[{"language":[{"iso":"eng"}],"article_type":"original","ec_funded":1,"date_published":"2018-05-10T00:00:00Z","volume":14,"has_accepted_license":"1","file_date_updated":"2020-07-14T12:45:53Z","project":[{"call_identifier":"H2020","grant_number":"720270","_id":"25CBA828-B435-11E9-9278-68D0E5697425","name":"Human Brain Project Specific Grant Agreement 1"},{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"month":"05","license":"https://creativecommons.org/licenses/by/4.0/","doi":"10.1371/journal.pcbi.1006057","scopus_import":"1","citation":{"ama":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. <i>PLoS Computational Biology</i>. 2018;14(5). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">10.1371/journal.pcbi.1006057</a>","apa":"Botella Soler, V., Deny, S., Martius, G. S., Marre, O., &#38; Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">https://doi.org/10.1371/journal.pcbi.1006057</a>","short":"V. Botella Soler, S. Deny, G.S. Martius, O. Marre, G. Tkačik, PLoS Computational Biology 14 (2018).","ista":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. 2018. Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. 14(5), e1006057.","chicago":"Botella Soler, Vicente, Stephane Deny, Georg S Martius, Olivier Marre, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” <i>PLoS Computational Biology</i>. Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">https://doi.org/10.1371/journal.pcbi.1006057</a>.","ieee":"V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina,” <i>PLoS Computational Biology</i>, vol. 14, no. 5. Public Library of Science, 2018.","mla":"Botella Soler, Vicente, et al. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” <i>PLoS Computational Biology</i>, vol. 14, no. 5, e1006057, Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">10.1371/journal.pcbi.1006057</a>."},"article_number":"e1006057","abstract":[{"text":"Retina is a paradigmatic system for studying sensory encoding: the transformation of light into spiking activity of ganglion cells. The inverse problem, where stimulus is reconstructed from spikes, has received less attention, especially for complex stimuli that should be reconstructed “pixel-by-pixel”. We recorded around a hundred neurons from a dense patch in a rat retina and decoded movies of multiple small randomly-moving discs. We constructed nonlinear (kernelized and neural network) decoders that improved significantly over linear results. An important contribution to this was the ability of nonlinear decoders to reliably separate between neural responses driven by locally fluctuating light signals, and responses at locally constant light driven by spontaneous-like activity. This improvement crucially depended on the precise, non-Poisson temporal structure of individual spike trains, which originated in the spike-history dependence of neural responses. We propose a general principle by which downstream circuitry could discriminate between spontaneous and stimulus-driven activity based solely on higher-order statistical structure in the incoming spike trains.","lang":"eng"}],"oa_version":"Published Version","author":[{"last_name":"Botella Soler","first_name":"Vicent","orcid":"0000-0002-8790-1914","id":"421234E8-F248-11E8-B48F-1D18A9856A87","full_name":"Botella Soler, Vicent"},{"full_name":"Deny, Stephane","last_name":"Deny","first_name":"Stephane"},{"full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"}],"title":"Nonlinear decoding of a complex movie from the mammalian retina","related_material":{"link":[{"url":"https://ist.ac.at/en/news/video-of-moving-discs-reconstructed-from-rat-retinal-neuron-signals/","relation":"press_release","description":"News on IST Homepage"}],"record":[{"relation":"research_data","status":"public","id":"5584"}]},"year":"2018","date_updated":"2025-04-15T08:18:24Z","type":"journal_article","status":"public","publication":"PLoS Computational Biology","file":[{"file_name":"2018_Plos_Botella_Soler.pdf","checksum":"3026f94d235219e15514505fdbadf34e","date_created":"2019-02-13T11:07:15Z","file_size":3460786,"relation":"main_file","file_id":"5974","creator":"dernst","access_level":"open_access","content_type":"application/pdf","date_updated":"2020-07-14T12:45:53Z"}],"oa":1,"department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","isi":1,"article_processing_charge":"Yes","_id":"292","external_id":{"isi":["000434012100002"]},"quality_controlled":"1","ddc":["570"],"date_created":"2018-12-11T11:45:39Z","day":"10","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"},"issue":"5","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","intvolume":"        14","publication_status":"published"},{"acknowledgement":"This work was financially supported by FP7 of the EU through the project “Body on a chip,” ICT-FET-296257, and the ERC Advanced Grant “NeuroCMOS” (contract 267351), as well as by an individual Ambizione Grant 142440 from the Swiss National Science Foundation for Olivier Frey. The research leading to these results also 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]. We would like to thank Alexander Stettler, ETH Zurich for his expertise and support in the cleanroom, and we acknowledge the Single Cell Unit of D-BSSE, ETH Zurich for assistance in microscopy issues. M.L. is grateful to the members of the Guet and Tkačik groups, IST Austria, for valuable comments and support.","_id":"305","quality_controlled":"1","status":"public","alternative_title":["MIMB"],"publication":"Methods in Molecular Biology","page":"183 - 202","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"Springer","intvolume":"      1771","publication_status":"published","publist_id":"7574","day":"01","date_created":"2018-12-11T11:45:43Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"01","doi":"10.1007/978-1-4939-7792-5_15","scopus_import":1,"language":[{"iso":"eng"}],"ec_funded":1,"date_published":"2018-01-01T00:00:00Z","volume":1771,"project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"title":"Fabrication and operation of microfluidic hanging drop networks","year":"2018","date_updated":"2021-01-12T07:40:42Z","type":"journal_article","citation":{"mla":"Misun, Patrick, et al. “Fabrication and Operation of Microfluidic Hanging Drop Networks.” <i>Methods in Molecular Biology</i>, vol. 1771, Springer, 2018, pp. 183–202, doi:<a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">10.1007/978-1-4939-7792-5_15</a>.","ieee":"P. Misun, A. Birchler, M. Lang, A. Hierlemann, and O. Frey, “Fabrication and operation of microfluidic hanging drop networks,” <i>Methods in Molecular Biology</i>, vol. 1771. Springer, pp. 183–202, 2018.","chicago":"Misun, Patrick, Axel Birchler, Moritz Lang, Andreas Hierlemann, and Olivier Frey. “Fabrication and Operation of Microfluidic Hanging Drop Networks.” <i>Methods in Molecular Biology</i>. Springer, 2018. <a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">https://doi.org/10.1007/978-1-4939-7792-5_15</a>.","ista":"Misun P, Birchler A, Lang M, Hierlemann A, Frey O. 2018. Fabrication and operation of microfluidic hanging drop networks. Methods in Molecular Biology. 1771, 183–202.","short":"P. Misun, A. Birchler, M. Lang, A. Hierlemann, O. Frey, Methods in Molecular Biology 1771 (2018) 183–202.","apa":"Misun, P., Birchler, A., Lang, M., Hierlemann, A., &#38; Frey, O. (2018). Fabrication and operation of microfluidic hanging drop networks. <i>Methods in Molecular Biology</i>. Springer. <a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">https://doi.org/10.1007/978-1-4939-7792-5_15</a>","ama":"Misun P, Birchler A, Lang M, Hierlemann A, Frey O. Fabrication and operation of microfluidic hanging drop networks. <i>Methods in Molecular Biology</i>. 2018;1771:183-202. doi:<a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">10.1007/978-1-4939-7792-5_15</a>"},"abstract":[{"text":"The hanging-drop network (HDN) is a technology platform based on a completely open microfluidic network at the bottom of an inverted, surface-patterned substrate. The platform is predominantly used for the formation, culturing, and interaction of self-assembled spherical microtissues (spheroids) under precisely controlled flow conditions. Here, we describe design, fabrication, and operation of microfluidic hanging-drop networks.","lang":"eng"}],"oa_version":"None","author":[{"first_name":"Patrick","last_name":"Misun","full_name":"Misun, Patrick"},{"full_name":"Birchler, Axel","first_name":"Axel","last_name":"Birchler"},{"first_name":"Moritz","last_name":"Lang","full_name":"Lang, Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Hierlemann","first_name":"Andreas","full_name":"Hierlemann, Andreas"},{"last_name":"Frey","first_name":"Olivier","full_name":"Frey, Olivier"}]},{"_id":"306","ddc":["530"],"quality_controlled":"1","publication":"Heliyon","status":"public","publisher":"Elsevier","department":[{"_id":"GaTk"}],"file":[{"access_level":"open_access","date_updated":"2020-07-14T12:45:59Z","content_type":"application/pdf","file_id":"5929","relation":"main_file","creator":"dernst","file_name":"2018_Heliyon_DeMartino.pdf","checksum":"67010cf5e3b3e0637c659371714a715a","date_created":"2019-02-06T07:36:24Z","file_size":994490}],"oa":1,"publication_status":"published","intvolume":"         4","issue":"4","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"},"day":"01","date_created":"2018-12-11T11:45:44Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"04","doi":"10.1016/j.heliyon.2018.e00596","scopus_import":1,"language":[{"iso":"eng"}],"has_accepted_license":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"file_date_updated":"2020-07-14T12:45:59Z","corr_author":"1","date_published":"2018-04-01T00:00:00Z","volume":4,"ec_funded":1,"year":"2018","title":"An introduction to the maximum entropy approach and its application to inference problems in biology","type":"journal_article","date_updated":"2024-10-09T20:58:19Z","abstract":[{"text":"A cornerstone of statistical inference, the maximum entropy framework is being increasingly applied to construct descriptive and predictive models of biological systems, especially complex biological networks, from large experimental data sets. Both its broad applicability and the success it obtained in different contexts hinge upon its conceptual simplicity and mathematical soundness. Here we try to concisely review the basic elements of the maximum entropy principle, starting from the notion of ‘entropy’, and describe its usefulness for the analysis of biological systems. As examples, we focus specifically on the problem of reconstructing gene interaction networks from expression data and on recent work attempting to expand our system-level understanding of bacterial metabolism. Finally, we highlight some extensions and potential limitations of the maximum entropy approach, and point to more recent developments that are likely to play a key role in the upcoming challenges of extracting structures and information from increasingly rich, high-throughput biological data.","lang":"eng"}],"citation":{"short":"A. De Martino, D. De Martino, Heliyon 4 (2018).","mla":"De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” <i>Heliyon</i>, vol. 4, no. 4, e00596, Elsevier, 2018, doi:<a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">10.1016/j.heliyon.2018.e00596</a>.","ieee":"A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” <i>Heliyon</i>, vol. 4, no. 4. Elsevier, 2018.","chicago":"De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” <i>Heliyon</i>. Elsevier, 2018. <a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">https://doi.org/10.1016/j.heliyon.2018.e00596</a>.","ista":"De Martino A, De Martino D. 2018. An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. 4(4), e00596.","ama":"De Martino A, De Martino D. An introduction to the maximum entropy approach and its application to inference problems in biology. <i>Heliyon</i>. 2018;4(4). doi:<a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">10.1016/j.heliyon.2018.e00596</a>","apa":"De Martino, A., &#38; De Martino, D. (2018). An introduction to the maximum entropy approach and its application to inference problems in biology. <i>Heliyon</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">https://doi.org/10.1016/j.heliyon.2018.e00596</a>"},"article_number":"e00596","author":[{"full_name":"De Martino, Andrea","first_name":"Andrea","last_name":"De Martino"},{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele","last_name":"De Martino","first_name":"Daniele","orcid":"0000-0002-5214-4706"}],"oa_version":"Published Version"},{"article_processing_charge":"No","_id":"161","isi":1,"ddc":["570"],"quality_controlled":"1","external_id":{"isi":["000440149300021"]},"publication":"Nature Communications","status":"public","oa":1,"file":[{"creator":"dernst","file_id":"5728","relation":"main_file","access_level":"open_access","date_updated":"2020-07-14T12:45:06Z","content_type":"application/pdf","file_name":"2018_NatureComm_DeMartino.pdf","date_created":"2018-12-17T16:44:28Z","file_size":1043205,"checksum":"3ba7ab27b27723c7dcf633e8fc1f8f18"}],"publisher":"Springer Nature","department":[{"_id":"GaTk"},{"_id":"CaGu"}],"intvolume":"         9","publication_status":"published","publist_id":"7760","date_created":"2018-12-11T11:44:57Z","day":"30","issue":"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"},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","month":"07","doi":"10.1038/s41467-018-05417-9","scopus_import":"1","language":[{"iso":"eng"}],"date_published":"2018-07-30T00:00:00Z","volume":9,"ec_funded":1,"project":[{"call_identifier":"FWF","grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425","name":"Biophysics of information processing in gene regulation"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"has_accepted_license":"1","file_date_updated":"2020-07-14T12:45:06Z","related_material":{"record":[{"id":"5587","relation":"popular_science","status":"public"}]},"title":"Statistical mechanics for metabolic networks during steady state growth","year":"2018","date_updated":"2025-04-15T06:50:08Z","type":"journal_article","abstract":[{"lang":"eng","text":"Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells."}],"article_number":"2988","citation":{"ieee":"D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” <i>Nature Communications</i>, vol. 9, no. 1. Springer Nature, 2018.","mla":"De Martino, Daniele, et al. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” <i>Nature Communications</i>, vol. 9, no. 1, 2988, Springer Nature, 2018, doi:<a href=\"https://doi.org/10.1038/s41467-018-05417-9\">10.1038/s41467-018-05417-9</a>.","ista":"De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. 2018. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 9(1), 2988.","chicago":"De Martino, Daniele, Andersson Anna Mc, Tobias Bergmiller, Calin C Guet, and Gašper Tkačik. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” <i>Nature Communications</i>. Springer Nature, 2018. <a href=\"https://doi.org/10.1038/s41467-018-05417-9\">https://doi.org/10.1038/s41467-018-05417-9</a>.","short":"D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkačik, Nature Communications 9 (2018).","apa":"De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., &#38; Tkačik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-018-05417-9\">https://doi.org/10.1038/s41467-018-05417-9</a>","ama":"De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. <i>Nature Communications</i>. 2018;9(1). doi:<a href=\"https://doi.org/10.1038/s41467-018-05417-9\">10.1038/s41467-018-05417-9</a>"},"oa_version":"Published Version","author":[{"last_name":"De Martino","first_name":"Daniele","orcid":"0000-0002-5214-4706","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele"},{"last_name":"Mc","first_name":"Andersson Anna","full_name":"Mc, Andersson Anna"},{"last_name":"Bergmiller","first_name":"Tobias","orcid":"0000-0001-5396-4346","id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","full_name":"Bergmiller, Tobias"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C","last_name":"Guet","orcid":"0000-0001-6220-2052","first_name":"Calin C"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper"}]},{"scopus_import":"1","doi":"10.1093/molbev/msy163","month":"08","date_published":"2018-08-28T00:00:00Z","volume":35,"language":[{"iso":"eng"}],"article_type":"original","date_updated":"2023-10-17T11:51:06Z","type":"journal_article","publication_identifier":{"issn":["0737-4038"]},"title":"Nonoptimal gene expression creates latent potential for antibiotic resistance","year":"2018","oa_version":"Submitted Version","author":[{"full_name":"Palmer, Adam","last_name":"Palmer","first_name":"Adam"},{"first_name":"Remy P","orcid":"0000-0003-0876-3187","last_name":"Chait","full_name":"Chait, Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Kishony, Roy","last_name":"Kishony","first_name":"Roy"}],"citation":{"ama":"Palmer A, Chait RP, Kishony R. Nonoptimal gene expression creates latent potential for antibiotic resistance. <i>Molecular Biology and Evolution</i>. 2018;35(11):2669-2684. doi:<a href=\"https://doi.org/10.1093/molbev/msy163\">10.1093/molbev/msy163</a>","apa":"Palmer, A., Chait, R. P., &#38; Kishony, R. (2018). Nonoptimal gene expression creates latent potential for antibiotic resistance. <i>Molecular Biology and Evolution</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/molbev/msy163\">https://doi.org/10.1093/molbev/msy163</a>","short":"A. Palmer, R.P. Chait, R. Kishony, Molecular Biology and Evolution 35 (2018) 2669–2684.","ista":"Palmer A, Chait RP, Kishony R. 2018. Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. 35(11), 2669–2684.","chicago":"Palmer, Adam, Remy P Chait, and Roy Kishony. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” <i>Molecular Biology and Evolution</i>. Oxford University Press, 2018. <a href=\"https://doi.org/10.1093/molbev/msy163\">https://doi.org/10.1093/molbev/msy163</a>.","mla":"Palmer, Adam, et al. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” <i>Molecular Biology and Evolution</i>, vol. 35, no. 11, Oxford University Press, 2018, pp. 2669–84, doi:<a href=\"https://doi.org/10.1093/molbev/msy163\">10.1093/molbev/msy163</a>.","ieee":"A. Palmer, R. P. Chait, and R. Kishony, “Nonoptimal gene expression creates latent potential for antibiotic resistance,” <i>Molecular Biology and Evolution</i>, vol. 35, no. 11. Oxford University Press, pp. 2669–2684, 2018."},"abstract":[{"lang":"eng","text":"Bacteria regulate genes to survive antibiotic stress, but regulation can be far from perfect. When regulation is not optimal, mutations that change gene expression can contribute to antibiotic resistance. It is not systematically understood to what extent natural gene regulation is or is not optimal for distinct antibiotics, and how changes in expression of specific genes quantitatively affect antibiotic resistance. Here we discover a simple quantitative relation between fitness, gene expression, and antibiotic potency, which rationalizes our observation that a multitude of genes and even innate antibiotic defense mechanisms have expression that is critically nonoptimal under antibiotic treatment. First, we developed a pooled-strain drug-diffusion assay and screened Escherichia coli overexpression and knockout libraries, finding that resistance to a range of 31 antibiotics could result from changing expression of a large and functionally diverse set of genes, in a primarily but not exclusively drug-specific manner. Second, by synthetically controlling the expression of single-drug and multidrug resistance genes, we observed that their fitness-expression functions changed dramatically under antibiotic treatment in accordance with a log-sensitivity relation. Thus, because many genes are nonoptimally expressed under antibiotic treatment, many regulatory mutations can contribute to resistance by altering expression and by activating latent defenses."}],"external_id":{"isi":["000452567200006"],"pmid":["30169679"]},"quality_controlled":"1","isi":1,"_id":"19","article_processing_charge":"No","oa":1,"page":"2669 - 2684","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publisher":"Oxford University Press","status":"public","publication":"Molecular Biology and Evolution","publist_id":"8036","intvolume":"        35","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pubmed/30169679"}],"date_created":"2018-12-11T11:44:11Z","day":"28","pmid":1,"issue":"11"},{"publist_id":"7364","intvolume":"         2","publication_status":"published","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","day":"01","date_created":"2018-12-11T11:46:35Z","issue":"2","external_id":{"isi":["000426516400027"]},"quality_controlled":"1","article_processing_charge":"No","_id":"457","isi":1,"publisher":"Springer Nature","page":"359 - 366","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publication":"Nature Ecology and Evolution","status":"public","date_updated":"2026-04-08T14:19:43Z","type":"journal_article","related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"202"}]},"title":"Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity","year":"2018","oa_version":"None","author":[{"id":"4569785E-F248-11E8-B48F-1D18A9856A87","full_name":"Pleska, Maros","last_name":"Pleska","orcid":"0000-0001-7460-7479","first_name":"Maros"},{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","full_name":"Lang, Moritz","last_name":"Lang","first_name":"Moritz"},{"first_name":"Dominik","last_name":"Refardt","full_name":"Refardt, Dominik"},{"first_name":"Bruce","last_name":"Levin","full_name":"Levin, Bruce"},{"full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet"}],"abstract":[{"lang":"eng","text":"Temperate bacteriophages integrate in bacterial genomes as prophages and represent an important source of genetic variation for bacterial evolution, frequently transmitting fitness-augmenting genes such as toxins responsible for virulence of major pathogens. However, only a fraction of bacteriophage infections are lysogenic and lead to prophage acquisition, whereas the majority are lytic and kill the infected bacteria. Unless able to discriminate lytic from lysogenic infections, mechanisms of immunity to bacteriophages are expected to act as a double-edged sword and increase the odds of survival at the cost of depriving bacteria of potentially beneficial prophages. We show that although restriction-modification systems as mechanisms of innate immunity prevent both lytic and lysogenic infections indiscriminately in individual bacteria, they increase the number of prophage-acquiring individuals at the population level. We find that this counterintuitive result is a consequence of phage-host population dynamics, in which restriction-modification systems delay infection onset until bacteria reach densities at which the probability of lysogeny increases. These results underscore the importance of population-level dynamics as a key factor modulating costs and benefits of immunity to temperate bacteriophages"}],"citation":{"mla":"Pleska, Maros, et al. “Phage-Host Population Dynamics Promotes Prophage Acquisition in Bacteria with Innate Immunity.” <i>Nature Ecology and Evolution</i>, vol. 2, no. 2, Springer Nature, 2018, pp. 359–66, doi:<a href=\"https://doi.org/10.1038/s41559-017-0424-z\">10.1038/s41559-017-0424-z</a>.","ieee":"M. Pleska, M. Lang, D. Refardt, B. Levin, and C. C. Guet, “Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity,” <i>Nature Ecology and Evolution</i>, vol. 2, no. 2. Springer Nature, pp. 359–366, 2018.","ista":"Pleska M, Lang M, Refardt D, Levin B, Guet CC. 2018. Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. 2(2), 359–366.","chicago":"Pleska, Maros, Moritz Lang, Dominik Refardt, Bruce Levin, and Calin C Guet. “Phage-Host Population Dynamics Promotes Prophage Acquisition in Bacteria with Innate Immunity.” <i>Nature Ecology and Evolution</i>. Springer Nature, 2018. <a href=\"https://doi.org/10.1038/s41559-017-0424-z\">https://doi.org/10.1038/s41559-017-0424-z</a>.","short":"M. Pleska, M. Lang, D. Refardt, B. Levin, C.C. Guet, Nature Ecology and Evolution 2 (2018) 359–366.","apa":"Pleska, M., Lang, M., Refardt, D., Levin, B., &#38; Guet, C. C. (2018). Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. <i>Nature Ecology and Evolution</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41559-017-0424-z\">https://doi.org/10.1038/s41559-017-0424-z</a>","ama":"Pleska M, Lang M, Refardt D, Levin B, Guet CC. Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. <i>Nature Ecology and Evolution</i>. 2018;2(2):359-366. doi:<a href=\"https://doi.org/10.1038/s41559-017-0424-z\">10.1038/s41559-017-0424-z</a>"},"scopus_import":"1","month":"02","doi":"10.1038/s41559-017-0424-z","date_published":"2018-02-01T00:00:00Z","volume":2,"ec_funded":1,"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"},{"grant_number":"RGY0079/2011","_id":"251BCBEC-B435-11E9-9278-68D0E5697425","name":"Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems"},{"name":"Effects of Stochasticity on the Function of Restriction-Modi cation Systems at the Single-Cell Level","_id":"251D65D8-B435-11E9-9278-68D0E5697425","grant_number":"24210"}],"corr_author":"1","language":[{"iso":"eng"}]},{"publist_id":"7273","intvolume":"       115","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/152660 "}],"date_created":"2018-12-11T11:47:04Z","day":"02","issue":"1","quality_controlled":"1","external_id":{"isi":["000419128700049"]},"article_processing_charge":"No","_id":"543","isi":1,"oa":1,"publisher":"National Academy of Sciences","department":[{"_id":"GaTk"}],"page":"186 - 191","publication":"Proceedings of the National Academy of Sciences of the United States of America","status":"public","date_updated":"2025-05-14T10:55:59Z","type":"journal_article","title":"Toward a unified theory of efficient, predictive, and sparse coding","year":"2018","oa_version":"Submitted Version","author":[{"id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","full_name":"Chalk, Matthew J","last_name":"Chalk","orcid":"0000-0001-7782-4436","first_name":"Matthew J"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"}],"abstract":[{"text":"A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, “efficient coding” posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.","lang":"eng"}],"citation":{"short":"M.J. Chalk, O. Marre, G. Tkačik, Proceedings of the National Academy of Sciences of the United States of America 115 (2018) 186–191.","ieee":"M. J. Chalk, O. Marre, and G. Tkačik, “Toward a unified theory of efficient, predictive, and sparse coding,” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 115, no. 1. National Academy of Sciences, pp. 186–191, 2018.","mla":"Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>, vol. 115, no. 1, National Academy of Sciences, 2018, pp. 186–91, doi:<a href=\"https://doi.org/10.1073/pnas.1711114115\">10.1073/pnas.1711114115</a>.","ista":"Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive, and sparse coding. Proceedings of the National Academy of Sciences of the United States of America. 115(1), 186–191.","chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences, 2018. <a href=\"https://doi.org/10.1073/pnas.1711114115\">https://doi.org/10.1073/pnas.1711114115</a>.","ama":"Chalk MJ, Marre O, Tkačik G. Toward a unified theory of efficient, predictive, and sparse coding. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. 2018;115(1):186-191. doi:<a href=\"https://doi.org/10.1073/pnas.1711114115\">10.1073/pnas.1711114115</a>","apa":"Chalk, M. J., Marre, O., &#38; Tkačik, G. (2018). Toward a unified theory of efficient, predictive, and sparse coding. <i>Proceedings of the National Academy of Sciences of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1711114115\">https://doi.org/10.1073/pnas.1711114115</a>"},"scopus_import":"1","doi":"10.1073/pnas.1711114115","month":"01","date_published":"2018-01-02T00:00:00Z","volume":115,"project":[{"name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","call_identifier":"FWF"}],"corr_author":"1","language":[{"iso":"eng"}]},{"file":[{"file_size":1142543971,"date_created":"2018-12-12T13:02:24Z","checksum":"6808748837b9afbbbabc2a356ca2b88a","file_name":"IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat","creator":"system","file_id":"5590","relation":"main_file","content_type":"application/octet-stream","date_updated":"2020-07-14T12:47:07Z","access_level":"open_access"},{"checksum":"d6d6cd07743038fe3a12352983fcf9dd","file_size":702336,"date_created":"2018-12-12T13:02:25Z","file_name":"IST-2018-98-v1+2_ExperimentStructure.pdf","file_id":"5591","relation":"main_file","creator":"system","content_type":"application/pdf","date_updated":"2020-07-14T12:47:07Z","access_level":"open_access"},{"creator":"system","relation":"main_file","file_id":"5592","access_level":"open_access","content_type":"application/octet-stream","date_updated":"2020-07-14T12:47:07Z","file_name":"IST-2018-98-v1+3_GoodLocations_area2_20x20.mat","date_created":"2018-12-12T13:02:26Z","file_size":432,"checksum":"0c9cfb4dab35bb3dc25a04395600b1c8"},{"date_updated":"2020-07-14T12:47:07Z","content_type":"text/plain","access_level":"open_access","creator":"system","file_id":"5593","relation":"main_file","file_size":986,"date_created":"2018-12-12T13:02:26Z","checksum":"2a83b011012e21e934b4596285b1a183","file_name":"IST-2018-98-v1+4_README.txt"}],"oa":1,"date_published":"2018-03-29T00:00:00Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"has_accepted_license":"1","project":[{"call_identifier":"FWF","grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes"}],"publisher":"Institute of Science and Technology Austria","file_date_updated":"2020-07-14T12:47:07Z","status":"public","ddc":["570"],"license":"https://creativecommons.org/publicdomain/zero/1.0/","_id":"5584","article_processing_charge":"No","month":"03","doi":"10.15479/AT:ISTA:98","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","author":[{"last_name":"Deny","first_name":"Stephane","full_name":"Deny, Stephane"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"full_name":"Botella-Soler, Vicente","first_name":"Vicente","last_name":"Botella-Soler"},{"first_name":"Georg S","last_name":"Martius","full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik"}],"day":"29","date_created":"2018-12-12T12:31:39Z","citation":{"apa":"Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., &#38; Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:98\">https://doi.org/10.15479/AT:ISTA:98</a>","ama":"Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>","ieee":"S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina.” Institute of Science and Technology Austria, 2018.","mla":"Deny, Stephane, et al. <i>Nonlinear Decoding of a Complex Movie from the Mammalian Retina</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>.","chicago":"Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:98\">https://doi.org/10.15479/AT:ISTA:98</a>.","ista":"Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding of a complex movie from the mammalian retina, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>.","short":"S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018)."},"abstract":[{"text":"This package contains data for the publication \"Nonlinear decoding of a complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018). \r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin.\r\n(ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs  were displayed, and which stimulus segments are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. ","lang":"eng"}],"tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"date_updated":"2025-04-15T08:18:24Z","type":"research_data","title":"Nonlinear decoding of a complex movie from the mammalian retina","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"292"}]},"datarep_id":"98","year":"2018","keyword":["retina","decoding","regression","neural networks","complex stimulus"]},{"_id":"5587","month":"09","doi":"10.15479/AT:ISTA:62","article_processing_charge":"No","ddc":["530"],"status":"public","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:47:08Z","project":[{"name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","call_identifier":"FP7"},{"grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF"}],"publisher":"Institute of Science and Technology Austria","has_accepted_license":"1","ec_funded":1,"file":[{"access_level":"open_access","date_updated":"2020-07-14T12:47:08Z","content_type":"application/zip","creator":"system","relation":"main_file","file_id":"5641","file_name":"IST-2018-111-v1+1_CODES.zip","date_created":"2018-12-12T13:05:13Z","file_size":14376,"checksum":"97992e3e8cf8544ec985a48971708726"}],"oa":1,"date_published":"2018-09-21T00:00:00Z","year":"2018","keyword":["metabolic networks","e.coli core","maximum entropy","monte carlo markov chain sampling","ellipsoidal rounding"],"title":"Supporting materials \"STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH\"","related_material":{"record":[{"id":"161","relation":"research_paper","status":"public"}]},"datarep_id":"111","type":"research_data","date_updated":"2025-04-15T06:50:08Z","citation":{"ista":"De Martino D, Tkačik G. 2018. Supporting materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:62\">10.15479/AT:ISTA:62</a>.","chicago":"De Martino, Daniele, and Gašper Tkačik. “Supporting Materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:62\">https://doi.org/10.15479/AT:ISTA:62</a>.","mla":"De Martino, Daniele, and Gašper Tkačik. <i>Supporting Materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.”</i> Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:62\">10.15479/AT:ISTA:62</a>.","ieee":"D. De Martino and G. Tkačik, “Supporting materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology Austria, 2018.","short":"D. De Martino, G. Tkačik, (2018).","apa":"De Martino, D., &#38; Tkačik, G. (2018). Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:62\">https://doi.org/10.15479/AT:ISTA:62</a>","ama":"De Martino D, Tkačik G. Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:62\">10.15479/AT:ISTA:62</a>"},"abstract":[{"lang":"eng","text":"Supporting material to the article \r\nSTATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH\r\n\r\nboundscoli.dat\r\nFlux Bounds of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium. \r\n\r\npolcoli.dat\r\nMatrix enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium, \r\nobtained from the soichiometric matrix by standard linear algebra  (reduced row echelon form).\r\n\r\nellis.dat\r\nApproximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\npoint0.dat\r\nCenter of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\nlovasz.cpp  \r\nThis c++ code file receives in input the polytope of the feasible steady states of a metabolic network, \r\n(matrix and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding the polytope\r\nwith the Lovasz method \r\nNB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we refer to  PLoS ONE 10.4 e0122670 (2015).\r\n\r\nsampleHRnew.cpp  \r\nThis c++ code file receives in input the polytope of the feasible steady states of a metabolic network, \r\n(matrix and bounds), the ellipsoid rounding the polytope, a point inside and  \r\nit gives in output a max entropy sampling at fixed average growth rate \r\nof the steady states by performing an Hit-and-Run Monte Carlo Markov chain.\r\nNB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we refer to  PLoS ONE 10.4 e0122670 (2015)."}],"tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"day":"21","date_created":"2018-12-12T12:31:41Z","author":[{"last_name":"De Martino","first_name":"Daniele","orcid":"0000-0002-5214-4706","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele"},{"first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version"},{"acknowledgement":"JH and PM are funded by KAUST baseline funds and grant no. 1000000193 .\r\nWe thank Nicholas Barton (IST Austria) for his useful comments and suggestions. \r\n\r\n","isi":1,"article_processing_charge":"No","_id":"607","external_id":{"isi":["000437962900012"],"arxiv":["1704.08757"]},"quality_controlled":"1","status":"public","publication":"Physica D: Nonlinear Phenomena","oa":1,"page":"108-120","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publisher":"Elsevier","publication_status":"published","publist_id":"7198","date_created":"2018-12-11T11:47:28Z","day":"01","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1704.08757"}],"doi":"10.1016/j.physd.2017.10.015","month":"08","scopus_import":"1","language":[{"iso":"eng"}],"volume":"376-377","date_published":"2018-08-01T00:00:00Z","corr_author":"1","title":"Well posedness and maximum entropy approximation for the dynamics of quantitative traits","year":"2018","date_updated":"2024-10-09T20:58:45Z","arxiv":1,"type":"journal_article","citation":{"short":"K. Bodova, J. Haskovec, P. Markowich, Physica D: Nonlinear Phenomena 376–377 (2018) 108–120.","ieee":"K. Bodova, J. Haskovec, and P. Markowich, “Well posedness and maximum entropy approximation for the dynamics of quantitative traits,” <i>Physica D: Nonlinear Phenomena</i>, vol. 376–377. Elsevier, pp. 108–120, 2018.","mla":"Bodova, Katarina, et al. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” <i>Physica D: Nonlinear Phenomena</i>, vol. 376–377, Elsevier, 2018, pp. 108–20, doi:<a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">10.1016/j.physd.2017.10.015</a>.","chicago":"Bodova, Katarina, Jan Haskovec, and Peter Markowich. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” <i>Physica D: Nonlinear Phenomena</i>. Elsevier, 2018. <a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">https://doi.org/10.1016/j.physd.2017.10.015</a>.","ista":"Bodova K, Haskovec J, Markowich P. 2018. Well posedness and maximum entropy approximation for the dynamics of quantitative traits. Physica D: Nonlinear Phenomena. 376–377, 108–120.","ama":"Bodova K, Haskovec J, Markowich P. Well posedness and maximum entropy approximation for the dynamics of quantitative traits. <i>Physica D: Nonlinear Phenomena</i>. 2018;376-377:108-120. doi:<a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">10.1016/j.physd.2017.10.015</a>","apa":"Bodova, K., Haskovec, J., &#38; Markowich, P. (2018). Well posedness and maximum entropy approximation for the dynamics of quantitative traits. <i>Physica D: Nonlinear Phenomena</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">https://doi.org/10.1016/j.physd.2017.10.015</a>"},"abstract":[{"text":"We study the Fokker-Planck equation derived in the large system limit of the Markovian process describing the dynamics of quantitative traits. The Fokker-Planck equation is posed on a bounded domain and its transport and diffusion coefficients vanish on the domain's boundary. We first argue that, despite this degeneracy, the standard no-flux boundary condition is valid. We derive the weak formulation of the problem and prove the existence and uniqueness of its solutions by constructing the corresponding contraction semigroup on a suitable function space. Then, we prove that for the parameter regime with high enough mutation rate the problem exhibits a positive spectral gap, which implies exponential convergence to equilibrium.Next, we provide a simple derivation of the so-called Dynamic Maximum Entropy (DynMaxEnt) method for approximation of observables (moments) of the Fokker-Planck solution, which can be interpreted as a nonlinear Galerkin approximation. The limited applicability of the DynMaxEnt method inspires us to introduce its modified version that is valid for the whole range of admissible parameters. Finally, we present several numerical experiments to demonstrate the performance of both the original and modified DynMaxEnt methods. We observe that in the parameter regimes where both methods are valid, the modified one exhibits slightly better approximation properties compared to the original one.","lang":"eng"}],"oa_version":"Submitted Version","author":[{"first_name":"Katarina","orcid":"0000-0002-7214-0171","last_name":"Bodova","full_name":"Bodova, Katarina","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Haskovec, Jan","last_name":"Haskovec","first_name":"Jan"},{"full_name":"Markowich, Peter","last_name":"Markowich","first_name":"Peter"}]},{"status":"public","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"publisher":"Genetics Society of America","oa":1,"date_published":"2018-04-30T00:00:00Z","month":"04","_id":"9813","article_processing_charge":"No","doi":"10.25386/genetics.6148304.v1","citation":{"short":"K. Bodova, T. Priklopil, D. Field, N.H. Barton, M. Pickup, (2018).","mla":"Bodova, Katarina, et al. <i>Supplemental Material for Bodova et Al., 2018</i>. Genetics Society of America, 2018, doi:<a href=\"https://doi.org/10.25386/genetics.6148304.v1\">10.25386/genetics.6148304.v1</a>.","ieee":"K. Bodova, T. Priklopil, D. Field, N. H. Barton, and M. Pickup, “Supplemental material for Bodova et al., 2018.” Genetics Society of America, 2018.","ista":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. 2018. Supplemental material for Bodova et al., 2018, Genetics Society of America, <a href=\"https://doi.org/10.25386/genetics.6148304.v1\">10.25386/genetics.6148304.v1</a>.","chicago":"Bodova, Katarina, Tadeas Priklopil, David Field, Nicholas H Barton, and Melinda Pickup. “Supplemental Material for Bodova et Al., 2018.” Genetics Society of America, 2018. <a href=\"https://doi.org/10.25386/genetics.6148304.v1\">https://doi.org/10.25386/genetics.6148304.v1</a>.","ama":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. Supplemental material for Bodova et al., 2018. 2018. doi:<a href=\"https://doi.org/10.25386/genetics.6148304.v1\">10.25386/genetics.6148304.v1</a>","apa":"Bodova, K., Priklopil, T., Field, D., Barton, N. H., &#38; Pickup, M. (2018). Supplemental material for Bodova et al., 2018. Genetics Society of America. <a href=\"https://doi.org/10.25386/genetics.6148304.v1\">https://doi.org/10.25386/genetics.6148304.v1</a>"},"abstract":[{"lang":"eng","text":"File S1 contains figures that clarify the following features: (i) effect of population size on the average number/frequency of SI classes, (ii) changes in the minimal completeness deficit in time for a single class, and (iii) diversification diagrams for all studied pathways, including the summary figure for k = 8. File S2 contains the code required for a stochastic simulation of the SLF system with an example. This file also includes the output in the form of figures and tables."}],"date_created":"2021-08-06T13:04:32Z","day":"30","main_file_link":[{"url":"https://doi.org/10.25386/genetics.6148304.v1","open_access":"1"}],"author":[{"id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'ová, Katarína","last_name":"Bod'ová","orcid":"0000-0002-7214-0171","first_name":"Katarína"},{"id":"3C869AA0-F248-11E8-B48F-1D18A9856A87","full_name":"Priklopil, Tadeas","last_name":"Priklopil","first_name":"Tadeas"},{"id":"419049E2-F248-11E8-B48F-1D18A9856A87","full_name":"Field, David","last_name":"Field","orcid":"0000-0002-4014-8478","first_name":"David"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","last_name":"Barton","orcid":"0000-0002-8548-5240","first_name":"Nicholas H"},{"last_name":"Pickup","orcid":"0000-0001-6118-0541","first_name":"Melinda","id":"2C78037E-F248-11E8-B48F-1D18A9856A87","full_name":"Pickup, Melinda"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","year":"2018","title":"Supplemental material for Bodova et al., 2018","related_material":{"record":[{"id":"316","status":"public","relation":"used_in_publication"}]},"type":"research_data_reference","date_updated":"2025-04-15T07:17:08Z"},{"doi":"10.1371/journal.pone.0193049.s001","_id":"9831","month":"03","article_processing_charge":"No","status":"public","date_published":"2018-03-07T00:00:00Z","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"406"}]},"title":"Implementation of the inference method in Matlab","year":"2018","date_updated":"2025-04-15T06:44:30Z","type":"research_data_reference","date_created":"2021-08-09T07:01:24Z","day":"07","abstract":[{"lang":"eng","text":"Implementation of the inference method in Matlab, including three applications of the method: The first one for the model of ant motion, the second one for bacterial chemotaxis, and the third one for the motion of fish."}],"citation":{"ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Implementation of the inference method in Matlab. 2018. doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">10.1371/journal.pone.0193049.s001</a>","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &#38; Tkačik, G. (2018). Implementation of the inference method in Matlab. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">https://doi.org/10.1371/journal.pone.0193049.s001</a>","short":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, (2018).","ieee":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Implementation of the inference method in Matlab.” Public Library of Science, 2018.","mla":"Bod’Ová, Katarína, et al. <i>Implementation of the Inference Method in Matlab</i>. Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">10.1371/journal.pone.0193049.s001</a>.","chicago":"Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper Tkačik. “Implementation of the Inference Method in Matlab.” Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">https://doi.org/10.1371/journal.pone.0193049.s001</a>.","ista":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Implementation of the inference method in Matlab, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">10.1371/journal.pone.0193049.s001</a>."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","author":[{"first_name":"Katarína","last_name":"Bod’Ová","full_name":"Bod’Ová, Katarína"},{"last_name":"Mitchell","first_name":"Gabriel","id":"315BCD80-F248-11E8-B48F-1D18A9856A87","full_name":"Mitchell, Gabriel"},{"full_name":"Harpaz, Roy","first_name":"Roy","last_name":"Harpaz"},{"last_name":"Schneidman","first_name":"Elad","full_name":"Schneidman, Elad"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}]},{"issue":"4","date_created":"2018-12-11T11:44:15Z","day":"17","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/243816v2.full"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publication_status":"published","intvolume":"        98","publist_id":"8024","publication":"Physical Review E","status":"public","publisher":"American Physical Society","department":[{"_id":"GaTk"}],"oa":1,"_id":"31","article_processing_charge":"No","isi":1,"acknowledgement":"This work was supported by ANR Trajectory, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche (LIFESENSES; ANR-10-LABX-65), EC Grant No. H2020-785907 from the Human Brain Project, NIH Grant No. U01NS090501, and an AVIESAN-UNADEV grant to O.M. M.C. was supported by the Agence Nationale de la Recherche Jeune Chercheur/Jeune Chercheuse grant (ANR-17-CE37-0013).","quality_controlled":"1","external_id":{"isi":["000447486100004"]},"abstract":[{"text":"Correlations in sensory neural networks have both extrinsic and intrinsic origins. Extrinsic or stimulus correlations arise from shared inputs to the network and, thus, depend strongly on the stimulus ensemble. Intrinsic or noise correlations reflect biophysical mechanisms of interactions between neurons, which are expected to be robust to changes in the stimulus ensemble. Despite the importance of this distinction for understanding how sensory networks encode information collectively, no method exists to reliably separate intrinsic interactions from extrinsic correlations in neural activity data, limiting our ability to build predictive models of the network response. In this paper we introduce a general strategy to infer population models of interacting neurons that collectively encode stimulus information. The key to disentangling intrinsic from extrinsic correlations is to infer the couplings between neurons separately from the encoding model and to combine the two using corrections calculated in a mean-field approximation. We demonstrate the effectiveness of this approach in retinal recordings. The same coupling network is inferred from responses to radically different stimulus ensembles, showing that these couplings indeed reflect stimulus-independent interactions between neurons. The inferred model predicts accurately the collective response of retinal ganglion cell populations as a function of the stimulus.","lang":"eng"}],"article_number":"042410","citation":{"apa":"Ferrari, U., Deny, S., Chalk, M. J., Tkačik, G., Marre, O., &#38; Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. <i>Physical Review E</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">https://doi.org/10.1103/PhysRevE.98.042410</a>","ama":"Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. <i>Physical Review E</i>. 2018;98(4). doi:<a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">10.1103/PhysRevE.98.042410</a>","chicago":"Ferrari, Ulisse, Stephane Deny, Matthew J Chalk, Gašper Tkačik, Olivier Marre, and Thierry Mora. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” <i>Physical Review E</i>. American Physical Society, 2018. <a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">https://doi.org/10.1103/PhysRevE.98.042410</a>.","ista":"Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. 2018. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. 98(4), 042410.","ieee":"U. Ferrari, S. Deny, M. J. Chalk, G. Tkačik, O. Marre, and T. Mora, “Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons,” <i>Physical Review E</i>, vol. 98, no. 4. American Physical Society, 2018.","mla":"Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” <i>Physical Review E</i>, vol. 98, no. 4, 042410, American Physical Society, 2018, doi:<a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">10.1103/PhysRevE.98.042410</a>.","short":"U. Ferrari, S. Deny, M.J. Chalk, G. Tkačik, O. Marre, T. Mora, Physical Review E 98 (2018)."},"author":[{"full_name":"Ferrari, Ulisse","first_name":"Ulisse","last_name":"Ferrari"},{"full_name":"Deny, Stephane","first_name":"Stephane","last_name":"Deny"},{"full_name":"Chalk, Matthew J","last_name":"Chalk","first_name":"Matthew J"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"full_name":"Mora, Thierry","first_name":"Thierry","last_name":"Mora"}],"oa_version":"Preprint","year":"2018","title":"Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons","publication_identifier":{"issn":["2470-0045"]},"type":"journal_article","date_updated":"2025-05-05T13:48:04Z","article_type":"original","language":[{"iso":"eng"}],"project":[{"call_identifier":"H2020","name":"Human Brain Project Specific Grant Agreement 2","grant_number":"785907","_id":"26436750-B435-11E9-9278-68D0E5697425"}],"date_published":"2018-10-17T00:00:00Z","volume":98,"ec_funded":1,"month":"10","doi":"10.1103/PhysRevE.98.042410","scopus_import":"1"},{"issue":"3","day":"01","date_created":"2018-12-11T11:45:47Z","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/node/80098.abstract"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","publication_status":"published","intvolume":"       209","status":"public","publication":"Genetics","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"page":"861-883","publisher":"Genetics Society of America","oa":1,"isi":1,"_id":"316","article_processing_charge":"No","quality_controlled":"1","external_id":{"isi":["000437171700017"]},"citation":{"chicago":"Bodova, Katarina, Tadeas Priklopil, David Field, Nicholas H Barton, and Melinda Pickup. “Evolutionary Pathways for the Generation of New Self-Incompatibility Haplotypes in a Non-Self Recognition System.” <i>Genetics</i>. Genetics Society of America, 2018. <a href=\"https://doi.org/10.1534/genetics.118.300748\">https://doi.org/10.1534/genetics.118.300748</a>.","ista":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. 2018. Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. Genetics. 209(3), 861–883.","ieee":"K. Bodova, T. Priklopil, D. Field, N. H. Barton, and M. Pickup, “Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system,” <i>Genetics</i>, vol. 209, no. 3. Genetics Society of America, pp. 861–883, 2018.","mla":"Bodova, Katarina, et al. “Evolutionary Pathways for the Generation of New Self-Incompatibility Haplotypes in a Non-Self Recognition System.” <i>Genetics</i>, vol. 209, no. 3, Genetics Society of America, 2018, pp. 861–83, doi:<a href=\"https://doi.org/10.1534/genetics.118.300748\">10.1534/genetics.118.300748</a>.","short":"K. Bodova, T. Priklopil, D. Field, N.H. Barton, M. Pickup, Genetics 209 (2018) 861–883.","apa":"Bodova, K., Priklopil, T., Field, D., Barton, N. H., &#38; Pickup, M. (2018). Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.118.300748\">https://doi.org/10.1534/genetics.118.300748</a>","ama":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. <i>Genetics</i>. 2018;209(3):861-883. doi:<a href=\"https://doi.org/10.1534/genetics.118.300748\">10.1534/genetics.118.300748</a>"},"abstract":[{"lang":"eng","text":"Self-incompatibility (SI) is a genetically based recognition system that functions to prevent self-fertilization and mating among related plants. An enduring puzzle in SI is how the high diversity observed in nature arises and is maintained. Based on the underlying recognition mechanism, SI can be classified into two main groups: self- and non-self recognition. Most work has focused on diversification within self-recognition systems despite expected differences between the two groups in the evolutionary pathways and outcomes of diversification. Here, we use a deterministic population genetic model and stochastic simulations to investigate how novel S-haplotypes evolve in a gametophytic non-self recognition (SRNase/S Locus F-box (SLF)) SI system. For this model the pathways for diversification involve either the maintenance or breakdown of SI and can vary in the order of mutations of the female (SRNase) and male (SLF) components. We show analytically that diversification can occur with high inbreeding depression and self-pollination, but this varies with evolutionary pathway and level of completeness (which determines the number of potential mating partners in the population), and in general is more likely for lower haplotype number. The conditions for diversification are broader in stochastic simulations of finite population size. However, the number of haplotypes observed under high inbreeding and moderate to high self-pollination is less than that commonly observed in nature. Diversification was observed through pathways that maintain SI as well as through self-compatible intermediates. Yet the lifespan of diversified haplotypes was sensitive to their level of completeness. By examining diversification in a non-self recognition SI system, this model extends our understanding of the evolution and maintenance of haplotype diversity observed in a self recognition system common in flowering plants."}],"author":[{"full_name":"Bodova, Katarina","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","first_name":"Katarina","orcid":"0000-0002-7214-0171","last_name":"Bodova"},{"first_name":"Tadeas","last_name":"Priklopil","full_name":"Priklopil, Tadeas","id":"3C869AA0-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Field","first_name":"David","orcid":"0000-0002-4014-8478","id":"419049E2-F248-11E8-B48F-1D18A9856A87","full_name":"Field, David"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","last_name":"Barton","orcid":"0000-0002-8548-5240","first_name":"Nicholas H"},{"orcid":"0000-0001-6118-0541","first_name":"Melinda","last_name":"Pickup","full_name":"Pickup, Melinda","id":"2C78037E-F248-11E8-B48F-1D18A9856A87"}],"oa_version":"Preprint","year":"2018","title":"Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system","related_material":{"record":[{"relation":"research_data","status":"public","id":"9813"}],"link":[{"url":"https://ist.ac.at/en/news/recognizing-others-but-not-yourself-new-insights-into-the-evolution-of-plant-mating/","description":"News on IST Homepage","relation":"press_release"}]},"type":"journal_article","date_updated":"2025-04-15T06:50:00Z","article_type":"original","language":[{"iso":"eng"}],"project":[{"name":"Mating system and the evolutionary dynamics of hybrid zones","grant_number":"329960","_id":"25B36484-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152","call_identifier":"FP7"},{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"ec_funded":1,"date_published":"2018-07-01T00:00:00Z","volume":209,"doi":"10.1534/genetics.118.300748","month":"07","scopus_import":"1"},{"language":[{"iso":"eng"}],"date_published":"2018-03-07T00:00:00Z","volume":13,"corr_author":"1","has_accepted_license":"1","project":[{"grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425","name":"Information processing and computation in fish groups"}],"file_date_updated":"2020-07-14T12:46:22Z","doi":"10.1371/journal.pone.0193049","pubrep_id":"995","month":"03","scopus_import":"1","citation":{"chicago":"Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper Tkačik. “Probabilistic Models of Individual and Collective Animal Behavior.” <i>PLoS One</i>. Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pone.0193049\">https://doi.org/10.1371/journal.pone.0193049</a>.","ista":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Probabilistic models of individual and collective animal behavior. PLoS One. 13(3).","mla":"Bod’Ová, Katarína, et al. “Probabilistic Models of Individual and Collective Animal Behavior.” <i>PLoS One</i>, vol. 13, no. 3, Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049\">10.1371/journal.pone.0193049</a>.","ieee":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Probabilistic models of individual and collective animal behavior,” <i>PLoS One</i>, vol. 13, no. 3. Public Library of Science, 2018.","short":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, PLoS One 13 (2018).","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &#38; Tkačik, G. (2018). Probabilistic models of individual and collective animal behavior. <i>PLoS One</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0193049\">https://doi.org/10.1371/journal.pone.0193049</a>","ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Probabilistic models of individual and collective animal behavior. <i>PLoS One</i>. 2018;13(3). doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049\">10.1371/journal.pone.0193049</a>"},"abstract":[{"lang":"eng","text":"Recent developments in automated tracking allow uninterrupted, high-resolution recording of animal trajectories, sometimes coupled with the identification of stereotyped changes of body pose or other behaviors of interest. Analysis and interpretation of such data represents a challenge: the timing of animal behaviors may be stochastic and modulated by kinematic variables, by the interaction with the environment or with the conspecifics within the animal group, and dependent on internal cognitive or behavioral state of the individual. Existing models for collective motion typically fail to incorporate the discrete, stochastic, and internal-state-dependent aspects of behavior, while models focusing on individual animal behavior typically ignore the spatial aspects of the problem. Here we propose a probabilistic modeling framework to address this gap. Each animal can switch stochastically between different behavioral states, with each state resulting in a possibly different law of motion through space. Switching rates for behavioral transitions can depend in a very general way, which we seek to identify from data, on the effects of the environment as well as the interaction between the animals. We represent the switching dynamics as a Generalized Linear Model and show that: (i) forward simulation of multiple interacting animals is possible using a variant of the Gillespie’s Stochastic Simulation Algorithm; (ii) formulated properly, the maximum likelihood inference of switching rate functions is tractably solvable by gradient descent; (iii) model selection can be used to identify factors that modulate behavioral state switching and to appropriately adjust model complexity to data. To illustrate our framework, we apply it to two synthetic models of animal motion and to real zebrafish tracking data. "}],"oa_version":"Submitted Version","author":[{"first_name":"Katarína","last_name":"Bod’Ová","full_name":"Bod’Ová, Katarína"},{"first_name":"Gabriel","last_name":"Mitchell","full_name":"Mitchell, Gabriel","id":"315BCD80-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Harpaz","first_name":"Roy","full_name":"Harpaz, Roy"},{"full_name":"Schneidman, Elad","first_name":"Elad","last_name":"Schneidman"},{"orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"title":"Probabilistic models of individual and collective animal behavior","related_material":{"record":[{"id":"9831","status":"public","relation":"research_data"}]},"year":"2018","date_updated":"2025-04-15T06:44:30Z","type":"journal_article","status":"public","publication":"PLoS One","oa":1,"file":[{"file_name":"IST-2018-995-v1+1_2018_Bodova_Probabilistic.pdf","date_created":"2018-12-12T10:15:43Z","file_size":6887358,"checksum":"684229493db75b43e98a46cd922da497","access_level":"open_access","date_updated":"2020-07-14T12:46:22Z","content_type":"application/pdf","creator":"system","file_id":"5165","relation":"main_file"}],"department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","acknowledgement":"This work was supported by the Human Frontier Science Program RGP0065/2012 (GT, ES).","isi":1,"_id":"406","article_processing_charge":"Yes","external_id":{"isi":["000426896800032"]},"quality_controlled":"1","ddc":["530","571"],"date_created":"2018-12-11T11:46:18Z","day":"07","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"},"issue":"3","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","intvolume":"        13","publication_status":"published","publist_id":"7423"},{"external_id":{"isi":["000447947600021"]},"quality_controlled":"1","ddc":["570"],"isi":1,"article_processing_charge":"No","_id":"67","oa":1,"file":[{"date_created":"2020-05-14T11:28:52Z","file_size":1135973,"checksum":"383a2e2c944a856e2e821ec8e7bf71b6","file_name":"2018_NatureEcology_Igler.pdf","creator":"dernst","relation":"main_file","file_id":"7830","content_type":"application/pdf","date_updated":"2020-07-14T12:47:37Z","access_level":"open_access"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"JoBo"}],"page":"1633 - 1643","publisher":"Nature Publishing Group","status":"public","publication":"Nature Ecology and Evolution","publist_id":"7987","intvolume":"         2","publication_status":"published","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","day":"10","date_created":"2018-12-11T11:44:27Z","issue":"10","scopus_import":"1","month":"09","doi":"10.1038/s41559-018-0651-y","ec_funded":1,"date_published":"2018-09-10T00:00:00Z","volume":2,"project":[{"call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"},{"call_identifier":"H2020","name":"Selective Barriers to Horizontal Gene Transfer","_id":"2578D616-B435-11E9-9278-68D0E5697425","grant_number":"648440"},{"grant_number":"24573","_id":"251EE76E-B435-11E9-9278-68D0E5697425","name":"Design principles underlying genetic switch architecture"}],"has_accepted_license":"1","file_date_updated":"2020-07-14T12:47:37Z","language":[{"iso":"eng"}],"article_type":"original","date_updated":"2026-08-11T22:30:39Z","type":"journal_article","title":"Evolutionary potential of transcription factors for gene regulatory rewiring","related_material":{"record":[{"id":"5585","status":"public","relation":"popular_science"},{"id":"6371","relation":"dissertation_contains","status":"public"}]},"year":"2018","oa_version":"Submitted Version","author":[{"id":"46613666-F248-11E8-B48F-1D18A9856A87","full_name":"Igler, Claudia","last_name":"Igler","first_name":"Claudia","orcid":"0000-0001-7777-546X"},{"first_name":"Mato","last_name":"Lagator","full_name":"Lagator, Mato","id":"345D25EC-F248-11E8-B48F-1D18A9856A87"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","orcid":"0000-0002-6699-1455"},{"first_name":"Jonathan P","orcid":"0000-0002-4624-4612","last_name":"Bollback","full_name":"Bollback, Jonathan P","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"}],"citation":{"apa":"Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., &#38; Guet, C. C. (2018). Evolutionary potential of transcription factors for gene regulatory rewiring. <i>Nature Ecology and Evolution</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/s41559-018-0651-y\">https://doi.org/10.1038/s41559-018-0651-y</a>","ama":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Evolutionary potential of transcription factors for gene regulatory rewiring. <i>Nature Ecology and Evolution</i>. 2018;2(10):1633-1643. doi:<a href=\"https://doi.org/10.1038/s41559-018-0651-y\">10.1038/s41559-018-0651-y</a>","mla":"Igler, Claudia, et al. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” <i>Nature Ecology and Evolution</i>, vol. 2, no. 10, Nature Publishing Group, 2018, pp. 1633–43, doi:<a href=\"https://doi.org/10.1038/s41559-018-0651-y\">10.1038/s41559-018-0651-y</a>.","ieee":"C. Igler, M. Lagator, G. Tkačik, J. P. Bollback, and C. C. Guet, “Evolutionary potential of transcription factors for gene regulatory rewiring,” <i>Nature Ecology and Evolution</i>, vol. 2, no. 10. Nature Publishing Group, pp. 1633–1643, 2018.","chicago":"Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” <i>Nature Ecology and Evolution</i>. Nature Publishing Group, 2018. <a href=\"https://doi.org/10.1038/s41559-018-0651-y\">https://doi.org/10.1038/s41559-018-0651-y</a>.","ista":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. 2018. Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. 2(10), 1633–1643.","short":"C. Igler, M. Lagator, G. Tkačik, J.P. Bollback, C.C. Guet, Nature Ecology and Evolution 2 (2018) 1633–1643."},"abstract":[{"text":"Gene regulatory networks evolve through rewiring of individual components—that is, through changes in regulatory connections. However, the mechanistic basis of regulatory rewiring is poorly understood. Using a canonical gene regulatory system, we quantify the properties of transcription factors that determine the evolutionary potential for rewiring of regulatory connections: robustness, tunability and evolvability. In vivo repression measurements of two repressors at mutated operator sites reveal their contrasting evolutionary potential: while robustness and evolvability were positively correlated, both were in trade-off with tunability. Epistatic interactions between adjacent operators alleviated this trade-off. A thermodynamic model explains how the differences in robustness, tunability and evolvability arise from biophysical characteristics of repressor–DNA binding. The model also uncovers that the energy matrix, which describes how mutations affect repressor–DNA binding, encodes crucial information about the evolutionary potential of a repressor. The biophysical determinants of evolutionary potential for regulatory rewiring constitute a mechanistic framework for understanding network evolution.","lang":"eng"}]},{"date_published":"2018-07-20T00:00:00Z","ec_funded":1,"file":[{"file_id":"5611","relation":"main_file","creator":"system","access_level":"open_access","date_updated":"2020-07-14T12:47:07Z","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","file_name":"IST-2018-108-v1+1_data_figures.xlsx","checksum":"1435781526c77413802adee0d4583cce","file_size":16507,"date_created":"2018-12-12T13:02:45Z"}],"oa":1,"file_date_updated":"2020-07-14T12:47:07Z","publisher":"Institute of Science and Technology Austria","has_accepted_license":"1","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"},{"call_identifier":"H2020","_id":"2578D616-B435-11E9-9278-68D0E5697425","grant_number":"648440","name":"Selective Barriers to Horizontal Gene Transfer"},{"name":"Design principles underlying genetic switch architecture","grant_number":"24573","_id":"251EE76E-B435-11E9-9278-68D0E5697425"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"status":"public","ddc":["576"],"_id":"5585","doi":"10.15479/AT:ISTA:108","month":"07","article_processing_charge":"No","oa_version":"Published Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Igler, Claudia","id":"46613666-F248-11E8-B48F-1D18A9856A87","first_name":"Claudia","orcid":"0000-0001-7777-546X","last_name":"Igler"},{"id":"345D25EC-F248-11E8-B48F-1D18A9856A87","full_name":"Lagator, Mato","last_name":"Lagator","first_name":"Mato"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"},{"id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","full_name":"Bollback, Jonathan P","last_name":"Bollback","first_name":"Jonathan P","orcid":"0000-0002-4624-4612"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C","last_name":"Guet","orcid":"0000-0001-6220-2052","first_name":"Calin C"}],"day":"20","date_created":"2018-12-12T12:31:40Z","tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"abstract":[{"lang":"eng","text":"Mean repression values and standard error of the mean are given for all operator mutant libraries."}],"citation":{"short":"C. Igler, M. Lagator, G. Tkačik, J.P. Bollback, C.C. Guet, (2018).","mla":"Igler, Claudia, et al. <i>Data for the Paper Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:108\">10.15479/AT:ISTA:108</a>.","ieee":"C. Igler, M. Lagator, G. Tkačik, J. P. Bollback, and C. C. Guet, “Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring.” Institute of Science and Technology Austria, 2018.","chicago":"Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Data for the Paper Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:108\">https://doi.org/10.15479/AT:ISTA:108</a>.","ista":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. 2018. Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:108\">10.15479/AT:ISTA:108</a>.","ama":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:108\">10.15479/AT:ISTA:108</a>","apa":"Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., &#38; Guet, C. C. (2018). Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:108\">https://doi.org/10.15479/AT:ISTA:108</a>"},"date_updated":"2026-08-11T22:30:39Z","type":"research_data","datarep_id":"108","related_material":{"record":[{"status":"public","relation":"research_paper","id":"67"},{"id":"6371","status":"public","relation":"research_paper"}]},"title":"Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring","year":"2018"},{"ddc":["571"],"quality_controlled":"1","external_id":{"isi":["000417241200004"]},"article_processing_charge":"No","_id":"1104","isi":1,"oa":1,"file":[{"access_level":"open_access","date_updated":"2018-12-12T10:16:06Z","content_type":"application/pdf","creator":"system","file_id":"5191","relation":"main_file","file_name":"IST-2018-921-v1+1_s41467-017-02159-y.pdf","date_created":"2018-12-12T10:16:06Z","file_size":2872887}],"publisher":"Nature Publishing Group","department":[{"_id":"GaTk"}],"publication":"Nature Communications","status":"public","publist_id":"6266","intvolume":"         8","publication_status":"published","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T11:50:10Z","day":"06","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"},"issue":"1","scopus_import":"1","pubrep_id":"921","doi":"10.1038/s41467-017-02159-y","month":"12","volume":8,"date_published":"2017-12-06T00:00:00Z","ec_funded":1,"has_accepted_license":"1","project":[{"call_identifier":"FP7","grant_number":"604102","_id":"25CD3DD2-B435-11E9-9278-68D0E5697425","name":"Localization of ion channels and receptors by two and three-dimensional immunoelectron microscopic approaches"},{"grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"file_date_updated":"2018-12-12T10:16:06Z","language":[{"iso":"eng"}],"date_updated":"2025-07-10T11:50:05Z","publication_identifier":{"issn":["2041-1723"]},"type":"journal_article","title":"Multiplexed computations in retinal ganglion cells of a single type","year":"2017","oa_version":"Published Version","author":[{"first_name":"Stephane","last_name":"Deny","full_name":"Deny, Stephane"},{"last_name":"Ferrari","first_name":"Ulisse","full_name":"Ferrari, Ulisse"},{"full_name":"Mace, Emilie","first_name":"Emilie","last_name":"Mace"},{"full_name":"Yger, Pierre","first_name":"Pierre","last_name":"Yger"},{"first_name":"Romain","last_name":"Caplette","full_name":"Caplette, Romain"},{"full_name":"Picaud, Serge","last_name":"Picaud","first_name":"Serge"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper"},{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"}],"abstract":[{"text":"In the early visual system, cells of the same type perform the same computation in different places of the visual field. How these cells code together a complex visual scene is unclear. A common assumption is that cells of a single-type extract a single-stimulus feature to form a feature map, but this has rarely been observed directly. Using large-scale recordings in the rat retina, we show that a homogeneous population of fast OFF ganglion cells simultaneously encodes two radically different features of a visual scene. Cells close to a moving object code quasilinearly for its position, while distant cells remain largely invariant to the object's position and, instead, respond nonlinearly to changes in the object's speed. We develop a quantitative model that accounts for this effect and identify a disinhibitory circuit that mediates it. Ganglion cells of a single type thus do not code for one, but two features simultaneously. This richer, flexible neural map might also be present in other sensory systems.","lang":"eng"}],"citation":{"ista":"Deny S, Ferrari U, Mace E, Yger P, Caplette R, Picaud S, Tkačik G, Marre O. 2017. Multiplexed computations in retinal ganglion cells of a single type. Nature Communications. 8(1), 1964.","chicago":"Deny, Stephane, Ulisse Ferrari, Emilie Mace, Pierre Yger, Romain Caplette, Serge Picaud, Gašper Tkačik, and Olivier Marre. “Multiplexed Computations in Retinal Ganglion Cells of a Single Type.” <i>Nature Communications</i>. Nature Publishing Group, 2017. <a href=\"https://doi.org/10.1038/s41467-017-02159-y\">https://doi.org/10.1038/s41467-017-02159-y</a>.","ieee":"S. Deny <i>et al.</i>, “Multiplexed computations in retinal ganglion cells of a single type,” <i>Nature Communications</i>, vol. 8, no. 1. Nature Publishing Group, 2017.","mla":"Deny, Stephane, et al. “Multiplexed Computations in Retinal Ganglion Cells of a Single Type.” <i>Nature Communications</i>, vol. 8, no. 1, 1964, Nature Publishing Group, 2017, doi:<a href=\"https://doi.org/10.1038/s41467-017-02159-y\">10.1038/s41467-017-02159-y</a>.","short":"S. Deny, U. Ferrari, E. Mace, P. Yger, R. Caplette, S. Picaud, G. Tkačik, O. Marre, Nature Communications 8 (2017).","apa":"Deny, S., Ferrari, U., Mace, E., Yger, P., Caplette, R., Picaud, S., … Marre, O. (2017). Multiplexed computations in retinal ganglion cells of a single type. <i>Nature Communications</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/s41467-017-02159-y\">https://doi.org/10.1038/s41467-017-02159-y</a>","ama":"Deny S, Ferrari U, Mace E, et al. Multiplexed computations in retinal ganglion cells of a single type. <i>Nature Communications</i>. 2017;8(1). doi:<a href=\"https://doi.org/10.1038/s41467-017-02159-y\">10.1038/s41467-017-02159-y</a>"},"article_number":"1964"},{"main_file_link":[{"url":"https://arxiv.org/abs/1707.00320","open_access":"1"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","issue":"6","date_created":"2018-12-11T11:47:06Z","day":"21","publist_id":"7266","publication_status":"published","intvolume":"        96","publisher":"American Physical Society","department":[{"_id":"GaTk"}],"oa":1,"publication":"Physical Review E","alternative_title":["Rapid Communications"],"status":"public","quality_controlled":"1","external_id":{"arxiv":["1707.00320"],"isi":["000418574400001"]},"article_processing_charge":"No","_id":"548","isi":1,"author":[{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele","last_name":"De Martino","orcid":"0000-0002-5214-4706","first_name":"Daniele"}],"oa_version":"Submitted Version","abstract":[{"lang":"eng","text":"In this work maximum entropy distributions in the space of steady states of metabolic networks are considered upon constraining the first and second moments of the growth rate. Coexistence of fast and slow phenotypes, with bimodal flux distributions, emerges upon considering control on the average growth (optimization) and its fluctuations (heterogeneity). This is applied to the carbon catabolic core of Escherichia coli where it quantifies the metabolic activity of slow growing phenotypes and it provides a quantitative map with metabolic fluxes, opening the possibility to detect coexistence from flux data. A preliminary analysis on data for E. coli cultures in standard conditions shows degeneracy for the inferred parameters that extend in the coexistence region."}],"citation":{"ista":"De Martino D. 2017. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. Physical Review E. 96(6), 060401.","chicago":"De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” <i>Physical Review E</i>. American Physical Society, 2017. <a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">https://doi.org/10.1103/PhysRevE.96.060401</a>.","mla":"De Martino, Daniele. “Maximum Entropy Modeling of Metabolic Networks by Constraining Growth-Rate Moments Predicts Coexistence of Phenotypes.” <i>Physical Review E</i>, vol. 96, no. 6, 060401, American Physical Society, 2017, doi:<a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">10.1103/PhysRevE.96.060401</a>.","ieee":"D. De Martino, “Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes,” <i>Physical Review E</i>, vol. 96, no. 6. American Physical Society, 2017.","short":"D. De Martino, Physical Review E 96 (2017).","apa":"De Martino, D. (2017). Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. <i>Physical Review E</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">https://doi.org/10.1103/PhysRevE.96.060401</a>","ama":"De Martino D. Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes. <i>Physical Review E</i>. 2017;96(6). doi:<a href=\"https://doi.org/10.1103/PhysRevE.96.060401\">10.1103/PhysRevE.96.060401</a>"},"article_number":"060401","publication_identifier":{"issn":["2470-0045"]},"arxiv":1,"type":"journal_article","date_updated":"2025-09-18T09:41:11Z","year":"2017","title":"Maximum entropy modeling of metabolic networks by constraining growth-rate moments predicts coexistence of phenotypes","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"corr_author":"1","date_published":"2017-12-21T00:00:00Z","volume":96,"ec_funded":1,"language":[{"iso":"eng"}],"scopus_import":"1","doi":"10.1103/PhysRevE.96.060401","month":"12"},{"abstract":[{"lang":"eng","text":"This repository contains the data collected for the manuscript \"Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity\".\r\nThe data is compressed into a single archive. Within the archive, different folders correspond to figures of the main text and the SI of the related publication.\r\nData is saved as plain text, with each folder containing a separate readme file describing the format. Typically, the data is from fluorescence microscopy measurements of single cells growing in a microfluidic \"mother machine\" device, and consists of relevant values (primarily arbitrary unit or normalized fluorescence measurements, and division times / growth rates) after raw microscopy images have been processed, segmented, and their features extracted, as described in the methods section of the related publication."}],"tmp":{"image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)"},"citation":{"chicago":"Bergmiller, Tobias, Anna M Andersson, Kathrin Tomasek, Enrique Balleza, Daniel Kiviet, Robert Hauschild, Gašper Tkačik, and Calin C Guet. “Biased Partitioning of the Multi-Drug Efflux Pump AcrAB-TolC Underlies Long-Lived Phenotypic Heterogeneity.” Institute of Science and Technology Austria, 2017. <a href=\"https://doi.org/10.15479/AT:ISTA:53\">https://doi.org/10.15479/AT:ISTA:53</a>.","ista":"Bergmiller T, Andersson AM, Tomasek K, Balleza E, Kiviet D, Hauschild R, Tkačik G, Guet CC. 2017. Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:53\">10.15479/AT:ISTA:53</a>.","mla":"Bergmiller, Tobias, et al. <i>Biased Partitioning of the Multi-Drug Efflux Pump AcrAB-TolC Underlies Long-Lived Phenotypic Heterogeneity</i>. Institute of Science and Technology Austria, 2017, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:53\">10.15479/AT:ISTA:53</a>.","ieee":"T. Bergmiller <i>et al.</i>, “Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity.” Institute of Science and Technology Austria, 2017.","short":"T. Bergmiller, A.M. Andersson, K. Tomasek, E. Balleza, D. Kiviet, R. Hauschild, G. Tkačik, C.C. Guet, (2017).","apa":"Bergmiller, T., Andersson, A. M., Tomasek, K., Balleza, E., Kiviet, D., Hauschild, R., … Guet, C. C. (2017). Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:53\">https://doi.org/10.15479/AT:ISTA:53</a>","ama":"Bergmiller T, Andersson AM, Tomasek K, et al. Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity. 2017. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:53\">10.15479/AT:ISTA:53</a>"},"day":"10","date_created":"2018-12-12T12:31:32Z","author":[{"orcid":"0000-0001-5396-4346","first_name":"Tobias","last_name":"Bergmiller","full_name":"Bergmiller, Tobias","id":"2C471CFA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Anna M","orcid":"0000-0003-2912-6769","last_name":"Andersson","full_name":"Andersson, Anna M","id":"2B8A40DA-F248-11E8-B48F-1D18A9856A87"},{"id":"3AEC8556-F248-11E8-B48F-1D18A9856A87","full_name":"Tomasek, Kathrin","last_name":"Tomasek","orcid":"0000-0003-3768-877X","first_name":"Kathrin"},{"full_name":"Balleza, Enrique","last_name":"Balleza","first_name":"Enrique"},{"first_name":"Daniel","last_name":"Kiviet","full_name":"Kiviet, Daniel"},{"last_name":"Hauschild","first_name":"Robert","orcid":"0000-0001-9843-3522","id":"4E01D6B4-F248-11E8-B48F-1D18A9856A87","full_name":"Hauschild, Robert"},{"orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052","first_name":"Calin C","last_name":"Guet"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","keyword":["single cell microscopy","mother machine microfluidic device","AcrAB-TolC pump","multi-drug efflux","Escherichia coli"],"year":"2017","datarep_id":"53","related_material":{"record":[{"id":"665","relation":"research_paper","status":"public"}]},"title":"Biased partitioning of the multi-drug efflux pump AcrAB-TolC underlies long-lived phenotypic heterogeneity","type":"research_data","date_updated":"2025-09-11T07:05:03Z","status":"public","publisher":"Institute of Science and Technology Austria","file_date_updated":"2020-07-14T12:47:03Z","has_accepted_license":"1","department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"Bio"}],"date_published":"2017-03-10T00:00:00Z","file":[{"access_level":"open_access","date_updated":"2020-07-14T12:47:03Z","content_type":"application/zip","relation":"main_file","file_id":"5603","creator":"system","file_name":"IST-2017-53-v1+1_Data_MDE.zip","checksum":"d77859af757ac8025c50c7b12b52eaf3","file_size":6773204,"date_created":"2018-12-12T13:02:38Z"}],"oa":1,"article_processing_charge":"No","_id":"5560","month":"03","doi":"10.15479/AT:ISTA:53","ddc":["571"]}]