,"day":"01"},{"intvolume":" 11","status":"public","title":"High accuracy decoding of dynamical motion from a large retinal population","ddc":["570"],"_id":"1697","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"access_level":"open_access","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","content_type":"application/pdf","file_size":4673930,"creator":"system","relation":"main_file","file_id":"5212","checksum":"472b979f3f1cffb37b3e503f085115ca","date_updated":"2020-07-14T12:45:12Z","date_created":"2018-12-12T10:16:25Z"}],"oa_version":"Published Version","pubrep_id":"455","type":"journal_article","issue":"7","abstract":[{"lang":"eng","text":"Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits."}],"citation":{"ama":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 2015;11(7). doi:10.1371/journal.pcbi.1004304","ieee":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” PLoS Computational Biology, vol. 11, no. 7. Public Library of Science, 2015.","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304","ista":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 11(7), e1004304.","short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015).","mla":"Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004304.","chicago":"Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora, Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004304."},"publication":"PLoS Computational Biology","date_published":"2015-07-01T00:00:00Z","scopus_import":1,"has_accepted_license":"1","day":"01","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","publication_status":"published","acknowledgement":"This work was supported by grants EY 014196 and EY 017934 to MJB, ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010- 22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).","year":"2015","volume":11,"date_created":"2018-12-11T11:53:31Z","date_updated":"2021-01-12T06:52:35Z","author":[{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"orcid":"0000-0002-8790-1914","id":"421234E8-F248-11E8-B48F-1D18A9856A87","last_name":"Botella Soler","first_name":"Vicente","full_name":"Botella Soler, Vicente"},{"full_name":"Simmons, Kristina","last_name":"Simmons","first_name":"Kristina"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"},{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik"},{"last_name":"Berry","first_name":"Michael","full_name":"Berry, Michael"}],"article_number":"e1004304","publist_id":"5447","file_date_updated":"2020-07-14T12:45:12Z","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26"}],"quality_controlled":"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"},"oa":1,"language":[{"iso":"eng"}],"doi":"10.1371/journal.pcbi.1004304","month":"07"},{"month":"09","doi":"10.1073/pnas.1514188112","language":[{"iso":"eng"}],"external_id":{"pmid":["26330611"]},"oa":1,"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/","open_access":"1"}],"project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"quality_controlled":"1","publist_id":"5440","author":[{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"first_name":"Dario","last_name":"Amodei","full_name":"Amodei, Dario"},{"last_name":"Palmer","first_name":"Stephanie","full_name":"Palmer, Stephanie"},{"full_name":"Berry Ii, Michael","last_name":"Berry Ii","first_name":"Michael"},{"full_name":"Bialek, William","last_name":"Bialek","first_name":"William"}],"volume":112,"date_created":"2018-12-11T11:53:33Z","date_updated":"2021-01-12T06:52:37Z","pmid":1,"acknowledgement":"Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation.","year":"2015","publisher":"National Academy of Sciences","department":[{"_id":"GaTk"}],"publication_status":"published","day":"15","scopus_import":1,"date_published":"2015-09-15T00:00:00Z","citation":{"ama":"Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 2015;112(37):11508-11513. doi:10.1073/pnas.1514188112","apa":"Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., & Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1514188112","ieee":"G. Tkačik et al., “Thermodynamics and signatures of criticality in a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015.","ista":"Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513.","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513.","mla":"Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:10.1073/pnas.1514188112.","chicago":"Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1514188112."},"publication":"PNAS","page":"11508 - 11513","issue":"37","abstract":[{"text":"The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. ","lang":"eng"}],"type":"journal_article","oa_version":"Submitted Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1701","intvolume":" 112","title":"Thermodynamics and signatures of criticality in a network of neurons","status":"public"},{"oa_version":"None","volume":25,"date_created":"2018-12-11T11:54:25Z","date_updated":"2021-01-12T06:53:41Z","author":[{"full_name":"Ruess, Jakob","last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Lygeros, John","last_name":"Lygeros","first_name":"John"}],"publisher":"ACM","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"intvolume":" 25","title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","status":"public","publication_status":"published","_id":"1861","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2015","acknowledgement":"HYCON2; EC; European Commission\r\n","publist_id":"5238","issue":"2","abstract":[{"text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.","lang":"eng"}],"type":"journal_article","article_number":"8","language":[{"iso":"eng"}],"doi":"10.1145/2688906","date_published":"2015-02-01T00:00:00Z","quality_controlled":"1","citation":{"ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 2015;25(2). doi:10.1145/2688906","ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2. ACM, 2015.","apa":"Ruess, J., & Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. ACM. https://doi.org/10.1145/2688906","ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8.","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2, 8, ACM, 2015, doi:10.1145/2688906.","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation. ACM, 2015. https://doi.org/10.1145/2688906."},"publication":"ACM Transactions on Modeling and Computer Simulation","month":"02","day":"01","scopus_import":1},{"abstract":[{"lang":"eng","text":"The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. "}],"publist_id":"5210","issue":"1","type":"journal_article","author":[{"full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455"},{"first_name":"Julien","last_name":"Dubuis","full_name":"Dubuis, Julien"},{"full_name":"Petkova, Mariela","first_name":"Mariela","last_name":"Petkova"},{"last_name":"Gregor","first_name":"Thomas","full_name":"Gregor, Thomas"}],"date_updated":"2021-01-12T06:53:50Z","date_created":"2018-12-11T11:54:32Z","oa_version":"Preprint","volume":199,"_id":"1885","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2015","title":"Positional information, positional error, and readout precision in morphogenesis: A mathematical framework","status":"public","publication_status":"published","department":[{"_id":"GaTk"}],"intvolume":" 199","publisher":"Genetics Society of America","day":"01","month":"01","scopus_import":1,"date_published":"2015-01-01T00:00:00Z","doi":"10.1534/genetics.114.171850","language":[{"iso":"eng"}],"publication":"Genetics","citation":{"chicago":"Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” Genetics. Genetics Society of America, 2015. https://doi.org/10.1534/genetics.114.171850.","mla":"Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” Genetics, vol. 199, no. 1, Genetics Society of America, 2015, pp. 39–59, doi:10.1534/genetics.114.171850.","short":"G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59.","ista":"Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 199(1), 39–59.","ieee":"G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” Genetics, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.","apa":"Tkačik, G., Dubuis, J., Petkova, M., & Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.114.171850","ama":"Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 2015;199(1):39-59. doi:10.1534/genetics.114.171850"},"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1404.5599"}],"oa":1,"quality_controlled":"1","page":"39 - 59"},{"type":"journal_article","article_number":"062710","issue":"6","publist_id":"5145","abstract":[{"lang":"eng","text":"We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the "input") by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models."}],"_id":"1940","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2015","department":[{"_id":"GaTk"}],"publisher":"American Institute of Physics","intvolume":" 91","status":"public","title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","publication_status":"published","author":[{"full_name":"Sokolowski, Thomas R","orcid":"0000-0002-1287-3779","id":"3E999752-F248-11E8-B48F-1D18A9856A87","last_name":"Sokolowski","first_name":"Thomas R"},{"first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"volume":91,"oa_version":"Preprint","date_created":"2018-12-11T11:54:49Z","date_updated":"2021-01-12T06:54:13Z","scopus_import":1,"day":"15","month":"06","main_file_link":[{"url":"http://arxiv.org/abs/1501.04015","open_access":"1"}],"citation":{"ama":"Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 2015;91(6). doi:10.1103/PhysRevE.91.062710","ista":"Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 91(6), 062710.","ieee":"T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6. American Institute of Physics, 2015.","apa":"Sokolowski, T. R., & Tkačik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.91.062710","mla":"Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6, 062710, American Institute of Physics, 2015, doi:10.1103/PhysRevE.91.062710.","short":"T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (2015).","chicago":"Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics, 2015. https://doi.org/10.1103/PhysRevE.91.062710."},"oa":1,"publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","quality_controlled":"1","date_published":"2015-06-15T00:00:00Z","doi":"10.1103/PhysRevE.91.062710","language":[{"iso":"eng"}]},{"publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"title":"Supporting information text","status":"public","_id":"9718","year":"2015","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","date_updated":"2023-02-23T10:16:13Z","date_created":"2021-07-26T08:35:23Z","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1827"}]},"author":[{"first_name":"Tamar","last_name":"Friedlander","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","full_name":"Friedlander, Tamar"},{"first_name":"Avraham E.","last_name":"Mayo","full_name":"Mayo, Avraham E."},{"full_name":"Tlusty, Tsvi","first_name":"Tsvi","last_name":"Tlusty"},{"last_name":"Alon","first_name":"Uri","full_name":"Alon, Uri"}],"type":"research_data_reference","citation":{"apa":"Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Supporting information text. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s001","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information text.” Public Library of Science, 2015.","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Supporting information text, Public Library of Science, 10.1371/journal.pcbi.1004055.s001.","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Supporting information text. 2015. doi:10.1371/journal.pcbi.1004055.s001","chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Supporting Information Text.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s001.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","mla":"Friedlander, Tamar, et al. Supporting Information Text. Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.s001."},"doi":"10.1371/journal.pcbi.1004055.s001","date_published":"2015-03-23T00:00:00Z","article_processing_charge":"No","month":"03","day":"23"},{"file_date_updated":"2020-07-14T12:45:17Z","ec_funded":1,"publist_id":"5278","date_updated":"2023-02-23T14:07:51Z","date_created":"2018-12-11T11:54:14Z","volume":11,"author":[{"first_name":"Tamar","last_name":"Friedlander","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","full_name":"Friedlander, Tamar"},{"first_name":"Avraham","last_name":"Mayo","full_name":"Mayo, Avraham"},{"first_name":"Tsvi","last_name":"Tlusty","full_name":"Tlusty, Tsvi"},{"last_name":"Alon","first_name":"Uri","full_name":"Alon, Uri"}],"related_material":{"record":[{"relation":"research_data","status":"public","id":"9718"},{"status":"public","relation":"research_data","id":"9773"}]},"publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","year":"2015","month":"03","language":[{"iso":"eng"}],"doi":"10.1371/journal.pcbi.1004055","quality_controlled":"1","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"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,"abstract":[{"text":"Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved.","lang":"eng"}],"issue":"3","type":"journal_article","oa_version":"Published Version","file":[{"date_created":"2018-12-12T10:15:39Z","date_updated":"2020-07-14T12:45:17Z","checksum":"b8aa66f450ff8de393014b87ec7d2efb","relation":"main_file","file_id":"5161","content_type":"application/pdf","file_size":1811647,"creator":"system","file_name":"IST-2016-452-v1+1_journal.pcbi.1004055.pdf","access_level":"open_access"}],"pubrep_id":"452","status":"public","title":"Evolution of bow-tie architectures in biology","ddc":["576"],"intvolume":" 11","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"1827","day":"23","article_processing_charge":"No","has_accepted_license":"1","scopus_import":1,"date_published":"2015-03-23T00:00:00Z","publication":"PLoS Computational Biology","citation":{"chicago":"Friedlander, Tamar, Avraham Mayo, Tsvi Tlusty, and Uri Alon. “Evolution of Bow-Tie Architectures in Biology.” PLoS Computational Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.","mla":"Friedlander, Tamar, et al. “Evolution of Bow-Tie Architectures in Biology.” PLoS Computational Biology, vol. 11, no. 3, Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.","short":"T. Friedlander, A. Mayo, T. Tlusty, U. Alon, PLoS Computational Biology 11 (2015).","ista":"Friedlander T, Mayo A, Tlusty T, Alon U. 2015. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 11(3).","apa":"Friedlander, T., Mayo, A., Tlusty, T., & Alon, U. (2015). Evolution of bow-tie architectures in biology. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055","ieee":"T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures in biology,” PLoS Computational Biology, vol. 11, no. 3. Public Library of Science, 2015.","ama":"Friedlander T, Mayo A, Tlusty T, Alon U. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 2015;11(3). doi:10.1371/journal.pcbi.1004055"}},{"month":"03","day":"23","article_processing_charge":"No","citation":{"chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Evolutionary Simulation Code.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s002.","mla":"Friedlander, Tamar, et al. Evolutionary Simulation Code. Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.s002.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Evolutionary simulation code, Public Library of Science, 10.1371/journal.pcbi.1004055.s002.","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Evolutionary simulation code.” Public Library of Science, 2015.","apa":"Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Evolutionary simulation code. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s002","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Evolutionary simulation code. 2015. doi:10.1371/journal.pcbi.1004055.s002"},"doi":"10.1371/journal.pcbi.1004055.s002","date_published":"2015-03-23T00:00:00Z","type":"research_data_reference","_id":"9773","year":"2015","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","status":"public","title":"Evolutionary simulation code","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"author":[{"id":"36A5845C-F248-11E8-B48F-1D18A9856A87","first_name":"Tamar","last_name":"Friedlander","full_name":"Friedlander, Tamar"},{"full_name":"Mayo, Avraham E.","last_name":"Mayo","first_name":"Avraham E."},{"first_name":"Tsvi","last_name":"Tlusty","full_name":"Tlusty, Tsvi"},{"last_name":"Alon","first_name":"Uri","full_name":"Alon, Uri"}],"related_material":{"record":[{"id":"1827","relation":"used_in_publication","status":"public"}]},"date_updated":"2023-02-23T10:16:13Z","date_created":"2021-08-05T12:58:07Z","oa_version":"Published Version"},{"article_processing_charge":"No","month":"11","day":"06","citation":{"ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Other fitness models for comparison & for interacting TFBSs. 2015. doi:10.1371/journal.pgen.1005639.s001","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Other fitness models for comparison & for interacting TFBSs, Public Library of Science, 10.1371/journal.pgen.1005639.s001.","ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Other fitness models for comparison & for interacting TFBSs.” Public Library of Science, 2015.","apa":"Tugrul, M., Paixao, T., Barton, N. H., & Tkačik, G. (2015). Other fitness models for comparison & for interacting TFBSs. Public Library of Science. https://doi.org/10.1371/journal.pgen.1005639.s001","mla":"Tugrul, Murat, et al. Other Fitness Models for Comparison & for Interacting TFBSs. Public Library of Science, 2015, doi:10.1371/journal.pgen.1005639.s001.","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, (2015).","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Other Fitness Models for Comparison & for Interacting TFBSs.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pgen.1005639.s001."},"date_published":"2015-11-06T00:00:00Z","doi":"10.1371/journal.pgen.1005639.s001","type":"research_data_reference","department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"Public Library of Science","status":"public","title":"Other fitness models for comparison & for interacting TFBSs","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","_id":"9712","year":"2015","oa_version":"Published Version","date_updated":"2023-02-23T10:09:08Z","date_created":"2021-07-23T12:00:37Z","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1666"}]},"author":[{"last_name":"Tugrul","first_name":"Murat","orcid":"0000-0002-8523-0758","id":"37C323C6-F248-11E8-B48F-1D18A9856A87","full_name":"Tugrul, Murat"},{"first_name":"Tiago","last_name":"Paixao","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2361-3953","full_name":"Paixao, Tiago"},{"full_name":"Barton, Nicholas H","first_name":"Nicholas H","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","first_name":"Gašper","full_name":"Tkačik, Gašper"}]},{"type":"journal_article","abstract":[{"lang":"eng","text":"Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ∼ 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of “pre-sites” or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics."}],"issue":"11","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1666","title":"Dynamics of transcription factor binding site evolution","status":"public","ddc":["576"],"intvolume":" 11","pubrep_id":"463","oa_version":"Published Version","file":[{"checksum":"a4e72fca5ccf40ddacf4d08c8e46b554","date_updated":"2020-07-14T12:45:10Z","date_created":"2018-12-12T10:07:58Z","file_id":"4657","relation":"main_file","creator":"system","content_type":"application/pdf","file_size":2580778,"access_level":"open_access","file_name":"IST-2016-463-v1+1_journal.pgen.1005639.pdf"}],"scopus_import":1,"day":"06","has_accepted_license":"1","publication":"PLoS Genetics","citation":{"mla":"Tugrul, Murat, et al. “Dynamics of Transcription Factor Binding Site Evolution.” PLoS Genetics, vol. 11, no. 11, Public Library of Science, 2015, doi:10.1371/journal.pgen.1005639.","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, PLoS Genetics 11 (2015).","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Dynamics of Transcription Factor Binding Site Evolution.” PLoS Genetics. Public Library of Science, 2015. https://doi.org/10.1371/journal.pgen.1005639.","ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Dynamics of transcription factor binding site evolution. PLoS Genetics. 2015;11(11). doi:10.1371/journal.pgen.1005639","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Dynamics of transcription factor binding site evolution. PLoS Genetics. 11(11).","ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Dynamics of transcription factor binding site evolution,” PLoS Genetics, vol. 11, no. 11. Public Library of Science, 2015.","apa":"Tugrul, M., Paixao, T., Barton, N. H., & Tkačik, G. (2015). Dynamics of transcription factor binding site evolution. PLoS Genetics. Public Library of Science. https://doi.org/10.1371/journal.pgen.1005639"},"date_published":"2015-11-06T00:00:00Z","file_date_updated":"2020-07-14T12:45:10Z","publist_id":"5483","ec_funded":1,"year":"2015","publication_status":"published","publisher":"Public Library of Science","department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"author":[{"full_name":"Tugrul, Murat","first_name":"Murat","last_name":"Tugrul","id":"37C323C6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8523-0758"},{"full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-2361-3953","first_name":"Tiago","last_name":"Paixao"},{"orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","first_name":"Nicholas H","full_name":"Barton, Nicholas H"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper"}],"related_material":{"record":[{"relation":"research_data","status":"public","id":"9712"},{"status":"public","relation":"dissertation_contains","id":"1131"}]},"date_created":"2018-12-11T11:53:21Z","date_updated":"2023-09-07T11:53:49Z","volume":11,"month":"11","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,"quality_controlled":"1","project":[{"name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152"}],"doi":"10.1371/journal.pgen.1005639","language":[{"iso":"eng"}]},{"ec_funded":1,"publist_id":"5595","article_number":"248101","author":[{"id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","first_name":"Sarah A","last_name":"Cepeda Humerez","full_name":"Cepeda Humerez, Sarah A"},{"first_name":"Georg","last_name":"Rieckh","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg"},{"full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455"}],"related_material":{"record":[{"id":"6473","relation":"part_of_dissertation","status":"public"}]},"date_updated":"2023-09-07T12:55:21Z","date_created":"2018-12-11T11:52:49Z","volume":115,"year":"2015","publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"American Physical Society","month":"12","doi":"10.1103/PhysRevLett.115.248101","language":[{"iso":"eng"}],"oa":1,"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1504.05716"}],"quality_controlled":"1","project":[{"name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152"}],"abstract":[{"lang":"eng","text":"Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that noncognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here, we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative nonequilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and predicts increased noise in gene expression as a trade-off for improved specificity. Using information theory, we quantify this trade-off to find when optimal proofreading architectures are favored over their equilibrium counterparts. Such architectures exhibit significant super-Poisson noise at low expression in steady state."}],"issue":"24","type":"journal_article","oa_version":"Preprint","_id":"1576","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public","title":"Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation","intvolume":" 115","day":"08","scopus_import":1,"date_published":"2015-12-08T00:00:00Z","publication":"Physical Review Letters","citation":{"chicago":"Cepeda Humerez, Sarah A, Georg Rieckh, and Gašper Tkačik. “Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation.” Physical Review Letters. American Physical Society, 2015. https://doi.org/10.1103/PhysRevLett.115.248101.","mla":"Cepeda Humerez, Sarah A., et al. “Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation.” Physical Review Letters, vol. 115, no. 24, 248101, American Physical Society, 2015, doi:10.1103/PhysRevLett.115.248101.","short":"S.A. Cepeda Humerez, G. Rieckh, G. Tkačik, Physical Review Letters 115 (2015).","ista":"Cepeda Humerez SA, Rieckh G, Tkačik G. 2015. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. 115(24), 248101.","ieee":"S. A. Cepeda Humerez, G. Rieckh, and G. Tkačik, “Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation,” Physical Review Letters, vol. 115, no. 24. American Physical Society, 2015.","apa":"Cepeda Humerez, S. A., Rieckh, G., & Tkačik, G. (2015). Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. American Physical Society. https://doi.org/10.1103/PhysRevLett.115.248101","ama":"Cepeda Humerez SA, Rieckh G, Tkačik G. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. 2015;115(24). doi:10.1103/PhysRevLett.115.248101"}},{"article_processing_charge":"No","has_accepted_license":"1","day":"23","scopus_import":"1","date_published":"2015-10-23T00:00:00Z","page":"7266 - 7297","citation":{"apa":"Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy. MDPI. https://doi.org/10.3390/e17107266","ieee":"G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.","ista":"Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots. Entropy. 17(10), 7266–7297.","ama":"Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266","chicago":"Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” Entropy. MDPI, 2015. https://doi.org/10.3390/e17107266.","short":"G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.","mla":"Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266."},"publication":"Entropy","issue":"10","abstract":[{"text":"Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration.","lang":"eng"}],"type":"journal_article","oa_version":"Published Version","file":[{"date_updated":"2020-07-14T12:45:08Z","date_created":"2018-12-12T10:12:25Z","checksum":"945d99631a96e0315acb26dc8541dcf9","relation":"main_file","file_id":"4943","content_type":"application/pdf","file_size":6455007,"creator":"system","file_name":"IST-2016-464-v1+1_entropy-17-07266.pdf","access_level":"open_access"}],"pubrep_id":"464","intvolume":" 17","status":"public","title":"Quantifying emergent behavior of autonomous robots","ddc":["000"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1655","month":"10","language":[{"iso":"eng"}],"doi":"10.3390/e17107266","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme"}],"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"},"publist_id":"5495","ec_funded":1,"file_date_updated":"2020-07-14T12:45:08Z","volume":17,"date_created":"2018-12-11T11:53:17Z","date_updated":"2023-10-17T11:42:00Z","author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","first_name":"Georg S","full_name":"Martius, Georg S"},{"full_name":"Olbrich, Eckehard","first_name":"Eckehard","last_name":"Olbrich"}],"publisher":"MDPI","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"publication_status":"published","year":"2015","acknowledgement":"This work was supported by the DFG priority program 1527 (Autonomous Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734."},{"citation":{"apa":"Savin, C., & Denève, S. (2014). Spatio-temporal representations of uncertainty in spiking neural networks (Vol. 3, pp. 2024–2032). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.","ieee":"C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in spiking neural networks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2014, vol. 3, no. January, pp. 2024–2032.","ista":"Savin C, Denève S. 2014. Spatio-temporal representations of uncertainty in spiking neural networks. NIPS: Neural Information Processing Systems vol. 3, 2024–2032.","ama":"Savin C, Denève S. Spatio-temporal representations of uncertainty in spiking neural networks. In: Vol 3. Neural Information Processing Systems; 2014:2024-2032.","chicago":"Savin, Cristina, and Sophie Denève. “Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks,” 3:2024–32. Neural Information Processing Systems, 2014.","short":"C. Savin, S. Denève, in:, Neural Information Processing Systems, 2014, pp. 2024–2032.","mla":"Savin, Cristina, and Sophie Denève. Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks. Vol. 3, no. January, Neural Information Processing Systems, 2014, pp. 2024–32."},"main_file_link":[{"url":"http://papers.nips.cc/paper/5343-spatio-temporal-representations-of-uncertainty-in-spiking-neural-networks.pdf"}],"page":"2024 - 2032","quality_controlled":"1","date_published":"2014-01-01T00:00:00Z","conference":{"end_date":"2014-12-13","location":"Montreal, Canada","start_date":"2014-12-08","name":"NIPS: Neural Information Processing Systems"},"language":[{"iso":"eng"}],"scopus_import":1,"day":"01","month":"01","_id":"1708","year":"2014","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","publisher":"Neural Information Processing Systems","department":[{"_id":"GaTk"}],"intvolume":" 3","publication_status":"published","title":"Spatio-temporal representations of uncertainty in spiking neural networks","status":"public","author":[{"last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina"},{"first_name":"Sophie","last_name":"Denève","full_name":"Denève, Sophie"}],"volume":3,"oa_version":"None","date_updated":"2021-01-12T06:52:40Z","date_created":"2018-12-11T11:53:35Z","type":"conference","issue":"January","publist_id":"5427","abstract":[{"text":"It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations.","lang":"eng"}]},{"month":"11","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,"quality_controlled":"1","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF","grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425"}],"doi":"10.7554/eLife.03722","language":[{"iso":"eng"}],"article_number":"e03722","file_date_updated":"2020-07-14T12:45:20Z","publist_id":"5209","year":"2014","publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"eLife Sciences Publications","author":[{"last_name":"Hermundstad","first_name":"Ann","full_name":"Hermundstad, Ann"},{"last_name":"Briguglio","first_name":"John","full_name":"Briguglio, John"},{"full_name":"Conte, Mary","first_name":"Mary","last_name":"Conte"},{"full_name":"Victor, Jonathan","first_name":"Jonathan","last_name":"Victor"},{"full_name":"Balasubramanian, Vijay","first_name":"Vijay","last_name":"Balasubramanian"},{"first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"date_updated":"2021-01-12T06:53:50Z","date_created":"2018-12-11T11:54:32Z","scopus_import":1,"day":"14","has_accepted_license":"1","publication":"eLife","citation":{"mla":"Hermundstad, Ann, et al. “Variance Predicts Salience in Central Sensory Processing.” ELife, no. November, e03722, eLife Sciences Publications, 2014, doi:10.7554/eLife.03722.","short":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, G. Tkačik, ELife (2014).","chicago":"Hermundstad, Ann, John Briguglio, Mary Conte, Jonathan Victor, Vijay Balasubramanian, and Gašper Tkačik. “Variance Predicts Salience in Central Sensory Processing.” ELife. eLife Sciences Publications, 2014. https://doi.org/10.7554/eLife.03722.","ama":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. Variance predicts salience in central sensory processing. eLife. 2014;(November). doi:10.7554/eLife.03722","ista":"Hermundstad A, Briguglio J, Conte M, Victor J, Balasubramanian V, Tkačik G. 2014. Variance predicts salience in central sensory processing. eLife. (November), e03722.","ieee":"A. Hermundstad, J. Briguglio, M. Conte, J. Victor, V. Balasubramanian, and G. Tkačik, “Variance predicts salience in central sensory processing,” eLife, no. November. eLife Sciences Publications, 2014.","apa":"Hermundstad, A., Briguglio, J., Conte, M., Victor, J., Balasubramanian, V., & Tkačik, G. (2014). Variance predicts salience in central sensory processing. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.03722"},"date_published":"2014-11-14T00:00:00Z","type":"journal_article","abstract":[{"text":"Information processing in the sensory periphery is shaped by natural stimulus statistics. In the periphery, a transmission bottleneck constrains performance; thus efficient coding implies that natural signal components with a predictably wider range should be compressed. In a different regime—when sampling limitations constrain performance—efficient coding implies that more resources should be allocated to informative features that are more variable. We propose that this regime is relevant for sensory cortex when it extracts complex features from limited numbers of sensory samples. To test this prediction, we use central visual processing as a model: we show that visual sensitivity for local multi-point spatial correlations, described by dozens of independently-measured parameters, can be quantitatively predicted from the structure of natural images. This suggests that efficient coding applies centrally, where it extends to higher-order sensory features and operates in a regime in which sensitivity increases with feature variability.","lang":"eng"}],"issue":"November","_id":"1886","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","status":"public","ddc":["570"],"title":"Variance predicts salience in central sensory processing","pubrep_id":"420","oa_version":"Published Version","file":[{"relation":"main_file","file_id":"4922","date_created":"2018-12-12T10:12:04Z","date_updated":"2020-07-14T12:45:20Z","checksum":"766ac8999ac6e3364f10065a06024b8f","file_name":"IST-2016-420-v1+1_e03722.full.pdf","access_level":"open_access","content_type":"application/pdf","file_size":5117086,"creator":"system"}]},{"doi":"10.1103/PhysRevE.89.032701","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1402.0430"}],"oa":1,"month":"03","author":[{"full_name":"Kollár, Richard","last_name":"Kollár","first_name":"Richard"},{"orcid":"0000-0002-7214-0171","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","last_name":"Bod'ová","first_name":"Katarína","full_name":"Bod'ová, Katarína"},{"last_name":"Nosek","first_name":"Jozef","full_name":"Nosek, Jozef"},{"first_name":"Ľubomír","last_name":"Tomáška","full_name":"Tomáška, Ľubomír"}],"volume":89,"date_updated":"2022-08-01T10:50:10Z","date_created":"2018-12-11T11:54:35Z","year":"2014","acknowledgement":"The work was supported by the VEGA Grant No. 1/0459/13 (R.K. and K.B.).","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publisher":"American Institute of Physics","publication_status":"published","publist_id":"5198","article_number":"032701","date_published":"2014-03-04T00:00:00Z","citation":{"apa":"Kollár, R., Bodova, K., Nosek, J., & Tomáška, Ľ. (2014). Mathematical model of alternative mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.89.032701","ieee":"R. Kollár, K. Bodova, J. Nosek, and Ľ. Tomáška, “Mathematical model of alternative mechanism of telomere length maintenance,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 89, no. 3. American Institute of Physics, 2014.","ista":"Kollár R, Bodova K, Nosek J, Tomáška Ľ. 2014. Mathematical model of alternative mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(3), 032701.","ama":"Kollár R, Bodova K, Nosek J, Tomáška Ľ. Mathematical model of alternative mechanism of telomere length maintenance. Physical Review E Statistical Nonlinear and Soft Matter Physics. 2014;89(3). doi:10.1103/PhysRevE.89.032701","chicago":"Kollár, Richard, Katarina Bodova, Jozef Nosek, and Ľubomír Tomáška. “Mathematical Model of Alternative Mechanism of Telomere Length Maintenance.” Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics, 2014. https://doi.org/10.1103/PhysRevE.89.032701.","short":"R. Kollár, K. Bodova, J. Nosek, Ľ. Tomáška, Physical Review E Statistical Nonlinear and Soft Matter Physics 89 (2014).","mla":"Kollár, Richard, et al. “Mathematical Model of Alternative Mechanism of Telomere Length Maintenance.” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 89, no. 3, 032701, American Institute of Physics, 2014, doi:10.1103/PhysRevE.89.032701."},"publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","article_processing_charge":"No","day":"04","scopus_import":"1","oa_version":"Submitted Version","_id":"1896","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 89","status":"public","title":"Mathematical model of alternative mechanism of telomere length maintenance","issue":"3","abstract":[{"text":"Biopolymer length regulation is a complex process that involves a large number of biological, chemical, and physical subprocesses acting simultaneously across multiple spatial and temporal scales. An illustrative example important for genomic stability is the length regulation of telomeres - nucleoprotein structures at the ends of linear chromosomes consisting of tandemly repeated DNA sequences and a specialized set of proteins. Maintenance of telomeres is often facilitated by the enzyme telomerase but, particularly in telomerase-free systems, the maintenance of chromosomal termini depends on alternative lengthening of telomeres (ALT) mechanisms mediated by recombination. Various linear and circular DNA structures were identified to participate in ALT, however, dynamics of the whole process is still poorly understood. We propose a chemical kinetics model of ALT with kinetic rates systematically derived from the biophysics of DNA diffusion and looping. The reaction system is reduced to a coagulation-fragmentation system by quasi-steady-state approximation. The detailed treatment of kinetic rates yields explicit formulas for expected size distributions of telomeres that demonstrate the key role played by the J factor, a quantitative measure of bending of polymers. The results are in agreement with experimental data and point out interesting phenomena: an appearance of very long telomeric circles if the total telomere density exceeds a critical value (excess mass) and a nonlinear response of the telomere size distributions to the amount of telomeric DNA in the system. The results can be of general importance for understanding dynamics of telomeres in telomerase-independent systems as this mode of telomere maintenance is similar to the situation in tumor cells lacking telomerase activity. Furthermore, due to its universality, the model may also serve as a prototype of an interaction between linear and circular DNA structures in various settings.","lang":"eng"}],"type":"journal_article"},{"type":"journal_article","issue":"3","abstract":[{"lang":"eng","text":"Summary: Phenotypes are often environmentally dependent, which requires organisms to track environmental change. The challenge for organisms is to construct phenotypes using the most accurate environmental cue. Here, we use a quantitative genetic model of adaptation by additive genetic variance, within- and transgenerational plasticity via linear reaction norms and indirect genetic effects respectively. We show how the relative influence on the eventual phenotype of these components depends on the predictability of environmental change (fast or slow, sinusoidal or stochastic) and the developmental lag τ between when the environment is perceived and when selection acts. We then decompose expected mean fitness into three components (variance load, adaptation and fluctuation load) to study the fitness costs of within- and transgenerational plasticity. A strongly negative maternal effect coefficient m minimizes the variance load, but a strongly positive m minimises the fluctuation load. The adaptation term is maximized closer to zero, with positive or negative m preferred under different environmental scenarios. Phenotypic plasticity is higher when τ is shorter and when the environment changes frequently between seasonal extremes. Expected mean population fitness is highest away from highest observed levels of phenotypic plasticity. Within- and transgenerational plasticity act in concert to deliver well-adapted phenotypes, which emphasizes the need to study both simultaneously when investigating phenotypic evolution."}],"_id":"1909","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","intvolume":" 28","status":"public","title":"The fitness costs of adaptation via phenotypic plasticity and maternal effects","ddc":["570"],"pubrep_id":"419","file":[{"access_level":"open_access","file_name":"IST-2016-419-v1+1_Ezard_et_al-2014-Functional_Ecology.pdf","file_size":536154,"content_type":"application/pdf","creator":"system","relation":"main_file","file_id":"5167","checksum":"3cbe8623174709a8ceec2103246f8fe0","date_created":"2018-12-12T10:15:45Z","date_updated":"2020-07-14T12:45:20Z"}],"oa_version":"Published Version","scopus_import":1,"has_accepted_license":"1","day":"01","citation":{"ama":"Ezard T, Prizak R, Hoyle R. The fitness costs of adaptation via phenotypic plasticity and maternal effects. Functional Ecology. 2014;28(3):693-701. doi:10.1111/1365-2435.12207","ista":"Ezard T, Prizak R, Hoyle R. 2014. The fitness costs of adaptation via phenotypic plasticity and maternal effects. Functional Ecology. 28(3), 693–701.","apa":"Ezard, T., Prizak, R., & Hoyle, R. (2014). The fitness costs of adaptation via phenotypic plasticity and maternal effects. Functional Ecology. Wiley-Blackwell. https://doi.org/10.1111/1365-2435.12207","ieee":"T. Ezard, R. Prizak, and R. Hoyle, “The fitness costs of adaptation via phenotypic plasticity and maternal effects,” Functional Ecology, vol. 28, no. 3. Wiley-Blackwell, pp. 693–701, 2014.","mla":"Ezard, Thomas, et al. “The Fitness Costs of Adaptation via Phenotypic Plasticity and Maternal Effects.” Functional Ecology, vol. 28, no. 3, Wiley-Blackwell, 2014, pp. 693–701, doi:10.1111/1365-2435.12207.","short":"T. Ezard, R. Prizak, R. Hoyle, Functional Ecology 28 (2014) 693–701.","chicago":"Ezard, Thomas, Roshan Prizak, and Rebecca Hoyle. “The Fitness Costs of Adaptation via Phenotypic Plasticity and Maternal Effects.” Functional Ecology. Wiley-Blackwell, 2014. https://doi.org/10.1111/1365-2435.12207."},"publication":"Functional Ecology","page":"693 - 701","date_published":"2014-06-01T00:00:00Z","publist_id":"5186","file_date_updated":"2020-07-14T12:45:20Z","year":"2014","acknowledgement":"Engineering and Physical Sciences Research Council. Grant Number: EP/H031928/1","publisher":"Wiley-Blackwell","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publication_status":"published","author":[{"last_name":"Ezard","first_name":"Thomas","full_name":"Ezard, Thomas"},{"last_name":"Prizak","first_name":"Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87","full_name":"Prizak, Roshan"},{"full_name":"Hoyle, Rebecca","last_name":"Hoyle","first_name":"Rebecca"}],"volume":28,"date_created":"2018-12-11T11:54:40Z","date_updated":"2021-01-12T06:54:00Z","month":"06","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"},"doi":"10.1111/1365-2435.12207","language":[{"iso":"eng"}]},{"_id":"1928","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","year":"2014","acknowledgement":"J.H. received support from the Zdenek Bakala Foundation and the Mobility Fund of Charles University in Prague.","publication_status":"published","status":"public","title":"Evolutionary dynamics of infectious diseases in finite populations","intvolume":" 360","department":[{"_id":"GaTk"}],"publisher":"Elsevier","author":[{"full_name":"Humplik, Jan","id":"2E9627A8-F248-11E8-B48F-1D18A9856A87","last_name":"Humplik","first_name":"Jan"},{"full_name":"Hill, Alison","last_name":"Hill","first_name":"Alison"},{"last_name":"Nowak","first_name":"Martin","full_name":"Nowak, Martin"}],"date_updated":"2021-01-12T06:54:08Z","date_created":"2018-12-11T11:54:46Z","volume":360,"oa_version":"None","type":"journal_article","abstract":[{"lang":"eng","text":"In infectious disease epidemiology the basic reproductive ratio, R0, is defined as the average number of new infections caused by a single infected individual in a fully susceptible population. Many models describing competition for hosts between non-interacting pathogen strains in an infinite population lead to the conclusion that selection favors invasion of new strains if and only if they have higher R0 values than the resident. Here we demonstrate that this picture fails in finite populations. Using a simple stochastic SIS model, we show that in general there is no analogous optimization principle. We find that successive invasions may in some cases lead to strains that infect a smaller fraction of the host population, and that mutually invasible pathogen strains exist. In the limit of weak selection we demonstrate that an optimization principle does exist, although it differs from R0 maximization. For strains with very large R0, we derive an expression for this local fitness function and use it to establish a lower bound for the error caused by neglecting stochastic effects. Furthermore, we apply this weak selection limit to investigate the selection dynamics in the presence of a trade-off between the virulence and the transmission rate of a pathogen."}],"publist_id":"5166","publication":"Journal of Theoretical Biology","citation":{"ama":"Humplik J, Hill A, Nowak M. Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. 2014;360:149-162. doi:10.1016/j.jtbi.2014.06.039","ista":"Humplik J, Hill A, Nowak M. 2014. Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. 360, 149–162.","apa":"Humplik, J., Hill, A., & Nowak, M. (2014). Evolutionary dynamics of infectious diseases in finite populations. Journal of Theoretical Biology. Elsevier. https://doi.org/10.1016/j.jtbi.2014.06.039","ieee":"J. Humplik, A. Hill, and M. Nowak, “Evolutionary dynamics of infectious diseases in finite populations,” Journal of Theoretical Biology, vol. 360. Elsevier, pp. 149–162, 2014.","mla":"Humplik, Jan, et al. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” Journal of Theoretical Biology, vol. 360, Elsevier, 2014, pp. 149–62, doi:10.1016/j.jtbi.2014.06.039.","short":"J. Humplik, A. Hill, M. Nowak, Journal of Theoretical Biology 360 (2014) 149–162.","chicago":"Humplik, Jan, Alison Hill, and Martin Nowak. “Evolutionary Dynamics of Infectious Diseases in Finite Populations.” Journal of Theoretical Biology. Elsevier, 2014. https://doi.org/10.1016/j.jtbi.2014.06.039."},"page":"149 - 162","doi":"10.1016/j.jtbi.2014.06.039","date_published":"2014-11-07T00:00:00Z","language":[{"iso":"eng"}],"scopus_import":1,"month":"11","day":"07"},{"article_number":"57","publist_id":"5163","publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Frontiers Research Foundation","year":"2014","acknowledgement":"Supported in part by EC MEXT project PLICON and the LOEWE-Program “Neuronal Coordination Research Focus Frankfurt” (NeFF). Jochen Triesch was supported by the Quandt foundation.","date_created":"2018-12-11T11:54:46Z","date_updated":"2021-01-12T06:54:09Z","volume":8,"author":[{"full_name":"Savin, Cristina","first_name":"Cristina","last_name":"Savin","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Triesch, Jochen","first_name":"Jochen","last_name":"Triesch"}],"month":"05","quality_controlled":"1","main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4035833/"}],"oa":1,"language":[{"iso":"eng"}],"doi":"10.3389/fncom.2014.00057","type":"journal_article","abstract":[{"text":"A wealth of experimental evidence suggests that working memory circuits preferentially represent information that is behaviorally relevant. Still, we are missing a mechanistic account of how these representations come about. Here we provide a simple explanation for a range of experimental findings, in light of prefrontal circuits adapting to task constraints by reward-dependent learning. In particular, we model a neural network shaped by reward-modulated spike-timing dependent plasticity (r-STDP) and homeostatic plasticity (intrinsic excitability and synaptic scaling). We show that the experimentally-observed neural representations naturally emerge in an initially unstructured circuit as it learns to solve several working memory tasks. These results point to a critical, and previously unappreciated, role for reward-dependent learning in shaping prefrontal cortex activity.","lang":"eng"}],"issue":"MAY","status":"public","title":"Emergence of task-dependent representations in working memory circuits","intvolume":" 8","_id":"1931","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","oa_version":"Submitted Version","scopus_import":1,"day":"28","publication":"Frontiers in Computational Neuroscience","citation":{"ama":"Savin C, Triesch J. Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. 2014;8(MAY). doi:10.3389/fncom.2014.00057","ista":"Savin C, Triesch J. 2014. Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. 8(MAY), 57.","apa":"Savin, C., & Triesch, J. (2014). Emergence of task-dependent representations in working memory circuits. Frontiers in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2014.00057","ieee":"C. Savin and J. Triesch, “Emergence of task-dependent representations in working memory circuits,” Frontiers in Computational Neuroscience, vol. 8, no. MAY. Frontiers Research Foundation, 2014.","mla":"Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations in Working Memory Circuits.” Frontiers in Computational Neuroscience, vol. 8, no. MAY, 57, Frontiers Research Foundation, 2014, doi:10.3389/fncom.2014.00057.","short":"C. Savin, J. Triesch, Frontiers in Computational Neuroscience 8 (2014).","chicago":"Savin, Cristina, and Jochen Triesch. “Emergence of Task-Dependent Representations in Working Memory Circuits.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2014. https://doi.org/10.3389/fncom.2014.00057."},"date_published":"2014-05-28T00:00:00Z"},{"file_date_updated":"2020-07-14T12:45:25Z","publist_id":"5043","license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","acknowledgement":"This work is supported by AFOSR grant FA 9550-11-1-0165, program grant RPG 24/2012 from the Human Frontiers of Science (DBF) and travel support from the European Commission Marie Curie International Reintegration Grant PIRG04-GA-2008-239429 (KB). DP was supported by NIHR01 GM104987 and the Wyss Institute of Biologically Inspired Engineering. ","year":"2014","publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Academic Press","author":[{"full_name":"Bodova, Katarina","first_name":"Katarina","last_name":"Bodova","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-7214-0171"},{"full_name":"Paydarfar, David","first_name":"David","last_name":"Paydarfar"},{"last_name":"Forger","first_name":"Daniel","full_name":"Forger, Daniel"}],"related_material":{"link":[{"url":"https://doi.org/10.1016/j.jtbi.2015.03.013","relation":"erratum"}]},"date_updated":"2022-08-25T14:00:47Z","date_created":"2018-12-11T11:55:18Z","volume":365,"month":"10","oa":1,"tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","short":"CC BY-NC-ND (4.0)","image":"/images/cc_by_nc_nd.png"},"quality_controlled":"1","doi":"10.1016/j.jtbi.2014.09.041","language":[{"iso":"eng"}],"type":"journal_article","abstract":[{"text":"Understanding the dynamics of noisy neurons remains an important challenge in neuroscience. Here, we describe a simple probabilistic model that accurately describes the firing behavior in a large class (type II) of neurons. To demonstrate the usefulness of this model, we show how it accurately predicts the interspike interval (ISI) distributions, bursting patterns and mean firing rates found by: (1) simulations of the classic Hodgkin-Huxley model with channel noise, (2) experimental data from squid giant axon with a noisy input current and (3) experimental data on noisy firing from a neuron within the suprachiasmatic nucleus (SCN). This simple model has 6 parameters, however, in some cases, two of these parameters are coupled and only 5 parameters account for much of the known behavior. From these parameters, many properties of spiking can be found through simple calculation. Thus, we show how the complex effects of noise can be understood through a simple and general probabilistic model.","lang":"eng"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"2028","ddc":["570"],"status":"public","title":"Characterizing spiking in noisy type II neurons","intvolume":" 365","pubrep_id":"444","oa_version":"Published Version","file":[{"relation":"main_file","file_id":"5316","checksum":"a9dbae18d3233b3dab6944fd3f2cd49e","date_created":"2018-12-12T10:17:58Z","date_updated":"2020-07-14T12:45:25Z","access_level":"open_access","file_name":"IST-2016-444-v1+1_1-s2.0-S0022519314005888-main.pdf","file_size":2679222,"content_type":"application/pdf","creator":"system"}],"scopus_import":"1","day":"12","article_processing_charge":"No","has_accepted_license":"1","publication":" Journal of Theoretical Biology","citation":{"short":"K. Bodova, D. Paydarfar, D. Forger, Journal of Theoretical Biology 365 (2014) 40–54.","mla":"Bodova, Katarina, et al. “Characterizing Spiking in Noisy Type II Neurons.” Journal of Theoretical Biology, vol. 365, Academic Press, 2014, pp. 40–54, doi:10.1016/j.jtbi.2014.09.041.","chicago":"Bodova, Katarina, David Paydarfar, and Daniel Forger. “Characterizing Spiking in Noisy Type II Neurons.” Journal of Theoretical Biology. Academic Press, 2014. https://doi.org/10.1016/j.jtbi.2014.09.041.","ama":"Bodova K, Paydarfar D, Forger D. Characterizing spiking in noisy type II neurons. Journal of Theoretical Biology. 2014;365:40-54. doi:10.1016/j.jtbi.2014.09.041","apa":"Bodova, K., Paydarfar, D., & Forger, D. (2014). Characterizing spiking in noisy type II neurons. Journal of Theoretical Biology. Academic Press. https://doi.org/10.1016/j.jtbi.2014.09.041","ieee":"K. Bodova, D. Paydarfar, and D. Forger, “Characterizing spiking in noisy type II neurons,” Journal of Theoretical Biology, vol. 365. Academic Press, pp. 40–54, 2014.","ista":"Bodova K, Paydarfar D, Forger D. 2014. Characterizing spiking in noisy type II neurons. Journal of Theoretical Biology. 365, 40–54."},"page":"40 - 54","date_published":"2014-10-12T00:00:00Z"},{"date_published":"2014-06-16T00:00:00Z","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","citation":{"ista":"Botella Soler V, Glendinning P. 2014. Hierarchy and polysynchrony in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter Physics. 89(6), 062809.","ieee":"V. Botella Soler and P. Glendinning, “Hierarchy and polysynchrony in an adaptive network ,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 89, no. 6. American Institute of Physics, 2014.","apa":"Botella Soler, V., & Glendinning, P. (2014). Hierarchy and polysynchrony in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.89.062809","ama":"Botella Soler V, Glendinning P. Hierarchy and polysynchrony in an adaptive network . Physical Review E Statistical Nonlinear and Soft Matter Physics. 2014;89(6). doi:10.1103/PhysRevE.89.062809","chicago":"Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony in an Adaptive Network .” Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics, 2014. https://doi.org/10.1103/PhysRevE.89.062809.","mla":"Botella Soler, Vicente, and Paul Glendinning. “Hierarchy and Polysynchrony in an Adaptive Network .” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 89, no. 6, 062809, American Institute of Physics, 2014, doi:10.1103/PhysRevE.89.062809.","short":"V. Botella Soler, P. Glendinning, Physical Review E Statistical Nonlinear and Soft Matter Physics 89 (2014)."},"day":"16","article_processing_charge":"No","scopus_import":"1","oa_version":"Preprint","title":"Hierarchy and polysynchrony in an adaptive network ","status":"public","intvolume":" 89","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","_id":"2183","abstract":[{"text":"We describe a simple adaptive network of coupled chaotic maps. The network reaches a stationary state (frozen topology) for all values of the coupling parameter, although the dynamics of the maps at the nodes of the network can be nontrivial. The structure of the network shows interesting hierarchical properties and in certain parameter regions the dynamics is polysynchronous: Nodes can be divided in differently synchronized classes but, contrary to cluster synchronization, nodes in the same class need not be connected to each other. These complicated synchrony patterns have been conjectured to play roles in systems biology and circuits. The adaptive system we study describes ways whereby this behavior can evolve from undifferentiated nodes.","lang":"eng"}],"issue":"6","type":"journal_article","language":[{"iso":"eng"}],"doi":"10.1103/PhysRevE.89.062809","quality_controlled":"1","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1403.3209"}],"oa":1,"month":"06","date_updated":"2022-08-25T14:04:45Z","date_created":"2018-12-11T11:56:11Z","volume":89,"author":[{"id":"421234E8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8790-1914","first_name":"Vicente","last_name":"Botella Soler","full_name":"Botella Soler, Vicente"},{"full_name":"Glendinning, Paul","first_name":"Paul","last_name":"Glendinning"}],"publication_status":"published","publisher":"American Institute of Physics","department":[{"_id":"GaTk"}],"year":"2014","acknowledgement":"V.B.S. is partially supported by contract MEC (Grant No. AYA2010-22111-C03-02).\r\n","ec_funded":1,"publist_id":"4798","article_number":"062809"},{"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4026790/","open_access":"1"}],"oa":1,"external_id":{"pmid":["24606943"]},"quality_controlled":"1","doi":"10.1016/j.bpj.2014.01.014","language":[{"iso":"eng"}],"month":"03","publication_identifier":{"issn":["00063495"]},"year":"2014","pmid":1,"publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Biophysical Society","author":[{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","last_name":"Rieckh","first_name":"Georg","full_name":"Rieckh, Georg"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper"}],"date_created":"2018-12-11T11:56:28Z","date_updated":"2021-01-12T06:56:10Z","volume":106,"publist_id":"4730","publication":"Biophysical Journal","citation":{"short":"G. Rieckh, G. Tkačik, Biophysical Journal 106 (2014) 1194–1204.","mla":"Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in Promoters with Multiple Internal States.” Biophysical Journal, vol. 106, no. 5, Biophysical Society, 2014, pp. 1194–204, doi:10.1016/j.bpj.2014.01.014.","chicago":"Rieckh, Georg, and Gašper Tkačik. “Noise and Information Transmission in Promoters with Multiple Internal States.” Biophysical Journal. Biophysical Society, 2014. https://doi.org/10.1016/j.bpj.2014.01.014.","ama":"Rieckh G, Tkačik G. Noise and information transmission in promoters with multiple internal states. Biophysical Journal. 2014;106(5):1194-1204. doi:10.1016/j.bpj.2014.01.014","apa":"Rieckh, G., & Tkačik, G. (2014). Noise and information transmission in promoters with multiple internal states. Biophysical Journal. Biophysical Society. https://doi.org/10.1016/j.bpj.2014.01.014","ieee":"G. Rieckh and G. Tkačik, “Noise and information transmission in promoters with multiple internal states,” Biophysical Journal, vol. 106, no. 5. Biophysical Society, pp. 1194–1204, 2014.","ista":"Rieckh G, Tkačik G. 2014. Noise and information transmission in promoters with multiple internal states. Biophysical Journal. 106(5), 1194–1204."},"page":"1194 - 1204","date_published":"2014-03-04T00:00:00Z","scopus_import":1,"day":"04","_id":"2231","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Noise and information transmission in promoters with multiple internal states","status":"public","intvolume":" 106","oa_version":"Submitted Version","type":"journal_article","abstract":[{"lang":"eng","text":"Based on the measurements of noise in gene expression performed during the past decade, it has become customary to think of gene regulation in terms of a two-state model, where the promoter of a gene can stochastically switch between an ON and an OFF state. As experiments are becoming increasingly precise and the deviations from the two-state model start to be observable, we ask about the experimental signatures of complex multistate promoters, as well as the functional consequences of this additional complexity. In detail, we i), extend the calculations for noise in gene expression to promoters described by state transition diagrams with multiple states, ii), systematically compute the experimentally accessible noise characteristics for these complex promoters, and iii), use information theory to evaluate the channel capacities of complex promoter architectures and compare them with the baseline provided by the two-state model. We find that adding internal states to the promoter generically decreases channel capacity, except in certain cases, three of which (cooperativity, dual-role regulation, promoter cycling) we analyze in detail."}],"issue":"5"},{"date_published":"2014-01-21T00:00:00Z","citation":{"ista":"Tkačik G, Ghosh A, Schneidman E, Segev R. 2014. Adaptation to changes in higher-order stimulus statistics in the salamander retina. PLoS One. 9(1), e85841.","ieee":"G. Tkačik, A. Ghosh, E. Schneidman, and R. Segev, “Adaptation to changes in higher-order stimulus statistics in the salamander retina,” PLoS One, vol. 9, no. 1. Public Library of Science, 2014.","apa":"Tkačik, G., Ghosh, A., Schneidman, E., & Segev, R. (2014). Adaptation to changes in higher-order stimulus statistics in the salamander retina. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0085841","ama":"Tkačik G, Ghosh A, Schneidman E, Segev R. Adaptation to changes in higher-order stimulus statistics in the salamander retina. PLoS One. 2014;9(1). doi:10.1371/journal.pone.0085841","chicago":"Tkačik, Gašper, Anandamohan Ghosh, Elad Schneidman, and Ronen Segev. “Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” PLoS One. Public Library of Science, 2014. https://doi.org/10.1371/journal.pone.0085841.","mla":"Tkačik, Gašper, et al. “Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina.” PLoS One, vol. 9, no. 1, e85841, Public Library of Science, 2014, doi:10.1371/journal.pone.0085841.","short":"G. Tkačik, A. Ghosh, E. Schneidman, R. Segev, PLoS One 9 (2014)."},"publication":"PLoS One","has_accepted_license":"1","day":"21","scopus_import":1,"oa_version":"Published Version","file":[{"file_name":"IST-2016-432-v1+1_journal.pone.0085841.pdf","access_level":"open_access","file_size":1568524,"content_type":"application/pdf","creator":"system","relation":"main_file","file_id":"5011","date_created":"2018-12-12T10:13:28Z","date_updated":"2020-07-14T12:46:06Z","checksum":"1d5816b343abe5eadc3eb419bcece971"}],"pubrep_id":"432","intvolume":" 9","status":"public","title":"Adaptation to changes in higher-order stimulus statistics in the salamander retina","ddc":["570"],"_id":"3263","user_id":"3FFCCD3A-F248-11E8-B48F-1D18A9856A87","issue":"1","abstract":[{"lang":"eng","text":"Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. While adaptive changes in retinal processing to the variations of the mean luminance level and second-order stimulus statistics have been documented before, no such measurements have been performed when higher-order moments of the light distribution change. We therefore measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the encoding properties of retinal ganglion cells change only marginally when higher-order statistics change, compared to the changes observed in response to the variation in contrast. By analyzing optimal coding in LN-type models, we showed that neurons can maintain a high information rate without large dynamic adaptation to changes in skew or kurtosis. This is because, for uncorrelated stimuli, spatio-temporal summation within the receptive field averages away non-gaussian aspects of the light intensity distribution."}],"type":"journal_article","language":[{"iso":"eng"}],"doi":"10.1371/journal.pone.0085841","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","volume":9,"date_created":"2018-12-11T12:02:20Z","date_updated":"2021-01-12T07:42:14Z","author":[{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper"},{"full_name":"Ghosh, Anandamohan","first_name":"Anandamohan","last_name":"Ghosh"},{"last_name":"Schneidman","first_name":"Elad","full_name":"Schneidman, Elad"},{"full_name":"Segev, Ronen","last_name":"Segev","first_name":"Ronen"}],"publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"publication_status":"published","acknowledgement":"This work was supported by The Israel Science Foundation and The Human Frontiers Science Program.\r\nWe thank the referees for helping significantly improve this paper. We also thank Vijay Balasubramanian, Kristina Simmons, and Jason Prentice for stimulating discussions. GT wishes to thank the faculty and students of the “Methods in Computational Neuroscience” course at Marine Biological Laboratory, Woods Hole.\r\n","year":"2014","publist_id":"3385","file_date_updated":"2020-07-14T12:46:06Z","article_number":"e85841"},{"doi":"10.1002/ece3.1150","language":[{"iso":"eng"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"month":"07","author":[{"id":"4456104E-F248-11E8-B48F-1D18A9856A87","first_name":"Roshan","last_name":"Prizak","full_name":"Prizak, Roshan"},{"full_name":"Ezard, Thomas","first_name":"Thomas","last_name":"Ezard"},{"first_name":"Rebecca","last_name":"Hoyle","full_name":"Hoyle, Rebecca"}],"volume":4,"date_updated":"2021-01-12T08:01:30Z","date_created":"2018-12-11T11:47:02Z","year":"2014","publisher":"Wiley-Blackwell","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publication_status":"published","publist_id":"7280","file_date_updated":"2020-07-14T12:46:38Z","date_published":"2014-07-19T00:00:00Z","citation":{"ama":"Prizak R, Ezard T, Hoyle R. Fitness consequences of maternal and grandmaternal effects. Ecology and Evolution. 2014;4(15):3139-3145. doi:10.1002/ece3.1150","ista":"Prizak R, Ezard T, Hoyle R. 2014. Fitness consequences of maternal and grandmaternal effects. Ecology and Evolution. 4(15), 3139–3145.","ieee":"R. Prizak, T. Ezard, and R. Hoyle, “Fitness consequences of maternal and grandmaternal effects,” Ecology and Evolution, vol. 4, no. 15. Wiley-Blackwell, pp. 3139–3145, 2014.","apa":"Prizak, R., Ezard, T., & Hoyle, R. (2014). Fitness consequences of maternal and grandmaternal effects. Ecology and Evolution. Wiley-Blackwell. https://doi.org/10.1002/ece3.1150","mla":"Prizak, Roshan, et al. “Fitness Consequences of Maternal and Grandmaternal Effects.” Ecology and Evolution, vol. 4, no. 15, Wiley-Blackwell, 2014, pp. 3139–45, doi:10.1002/ece3.1150.","short":"R. Prizak, T. Ezard, R. Hoyle, Ecology and Evolution 4 (2014) 3139–3145.","chicago":"Prizak, Roshan, Thomas Ezard, and Rebecca Hoyle. “Fitness Consequences of Maternal and Grandmaternal Effects.” Ecology and Evolution. Wiley-Blackwell, 2014. https://doi.org/10.1002/ece3.1150."},"publication":"Ecology and Evolution","page":"3139 - 3145","has_accepted_license":"1","day":"19","scopus_import":1,"pubrep_id":"934","oa_version":"Published Version","file":[{"file_id":"4886","relation":"main_file","checksum":"e32abf75a248e7a11811fd7f60858769","date_created":"2018-12-12T10:11:31Z","date_updated":"2020-07-14T12:46:38Z","access_level":"open_access","file_name":"IST-2018-934-v1+1_Prizak_et_al-2014-Ecology_and_Evolution.pdf","creator":"system","content_type":"application/pdf","file_size":621582}],"_id":"537","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","intvolume":" 4","title":"Fitness consequences of maternal and grandmaternal effects","ddc":["530","571"],"status":"public","issue":"15","abstract":[{"lang":"eng","text":"Transgenerational effects are broader than only parental relationships. Despite mounting evidence that multigenerational effects alter phenotypic and life-history traits, our understanding of how they combine to determine fitness is not well developed because of the added complexity necessary to study them. Here, we derive a quantitative genetic model of adaptation to an extraordinary new environment by an additive genetic component, phenotypic plasticity, maternal and grandmaternal effects. We show how, at equilibrium, negative maternal and negative grandmaternal effects maximize expected population mean fitness. We define negative transgenerational effects as those that have a negative effect on trait expression in the subsequent generation, that is, they slow, or potentially reverse, the expected evolutionary dynamic. When maternal effects are positive, negative grandmaternal effects are preferred. As expected under Mendelian inheritance, the grandmaternal effects have a lower impact on fitness than the maternal effects, but this dual inheritance model predicts a more complex relationship between maternal and grandmaternal effects to constrain phenotypic variance and so maximize expected population mean fitness in the offspring."}],"type":"journal_article"},{"date_published":"2014-11-07T00:00:00Z","doi":"10.5061/dryad.246qg","oa":1,"citation":{"ista":"Simmons K, Prentice J, Tkačik G, Homann J, Yee H, Palmer S, Nelson P, Balasubramanian V. 2014. Data from: Transformation of stimulus correlations by the retina, Dryad, 10.5061/dryad.246qg.","apa":"Simmons, K., Prentice, J., Tkačik, G., Homann, J., Yee, H., Palmer, S., … Balasubramanian, V. (2014). Data from: Transformation of stimulus correlations by the retina. Dryad. https://doi.org/10.5061/dryad.246qg","ieee":"K. Simmons et al., “Data from: Transformation of stimulus correlations by the retina.” Dryad, 2014.","ama":"Simmons K, Prentice J, Tkačik G, et al. Data from: Transformation of stimulus correlations by the retina. 2014. doi:10.5061/dryad.246qg","chicago":"Simmons, Kristina, Jason Prentice, Gašper Tkačik, Jan Homann, Heather Yee, Stephanie Palmer, Philip Nelson, and Vijay Balasubramanian. “Data from: Transformation of Stimulus Correlations by the Retina.” Dryad, 2014. https://doi.org/10.5061/dryad.246qg.","mla":"Simmons, Kristina, et al. Data from: Transformation of Stimulus Correlations by the Retina. Dryad, 2014, doi:10.5061/dryad.246qg.","short":"K. Simmons, J. Prentice, G. Tkačik, J. Homann, H. Yee, S. Palmer, P. Nelson, V. Balasubramanian, (2014)."},"main_file_link":[{"open_access":"1","url":"https://doi.org/10.5061/dryad.246qg"}],"month":"11","day":"07","article_processing_charge":"No","date_updated":"2023-02-23T10:35:57Z","date_created":"2021-07-30T08:13:52Z","oa_version":"Published Version","author":[{"first_name":"Kristina","last_name":"Simmons","full_name":"Simmons, Kristina"},{"full_name":"Prentice, Jason","first_name":"Jason","last_name":"Prentice"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper"},{"first_name":"Jan","last_name":"Homann","full_name":"Homann, Jan"},{"last_name":"Yee","first_name":"Heather","full_name":"Yee, Heather"},{"last_name":"Palmer","first_name":"Stephanie","full_name":"Palmer, Stephanie"},{"first_name":"Philip","last_name":"Nelson","full_name":"Nelson, Philip"},{"full_name":"Balasubramanian, Vijay","first_name":"Vijay","last_name":"Balasubramanian"}],"related_material":{"record":[{"id":"2277","status":"public","relation":"used_in_publication"}]},"title":"Data from: Transformation of stimulus correlations by the retina","status":"public","department":[{"_id":"GaTk"}],"publisher":"Dryad","_id":"9752","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","year":"2014","abstract":[{"lang":"eng","text":"Redundancies and correlations in the responses of sensory neurons may seem to waste neural resources, but they can also carry cues about structured stimuli and may help the brain to correct for response errors. To investigate the effect of stimulus structure on redundancy in retina, we measured simultaneous responses from populations of retinal ganglion cells presented with natural and artificial stimuli that varied greatly in correlation structure; these stimuli and recordings are publicly available online. Responding to spatio-temporally structured stimuli such as natural movies, pairs of ganglion cells were modestly more correlated than in response to white noise checkerboards, but they were much less correlated than predicted by a non-adapting functional model of retinal response. Meanwhile, responding to stimuli with purely spatial correlations, pairs of ganglion cells showed increased correlations consistent with a static, non-adapting receptive field and nonlinearity. We found that in response to spatio-temporally correlated stimuli, ganglion cells had faster temporal kernels and tended to have stronger surrounds. These properties of individual cells, along with gain changes that opposed changes in effective contrast at the ganglion cell input, largely explained the pattern of pairwise correlations across stimuli where receptive field measurements were possible."}],"type":"research_data_reference"},{"doi":"10.1371/journal.pcbi.1003408","language":[{"iso":"eng"}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"main_file_link":[{"url":"http://repository.ist.ac.at/id/eprint/436","open_access":"1"}],"oa":1,"quality_controlled":"1","publication_identifier":{"issn":["1553734X"]},"month":"01","related_material":{"record":[{"status":"public","relation":"popular_science","id":"5562"}]},"author":[{"first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"first_name":"Dario","last_name":"Amodei","full_name":"Amodei, Dario"},{"full_name":"Schneidman, Elad","last_name":"Schneidman","first_name":"Elad"},{"full_name":"Bialek, William","first_name":"William","last_name":"Bialek"},{"full_name":"Berry, Michael","first_name":"Michael","last_name":"Berry"}],"volume":10,"date_updated":"2024-02-21T13:46:14Z","date_created":"2018-12-11T11:56:36Z","year":"2014","acknowledgement":"\r\n\r\n\r\n\r\nThis work was funded by NSF grant IIS-0613435, NSF grant PHY-0957573, NSF grant CCF-0939370, NIH grant R01 EY14196, NIH grant P50 GM071508, the Fannie and John Hertz Foundation, the Swartz Foundation, the WM Keck Foundation, ANR Optima and the French State program “Investissements d'Avenir” [LIFESENSES: ANR-10-LABX-65], and the Austrian Research Foundation FWF P25651.","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"publication_status":"published","publist_id":"4689","file_date_updated":"2020-07-14T12:45:35Z","article_number":"e1003408","date_published":"2014-01-02T00:00:00Z","citation":{"apa":"Tkačik, G., Marre, O., Amodei, D., Schneidman, E., Bialek, W., & Berry, M. (2014). Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1003408","ieee":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, and M. Berry, “Searching for collective behavior in a large network of sensory neurons,” PLoS Computational Biology, vol. 10, no. 1. Public Library of Science, 2014.","ista":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. 2014. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 10(1), e1003408.","ama":"Tkačik G, Marre O, Amodei D, Schneidman E, Bialek W, Berry M. Searching for collective behavior in a large network of sensory neurons. PLoS Computational Biology. 2014;10(1). doi:10.1371/journal.pcbi.1003408","chicago":"Tkačik, Gašper, Olivier Marre, Dario Amodei, Elad Schneidman, William Bialek, and Michael Berry. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” PLoS Computational Biology. Public Library of Science, 2014. https://doi.org/10.1371/journal.pcbi.1003408.","short":"G. Tkačik, O. Marre, D. Amodei, E. Schneidman, W. Bialek, M. Berry, PLoS Computational Biology 10 (2014).","mla":"Tkačik, Gašper, et al. “Searching for Collective Behavior in a Large Network of Sensory Neurons.” PLoS Computational Biology, vol. 10, no. 1, e1003408, Public Library of Science, 2014, doi:10.1371/journal.pcbi.1003408."},"publication":"PLoS Computational Biology","has_accepted_license":"1","day":"02","scopus_import":1,"pubrep_id":"436","file":[{"date_created":"2018-12-12T10:12:46Z","date_updated":"2020-07-14T12:45:35Z","checksum":"c720222c5e924a4acb17f23b9381a6ca","file_id":"4965","relation":"main_file","creator":"system","content_type":"application/pdf","file_size":2194790,"file_name":"IST-2016-436-v1+1_journal.pcbi.1003408.pdf","access_level":"open_access"}],"oa_version":"Published Version","user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","_id":"2257","intvolume":" 10","status":"public","ddc":["570"],"title":"Searching for collective behavior in a large network of sensory neurons","issue":"1","abstract":[{"lang":"eng","text":"Maximum entropy models are the least structured probability distributions that exactly reproduce a chosen set of statistics measured in an interacting network. Here we use this principle to construct probabilistic models which describe the correlated spiking activity of populations of up to 120 neurons in the salamander retina as it responds to natural movies. Already in groups as small as 10 neurons, interactions between spikes can no longer be regarded as small perturbations in an otherwise independent system; for 40 or more neurons pairwise interactions need to be supplemented by a global interaction that controls the distribution of synchrony in the population. Here we show that such “K-pairwise” models—being systematic extensions of the previously used pairwise Ising models—provide an excellent account of the data. We explore the properties of the neural vocabulary by: 1) estimating its entropy, which constrains the population's capacity to represent visual information; 2) classifying activity patterns into a small set of metastable collective modes; 3) showing that the neural codeword ensembles are extremely inhomogenous; 4) demonstrating that the state of individual neurons is highly predictable from the rest of the population, allowing the capacity for error correction."}],"type":"journal_article"},{"_id":"2413","year":"2013","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","title":"Neuronal oscillations scale up and scale down the brain dynamics ","status":"public","publication_status":"published","editor":[{"full_name":"Meyer, Misha","last_name":"Meyer","first_name":"Misha"},{"last_name":"Pesenson","first_name":"Z.","full_name":"Pesenson, Z."}],"department":[{"_id":"GaTk"}],"publisher":"Wiley-VCH","author":[{"full_name":"Valderrama, Mario","first_name":"Mario","last_name":"Valderrama"},{"full_name":"Botella Soler, Vicente","orcid":"0000-0002-8790-1914","id":"421234E8-F248-11E8-B48F-1D18A9856A87","last_name":"Botella Soler","first_name":"Vicente"},{"last_name":"Le Van Quyen","first_name":"Michel","full_name":"Le Van Quyen, Michel"}],"date_updated":"2021-01-12T06:57:20Z","date_created":"2018-12-11T11:57:31Z","oa_version":"None","type":"book_chapter","alternative_title":["Reviews of Nonlinear Dynamics and Complexity"],"abstract":[{"lang":"eng","text":"Progress in understanding the global brain dynamics has remained slow to date in large part because of the highly multiscale nature of brain activity. Indeed, normal brain dynamics is characterized by complex interactions between multiple levels: from the microscopic scale of single neurons to the mesoscopic level of local groups of neurons, and finally to the macroscopic level of the whole brain. Among the most difficult tasks are those of identifying which scales are significant for a given particular function and describing how the scales affect each other. It is important to realize that the scales of time and space are linked together, or even intertwined, and that causal inference is far more ambiguous between than within levels. We approach this problem from the perspective of our recent work on simultaneous recording from micro- and macroelectrodes in the human brain. We propose a physiological description of these multilevel interactions, based on phase–amplitude coupling of neuronal oscillations that operate at multiple frequencies and on different spatial scales. Specifically, the amplitude of the oscillations on a particular spatial scale is modulated by phasic variations in neuronal excitability induced by lower frequency oscillations that emerge on a larger spatial scale. Following this general principle, it is possible to scale up or scale down the multiscale brain dynamics. It is expected that large-scale network oscillations in the low-frequency range, mediating downward effects, may play an important role in attention and consciousness."}],"publist_id":"4513","publication":"Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain","citation":{"mla":"Valderrama, Mario, et al. “Neuronal Oscillations Scale up and Scale down the Brain Dynamics .” Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, edited by Misha Meyer and Z. Pesenson, Wiley-VCH, 2013, doi:10.1002/9783527671632.ch08.","short":"M. Valderrama, V. Botella Soler, M. Le Van Quyen, in:, M. Meyer, Z. Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, Wiley-VCH, 2013.","chicago":"Valderrama, Mario, Vicente Botella Soler, and Michel Le Van Quyen. “Neuronal Oscillations Scale up and Scale down the Brain Dynamics .” In Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, edited by Misha Meyer and Z. Pesenson. Wiley-VCH, 2013. https://doi.org/10.1002/9783527671632.ch08.","ama":"Valderrama M, Botella Soler V, Le Van Quyen M. Neuronal oscillations scale up and scale down the brain dynamics . In: Meyer M, Pesenson Z, eds. Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH; 2013. doi:10.1002/9783527671632.ch08","ista":"Valderrama M, Botella Soler V, Le Van Quyen M. 2013.Neuronal oscillations scale up and scale down the brain dynamics . In: Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Reviews of Nonlinear Dynamics and Complexity, .","apa":"Valderrama, M., Botella Soler, V., & Le Van Quyen, M. (2013). Neuronal oscillations scale up and scale down the brain dynamics . In M. Meyer & Z. Pesenson (Eds.), Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain. Wiley-VCH. https://doi.org/10.1002/9783527671632.ch08","ieee":"M. Valderrama, V. Botella Soler, and M. Le Van Quyen, “Neuronal oscillations scale up and scale down the brain dynamics ,” in Multiscale Analysis and Nonlinear Dynamics: From Genes to the Brain, M. Meyer and Z. Pesenson, Eds. Wiley-VCH, 2013."},"quality_controlled":"1","date_published":"2013-08-01T00:00:00Z","doi":"10.1002/9783527671632.ch08","language":[{"iso":"eng"}],"scopus_import":1,"month":"08","day":"01","publication_identifier":{"isbn":["9783527411986 "],"eisbn":["9783527671632"]}}]