[{"quality_controlled":"1","publisher":"IEEE","oa":1,"date_published":"2019-08-01T00:00:00Z","doi":"10.1109/ITW44776.2019.8989292","date_created":"2020-03-22T23:00:47Z","isi":1,"year":"2019","day":"01","publication":"IEEE Information Theory Workshop, ITW 2019","project":[{"name":"International IST Doctoral Program","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"}],"article_number":"8989292","author":[{"last_name":"Hledik","full_name":"Hledik, Michal","id":"4171253A-F248-11E8-B48F-1D18A9856A87","first_name":"Michal"},{"first_name":"Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","full_name":"Sokolowski, Thomas R","orcid":"0000-0002-1287-3779","last_name":"Sokolowski"},{"first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455"}],"external_id":{"arxiv":["1812.01475"],"isi":["000540384500015"]},"article_processing_charge":"No","title":"A tight upper bound on mutual information","citation":{"mla":"Hledik, Michal, et al. “A Tight Upper Bound on Mutual Information.” IEEE Information Theory Workshop, ITW 2019, 8989292, IEEE, 2019, doi:10.1109/ITW44776.2019.8989292.","ieee":"M. Hledik, T. R. Sokolowski, and G. Tkačik, “A tight upper bound on mutual information,” in IEEE Information Theory Workshop, ITW 2019, Visby, Sweden, 2019.","short":"M. Hledik, T.R. Sokolowski, G. Tkačik, in:, IEEE Information Theory Workshop, ITW 2019, IEEE, 2019.","ama":"Hledik M, Sokolowski TR, Tkačik G. A tight upper bound on mutual information. In: IEEE Information Theory Workshop, ITW 2019. IEEE; 2019. doi:10.1109/ITW44776.2019.8989292","apa":"Hledik, M., Sokolowski, T. R., & Tkačik, G. (2019). A tight upper bound on mutual information. In IEEE Information Theory Workshop, ITW 2019. Visby, Sweden: IEEE. https://doi.org/10.1109/ITW44776.2019.8989292","chicago":"Hledik, Michal, Thomas R Sokolowski, and Gašper Tkačik. “A Tight Upper Bound on Mutual Information.” In IEEE Information Theory Workshop, ITW 2019. IEEE, 2019. https://doi.org/10.1109/ITW44776.2019.8989292.","ista":"Hledik M, Sokolowski TR, Tkačik G. 2019. A tight upper bound on mutual information. IEEE Information Theory Workshop, ITW 2019. Information Theory Workshop, 8989292."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1812.01475"}],"month":"08","abstract":[{"lang":"eng","text":"We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound."}],"oa_version":"Preprint","related_material":{"record":[{"status":"public","id":"15020","relation":"dissertation_contains"}]},"ec_funded":1,"publication_identifier":{"isbn":["9781538669006"]},"publication_status":"published","language":[{"iso":"eng"}],"type":"conference","conference":{"name":"Information Theory Workshop","location":"Visby, Sweden","end_date":"2019-08-28","start_date":"2019-08-25"},"status":"public","_id":"7606","department":[{"_id":"GaTk"}],"date_updated":"2024-03-06T14:22:51Z"},{"issue":"4","volume":4,"ec_funded":1,"publication_status":"published","file":[{"content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_id":"5929","checksum":"67010cf5e3b3e0637c659371714a715a","date_updated":"2020-07-14T12:45:59Z","file_size":994490,"creator":"dernst","date_created":"2019-02-06T07:36:24Z","file_name":"2018_Heliyon_DeMartino.pdf"}],"language":[{"iso":"eng"}],"scopus_import":1,"month":"04","intvolume":" 4","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"}],"oa_version":"Published Version","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:45:59Z","date_updated":"2021-01-12T07:40:46Z","ddc":["530"],"type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"status":"public","_id":"306","doi":"10.1016/j.heliyon.2018.e00596","date_published":"2018-04-01T00:00:00Z","date_created":"2018-12-11T11:45:44Z","has_accepted_license":"1","year":"2018","day":"01","publication":"Heliyon","quality_controlled":"1","publisher":"Elsevier","oa":1,"author":[{"last_name":"De Martino","full_name":"De Martino, Andrea","first_name":"Andrea"},{"full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706","last_name":"De Martino","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele"}],"title":"An introduction to the maximum entropy approach and its application to inference problems in biology","citation":{"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.","chicago":"De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” Heliyon. Elsevier, 2018. https://doi.org/10.1016/j.heliyon.2018.e00596.","ieee":"A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” Heliyon, vol. 4, no. 4. Elsevier, 2018.","short":"A. De Martino, D. De Martino, Heliyon 4 (2018).","apa":"De Martino, A., & De Martino, D. (2018). An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. Elsevier. https://doi.org/10.1016/j.heliyon.2018.e00596","ama":"De Martino A, De Martino D. An introduction to the maximum entropy approach and its application to inference problems in biology. Heliyon. 2018;4(4). doi:10.1016/j.heliyon.2018.e00596","mla":"De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” Heliyon, vol. 4, no. 4, e00596, Elsevier, 2018, doi:10.1016/j.heliyon.2018.e00596."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"article_number":"e00596"},{"language":[{"iso":"eng"}],"publication_status":"published","volume":1771,"ec_funded":1,"oa_version":"None","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"}],"month":"01","intvolume":" 1771","scopus_import":1,"alternative_title":["MIMB"],"date_updated":"2021-01-12T07:40:42Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"_id":"305","status":"public","type":"journal_article","day":"01","publication":"Methods in Molecular Biology","year":"2018","date_published":"2018-01-01T00:00:00Z","doi":"10.1007/978-1-4939-7792-5_15","date_created":"2018-12-11T11:45:43Z","page":"183 - 202","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.","publisher":"Springer","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Misun, Patrick, et al. “Fabrication and Operation of Microfluidic Hanging Drop Networks.” Methods in Molecular Biology, vol. 1771, Springer, 2018, pp. 183–202, doi:10.1007/978-1-4939-7792-5_15.","ieee":"P. Misun, A. Birchler, M. Lang, A. Hierlemann, and O. Frey, “Fabrication and operation of microfluidic hanging drop networks,” Methods in Molecular Biology, vol. 1771. Springer, pp. 183–202, 2018.","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., & Frey, O. (2018). Fabrication and operation of microfluidic hanging drop networks. Methods in Molecular Biology. Springer. https://doi.org/10.1007/978-1-4939-7792-5_15","ama":"Misun P, Birchler A, Lang M, Hierlemann A, Frey O. Fabrication and operation of microfluidic hanging drop networks. Methods in Molecular Biology. 2018;1771:183-202. doi:10.1007/978-1-4939-7792-5_15","chicago":"Misun, Patrick, Axel Birchler, Moritz Lang, Andreas Hierlemann, and Olivier Frey. “Fabrication and Operation of Microfluidic Hanging Drop Networks.” Methods in Molecular Biology. Springer, 2018. https://doi.org/10.1007/978-1-4939-7792-5_15.","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."},"title":"Fabrication and operation of microfluidic hanging drop networks","publist_id":"7574","author":[{"last_name":"Misun","full_name":"Misun, Patrick","first_name":"Patrick"},{"first_name":"Axel","last_name":"Birchler","full_name":"Birchler, Axel"},{"full_name":"Lang, Moritz","last_name":"Lang","first_name":"Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Andreas","last_name":"Hierlemann","full_name":"Hierlemann, Andreas"},{"last_name":"Frey","full_name":"Frey, Olivier","first_name":"Olivier"}],"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}]},{"issue":"23","volume":115,"related_material":{"record":[{"status":"public","id":"6473","relation":"part_of_dissertation"}]},"publication_status":"published","language":[{"iso":"eng"}],"main_file_link":[{"url":"https://www.biorxiv.org/content/early/2017/09/21/192039","open_access":"1"}],"scopus_import":"1","intvolume":" 115","month":"06","abstract":[{"lang":"eng","text":"Although cells respond specifically to environments, how environmental identity is encoded intracellularly is not understood. Here, we study this organization of information in budding yeast by estimating the mutual information between environmental transitions and the dynamics of nuclear translocation for 10 transcription factors. Our method of estimation is general, scalable, and based on decoding from single cells. The dynamics of the transcription factors are necessary to encode the highest amounts of extracellular information, and we show that information is transduced through two channels: Generalists (Msn2/4, Tod6 and Dot6, Maf1, and Sfp1) can encode the nature of multiple stresses, but only if stress is high; specialists (Hog1, Yap1, and Mig1/2) encode one particular stress, but do so more quickly and for a wider range of magnitudes. In particular, Dot6 encodes almost as much information as Msn2, the master regulator of the environmental stress response. Each transcription factor reports differently, and it is only their collective behavior that distinguishes between multiple environmental states. Changes in the dynamics of the localization of transcription factors thus constitute a precise, distributed internal representation of extracellular change. We predict that such multidimensional representations are common in cellular decision-making."}],"oa_version":"Preprint","pmid":1,"department":[{"_id":"GaTk"}],"date_updated":"2023-09-11T12:58:24Z","article_type":"original","type":"journal_article","status":"public","_id":"281","page":"6088 - 6093","date_created":"2018-12-11T11:45:35Z","doi":"10.1073/pnas.1716659115","date_published":"2018-06-05T00:00:00Z","year":"2018","isi":1,"publication":"PNAS","day":"05","oa":1,"publisher":"National Academy of Sciences","quality_controlled":"1","acknowledgement":"This work was supported by the Biotechnology and Biological Sciences Research Council (J.M.J.P., I.F., and P.S.S.), the Engineering and Physical Sciences Research Council (EPSRC) (A.A.G.), and Austrian Science Fund Grant FWF P28844 (to G.T.).","external_id":{"isi":["000434114900071"],"pmid":["29784812"]},"article_processing_charge":"No","publist_id":"7618","author":[{"full_name":"Granados, Alejandro","last_name":"Granados","first_name":"Alejandro"},{"full_name":"Pietsch, Julian","last_name":"Pietsch","first_name":"Julian"},{"id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","first_name":"Sarah A","last_name":"Cepeda Humerez","full_name":"Cepeda Humerez, Sarah A"},{"first_name":"Isebail","full_name":"Farquhar, Isebail","last_name":"Farquhar"},{"last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"last_name":"Swain","full_name":"Swain, Peter","first_name":"Peter"}],"title":"Distributed and dynamic intracellular organization of extracellular information","citation":{"apa":"Granados, A., Pietsch, J., Cepeda Humerez, S. A., Farquhar, I., Tkačik, G., & Swain, P. (2018). Distributed and dynamic intracellular organization of extracellular information. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1716659115","ama":"Granados A, Pietsch J, Cepeda Humerez SA, Farquhar I, Tkačik G, Swain P. Distributed and dynamic intracellular organization of extracellular information. PNAS. 2018;115(23):6088-6093. doi:10.1073/pnas.1716659115","ieee":"A. Granados, J. Pietsch, S. A. Cepeda Humerez, I. Farquhar, G. Tkačik, and P. Swain, “Distributed and dynamic intracellular organization of extracellular information,” PNAS, vol. 115, no. 23. National Academy of Sciences, pp. 6088–6093, 2018.","short":"A. Granados, J. Pietsch, S.A. Cepeda Humerez, I. Farquhar, G. Tkačik, P. Swain, PNAS 115 (2018) 6088–6093.","mla":"Granados, Alejandro, et al. “Distributed and Dynamic Intracellular Organization of Extracellular Information.” PNAS, vol. 115, no. 23, National Academy of Sciences, 2018, pp. 6088–93, doi:10.1073/pnas.1716659115.","ista":"Granados A, Pietsch J, Cepeda Humerez SA, Farquhar I, Tkačik G, Swain P. 2018. Distributed and dynamic intracellular organization of extracellular information. PNAS. 115(23), 6088–6093.","chicago":"Granados, Alejandro, Julian Pietsch, Sarah A Cepeda Humerez, Isebail Farquhar, Gašper Tkačik, and Peter Swain. “Distributed and Dynamic Intracellular Organization of Extracellular Information.” PNAS. National Academy of Sciences, 2018. https://doi.org/10.1073/pnas.1716659115."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","project":[{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}]},{"project":[{"grant_number":"329960","name":"Mating system and the evolutionary dynamics of hybrid zones","call_identifier":"FP7","_id":"25B36484-B435-11E9-9278-68D0E5697425"},{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425"},{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"title":"Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system","external_id":{"isi":["000437171700017"]},"article_processing_charge":"No","author":[{"first_name":"Katarina","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","last_name":"Bodova","orcid":"0000-0002-7214-0171","full_name":"Bodova, Katarina"},{"full_name":"Priklopil, Tadeas","last_name":"Priklopil","first_name":"Tadeas","id":"3C869AA0-F248-11E8-B48F-1D18A9856A87"},{"id":"419049E2-F248-11E8-B48F-1D18A9856A87","first_name":"David","last_name":"Field","full_name":"Field, David","orcid":"0000-0002-4014-8478"},{"full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H"},{"id":"2C78037E-F248-11E8-B48F-1D18A9856A87","first_name":"Melinda","last_name":"Pickup","full_name":"Pickup, Melinda","orcid":"0000-0001-6118-0541"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"short":"K. Bodova, T. Priklopil, D. Field, N.H. Barton, M. Pickup, Genetics 209 (2018) 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,” Genetics, vol. 209, no. 3. Genetics Society of America, pp. 861–883, 2018.","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. Genetics. 2018;209(3):861-883. doi:10.1534/genetics.118.300748","apa":"Bodova, K., Priklopil, T., Field, D., Barton, N. H., & Pickup, M. (2018). Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.118.300748","mla":"Bodova, Katarina, et al. “Evolutionary Pathways for the Generation of New Self-Incompatibility Haplotypes in a Non-Self Recognition System.” Genetics, vol. 209, no. 3, Genetics Society of America, 2018, pp. 861–83, doi:10.1534/genetics.118.300748.","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.","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.” Genetics. Genetics Society of America, 2018. https://doi.org/10.1534/genetics.118.300748."},"oa":1,"publisher":"Genetics Society of America","quality_controlled":"1","date_created":"2018-12-11T11:45:47Z","date_published":"2018-07-01T00:00:00Z","doi":"10.1534/genetics.118.300748","page":"861-883","publication":"Genetics","day":"01","year":"2018","isi":1,"status":"public","article_type":"original","type":"journal_article","_id":"316","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"date_updated":"2023-09-11T13:57:43Z","intvolume":" 209","month":"07","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/node/80098.abstract"}],"scopus_import":"1","oa_version":"Preprint","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."}],"ec_funded":1,"issue":"3","volume":209,"related_material":{"link":[{"relation":"press_release","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"}],"record":[{"id":"9813","status":"public","relation":"research_data"}]},"language":[{"iso":"eng"}],"publication_status":"published"},{"title":"Supplemental material for Bodova et al., 2018","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"article_processing_charge":"No","author":[{"orcid":"0000-0002-7214-0171","full_name":"Bod'ová, Katarína","last_name":"Bod'ová","first_name":"Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Priklopil, Tadeas","last_name":"Priklopil","id":"3C869AA0-F248-11E8-B48F-1D18A9856A87","first_name":"Tadeas"},{"id":"419049E2-F248-11E8-B48F-1D18A9856A87","first_name":"David","last_name":"Field","orcid":"0000-0002-4014-8478","full_name":"Field, David"},{"orcid":"0000-0002-8548-5240","full_name":"Barton, Nicholas H","last_name":"Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Melinda","id":"2C78037E-F248-11E8-B48F-1D18A9856A87","full_name":"Pickup, Melinda","orcid":"0000-0001-6118-0541","last_name":"Pickup"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-09-11T13:57:42Z","citation":{"mla":"Bodova, Katarina, et al. Supplemental Material for Bodova et Al., 2018. Genetics Society of America, 2018, doi:10.25386/genetics.6148304.v1.","apa":"Bodova, K., Priklopil, T., Field, D., Barton, N. H., & Pickup, M. (2018). Supplemental material for Bodova et al., 2018. Genetics Society of America. https://doi.org/10.25386/genetics.6148304.v1","ama":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. Supplemental material for Bodova et al., 2018. 2018. doi:10.25386/genetics.6148304.v1","short":"K. Bodova, T. Priklopil, D. Field, N.H. Barton, M. Pickup, (2018).","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.","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. https://doi.org/10.25386/genetics.6148304.v1.","ista":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. 2018. Supplemental material for Bodova et al., 2018, Genetics Society of America, 10.25386/genetics.6148304.v1."},"status":"public","type":"research_data_reference","_id":"9813","date_created":"2021-08-06T13:04:32Z","doi":"10.25386/genetics.6148304.v1","related_material":{"record":[{"status":"public","id":"316","relation":"used_in_publication"}]},"date_published":"2018-04-30T00:00:00Z","day":"30","year":"2018","month":"04","main_file_link":[{"open_access":"1","url":"https://doi.org/10.25386/genetics.6148304.v1"}],"oa":1,"publisher":"Genetics Society of America","oa_version":"Published Version","abstract":[{"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.","lang":"eng"}]},{"ddc":["530","571"],"date_updated":"2023-09-15T12:06:19Z","file_date_updated":"2020-07-14T12:46:22Z","department":[{"_id":"GaTk"}],"_id":"406","status":"public","pubrep_id":"995","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"file":[{"file_name":"IST-2018-995-v1+1_2018_Bodova_Probabilistic.pdf","date_created":"2018-12-12T10:15:43Z","file_size":6887358,"date_updated":"2020-07-14T12:46:22Z","creator":"system","checksum":"684229493db75b43e98a46cd922da497","file_id":"5165","content_type":"application/pdf","relation":"main_file","access_level":"open_access"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":13,"related_material":{"record":[{"id":"9831","status":"public","relation":"research_data"}]},"issue":"3","oa_version":"Submitted Version","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. "}],"month":"03","intvolume":" 13","scopus_import":"1","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"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).","chicago":"Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper Tkačik. “Probabilistic Models of Individual and Collective Animal Behavior.” PLoS One. Public Library of Science, 2018. https://doi.org/10.1371/journal.pone.0193049.","ieee":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Probabilistic models of individual and collective animal behavior,” PLoS One, 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).","ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Probabilistic models of individual and collective animal behavior. PLoS One. 2018;13(3). doi:10.1371/journal.pone.0193049","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., & Tkačik, G. (2018). Probabilistic models of individual and collective animal behavior. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0193049","mla":"Bod’Ová, Katarína, et al. “Probabilistic Models of Individual and Collective Animal Behavior.” PLoS One, vol. 13, no. 3, Public Library of Science, 2018, doi:10.1371/journal.pone.0193049."},"title":"Probabilistic models of individual and collective animal behavior","publist_id":"7423","author":[{"first_name":"Katarína","full_name":"Bod’Ová, Katarína","last_name":"Bod’Ová"},{"first_name":"Gabriel","id":"315BCD80-F248-11E8-B48F-1D18A9856A87","last_name":"Mitchell","full_name":"Mitchell, Gabriel"},{"first_name":"Roy","last_name":"Harpaz","full_name":"Harpaz, Roy"},{"first_name":"Elad","full_name":"Schneidman, Elad","last_name":"Schneidman"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"article_processing_charge":"Yes","external_id":{"isi":["000426896800032"]},"project":[{"_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012","name":"Information processing and computation in fish groups"}],"day":"07","publication":"PLoS One","isi":1,"has_accepted_license":"1","year":"2018","doi":"10.1371/journal.pone.0193049","date_published":"2018-03-07T00:00:00Z","date_created":"2018-12-11T11:46:18Z","acknowledgement":"This work was supported by the Human Frontier Science Program RGP0065/2012 (GT, ES).","publisher":"Public Library of Science","quality_controlled":"1","oa":1},{"_id":"457","type":"journal_article","status":"public","date_updated":"2023-09-15T12:04:57Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"abstract":[{"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","lang":"eng"}],"oa_version":"None","scopus_import":"1","intvolume":" 2","month":"02","publication_status":"published","language":[{"iso":"eng"}],"ec_funded":1,"issue":"2","volume":2,"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"202"}]},"project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"grant_number":"RGY0079/2011","name":"Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems (HFSP Young investigators' grant)","_id":"251BCBEC-B435-11E9-9278-68D0E5697425"},{"name":"Effects of Stochasticity on the Function of Restriction-Modi cation Systems at the Single-Cell Level (DOC Fellowship)","grant_number":"24210","_id":"251D65D8-B435-11E9-9278-68D0E5697425"}],"citation":{"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.” Nature Ecology and Evolution. Springer Nature, 2018. https://doi.org/10.1038/s41559-017-0424-z.","apa":"Pleska, M., Lang, M., Refardt, D., Levin, B., & Guet, C. C. (2018). Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. Springer Nature. https://doi.org/10.1038/s41559-017-0424-z","ama":"Pleska M, Lang M, Refardt D, Levin B, Guet CC. Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. 2018;2(2):359-366. doi:10.1038/s41559-017-0424-z","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,” Nature Ecology and Evolution, vol. 2, no. 2. Springer Nature, pp. 359–366, 2018.","short":"M. Pleska, M. Lang, D. Refardt, B. Levin, C.C. Guet, Nature Ecology and Evolution 2 (2018) 359–366.","mla":"Pleska, Maros, et al. “Phage-Host Population Dynamics Promotes Prophage Acquisition in Bacteria with Innate Immunity.” Nature Ecology and Evolution, vol. 2, no. 2, Springer Nature, 2018, pp. 359–66, doi:10.1038/s41559-017-0424-z."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","article_processing_charge":"No","external_id":{"isi":["000426516400027"]},"publist_id":"7364","author":[{"last_name":"Pleska","full_name":"Pleska, Maros","orcid":"0000-0001-7460-7479","id":"4569785E-F248-11E8-B48F-1D18A9856A87","first_name":"Maros"},{"full_name":"Lang, Moritz","last_name":"Lang","first_name":"Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Refardt","full_name":"Refardt, Dominik","first_name":"Dominik"},{"full_name":"Levin, Bruce","last_name":"Levin","first_name":"Bruce"},{"full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C"}],"title":"Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity","quality_controlled":"1","publisher":"Springer Nature","year":"2018","isi":1,"publication":"Nature Ecology and Evolution","day":"01","page":"359 - 366","date_created":"2018-12-11T11:46:35Z","doi":"10.1038/s41559-017-0424-z","date_published":"2018-02-01T00:00:00Z"},{"_id":"9831","type":"research_data_reference","status":"public","date_updated":"2023-09-15T12:06:18Z","citation":{"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.","short":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, (2018).","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., & Tkačik, G. (2018). Implementation of the inference method in Matlab. Public Library of Science. https://doi.org/10.1371/journal.pone.0193049.s001","ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Implementation of the inference method in Matlab. 2018. doi:10.1371/journal.pone.0193049.s001","mla":"Bod’Ová, Katarína, et al. Implementation of the Inference Method in Matlab. Public Library of Science, 2018, doi:10.1371/journal.pone.0193049.s001.","ista":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Implementation of the inference method in Matlab, Public Library of Science, 10.1371/journal.pone.0193049.s001.","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. https://doi.org/10.1371/journal.pone.0193049.s001."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","author":[{"first_name":"Katarína","full_name":"Bod’Ová, Katarína","last_name":"Bod’Ová"},{"last_name":"Mitchell","full_name":"Mitchell, Gabriel","first_name":"Gabriel","id":"315BCD80-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Harpaz","full_name":"Harpaz, Roy","first_name":"Roy"},{"full_name":"Schneidman, Elad","last_name":"Schneidman","first_name":"Elad"},{"orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper"}],"title":"Implementation of the inference method in Matlab","department":[{"_id":"GaTk"}],"abstract":[{"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.","lang":"eng"}],"oa_version":"Published Version","publisher":"Public Library of Science","month":"03","year":"2018","day":"07","date_created":"2021-08-09T07:01:24Z","date_published":"2018-03-07T00:00:00Z","doi":"10.1371/journal.pone.0193049.s001","related_material":{"record":[{"status":"public","id":"406","relation":"used_in_publication"}]}},{"citation":{"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.” Physical Review E. American Physical Society, 2018. https://doi.org/10.1103/PhysRevE.98.042410.","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.","mla":"Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” Physical Review E, vol. 98, no. 4, 042410, American Physical Society, 2018, doi:10.1103/PhysRevE.98.042410.","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. Physical Review E. 2018;98(4). doi:10.1103/PhysRevE.98.042410","apa":"Ferrari, U., Deny, S., Chalk, M. J., Tkačik, G., Marre, O., & Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. American Physical Society. https://doi.org/10.1103/PhysRevE.98.042410","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,” Physical Review E, vol. 98, no. 4. American Physical Society, 2018.","short":"U. Ferrari, S. Deny, M.J. Chalk, G. Tkačik, O. Marre, T. Mora, Physical Review E 98 (2018)."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","author":[{"first_name":"Ulisse","last_name":"Ferrari","full_name":"Ferrari, Ulisse"},{"last_name":"Deny","full_name":"Deny, Stephane","first_name":"Stephane"},{"first_name":"Matthew J","last_name":"Chalk","full_name":"Chalk, Matthew J"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"first_name":"Thierry","full_name":"Mora, Thierry","last_name":"Mora"}],"publist_id":"8024","article_processing_charge":"No","external_id":{"isi":["000447486100004"]},"title":"Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons","article_number":"042410","project":[{"_id":"26436750-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","grant_number":"785907","name":"Human Brain Project Specific Grant Agreement 2 (HBP SGA 2)"}],"isi":1,"year":"2018","day":"17","publication":"Physical Review E","date_published":"2018-10-17T00:00:00Z","doi":"10.1103/PhysRevE.98.042410","date_created":"2018-12-11T11:44:15Z","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).","publisher":"American Physical Society","quality_controlled":"1","oa":1,"date_updated":"2023-09-18T09:18:44Z","department":[{"_id":"GaTk"}],"_id":"31","article_type":"original","type":"journal_article","status":"public","publication_identifier":{"issn":["24700045"]},"publication_status":"published","language":[{"iso":"eng"}],"issue":"4","volume":98,"ec_funded":1,"abstract":[{"lang":"eng","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."}],"oa_version":"Preprint","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/243816v2.full"}],"month":"10","intvolume":" 98"},{"status":"public","type":"journal_article","_id":"543","department":[{"_id":"GaTk"}],"date_updated":"2023-09-19T10:16:35Z","month":"01","intvolume":" 115","scopus_import":"1","main_file_link":[{"url":"https://doi.org/10.1101/152660 ","open_access":"1"}],"oa_version":"Submitted Version","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"}],"volume":115,"issue":"1","language":[{"iso":"eng"}],"publication_status":"published","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"title":"Toward a unified theory of efficient, predictive, and sparse coding","publist_id":"7273","author":[{"orcid":"0000-0001-7782-4436","full_name":"Chalk, Matthew J","last_name":"Chalk","first_name":"Matthew J","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Olivier","last_name":"Marre","full_name":"Marre, Olivier"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"external_id":{"isi":["000419128700049"]},"article_processing_charge":"No","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” PNAS. National Academy of Sciences, 2018. https://doi.org/10.1073/pnas.1711114115.","ista":"Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 115(1), 186–191.","mla":"Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” PNAS, vol. 115, no. 1, National Academy of Sciences, 2018, pp. 186–91, doi:10.1073/pnas.1711114115.","apa":"Chalk, M. J., Marre, O., & Tkačik, G. (2018). Toward a unified theory of efficient, predictive, and sparse coding. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1711114115","ama":"Chalk MJ, Marre O, Tkačik G. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 2018;115(1):186-191. doi:10.1073/pnas.1711114115","ieee":"M. J. Chalk, O. Marre, and G. Tkačik, “Toward a unified theory of efficient, predictive, and sparse coding,” PNAS, vol. 115, no. 1. National Academy of Sciences, pp. 186–191, 2018.","short":"M.J. Chalk, O. Marre, G. Tkačik, PNAS 115 (2018) 186–191."},"publisher":"National Academy of Sciences","quality_controlled":"1","oa":1,"date_published":"2018-01-02T00:00:00Z","doi":"10.1073/pnas.1711114115","date_created":"2018-12-11T11:47:04Z","page":"186 - 191","day":"02","publication":"PNAS","isi":1,"year":"2018"},{"volume":"376-377","language":[{"iso":"eng"}],"publication_status":"published","month":"08","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1704.08757"}],"scopus_import":"1","oa_version":"Submitted Version","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"}],"department":[{"_id":"NiBa"},{"_id":"GaTk"}],"date_updated":"2023-09-19T10:38:34Z","status":"public","type":"journal_article","_id":"607","date_created":"2018-12-11T11:47:28Z","doi":"10.1016/j.physd.2017.10.015","date_published":"2018-08-01T00:00:00Z","page":"108-120","publication":"Physica D: Nonlinear Phenomena","day":"01","year":"2018","isi":1,"oa":1,"publisher":"Elsevier","quality_controlled":"1","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","title":"Well posedness and maximum entropy approximation for the dynamics of quantitative traits","article_processing_charge":"No","external_id":{"isi":["000437962900012"],"arxiv":["1704.08757"]},"author":[{"last_name":"Bodova","full_name":"Bodova, Katarina","orcid":"0000-0002-7214-0171","first_name":"Katarina","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jan","last_name":"Haskovec","full_name":"Haskovec, Jan"},{"first_name":"Peter","last_name":"Markowich","full_name":"Markowich, Peter"}],"publist_id":"7198","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"apa":"Bodova, K., Haskovec, J., & Markowich, P. (2018). Well posedness and maximum entropy approximation for the dynamics of quantitative traits. Physica D: Nonlinear Phenomena. Elsevier. https://doi.org/10.1016/j.physd.2017.10.015","ama":"Bodova K, Haskovec J, Markowich P. Well posedness and maximum entropy approximation for the dynamics of quantitative traits. Physica D: Nonlinear Phenomena. 2018;376-377:108-120. doi:10.1016/j.physd.2017.10.015","ieee":"K. Bodova, J. Haskovec, and P. Markowich, “Well posedness and maximum entropy approximation for the dynamics of quantitative traits,” Physica D: Nonlinear Phenomena, vol. 376–377. Elsevier, pp. 108–120, 2018.","short":"K. Bodova, J. Haskovec, P. Markowich, Physica D: Nonlinear Phenomena 376–377 (2018) 108–120.","mla":"Bodova, Katarina, et al. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” Physica D: Nonlinear Phenomena, vol. 376–377, Elsevier, 2018, pp. 108–20, doi:10.1016/j.physd.2017.10.015.","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.","chicago":"Bodova, Katarina, Jan Haskovec, and Peter Markowich. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” Physica D: Nonlinear Phenomena. Elsevier, 2018. https://doi.org/10.1016/j.physd.2017.10.015."}},{"publist_id":"8036","author":[{"last_name":"Palmer","full_name":"Palmer, Adam","first_name":"Adam"},{"last_name":"Chait","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Roy","last_name":"Kishony","full_name":"Kishony, Roy"}],"external_id":{"pmid":["30169679"],"isi":["000452567200006"]},"article_processing_charge":"No","title":"Nonoptimal gene expression creates latent potential for antibiotic resistance","citation":{"chicago":"Palmer, Adam, Remy P Chait, and Roy Kishony. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” Molecular Biology and Evolution. Oxford University Press, 2018. https://doi.org/10.1093/molbev/msy163.","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.","mla":"Palmer, Adam, et al. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” Molecular Biology and Evolution, vol. 35, no. 11, Oxford University Press, 2018, pp. 2669–84, doi:10.1093/molbev/msy163.","ama":"Palmer A, Chait RP, Kishony R. Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. 2018;35(11):2669-2684. doi:10.1093/molbev/msy163","apa":"Palmer, A., Chait, R. P., & Kishony, R. (2018). Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. Oxford University Press. https://doi.org/10.1093/molbev/msy163","short":"A. Palmer, R.P. Chait, R. Kishony, Molecular Biology and Evolution 35 (2018) 2669–2684.","ieee":"A. Palmer, R. P. Chait, and R. Kishony, “Nonoptimal gene expression creates latent potential for antibiotic resistance,” Molecular Biology and Evolution, vol. 35, no. 11. Oxford University Press, pp. 2669–2684, 2018."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","page":"2669 - 2684","date_published":"2018-08-28T00:00:00Z","doi":"10.1093/molbev/msy163","date_created":"2018-12-11T11:44:11Z","isi":1,"year":"2018","day":"28","publication":"Molecular Biology and Evolution","quality_controlled":"1","publisher":"Oxford University Press","oa":1,"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_updated":"2023-10-17T11:51:06Z","type":"journal_article","article_type":"original","status":"public","_id":"19","volume":35,"issue":"11","publication_identifier":{"issn":["0737-4038"]},"publication_status":"published","language":[{"iso":"eng"}],"scopus_import":"1","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pubmed/30169679","open_access":"1"}],"month":"08","intvolume":" 35","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."}],"pmid":1,"oa_version":"Submitted Version"},{"publication_status":"published","language":[{"iso":"eng"}],"file":[{"creator":"dernst","date_updated":"2020-07-14T12:45:53Z","file_size":3460786,"date_created":"2019-02-13T11:07:15Z","file_name":"2018_Plos_Botella_Soler.pdf","access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_id":"5974","checksum":"3026f94d235219e15514505fdbadf34e"}],"ec_funded":1,"issue":"5","volume":14,"related_material":{"record":[{"relation":"research_data","status":"public","id":"5584"}],"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"}]},"abstract":[{"lang":"eng","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."}],"oa_version":"Published Version","scopus_import":"1","intvolume":" 14","month":"05","date_updated":"2024-02-21T13:45:25Z","ddc":["570"],"file_date_updated":"2020-07-14T12:45:53Z","department":[{"_id":"GaTk"}],"_id":"292","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"article_type":"original","type":"journal_article","status":"public","year":"2018","isi":1,"has_accepted_license":"1","publication":"PLoS Computational Biology","day":"10","date_created":"2018-12-11T11:45:39Z","date_published":"2018-05-10T00:00:00Z","doi":"10.1371/journal.pcbi.1006057","oa":1,"quality_controlled":"1","publisher":"Public Library of Science","citation":{"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.” PLoS Computational Biology. Public Library of Science, 2018. https://doi.org/10.1371/journal.pcbi.1006057.","ieee":"V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina,” PLoS Computational Biology, vol. 14, no. 5. Public Library of Science, 2018.","short":"V. Botella Soler, S. Deny, G.S. Martius, O. Marre, G. Tkačik, PLoS Computational Biology 14 (2018).","apa":"Botella Soler, V., Deny, S., Martius, G. S., Marre, O., & Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1006057","ama":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. 2018;14(5). doi:10.1371/journal.pcbi.1006057","mla":"Botella Soler, Vicente, et al. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” PLoS Computational Biology, vol. 14, no. 5, e1006057, Public Library of Science, 2018, doi:10.1371/journal.pcbi.1006057."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","article_processing_charge":"Yes","external_id":{"isi":["000434012100002"]},"author":[{"first_name":"Vicent","id":"421234E8-F248-11E8-B48F-1D18A9856A87","last_name":"Botella Soler","full_name":"Botella Soler, Vicent","orcid":"0000-0002-8790-1914"},{"last_name":"Deny","full_name":"Deny, Stephane","first_name":"Stephane"},{"last_name":"Martius","full_name":"Martius, Georg S","first_name":"Georg S"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik"}],"title":"Nonlinear decoding of a complex movie from the mammalian retina","article_number":"e1006057","project":[{"call_identifier":"H2020","_id":"25CBA828-B435-11E9-9278-68D0E5697425","name":"Human Brain Project Specific Grant Agreement 1 (HBP SGA 1)","grant_number":"720270"},{"grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}]},{"status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"161","file_date_updated":"2020-07-14T12:45:06Z","department":[{"_id":"GaTk"},{"_id":"CaGu"}],"ddc":["570"],"date_updated":"2024-02-21T13:45:39Z","intvolume":" 9","month":"07","scopus_import":"1","oa_version":"Published Version","abstract":[{"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.","lang":"eng"}],"ec_funded":1,"issue":"1","related_material":{"record":[{"relation":"popular_science","status":"public","id":"5587"}]},"volume":9,"language":[{"iso":"eng"}],"file":[{"date_created":"2018-12-17T16:44:28Z","file_name":"2018_NatureComm_DeMartino.pdf","date_updated":"2020-07-14T12:45:06Z","file_size":1043205,"creator":"dernst","checksum":"3ba7ab27b27723c7dcf633e8fc1f8f18","file_id":"5728","content_type":"application/pdf","access_level":"open_access","relation":"main_file"}],"publication_status":"published","project":[{"grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"article_number":"2988","title":"Statistical mechanics for metabolic networks during steady state growth","external_id":{"isi":["000440149300021"]},"article_processing_charge":"No","publist_id":"7760","author":[{"first_name":"Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706","last_name":"De Martino"},{"first_name":"Andersson Anna","last_name":"Mc","full_name":"Mc, Andersson Anna"},{"id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","first_name":"Tobias","last_name":"Bergmiller","full_name":"Bergmiller, Tobias","orcid":"0000-0001-5396-4346"},{"last_name":"Guet","full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"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.” Nature Communications. Springer Nature, 2018. https://doi.org/10.1038/s41467-018-05417-9.","short":"D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkačik, Nature Communications 9 (2018).","ieee":"D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” Nature Communications, vol. 9, no. 1. Springer Nature, 2018.","ama":"De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 2018;9(1). doi:10.1038/s41467-018-05417-9","apa":"De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., & Tkačik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-018-05417-9","mla":"De Martino, Daniele, et al. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” Nature Communications, vol. 9, no. 1, 2988, Springer Nature, 2018, doi:10.1038/s41467-018-05417-9."},"oa":1,"publisher":"Springer Nature","quality_controlled":"1","date_created":"2018-12-11T11:44:57Z","doi":"10.1038/s41467-018-05417-9","date_published":"2018-07-30T00:00:00Z","publication":"Nature Communications","day":"30","year":"2018","isi":1,"has_accepted_license":"1"},{"citation":{"apa":"Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., & Guet, C. C. (2018). Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. Nature Publishing Group. https://doi.org/10.1038/s41559-018-0651-y","ama":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Evolutionary potential of transcription factors for gene regulatory rewiring. Nature Ecology and Evolution. 2018;2(10):1633-1643. doi:10.1038/s41559-018-0651-y","short":"C. Igler, M. Lagator, G. Tkačik, J.P. Bollback, C.C. Guet, Nature Ecology and Evolution 2 (2018) 1633–1643.","ieee":"C. Igler, M. Lagator, G. Tkačik, J. P. Bollback, and C. C. Guet, “Evolutionary potential of transcription factors for gene regulatory rewiring,” Nature Ecology and Evolution, vol. 2, no. 10. Nature Publishing Group, pp. 1633–1643, 2018.","mla":"Igler, Claudia, et al. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Nature Ecology and Evolution, vol. 2, no. 10, Nature Publishing Group, 2018, pp. 1633–43, doi:10.1038/s41559-018-0651-y.","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.","chicago":"Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Nature Ecology and Evolution. Nature Publishing Group, 2018. https://doi.org/10.1038/s41559-018-0651-y."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","external_id":{"isi":["000447947600021"]},"article_processing_charge":"No","publist_id":"7987","author":[{"id":"46613666-F248-11E8-B48F-1D18A9856A87","first_name":"Claudia","full_name":"Igler, Claudia","last_name":"Igler"},{"last_name":"Lagator","full_name":"Lagator, Mato","id":"345D25EC-F248-11E8-B48F-1D18A9856A87","first_name":"Mato"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"last_name":"Bollback","full_name":"Bollback, Jonathan P","orcid":"0000-0002-4624-4612","first_name":"Jonathan P","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Guet","orcid":"0000-0001-6220-2052","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C"}],"title":"Evolutionary potential of transcription factors for gene regulatory rewiring","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"},{"name":"Selective Barriers to Horizontal Gene Transfer","grant_number":"648440","_id":"2578D616-B435-11E9-9278-68D0E5697425","call_identifier":"H2020"},{"_id":"251EE76E-B435-11E9-9278-68D0E5697425","grant_number":"24573","name":"Design principles underlying genetic switch architecture (DOC Fellowship)"}],"year":"2018","has_accepted_license":"1","isi":1,"publication":"Nature Ecology and Evolution","day":"10","page":"1633 - 1643","date_created":"2018-12-11T11:44:27Z","doi":"10.1038/s41559-018-0651-y","date_published":"2018-09-10T00:00:00Z","oa":1,"publisher":"Nature Publishing Group","quality_controlled":"1","date_updated":"2024-03-27T23:30:48Z","ddc":["570"],"file_date_updated":"2020-07-14T12:47:37Z","department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"JoBo"}],"_id":"67","type":"journal_article","article_type":"original","status":"public","publication_status":"published","language":[{"iso":"eng"}],"file":[{"file_id":"7830","checksum":"383a2e2c944a856e2e821ec8e7bf71b6","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2020-05-14T11:28:52Z","file_name":"2018_NatureEcology_Igler.pdf","date_updated":"2020-07-14T12:47:37Z","file_size":1135973,"creator":"dernst"}],"ec_funded":1,"volume":2,"issue":"10","related_material":{"record":[{"status":"public","id":"5585","relation":"popular_science"},{"id":"6371","status":"public","relation":"dissertation_contains"}]},"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"}],"oa_version":"Submitted Version","scopus_import":"1","intvolume":" 2","month":"09"},{"_id":"613","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","pubrep_id":"911","status":"public","date_updated":"2021-01-12T08:06:15Z","ddc":["576","579"],"file_date_updated":"2020-07-14T12:47:20Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"abstract":[{"text":"Bacteria in groups vary individually, and interact with other bacteria and the environment to produce population-level patterns of gene expression. Investigating such behavior in detail requires measuring and controlling populations at the single-cell level alongside precisely specified interactions and environmental characteristics. Here we present an automated, programmable platform that combines image-based gene expression and growth measurements with on-line optogenetic expression control for hundreds of individual Escherichia coli cells over days, in a dynamically adjustable environment. This integrated platform broadly enables experiments that bridge individual and population behaviors. We demonstrate: (i) population structuring by independent closed-loop control of gene expression in many individual cells, (ii) cell-cell variation control during antibiotic perturbation, (iii) hybrid bio-digital circuits in single cells, and freely specifiable digital communication between individual bacteria. These examples showcase the potential for real-time integration of theoretical models with measurement and control of many individual cells to investigate and engineer microbial population behavior.","lang":"eng"}],"oa_version":"Published Version","scopus_import":1,"intvolume":" 8","month":"12","publication_status":"published","publication_identifier":{"issn":["20411723"]},"language":[{"iso":"eng"}],"file":[{"checksum":"44bb5d0229926c23a9955d9fe0f9723f","file_id":"5190","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2018-12-12T10:16:05Z","file_name":"IST-2017-911-v1+1_s41467-017-01683-1.pdf","date_updated":"2020-07-14T12:47:20Z","file_size":1951699,"creator":"system"}],"ec_funded":1,"volume":8,"issue":"1","article_number":"1535","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"citation":{"mla":"Chait, Remy P., et al. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” Nature Communications, vol. 8, no. 1, 1535, Nature Publishing Group, 2017, doi:10.1038/s41467-017-01683-1.","apa":"Chait, R. P., Ruess, J., Bergmiller, T., Tkačik, G., & Guet, C. C. (2017). Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-01683-1","ama":"Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. 2017;8(1). doi:10.1038/s41467-017-01683-1","ieee":"R. P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, and C. C. Guet, “Shaping bacterial population behavior through computer interfaced control of individual cells,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.","short":"R.P. Chait, J. Ruess, T. Bergmiller, G. Tkačik, C.C. Guet, Nature Communications 8 (2017).","chicago":"Chait, Remy P, Jakob Ruess, Tobias Bergmiller, Gašper Tkačik, and Calin C Guet. “Shaping Bacterial Population Behavior through Computer Interfaced Control of Individual Cells.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/s41467-017-01683-1.","ista":"Chait RP, Ruess J, Bergmiller T, Tkačik G, Guet CC. 2017. Shaping bacterial population behavior through computer interfaced control of individual cells. Nature Communications. 8(1), 1535."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"Yes (in subscription journal)","author":[{"first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P"},{"first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"},{"first_name":"Tobias","id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","last_name":"Bergmiller","orcid":"0000-0001-5396-4346","full_name":"Bergmiller, Tobias"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"7191","title":"Shaping bacterial population behavior through computer interfaced control of individual cells","acknowledgement":"We are grateful to M. Lang, H. Janovjak, M. Khammash, A. Milias-Argeitis, M. Rullan, G. Batt, A. Bosma-Moody, Aryan, S. Leibler, and members of the Guet and Tkačik groups for helpful discussion, comments, and suggestions. We thank A. Moglich, T. Mathes, J. Tabor, and S. Schmidl for kind gifts of strains, and R. Hauschild, B. Knep, M. Lang, T. Asenov, E. Papusheva, T. Menner, T. Adletzberger, and J. Merrin for technical assistance. The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007–2013) under REA grant agreement no. [291734]. (to R.C. and J.R.), Austrian Science Fund grant FWF P28844 (to G.T.), and internal IST Austria Interdisciplinary Project Support. J.R. acknowledges support from the Agence Nationale de la Recherche (ANR) under Grant Nos. ANR-16-CE33-0018 (MEMIP), ANR-16-CE12-0025 (COGEX) and ANR-10-BINF-06-01 (ICEBERG).","oa":1,"publisher":"Nature Publishing Group","quality_controlled":"1","year":"2017","has_accepted_license":"1","publication":"Nature Communications","day":"01","date_created":"2018-12-11T11:47:30Z","date_published":"2017-12-01T00:00:00Z","doi":"10.1038/s41467-017-01683-1"},{"scopus_import":1,"publisher":"IEEE","quality_controlled":"1","month":"02","abstract":[{"text":"We present an approach that enables robots to self-organize their sensorimotor behavior from scratch without providing specific information about neither the robot nor its environment. This is achieved by a simple neural control law that increases the consistency between external sensor dynamics and internal neural dynamics of the utterly simple controller. In this way, the embodiment and the agent-environment coupling are the only source of individual development. We show how an anthropomorphic tendon driven arm-shoulder system develops different behaviors depending on that coupling. For instance: Given a bottle half-filled with water, the arm starts to shake it, driven by the physical response of the water. When attaching a brush, the arm can be manipulated into wiping a table, and when connected to a revolvable wheel it finds out how to rotate it. Thus, the robot may be said to discover the affordances of the world. When allowing two (simulated) humanoid robots to interact physically, they engage into a joint behavior development leading to, for instance, spontaneous cooperation. More social effects are observed if the robots can visually perceive each other. Although, as an observer, it is tempting to attribute an apparent intentionality, there is nothing of the kind put in. As a conclusion, we argue that emergent behavior may be much less rooted in explicit intentions, internal motivations, or specific reward systems than is commonly believed.","lang":"eng"}],"oa_version":"None","date_published":"2017-02-07T00:00:00Z","doi":"10.1109/DEVLRN.2016.7846789","date_created":"2018-12-11T11:47:43Z","publication_identifier":{"isbn":["978-150905069-7"]},"year":"2017","publication_status":"published","day":"07","language":[{"iso":"eng"}],"type":"conference","conference":{"start_date":"2016-09-19","location":"Cergy-Pontoise, France","end_date":"2016-09-22","name":"ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics "},"status":"public","_id":"652","article_number":"7846789","author":[{"last_name":"Der","full_name":"Der, Ralf","first_name":"Ralf"},{"full_name":"Martius, Georg S","last_name":"Martius","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S"}],"publist_id":"7100","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"title":"Dynamical self consistency leads to behavioral development and emergent social interactions in robots","citation":{"ama":"Der R, Martius GS. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. In: IEEE; 2017. doi:10.1109/DEVLRN.2016.7846789","apa":"Der, R., & Martius, G. S. (2017). Dynamical self consistency leads to behavioral development and emergent social interactions in robots. Presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France: IEEE. https://doi.org/10.1109/DEVLRN.2016.7846789","short":"R. Der, G.S. Martius, in:, IEEE, 2017.","ieee":"R. Der and G. S. Martius, “Dynamical self consistency leads to behavioral development and emergent social interactions in robots,” presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France, 2017.","mla":"Der, Ralf, and Georg S. Martius. Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots. 7846789, IEEE, 2017, doi:10.1109/DEVLRN.2016.7846789.","ista":"Der R, Martius GS. 2017. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , 7846789.","chicago":"Der, Ralf, and Georg S Martius. “Dynamical Self Consistency Leads to Behavioral Development and Emergent Social Interactions in Robots.” IEEE, 2017. https://doi.org/10.1109/DEVLRN.2016.7846789."},"date_updated":"2021-01-12T08:07:51Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87"},{"year":"2017","has_accepted_license":"1","publication":"Frontiers in Neurorobotics","day":"16","date_created":"2018-12-11T11:47:45Z","doi":"10.3389/fnbot.2017.00008","date_published":"2017-03-16T00:00:00Z","oa":1,"publisher":"Frontiers Research Foundation","quality_controlled":"1","citation":{"ama":"Der R, Martius GS. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008","apa":"Der, R., & Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. Frontiers Research Foundation. https://doi.org/10.3389/fnbot.2017.00008","ieee":"R. Der and G. S. Martius, “Self organized behavior generation for musculoskeletal robots,” Frontiers in Neurorobotics, vol. 11, no. MAR. Frontiers Research Foundation, 2017.","short":"R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).","mla":"Der, Ralf, and Georg S. Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.” Frontiers in Neurorobotics, vol. 11, no. MAR, 00008, Frontiers Research Foundation, 2017, doi:10.3389/fnbot.2017.00008.","ista":"Der R, Martius GS. 2017. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 11(MAR), 00008.","chicago":"Der, Ralf, and Georg S Martius. “Self Organized Behavior Generation for Musculoskeletal Robots.” Frontiers in Neurorobotics. Frontiers Research Foundation, 2017. https://doi.org/10.3389/fnbot.2017.00008."},"user_id":"2EBD1598-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"Yes","author":[{"full_name":"Der, Ralf","last_name":"Der","first_name":"Ralf"},{"first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","full_name":"Martius, Georg S"}],"publist_id":"7078","title":"Self organized behavior generation for musculoskeletal robots","article_number":"00008","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"publication_status":"published","publication_identifier":{"issn":["16625218"]},"language":[{"iso":"eng"}],"file":[{"file_id":"5371","checksum":"b1bc43f96d1df3313c03032c2a46388d","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"IST-2017-903-v1+1_fnbot-11-00008.pdf","date_created":"2018-12-12T10:18:49Z","file_size":8439566,"date_updated":"2020-07-14T12:47:33Z","creator":"system"}],"ec_funded":1,"volume":11,"issue":"MAR","abstract":[{"lang":"eng","text":"With the accelerated development of robot technologies, control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of specific objectives for the task at hand. While very successful in many applications, self-organized control schemes seem to be favored in large complex systems with unknown dynamics or which are difficult to model. Reasons are the expected scalability, robustness, and resilience of self-organizing systems. The paper presents a self-learning neurocontroller based on extrinsic differential plasticity introduced recently, applying it to an anthropomorphic musculoskeletal robot arm with attached objects of unknown physical dynamics. The central finding of the paper is the following effect: by the mere feedback through the internal dynamics of the object, the robot is learning to relate each of the objects with a very specific sensorimotor pattern. Specifically, an attached pendulum pilots the arm into a circular motion, a half-filled bottle produces axis oriented shaking behavior, a wheel is getting rotated, and wiping patterns emerge automatically in a table-plus-brush setting. By these object-specific dynamical patterns, the robot may be said to recognize the object's identity, or in other words, it discovers dynamical affordances of objects. Furthermore, when including hand coordinates obtained from a camera, a dedicated hand-eye coordination self-organizes spontaneously. These phenomena are discussed from a specific dynamical system perspective. Central is the dedicated working regime at the border to instability with its potentially infinite reservoir of (limit cycle) attractors "waiting" to be excited. Besides converging toward one of these attractors, variate behavior is also arising from a self-induced attractor morphing driven by the learning rule. We claim that experimental investigations with this anthropomorphic, self-learning robot not only generate interesting and potentially useful behaviors, but may also help to better understand what subjective human muscle feelings are, how they can be rooted in sensorimotor patterns, and how these concepts may feed back on robotics."}],"oa_version":"Published Version","scopus_import":1,"intvolume":" 11","month":"03","date_updated":"2021-01-12T08:08:04Z","ddc":["006"],"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:47:33Z","_id":"658","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","pubrep_id":"903","status":"public"},{"title":"Probabilistic models for neural populations that naturally capture global coupling and criticality","article_processing_charge":"Yes","author":[{"first_name":"Jan","id":"2E9627A8-F248-11E8-B48F-1D18A9856A87","full_name":"Humplik, Jan","last_name":"Humplik"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"publist_id":"6960","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Humplik J, Tkačik G. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 2017;13(9). doi:10.1371/journal.pcbi.1005763","apa":"Humplik, J., & Tkačik, G. (2017). Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005763","short":"J. Humplik, G. Tkačik, PLoS Computational Biology 13 (2017).","ieee":"J. Humplik and G. Tkačik, “Probabilistic models for neural populations that naturally capture global coupling and criticality,” PLoS Computational Biology, vol. 13, no. 9. Public Library of Science, 2017.","mla":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology, vol. 13, no. 9, e1005763, Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005763.","ista":"Humplik J, Tkačik G. 2017. Probabilistic models for neural populations that naturally capture global coupling and criticality. PLoS Computational Biology. 13(9), e1005763.","chicago":"Humplik, Jan, and Gašper Tkačik. “Probabilistic Models for Neural Populations That Naturally Capture Global Coupling and Criticality.” PLoS Computational Biology. Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005763."},"project":[{"grant_number":"RGP0065/2012","name":"Information processing and computation in fish groups","_id":"255008E4-B435-11E9-9278-68D0E5697425"},{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"article_number":"e1005763","date_created":"2018-12-11T11:48:08Z","date_published":"2017-09-19T00:00:00Z","doi":"10.1371/journal.pcbi.1005763","publication":"PLoS Computational Biology","day":"19","year":"2017","has_accepted_license":"1","oa":1,"quality_controlled":"1","publisher":"Public Library of Science","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:47:53Z","ddc":["530","571"],"date_updated":"2021-01-12T08:12:21Z","pubrep_id":"884","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"720","issue":"9","volume":13,"language":[{"iso":"eng"}],"file":[{"file_size":14167050,"date_updated":"2020-07-14T12:47:53Z","creator":"system","file_name":"IST-2017-884-v1+1_journal.pcbi.1005763.pdf","date_created":"2018-12-12T10:18:30Z","content_type":"application/pdf","relation":"main_file","access_level":"open_access","checksum":"81107096c19771c36ddbe6f0282a3acb","file_id":"5352"}],"publication_status":"published","publication_identifier":{"issn":["1553734X"]},"intvolume":" 13","month":"09","scopus_import":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Advances in multi-unit recordings pave the way for statistical modeling of activity patterns in large neural populations. Recent studies have shown that the summed activity of all neurons strongly shapes the population response. A separate recent finding has been that neural populations also exhibit criticality, an anomalously large dynamic range for the probabilities of different population activity patterns. Motivated by these two observations, we introduce a class of probabilistic models which takes into account the prior knowledge that the neural population could be globally coupled and close to critical. These models consist of an energy function which parametrizes interactions between small groups of neurons, and an arbitrary positive, strictly increasing, and twice differentiable function which maps the energy of a population pattern to its probability. We show that: 1) augmenting a pairwise Ising model with a nonlinearity yields an accurate description of the activity of retinal ganglion cells which outperforms previous models based on the summed activity of neurons; 2) prior knowledge that the population is critical translates to prior expectations about the shape of the nonlinearity; 3) the nonlinearity admits an interpretation in terms of a continuous latent variable globally coupling the system whose distribution we can infer from data. Our method is independent of the underlying system’s state space; hence, it can be applied to other systems such as natural scenes or amino acid sequences of proteins which are also known to exhibit criticality."}]}]