[{"oa_version":"Published Version","date_published":"2019-05-23T00:00:00Z","type":"dissertation","oa":1,"file":[{"relation":"source_file","creator":"scepeda","file_id":"6480","date_created":"2019-05-23T11:18:16Z","content_type":"application/zip","file_size":23937464,"date_updated":"2020-07-14T12:47:31Z","checksum":"75f9184c1346e10a5de5f9cc7338309a","file_name":"Thesis_Cepeda.zip","access_level":"closed"},{"creator":"scepeda","relation":"main_file","content_type":"application/pdf","date_created":"2019-05-23T11:18:13Z","file_id":"6481","date_updated":"2020-07-14T12:47:31Z","file_size":16646985,"access_level":"open_access","file_name":"CepedaThesis.pdf","checksum":"afdc0633ddbd71d5b13550d7fb4f4454"}],"month":"05","doi":"10.15479/AT:ISTA:6473","publisher":"Institute of Science and Technology Austria","date_updated":"2025-05-28T11:57:00Z","article_processing_charge":"No","day":"23","publication_status":"published","date_created":"2019-05-21T00:11:23Z","degree_awarded":"PhD","alternative_title":["ISTA Thesis"],"page":"135","author":[{"full_name":"Cepeda Humerez, Sarah A","first_name":"Sarah A","id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","last_name":"Cepeda Humerez"}],"keyword":["Information estimation","Time-series","data analysis"],"supervisor":[{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"related_material":{"record":[{"id":"6900","status":"public","relation":"dissertation_contains"},{"status":"public","relation":"dissertation_contains","id":"281"},{"relation":"dissertation_contains","status":"public","id":"2016"},{"relation":"dissertation_contains","status":"public","id":"1576"}]},"ddc":["004"],"year":"2019","language":[{"iso":"eng"}],"citation":{"apa":"Cepeda Humerez, S. A. (2019). <i>Estimating information flow in single cells</i>. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:6473\">https://doi.org/10.15479/AT:ISTA:6473</a>","ista":"Cepeda Humerez SA. 2019. Estimating information flow in single cells. Institute of Science and Technology Austria.","short":"S.A. Cepeda Humerez, Estimating Information Flow in Single Cells, Institute of Science and Technology Austria, 2019.","ieee":"S. A. Cepeda Humerez, “Estimating information flow in single cells,” Institute of Science and Technology Austria, 2019.","mla":"Cepeda Humerez, Sarah A. <i>Estimating Information Flow in Single Cells</i>. Institute of Science and Technology Austria, 2019, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:6473\">10.15479/AT:ISTA:6473</a>.","ama":"Cepeda Humerez SA. Estimating information flow in single cells. 2019. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:6473\">10.15479/AT:ISTA:6473</a>","chicago":"Cepeda Humerez, Sarah A. “Estimating Information Flow in Single Cells.” Institute of Science and Technology Austria, 2019. <a href=\"https://doi.org/10.15479/AT:ISTA:6473\">https://doi.org/10.15479/AT:ISTA:6473</a>."},"has_accepted_license":"1","status":"public","title":"Estimating information flow in single cells","_id":"6473","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","abstract":[{"text":"Single cells are constantly interacting with their environment and each other, more importantly, the accurate perception of environmental cues is crucial for growth, survival, and reproduction. This communication between cells and their environment can be formalized in mathematical terms and be quantified as the information flow between them, as prescribed by information theory. \r\nThe recent availability of real–time dynamical patterns of signaling molecules in single cells has allowed us to identify encoding about the identity of the environment in the time–series. However, efficient estimation of the information transmitted by these signals has been a data–analysis challenge due to the high dimensionality of the trajectories and the limited number of samples. In the first part of this thesis, we develop and evaluate decoding–based estimation methods to lower bound the mutual information and derive model–based precise information estimates for biological reaction networks governed by the chemical master equation. This is followed by applying the decoding-based methods to study the intracellular representation of extracellular changes in budding yeast, by observing the transient dynamics of nuclear translocation of 10 transcription factors in response to 3 stress conditions. Additionally, we apply these estimators to previously published data on ERK and Ca2+ signaling and yeast stress response. We argue that this single cell decoding-based measure of information provides an unbiased, quantitative and interpretable measure for the fidelity of biological signaling processes. \r\nFinally, in the last section, we deal with gene regulation which is primarily controlled by transcription factors (TFs) that bind to the DNA to activate gene expression. The possibility that non-cognate TFs activate transcription diminishes the accuracy of regulation with potentially disastrous effects for the cell. This ’crosstalk’ acts as a previously unexplored source of noise in biochemical networks and puts a strong constraint on their performance. To mitigate erroneous initiation we propose an out of equilibrium scheme that implements kinetic proofreading. We show that such architectures are favored  over their equilibrium counterparts for complex organisms despite introducing noise in gene expression. ","lang":"eng"}],"publication_identifier":{"issn":["2663-337X"]},"file_date_updated":"2020-07-14T12:47:31Z"},{"_id":"6784","title":"Molecular noise of innate immunity shapes bacteria-phage ecologies","abstract":[{"lang":"eng","text":"Mathematical models have been used successfully at diverse scales of biological organization, ranging from ecology and population dynamics to stochastic reaction events occurring between individual molecules in single cells. Generally, many biological processes unfold across multiple scales, with mutations being the best studied example of how stochasticity at the molecular scale can influence outcomes at the population scale. In many other contexts, however, an analogous link between micro- and macro-scale remains elusive, primarily due to the challenges involved in setting up and analyzing multi-scale models. Here, we employ such a model to investigate how stochasticity propagates from individual biochemical reaction events in the bacterial innate immune system to the ecology of bacteria and bacterial viruses. We show analytically how the dynamics of bacterial populations are shaped by the activities of immunity-conferring enzymes in single cells and how the ecological consequences imply optimal bacterial defense strategies against viruses. Our results suggest that bacterial populations in the presence of viruses can either optimize their initial growth rate or their population size, with the first strategy favoring simple immunity featuring a single restriction modification system and the second strategy favoring complex bacterial innate immunity featuring several simultaneously active restriction modification systems."}],"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","has_accepted_license":"1","intvolume":"        15","citation":{"short":"J. Ruess, M. Pleska, C.C. Guet, G. Tkačik, PLoS Computational Biology 15 (2019).","ieee":"J. Ruess, M. Pleska, C. C. Guet, and G. Tkačik, “Molecular noise of innate immunity shapes bacteria-phage ecologies,” <i>PLoS Computational Biology</i>, vol. 15, no. 7. Public Library of Science, 2019.","ista":"Ruess J, Pleska M, Guet CC, Tkačik G. 2019. Molecular noise of innate immunity shapes bacteria-phage ecologies. PLoS Computational Biology. 15(7), e1007168.","apa":"Ruess, J., Pleska, M., Guet, C. C., &#38; Tkačik, G. (2019). Molecular noise of innate immunity shapes bacteria-phage ecologies. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">https://doi.org/10.1371/journal.pcbi.1007168</a>","chicago":"Ruess, Jakob, Maros Pleska, Calin C Guet, and Gašper Tkačik. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” <i>PLoS Computational Biology</i>. Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">https://doi.org/10.1371/journal.pcbi.1007168</a>.","ama":"Ruess J, Pleska M, Guet CC, Tkačik G. Molecular noise of innate immunity shapes bacteria-phage ecologies. <i>PLoS Computational Biology</i>. 2019;15(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">10.1371/journal.pcbi.1007168</a>","mla":"Ruess, Jakob, et al. “Molecular Noise of Innate Immunity Shapes Bacteria-Phage Ecologies.” <i>PLoS Computational Biology</i>, vol. 15, no. 7, e1007168, Public Library of Science, 2019, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168\">10.1371/journal.pcbi.1007168</a>."},"status":"public","file_date_updated":"2020-07-14T12:47:40Z","isi":1,"scopus_import":"1","publication_identifier":{"eissn":["1553-7358"]},"issue":"7","article_number":"e1007168","oa":1,"file":[{"content_type":"application/pdf","date_created":"2019-08-12T12:27:26Z","file_id":"6803","creator":"dernst","relation":"main_file","file_name":"2019_PlosComputBiology_Ruess.pdf","access_level":"open_access","checksum":"7ded4721b41c2a0fc66a1c634540416a","date_updated":"2020-07-14T12:47:40Z","file_size":2200003}],"quality_controlled":"1","month":"07","publication":"PLoS Computational Biology","doi":"10.1371/journal.pcbi.1007168","publisher":"Public Library of Science","date_updated":"2023-08-29T07:10:06Z","article_processing_charge":"No","article_type":"original","oa_version":"Published Version","external_id":{"isi":["000481577700032"]},"volume":15,"date_published":"2019-07-02T00:00:00Z","type":"journal_article","project":[{"_id":"251D65D8-B435-11E9-9278-68D0E5697425","grant_number":"24210","name":"Effects of Stochasticity on the Function of Restriction-Modi cation Systems at the Single-Cell Level"},{"grant_number":"RGY0079/2011","_id":"251BCBEC-B435-11E9-9278-68D0E5697425","name":"Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"language":[{"iso":"eng"}],"ddc":["570"],"year":"2019","related_material":{"record":[{"id":"9786","relation":"research_data","status":"public"}]},"publication_status":"published","day":"02","date_created":"2019-08-11T21:59:19Z","author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess"},{"full_name":"Pleska, Maros","id":"4569785E-F248-11E8-B48F-1D18A9856A87","first_name":"Maros","orcid":"0000-0001-7460-7479","last_name":"Pleska"},{"orcid":"0000-0001-6220-2052","last_name":"Guet","full_name":"Guet, Calin C","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik"}]},{"publication_identifier":{"eissn":["15537358"]},"issue":"9","file_date_updated":"2020-07-14T12:47:44Z","scopus_import":"1","isi":1,"intvolume":"        15","citation":{"apa":"Cepeda Humerez, S. A., Ruess, J., &#38; Tkačik, G. (2019). Estimating information in time-varying signals. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">https://doi.org/10.1371/journal.pcbi.1007290</a>","ista":"Cepeda Humerez SA, Ruess J, Tkačik G. 2019. Estimating information in time-varying signals. PLoS computational biology. 15(9), e1007290.","short":"S.A. Cepeda Humerez, J. Ruess, G. Tkačik, PLoS Computational Biology 15 (2019) e1007290.","ieee":"S. A. Cepeda Humerez, J. Ruess, and G. Tkačik, “Estimating information in time-varying signals,” <i>PLoS computational biology</i>, vol. 15, no. 9. Public Library of Science, p. e1007290, 2019.","mla":"Cepeda Humerez, Sarah A., et al. “Estimating Information in Time-Varying Signals.” <i>PLoS Computational Biology</i>, vol. 15, no. 9, Public Library of Science, 2019, p. e1007290, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">10.1371/journal.pcbi.1007290</a>.","ama":"Cepeda Humerez SA, Ruess J, Tkačik G. Estimating information in time-varying signals. <i>PLoS computational biology</i>. 2019;15(9):e1007290. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">10.1371/journal.pcbi.1007290</a>","chicago":"Cepeda Humerez, Sarah A, Jakob Ruess, and Gašper Tkačik. “Estimating Information in Time-Varying Signals.” <i>PLoS Computational Biology</i>. Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">https://doi.org/10.1371/journal.pcbi.1007290</a>."},"has_accepted_license":"1","status":"public","pmid":1,"user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","abstract":[{"text":"Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.","lang":"eng"}],"title":"Estimating information in time-varying signals","_id":"6900","date_created":"2019-09-22T22:00:37Z","day":"03","publication_status":"published","author":[{"full_name":"Cepeda Humerez, Sarah A","id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87","first_name":"Sarah A","last_name":"Cepeda Humerez"},{"orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper"}],"page":"e1007290","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"related_material":{"record":[{"id":"6473","status":"public","relation":"part_of_dissertation"}]},"year":"2019","language":[{"iso":"eng"}],"ddc":["570"],"oa_version":"Published Version","external_id":{"isi":["000489741800021"],"pmid":["31479447"]},"project":[{"grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Biophysics of information processing in gene regulation"}],"type":"journal_article","date_published":"2019-09-03T00:00:00Z","volume":15,"month":"09","oa":1,"quality_controlled":"1","file":[{"creator":"kschuh","relation":"main_file","date_created":"2019-10-01T10:53:45Z","file_id":"6925","content_type":"application/pdf","file_size":3081855,"date_updated":"2020-07-14T12:47:44Z","checksum":"81bdce1361c9aa8395d6fa635fb6ab47","access_level":"open_access","file_name":"2019_PLoS_Cepeda-Humerez.pdf"}],"article_processing_charge":"No","date_updated":"2023-09-07T12:55:21Z","doi":"10.1371/journal.pcbi.1007290","publisher":"Public Library of Science","publication":"PLoS computational biology"},{"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"6784"}]},"year":"2019","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"author":[{"last_name":"Ruess","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob"},{"full_name":"Pleska, Maros","id":"4569785E-F248-11E8-B48F-1D18A9856A87","first_name":"Maros","last_name":"Pleska","orcid":"0000-0001-7460-7479"},{"full_name":"Guet, Calin C","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052","last_name":"Guet"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik"}],"day":"02","date_created":"2021-08-06T08:23:43Z","title":"Supporting text and results","date_updated":"2023-08-29T07:10:05Z","doi":"10.1371/journal.pcbi.1007168.s001","publisher":"Public Library of Science","_id":"9786","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","month":"07","status":"public","type":"research_data_reference","date_published":"2019-07-02T00:00:00Z","citation":{"chicago":"Ruess, Jakob, Maros Pleska, Calin C Guet, and Gašper Tkačik. “Supporting Text and Results.” Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">https://doi.org/10.1371/journal.pcbi.1007168.s001</a>.","mla":"Ruess, Jakob, et al. <i>Supporting Text and Results</i>. Public Library of Science, 2019, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">10.1371/journal.pcbi.1007168.s001</a>.","ama":"Ruess J, Pleska M, Guet CC, Tkačik G. Supporting text and results. 2019. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">10.1371/journal.pcbi.1007168.s001</a>","short":"J. Ruess, M. Pleska, C.C. Guet, G. Tkačik, (2019).","ieee":"J. Ruess, M. Pleska, C. C. Guet, and G. Tkačik, “Supporting text and results.” Public Library of Science, 2019.","apa":"Ruess, J., Pleska, M., Guet, C. C., &#38; Tkačik, G. (2019). Supporting text and results. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">https://doi.org/10.1371/journal.pcbi.1007168.s001</a>","ista":"Ruess J, Pleska M, Guet CC, Tkačik G. 2019. Supporting text and results, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pcbi.1007168.s001\">10.1371/journal.pcbi.1007168.s001</a>."},"oa_version":"Published Version"},{"author":[{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele","full_name":"De Martino, Daniele","last_name":"De Martino","orcid":"0000-0002-5214-4706"},{"last_name":"Mc","full_name":"Mc, Andersson Anna","first_name":"Andersson Anna"},{"orcid":"0000-0001-5396-4346","last_name":"Bergmiller","first_name":"Tobias","id":"2C471CFA-F248-11E8-B48F-1D18A9856A87","full_name":"Bergmiller, Tobias"},{"orcid":"0000-0001-6220-2052","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","full_name":"Guet, Calin C"},{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik"}],"date_created":"2018-12-11T11:44:57Z","publication_status":"published","publist_id":"7760","day":"30","language":[{"iso":"eng"}],"ddc":["570"],"year":"2018","related_material":{"record":[{"id":"5587","status":"public","relation":"popular_science"}]},"department":[{"_id":"GaTk"},{"_id":"CaGu"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"volume":9,"type":"journal_article","date_published":"2018-07-30T00:00:00Z","project":[{"call_identifier":"FWF","name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425"},{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"external_id":{"isi":["000440149300021"]},"oa_version":"Published Version","article_processing_charge":"No","publication":"Nature Communications","publisher":"Springer Nature","doi":"10.1038/s41467-018-05417-9","date_updated":"2024-02-21T13:45:39Z","month":"07","article_number":"2988","file":[{"creator":"dernst","relation":"main_file","content_type":"application/pdf","date_created":"2018-12-17T16:44:28Z","file_id":"5728","date_updated":"2020-07-14T12:45:06Z","file_size":1043205,"file_name":"2018_NatureComm_DeMartino.pdf","access_level":"open_access","checksum":"3ba7ab27b27723c7dcf633e8fc1f8f18"}],"oa":1,"quality_controlled":"1","issue":"1","isi":1,"scopus_import":"1","file_date_updated":"2020-07-14T12:45:06Z","ec_funded":1,"status":"public","has_accepted_license":"1","intvolume":"         9","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.","apa":"De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., &#38; Tkačik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. <i>Nature Communications</i>. Springer Nature. <a href=\"https://doi.org/10.1038/s41467-018-05417-9\">https://doi.org/10.1038/s41467-018-05417-9</a>","ieee":"D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” <i>Nature Communications</i>, vol. 9, no. 1. Springer Nature, 2018.","short":"D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkačik, Nature Communications 9 (2018).","ama":"De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. <i>Nature Communications</i>. 2018;9(1). doi:<a href=\"https://doi.org/10.1038/s41467-018-05417-9\">10.1038/s41467-018-05417-9</a>","mla":"De Martino, Daniele, et al. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” <i>Nature Communications</i>, vol. 9, no. 1, 2988, Springer Nature, 2018, doi:<a href=\"https://doi.org/10.1038/s41467-018-05417-9\">10.1038/s41467-018-05417-9</a>.","chicago":"De Martino, Daniele, Andersson Anna Mc, Tobias Bergmiller, Calin C Guet, and Gašper Tkačik. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” <i>Nature Communications</i>. Springer Nature, 2018. <a href=\"https://doi.org/10.1038/s41467-018-05417-9\">https://doi.org/10.1038/s41467-018-05417-9</a>."},"abstract":[{"lang":"eng","text":"Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells."}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"161","title":"Statistical mechanics for metabolic networks during steady state growth"},{"external_id":{"isi":["000452567200006"],"pmid":["30169679"]},"oa_version":"Submitted Version","article_type":"original","volume":35,"date_published":"2018-08-28T00:00:00Z","type":"journal_article","month":"08","oa":1,"quality_controlled":"1","article_processing_charge":"No","publication":"Molecular Biology and Evolution","date_updated":"2023-10-17T11:51:06Z","doi":"10.1093/molbev/msy163","publisher":"Oxford University Press","date_created":"2018-12-11T11:44:11Z","publist_id":"8036","publication_status":"published","day":"28","author":[{"full_name":"Palmer, Adam","first_name":"Adam","last_name":"Palmer"},{"last_name":"Chait","orcid":"0000-0003-0876-3187","first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","full_name":"Chait, Remy P"},{"last_name":"Kishony","full_name":"Kishony, Roy","first_name":"Roy"}],"page":"2669 - 2684","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"year":"2018","language":[{"iso":"eng"}],"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pubmed/30169679"}],"citation":{"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,” <i>Molecular Biology and Evolution</i>, vol. 35, no. 11. Oxford University Press, pp. 2669–2684, 2018.","apa":"Palmer, A., Chait, R. P., &#38; Kishony, R. (2018). Nonoptimal gene expression creates latent potential for antibiotic resistance. <i>Molecular Biology and Evolution</i>. Oxford University Press. <a href=\"https://doi.org/10.1093/molbev/msy163\">https://doi.org/10.1093/molbev/msy163</a>","ista":"Palmer A, Chait RP, Kishony R. 2018. Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. 35(11), 2669–2684.","chicago":"Palmer, Adam, Remy P Chait, and Roy Kishony. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” <i>Molecular Biology and Evolution</i>. Oxford University Press, 2018. <a href=\"https://doi.org/10.1093/molbev/msy163\">https://doi.org/10.1093/molbev/msy163</a>.","mla":"Palmer, Adam, et al. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” <i>Molecular Biology and Evolution</i>, vol. 35, no. 11, Oxford University Press, 2018, pp. 2669–84, doi:<a href=\"https://doi.org/10.1093/molbev/msy163\">10.1093/molbev/msy163</a>.","ama":"Palmer A, Chait RP, Kishony R. Nonoptimal gene expression creates latent potential for antibiotic resistance. <i>Molecular Biology and Evolution</i>. 2018;35(11):2669-2684. doi:<a href=\"https://doi.org/10.1093/molbev/msy163\">10.1093/molbev/msy163</a>"},"intvolume":"        35","status":"public","pmid":1,"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."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"19","title":"Nonoptimal gene expression creates latent potential for antibiotic resistance","publication_identifier":{"issn":["0737-4038"]},"issue":"11","isi":1,"scopus_import":"1"},{"issue":"1","scopus_import":"1","isi":1,"citation":{"chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” <i>PNAS</i>. National Academy of Sciences, 2018. <a href=\"https://doi.org/10.1073/pnas.1711114115\">https://doi.org/10.1073/pnas.1711114115</a>.","mla":"Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” <i>PNAS</i>, vol. 115, no. 1, National Academy of Sciences, 2018, pp. 186–91, doi:<a href=\"https://doi.org/10.1073/pnas.1711114115\">10.1073/pnas.1711114115</a>.","ama":"Chalk MJ, Marre O, Tkačik G. Toward a unified theory of efficient, predictive, and sparse coding. <i>PNAS</i>. 2018;115(1):186-191. doi:<a href=\"https://doi.org/10.1073/pnas.1711114115\">10.1073/pnas.1711114115</a>","short":"M.J. Chalk, O. Marre, G. Tkačik, PNAS 115 (2018) 186–191.","ieee":"M. J. Chalk, O. Marre, and G. Tkačik, “Toward a unified theory of efficient, predictive, and sparse coding,” <i>PNAS</i>, vol. 115, no. 1. National Academy of Sciences, pp. 186–191, 2018.","apa":"Chalk, M. J., Marre, O., &#38; Tkačik, G. (2018). Toward a unified theory of efficient, predictive, and sparse coding. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1711114115\">https://doi.org/10.1073/pnas.1711114115</a>","ista":"Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 115(1), 186–191."},"intvolume":"       115","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/152660 "}],"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","abstract":[{"lang":"eng","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."}],"title":"Toward a unified theory of efficient, predictive, and sparse coding","_id":"543","date_created":"2018-12-11T11:47:04Z","day":"02","publication_status":"published","publist_id":"7273","author":[{"last_name":"Chalk","orcid":"0000-0001-7782-4436","full_name":"Chalk, Matthew J","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","first_name":"Matthew J"},{"last_name":"Marre","full_name":"Marre, Olivier","first_name":"Olivier"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik"}],"page":"186 - 191","department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"year":"2018","oa_version":"Submitted Version","external_id":{"isi":["000419128700049"]},"project":[{"grant_number":"P 25651-N26","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"date_published":"2018-01-02T00:00:00Z","type":"journal_article","volume":115,"month":"01","oa":1,"quality_controlled":"1","article_processing_charge":"No","publisher":"National Academy of Sciences","date_updated":"2023-09-19T10:16:35Z","doi":"10.1073/pnas.1711114115","publication":"PNAS"},{"day":"29","date_created":"2018-12-12T12:31:39Z","license":"https://creativecommons.org/publicdomain/zero/1.0/","datarep_id":"98","author":[{"first_name":"Stephane","full_name":"Deny, Stephane","last_name":"Deny"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"full_name":"Botella-Soler, Vicente","first_name":"Vicente","last_name":"Botella-Soler"},{"last_name":"Martius","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","full_name":"Martius, Georg S"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper"}],"keyword":["retina","decoding","regression","neural networks","complex stimulus"],"file_date_updated":"2020-07-14T12:47:07Z","tmp":{"short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"292"}]},"ddc":["570"],"year":"2018","citation":{"apa":"Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., &#38; Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:98\">https://doi.org/10.15479/AT:ISTA:98</a>","ista":"Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding of a complex movie from the mammalian retina, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>.","short":"S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).","ieee":"S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina.” Institute of Science and Technology Austria, 2018.","mla":"Deny, Stephane, et al. <i>Nonlinear Decoding of a Complex Movie from the Mammalian Retina</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>.","ama":"Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:98\">10.15479/AT:ISTA:98</a>","chicago":"Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:98\">https://doi.org/10.15479/AT:ISTA:98</a>."},"has_accepted_license":"1","oa_version":"Published Version","status":"public","project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"date_published":"2018-03-29T00:00:00Z","type":"research_data","oa":1,"file":[{"creator":"system","relation":"main_file","file_id":"5590","date_created":"2018-12-12T13:02:24Z","content_type":"application/octet-stream","file_size":1142543971,"date_updated":"2020-07-14T12:47:07Z","checksum":"6808748837b9afbbbabc2a356ca2b88a","file_name":"IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat","access_level":"open_access"},{"file_id":"5591","date_created":"2018-12-12T13:02:25Z","content_type":"application/pdf","relation":"main_file","creator":"system","checksum":"d6d6cd07743038fe3a12352983fcf9dd","file_name":"IST-2018-98-v1+2_ExperimentStructure.pdf","access_level":"open_access","file_size":702336,"date_updated":"2020-07-14T12:47:07Z"},{"creator":"system","relation":"main_file","file_id":"5592","date_created":"2018-12-12T13:02:26Z","content_type":"application/octet-stream","file_size":432,"date_updated":"2020-07-14T12:47:07Z","checksum":"0c9cfb4dab35bb3dc25a04395600b1c8","access_level":"open_access","file_name":"IST-2018-98-v1+3_GoodLocations_area2_20x20.mat"},{"relation":"main_file","creator":"system","date_created":"2018-12-12T13:02:26Z","file_id":"5593","content_type":"text/plain","file_size":986,"date_updated":"2020-07-14T12:47:07Z","checksum":"2a83b011012e21e934b4596285b1a183","file_name":"IST-2018-98-v1+4_README.txt","access_level":"open_access"}],"month":"03","title":"Nonlinear decoding of a complex movie from the mammalian retina","publisher":"Institute of Science and Technology Austria","doi":"10.15479/AT:ISTA:98","date_updated":"2024-02-21T13:45:26Z","_id":"5584","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","abstract":[{"text":"This package contains data for the publication \"Nonlinear decoding of a complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018). \r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion cells that pass stability and quality criteria, recorded on the multi-electrode array, in response to the presentation of the complex movie with many randomly moving dark discs. The responses are represented as 648000 x 91 binary matrix, where the first index indicates the timebin of duration 12.5 ms, and the second index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike in the particular time bin.\r\n(ii) README file and a graphical illustration of the structure of the experiment, specifying how the 648000 timebins are split into epochs where 1, 2, 4, or 10 discs  were displayed, and which stimulus segments are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie frame, with time that is locked to the recorded raster. The luminance traces are produced as described in the manuscript by filtering the raw disc movie with a small gaussian spatial kernel. ","lang":"eng"}]},{"date_created":"2018-12-12T12:31:40Z","day":"20","author":[{"id":"46613666-F248-11E8-B48F-1D18A9856A87","first_name":"Claudia","full_name":"Igler, Claudia","last_name":"Igler"},{"full_name":"Lagator, Mato","id":"345D25EC-F248-11E8-B48F-1D18A9856A87","first_name":"Mato","last_name":"Lagator"},{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","first_name":"Jonathan P","full_name":"Bollback, Jonathan P","last_name":"Bollback","orcid":"0000-0002-4624-4612"},{"full_name":"Guet, Calin C","first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052","last_name":"Guet"}],"datarep_id":"108","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"tmp":{"short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"file_date_updated":"2020-07-14T12:47:07Z","year":"2018","ddc":["576"],"related_material":{"record":[{"id":"67","status":"public","relation":"research_paper"},{"id":"6371","relation":"research_paper","status":"public"}]},"oa_version":"Published Version","has_accepted_license":"1","citation":{"apa":"Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., &#38; Guet, C. C. (2018). Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:108\">https://doi.org/10.15479/AT:ISTA:108</a>","ista":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. 2018. Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:108\">10.15479/AT:ISTA:108</a>.","short":"C. Igler, M. Lagator, G. Tkačik, J.P. Bollback, C.C. Guet, (2018).","ieee":"C. Igler, M. Lagator, G. Tkačik, J. P. Bollback, and C. C. Guet, “Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring.” Institute of Science and Technology Austria, 2018.","mla":"Igler, Claudia, et al. <i>Data for the Paper Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring</i>. Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:108\">10.15479/AT:ISTA:108</a>.","ama":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring. 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:108\">10.15479/AT:ISTA:108</a>","chicago":"Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Data for the Paper Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:108\">https://doi.org/10.15479/AT:ISTA:108</a>."},"project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"_id":"2578D616-B435-11E9-9278-68D0E5697425","grant_number":"648440","name":"Selective Barriers to Horizontal Gene Transfer","call_identifier":"H2020"},{"name":"Design principles underlying genetic switch architecture (DOC Fellowship)","grant_number":"24573","_id":"251EE76E-B435-11E9-9278-68D0E5697425"}],"date_published":"2018-07-20T00:00:00Z","type":"research_data","ec_funded":1,"status":"public","month":"07","file":[{"relation":"main_file","creator":"system","date_created":"2018-12-12T13:02:45Z","file_id":"5611","content_type":"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet","file_size":16507,"date_updated":"2020-07-14T12:47:07Z","checksum":"1435781526c77413802adee0d4583cce","file_name":"IST-2018-108-v1+1_data_figures.xlsx","access_level":"open_access"}],"oa":1,"abstract":[{"lang":"eng","text":"Mean repression values and standard error of the mean are given for all operator mutant libraries."}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","_id":"5585","publisher":"Institute of Science and Technology Austria","date_updated":"2024-03-25T23:30:27Z","doi":"10.15479/AT:ISTA:108","title":"Data for the paper Evolutionary potential of transcription factors for gene regulatory rewiring"},{"_id":"5587","doi":"10.15479/AT:ISTA:62","publisher":"Institute of Science and Technology Austria","title":"Supporting materials \"STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH\"","date_updated":"2024-02-21T13:45:39Z","abstract":[{"text":"Supporting material to the article \r\nSTATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH\r\n\r\nboundscoli.dat\r\nFlux Bounds of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium. \r\n\r\npolcoli.dat\r\nMatrix enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium, \r\nobtained from the soichiometric matrix by standard linear algebra  (reduced row echelon form).\r\n\r\nellis.dat\r\nApproximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\npoint0.dat\r\nCenter of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\nlovasz.cpp  \r\nThis c++ code file receives in input the polytope of the feasible steady states of a metabolic network, \r\n(matrix and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding the polytope\r\nwith the Lovasz method \r\nNB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we refer to  PLoS ONE 10.4 e0122670 (2015).\r\n\r\nsampleHRnew.cpp  \r\nThis c++ code file receives in input the polytope of the feasible steady states of a metabolic network, \r\n(matrix and bounds), the ellipsoid rounding the polytope, a point inside and  \r\nit gives in output a max entropy sampling at fixed average growth rate \r\nof the steady states by performing an Hit-and-Run Monte Carlo Markov chain.\r\nNB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we refer to  PLoS ONE 10.4 e0122670 (2015).","lang":"eng"}],"article_processing_charge":"No","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa":1,"file":[{"file_size":14376,"date_updated":"2020-07-14T12:47:08Z","checksum":"97992e3e8cf8544ec985a48971708726","access_level":"open_access","file_name":"IST-2018-111-v1+1_CODES.zip","relation":"main_file","creator":"system","date_created":"2018-12-12T13:05:13Z","file_id":"5641","content_type":"application/zip"}],"month":"09","ec_funded":1,"status":"public","date_published":"2018-09-21T00:00:00Z","type":"research_data","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"},{"call_identifier":"FWF","name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"has_accepted_license":"1","citation":{"short":"D. De Martino, G. Tkačik, (2018).","ieee":"D. De Martino and G. Tkačik, “Supporting materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology Austria, 2018.","ista":"De Martino D, Tkačik G. 2018. Supporting materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH’, Institute of Science and Technology Austria, <a href=\"https://doi.org/10.15479/AT:ISTA:62\">10.15479/AT:ISTA:62</a>.","apa":"De Martino, D., &#38; Tkačik, G. (2018). Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” Institute of Science and Technology Austria. <a href=\"https://doi.org/10.15479/AT:ISTA:62\">https://doi.org/10.15479/AT:ISTA:62</a>","chicago":"De Martino, Daniele, and Gašper Tkačik. “Supporting Materials ‘STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.’” Institute of Science and Technology Austria, 2018. <a href=\"https://doi.org/10.15479/AT:ISTA:62\">https://doi.org/10.15479/AT:ISTA:62</a>.","ama":"De Martino D, Tkačik G. Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” 2018. doi:<a href=\"https://doi.org/10.15479/AT:ISTA:62\">10.15479/AT:ISTA:62</a>","mla":"De Martino, Daniele, and Gašper Tkačik. <i>Supporting Materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.”</i> Institute of Science and Technology Austria, 2018, doi:<a href=\"https://doi.org/10.15479/AT:ISTA:62\">10.15479/AT:ISTA:62</a>."},"oa_version":"Published Version","year":"2018","ddc":["530"],"related_material":{"record":[{"relation":"research_paper","status":"public","id":"161"}]},"department":[{"_id":"GaTk"}],"tmp":{"short":"CC0 (1.0)","name":"Creative Commons Public Domain Dedication (CC0 1.0)","image":"/images/cc_0.png","legal_code_url":"https://creativecommons.org/publicdomain/zero/1.0/legalcode"},"file_date_updated":"2020-07-14T12:47:08Z","datarep_id":"111","keyword":["metabolic networks","e.coli core","maximum entropy","monte carlo markov chain sampling","ellipsoidal rounding"],"author":[{"orcid":"0000-0002-5214-4706","last_name":"De Martino","full_name":"De Martino, Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele"},{"full_name":"Tkacik, Gasper","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","orcid":"0000-0002-6699-1455"}],"day":"21","date_created":"2018-12-12T12:31:41Z"},{"status":"public","main_file_link":[{"url":"https://arxiv.org/abs/1704.08757","open_access":"1"}],"citation":{"apa":"Bodova, K., Haskovec, J., &#38; Markowich, P. (2018). Well posedness and maximum entropy approximation for the dynamics of quantitative traits. <i>Physica D: Nonlinear Phenomena</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">https://doi.org/10.1016/j.physd.2017.10.015</a>","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.","short":"K. Bodova, J. Haskovec, P. Markowich, Physica D: Nonlinear Phenomena 376–377 (2018) 108–120.","ieee":"K. Bodova, J. Haskovec, and P. Markowich, “Well posedness and maximum entropy approximation for the dynamics of quantitative traits,” <i>Physica D: Nonlinear Phenomena</i>, vol. 376–377. Elsevier, pp. 108–120, 2018.","mla":"Bodova, Katarina, et al. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” <i>Physica D: Nonlinear Phenomena</i>, vol. 376–377, Elsevier, 2018, pp. 108–20, doi:<a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">10.1016/j.physd.2017.10.015</a>.","ama":"Bodova K, Haskovec J, Markowich P. Well posedness and maximum entropy approximation for the dynamics of quantitative traits. <i>Physica D: Nonlinear Phenomena</i>. 2018;376-377:108-120. doi:<a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">10.1016/j.physd.2017.10.015</a>","chicago":"Bodova, Katarina, Jan Haskovec, and Peter Markowich. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” <i>Physica D: Nonlinear Phenomena</i>. Elsevier, 2018. <a href=\"https://doi.org/10.1016/j.physd.2017.10.015\">https://doi.org/10.1016/j.physd.2017.10.015</a>."},"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"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","_id":"607","title":"Well posedness and maximum entropy approximation for the dynamics of quantitative traits","acknowledgement":"JH and PM are funded by KAUST baseline funds and grant no. 1000000193 .\r\nWe thank Nicholas Barton (IST Austria) for his useful comments and suggestions. \r\n\r\n","isi":1,"scopus_import":"1","volume":"376-377","type":"journal_article","date_published":"2018-08-01T00:00:00Z","oa_version":"Submitted Version","external_id":{"arxiv":["1704.08757"],"isi":["000437962900012"]},"article_processing_charge":"No","publication":"Physica D: Nonlinear Phenomena","date_updated":"2023-09-19T10:38:34Z","doi":"10.1016/j.physd.2017.10.015","publisher":"Elsevier","month":"08","oa":1,"quality_controlled":"1","author":[{"id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","first_name":"Katarina","full_name":"Bodova, Katarina","orcid":"0000-0002-7214-0171","last_name":"Bodova"},{"first_name":"Jan","full_name":"Haskovec, Jan","last_name":"Haskovec"},{"first_name":"Peter","full_name":"Markowich, Peter","last_name":"Markowich"}],"page":"108-120","arxiv":1,"date_created":"2018-12-11T11:47:28Z","publist_id":"7198","publication_status":"published","day":"01","language":[{"iso":"eng"}],"year":"2018","department":[{"_id":"NiBa"},{"_id":"GaTk"}]},{"title":"Evolutionary potential of transcription factors for gene regulatory rewiring","_id":"67","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","abstract":[{"lang":"eng","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."}],"status":"public","ec_funded":1,"intvolume":"         2","citation":{"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.","apa":"Igler, C., Lagator, M., Tkačik, G., Bollback, J. P., &#38; Guet, C. C. (2018). Evolutionary potential of transcription factors for gene regulatory rewiring. <i>Nature Ecology and Evolution</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/s41559-018-0651-y\">https://doi.org/10.1038/s41559-018-0651-y</a>","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,” <i>Nature Ecology and Evolution</i>, vol. 2, no. 10. Nature Publishing Group, pp. 1633–1643, 2018.","ama":"Igler C, Lagator M, Tkačik G, Bollback JP, Guet CC. Evolutionary potential of transcription factors for gene regulatory rewiring. <i>Nature Ecology and Evolution</i>. 2018;2(10):1633-1643. doi:<a href=\"https://doi.org/10.1038/s41559-018-0651-y\">10.1038/s41559-018-0651-y</a>","mla":"Igler, Claudia, et al. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” <i>Nature Ecology and Evolution</i>, vol. 2, no. 10, Nature Publishing Group, 2018, pp. 1633–43, doi:<a href=\"https://doi.org/10.1038/s41559-018-0651-y\">10.1038/s41559-018-0651-y</a>.","chicago":"Igler, Claudia, Mato Lagator, Gašper Tkačik, Jonathan P Bollback, and Calin C Guet. “Evolutionary Potential of Transcription Factors for Gene Regulatory Rewiring.” <i>Nature Ecology and Evolution</i>. Nature Publishing Group, 2018. <a href=\"https://doi.org/10.1038/s41559-018-0651-y\">https://doi.org/10.1038/s41559-018-0651-y</a>."},"has_accepted_license":"1","scopus_import":"1","isi":1,"file_date_updated":"2020-07-14T12:47:37Z","issue":"10","doi":"10.1038/s41559-018-0651-y","date_updated":"2024-03-25T23:30:27Z","publisher":"Nature Publishing Group","publication":"Nature Ecology and Evolution","article_processing_charge":"No","oa":1,"file":[{"relation":"main_file","creator":"dernst","file_id":"7830","date_created":"2020-05-14T11:28:52Z","content_type":"application/pdf","file_size":1135973,"date_updated":"2020-07-14T12:47:37Z","checksum":"383a2e2c944a856e2e821ec8e7bf71b6","access_level":"open_access","file_name":"2018_NatureEcology_Igler.pdf"}],"quality_controlled":"1","month":"09","type":"journal_article","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme"},{"name":"Selective Barriers to Horizontal Gene Transfer","call_identifier":"H2020","_id":"2578D616-B435-11E9-9278-68D0E5697425","grant_number":"648440"},{"name":"Design principles underlying genetic switch architecture (DOC Fellowship)","grant_number":"24573","_id":"251EE76E-B435-11E9-9278-68D0E5697425"}],"date_published":"2018-09-10T00:00:00Z","volume":2,"article_type":"original","external_id":{"isi":["000447947600021"]},"oa_version":"Submitted Version","related_material":{"record":[{"relation":"popular_science","status":"public","id":"5585"},{"relation":"dissertation_contains","status":"public","id":"6371"}]},"language":[{"iso":"eng"}],"ddc":["570"],"year":"2018","department":[{"_id":"CaGu"},{"_id":"GaTk"},{"_id":"JoBo"}],"page":"1633 - 1643","author":[{"full_name":"Igler, Claudia","id":"46613666-F248-11E8-B48F-1D18A9856A87","first_name":"Claudia","last_name":"Igler"},{"full_name":"Lagator, Mato","first_name":"Mato","id":"345D25EC-F248-11E8-B48F-1D18A9856A87","last_name":"Lagator"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-4624-4612","last_name":"Bollback","first_name":"Jonathan P","id":"2C6FA9CC-F248-11E8-B48F-1D18A9856A87","full_name":"Bollback, Jonathan P"},{"full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","last_name":"Guet","orcid":"0000-0001-6220-2052"}],"day":"10","publication_status":"published","publist_id":"7987","date_created":"2018-12-11T11:44:27Z"},{"status":"public","date_published":"2018-04-30T00:00:00Z","type":"research_data_reference","main_file_link":[{"open_access":"1","url":"https://doi.org/10.25386/genetics.6148304.v1"}],"citation":{"mla":"Bodova, Katarina, et al. <i>Supplemental Material for Bodova et Al., 2018</i>. Genetics Society of America, 2018, doi:<a href=\"https://doi.org/10.25386/genetics.6148304.v1\">10.25386/genetics.6148304.v1</a>.","ama":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. Supplemental material for Bodova et al., 2018. 2018. doi:<a href=\"https://doi.org/10.25386/genetics.6148304.v1\">10.25386/genetics.6148304.v1</a>","chicago":"Bodova, Katarina, Tadeas Priklopil, David Field, Nicholas H Barton, and Melinda Pickup. “Supplemental Material for Bodova et Al., 2018.” Genetics Society of America, 2018. <a href=\"https://doi.org/10.25386/genetics.6148304.v1\">https://doi.org/10.25386/genetics.6148304.v1</a>.","apa":"Bodova, K., Priklopil, T., Field, D., Barton, N. H., &#38; Pickup, M. (2018). Supplemental material for Bodova et al., 2018. Genetics Society of America. <a href=\"https://doi.org/10.25386/genetics.6148304.v1\">https://doi.org/10.25386/genetics.6148304.v1</a>","ista":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. 2018. Supplemental material for Bodova et al., 2018, Genetics Society of America, <a href=\"https://doi.org/10.25386/genetics.6148304.v1\">10.25386/genetics.6148304.v1</a>.","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."},"oa_version":"Published Version","_id":"9813","date_updated":"2025-05-28T11:57:01Z","title":"Supplemental material for Bodova et al., 2018","publisher":"Genetics Society of America","doi":"10.25386/genetics.6148304.v1","abstract":[{"lang":"eng","text":"File S1 contains figures that clarify the following features: (i) effect of population size on the average number/frequency of SI classes, (ii) changes in the minimal completeness deficit in time for a single class, and (iii) diversification diagrams for all studied pathways, including the summary figure for k = 8. File S2 contains the code required for a stochastic simulation of the SLF system with an example. This file also includes the output in the form of figures and tables."}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","oa":1,"month":"04","author":[{"orcid":"0000-0002-7214-0171","last_name":"Bod'ová","full_name":"Bod'ová, Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","first_name":"Katarína"},{"full_name":"Priklopil, Tadeas","first_name":"Tadeas","id":"3C869AA0-F248-11E8-B48F-1D18A9856A87","last_name":"Priklopil"},{"orcid":"0000-0002-4014-8478","last_name":"Field","first_name":"David","id":"419049E2-F248-11E8-B48F-1D18A9856A87","full_name":"Field, David"},{"orcid":"0000-0002-8548-5240","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","full_name":"Barton, Nicholas H"},{"full_name":"Pickup, Melinda","id":"2C78037E-F248-11E8-B48F-1D18A9856A87","first_name":"Melinda","orcid":"0000-0001-6118-0541","last_name":"Pickup"}],"day":"30","date_created":"2021-08-06T13:04:32Z","year":"2018","related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"316"}]},"department":[{"_id":"NiBa"},{"_id":"GaTk"}]},{"publisher":"Public Library of Science","doi":"10.1371/journal.pone.0193049.s001","date_updated":"2023-09-15T12:06:18Z","title":"Implementation of the inference method in Matlab","_id":"9831","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","article_processing_charge":"No","abstract":[{"lang":"eng","text":"Implementation of the inference method in Matlab, including three applications of the method: The first one for the model of ant motion, the second one for bacterial chemotaxis, and the third one for the motion of fish."}],"month":"03","status":"public","date_published":"2018-03-07T00:00:00Z","type":"research_data_reference","citation":{"chicago":"Bod’Ová, Katarína, Gabriel Mitchell, Roy Harpaz, Elad Schneidman, and Gašper Tkačik. “Implementation of the Inference Method in Matlab.” Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">https://doi.org/10.1371/journal.pone.0193049.s001</a>.","mla":"Bod’Ová, Katarína, et al. <i>Implementation of the Inference Method in Matlab</i>. Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">10.1371/journal.pone.0193049.s001</a>.","ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Implementation of the inference method in Matlab. 2018. doi:<a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">10.1371/journal.pone.0193049.s001</a>","short":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, (2018).","ieee":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Implementation of the inference method in Matlab.” Public Library of Science, 2018.","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., &#38; Tkačik, G. (2018). Implementation of the inference method in Matlab. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">https://doi.org/10.1371/journal.pone.0193049.s001</a>","ista":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Implementation of the inference method in Matlab, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pone.0193049.s001\">10.1371/journal.pone.0193049.s001</a>."},"oa_version":"Published Version","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"406"}]},"year":"2018","department":[{"_id":"GaTk"}],"author":[{"last_name":"Bod’Ová","first_name":"Katarína","full_name":"Bod’Ová, Katarína"},{"last_name":"Mitchell","first_name":"Gabriel","id":"315BCD80-F248-11E8-B48F-1D18A9856A87","full_name":"Mitchell, Gabriel"},{"last_name":"Harpaz","full_name":"Harpaz, Roy","first_name":"Roy"},{"last_name":"Schneidman","first_name":"Elad","full_name":"Schneidman, Elad"},{"full_name":"Tkačik, Gašper","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","orcid":"0000-0002-6699-1455"}],"day":"07","date_created":"2021-08-09T07:01:24Z"},{"scopus_import":"1","isi":1,"issue":"23","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","abstract":[{"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.","lang":"eng"}],"title":"Distributed and dynamic intracellular organization of extracellular information","_id":"281","pmid":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.).","status":"public","citation":{"short":"A. Granados, J. Pietsch, S.A. Cepeda Humerez, I. Farquhar, G. Tkačik, P. Swain, PNAS 115 (2018) 6088–6093.","ieee":"A. Granados, J. Pietsch, S. A. Cepeda Humerez, I. Farquhar, G. Tkačik, and P. Swain, “Distributed and dynamic intracellular organization of extracellular information,” <i>PNAS</i>, vol. 115, no. 23. National Academy of Sciences, pp. 6088–6093, 2018.","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.","apa":"Granados, A., Pietsch, J., Cepeda Humerez, S. A., Farquhar, I., Tkačik, G., &#38; Swain, P. (2018). Distributed and dynamic intracellular organization of extracellular information. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1716659115\">https://doi.org/10.1073/pnas.1716659115</a>","chicago":"Granados, Alejandro, Julian Pietsch, Sarah A Cepeda Humerez, Isebail Farquhar, Gašper Tkačik, and Peter Swain. “Distributed and Dynamic Intracellular Organization of Extracellular Information.” <i>PNAS</i>. National Academy of Sciences, 2018. <a href=\"https://doi.org/10.1073/pnas.1716659115\">https://doi.org/10.1073/pnas.1716659115</a>.","ama":"Granados A, Pietsch J, Cepeda Humerez SA, Farquhar I, Tkačik G, Swain P. Distributed and dynamic intracellular organization of extracellular information. <i>PNAS</i>. 2018;115(23):6088-6093. doi:<a href=\"https://doi.org/10.1073/pnas.1716659115\">10.1073/pnas.1716659115</a>","mla":"Granados, Alejandro, et al. “Distributed and Dynamic Intracellular Organization of Extracellular Information.” <i>PNAS</i>, vol. 115, no. 23, National Academy of Sciences, 2018, pp. 6088–93, doi:<a href=\"https://doi.org/10.1073/pnas.1716659115\">10.1073/pnas.1716659115</a>."},"intvolume":"       115","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/early/2017/09/21/192039"}],"related_material":{"record":[{"id":"6473","status":"public","relation":"part_of_dissertation"}]},"year":"2018","language":[{"iso":"eng"}],"department":[{"_id":"GaTk"}],"author":[{"full_name":"Granados, Alejandro","first_name":"Alejandro","last_name":"Granados"},{"last_name":"Pietsch","full_name":"Pietsch, Julian","first_name":"Julian"},{"last_name":"Cepeda Humerez","full_name":"Cepeda Humerez, Sarah A","first_name":"Sarah A","id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Farquhar","full_name":"Farquhar, Isebail","first_name":"Isebail"},{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455"},{"last_name":"Swain","first_name":"Peter","full_name":"Swain, Peter"}],"page":"6088 - 6093","date_created":"2018-12-11T11:45:35Z","day":"05","publication_status":"published","publist_id":"7618","article_processing_charge":"No","date_updated":"2023-09-11T12:58:24Z","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1716659115","publication":"PNAS","month":"06","oa":1,"quality_controlled":"1","date_published":"2018-06-05T00:00:00Z","project":[{"name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27"}],"type":"journal_article","volume":115,"article_type":"original","external_id":{"isi":["000434114900071"],"pmid":["29784812"]},"oa_version":"Preprint"},{"issue":"5","isi":1,"scopus_import":"1","file_date_updated":"2020-07-14T12:45:53Z","ec_funded":1,"status":"public","has_accepted_license":"1","intvolume":"        14","citation":{"chicago":"Botella Soler, Vicente, Stephane Deny, Georg S Martius, Olivier Marre, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” <i>PLoS Computational Biology</i>. Public Library of Science, 2018. <a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">https://doi.org/10.1371/journal.pcbi.1006057</a>.","ama":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. <i>PLoS Computational Biology</i>. 2018;14(5). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">10.1371/journal.pcbi.1006057</a>","mla":"Botella Soler, Vicente, et al. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” <i>PLoS Computational Biology</i>, vol. 14, no. 5, e1006057, Public Library of Science, 2018, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">10.1371/journal.pcbi.1006057</a>.","ieee":"V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina,” <i>PLoS Computational Biology</i>, vol. 14, no. 5. Public Library of Science, 2018.","short":"V. Botella Soler, S. Deny, G.S. Martius, O. Marre, G. Tkačik, PLoS Computational Biology 14 (2018).","ista":"Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. 2018. Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. 14(5), e1006057.","apa":"Botella Soler, V., Deny, S., Martius, G. S., Marre, O., &#38; Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1006057\">https://doi.org/10.1371/journal.pcbi.1006057</a>"},"_id":"292","title":"Nonlinear decoding of a complex movie from the mammalian retina","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."}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","author":[{"orcid":"0000-0002-8790-1914","last_name":"Botella Soler","first_name":"Vicent","id":"421234E8-F248-11E8-B48F-1D18A9856A87","full_name":"Botella Soler, Vicent"},{"last_name":"Deny","full_name":"Deny, Stephane","first_name":"Stephane"},{"full_name":"Martius, Georg S","first_name":"Georg S","last_name":"Martius"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"publication_status":"published","day":"10","date_created":"2018-12-11T11:45:39Z","ddc":["570"],"year":"2018","language":[{"iso":"eng"}],"related_material":{"record":[{"id":"5584","status":"public","relation":"research_data"}],"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"}]},"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"department":[{"_id":"GaTk"}],"volume":14,"type":"journal_article","date_published":"2018-05-10T00:00:00Z","project":[{"name":"Human Brain Project Specific Grant Agreement 1 (HBP SGA 1)","call_identifier":"H2020","grant_number":"720270","_id":"25CBA828-B435-11E9-9278-68D0E5697425"},{"name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26"}],"oa_version":"Published Version","article_type":"original","external_id":{"isi":["000434012100002"]},"publication":"PLoS Computational Biology","date_updated":"2024-02-21T13:45:25Z","publisher":"Public Library of Science","doi":"10.1371/journal.pcbi.1006057","article_processing_charge":"Yes","article_number":"e1006057","oa":1,"file":[{"date_updated":"2020-07-14T12:45:53Z","file_size":3460786,"access_level":"open_access","file_name":"2018_Plos_Botella_Soler.pdf","checksum":"3026f94d235219e15514505fdbadf34e","relation":"main_file","creator":"dernst","content_type":"application/pdf","date_created":"2019-02-13T11:07:15Z","file_id":"5974"}],"quality_controlled":"1","month":"05"},{"scopus_import":1,"_id":"305","title":"Fabrication and operation of microfluidic hanging drop networks","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"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","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.","ec_funded":1,"status":"public","citation":{"apa":"Misun, P., Birchler, A., Lang, M., Hierlemann, A., &#38; Frey, O. (2018). Fabrication and operation of microfluidic hanging drop networks. <i>Methods in Molecular Biology</i>. Springer. <a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">https://doi.org/10.1007/978-1-4939-7792-5_15</a>","ista":"Misun P, Birchler A, Lang M, Hierlemann A, Frey O. 2018. Fabrication and operation of microfluidic hanging drop networks. Methods in Molecular Biology. 1771, 183–202.","short":"P. Misun, A. Birchler, M. Lang, A. Hierlemann, O. Frey, Methods in Molecular Biology 1771 (2018) 183–202.","ieee":"P. Misun, A. Birchler, M. Lang, A. Hierlemann, and O. Frey, “Fabrication and operation of microfluidic hanging drop networks,” <i>Methods in Molecular Biology</i>, vol. 1771. Springer, pp. 183–202, 2018.","mla":"Misun, Patrick, et al. “Fabrication and Operation of Microfluidic Hanging Drop Networks.” <i>Methods in Molecular Biology</i>, vol. 1771, Springer, 2018, pp. 183–202, doi:<a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">10.1007/978-1-4939-7792-5_15</a>.","ama":"Misun P, Birchler A, Lang M, Hierlemann A, Frey O. Fabrication and operation of microfluidic hanging drop networks. <i>Methods in Molecular Biology</i>. 2018;1771:183-202. doi:<a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">10.1007/978-1-4939-7792-5_15</a>","chicago":"Misun, Patrick, Axel Birchler, Moritz Lang, Andreas Hierlemann, and Olivier Frey. “Fabrication and Operation of Microfluidic Hanging Drop Networks.” <i>Methods in Molecular Biology</i>. Springer, 2018. <a href=\"https://doi.org/10.1007/978-1-4939-7792-5_15\">https://doi.org/10.1007/978-1-4939-7792-5_15</a>."},"intvolume":"      1771","year":"2018","language":[{"iso":"eng"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"page":"183 - 202","alternative_title":["MIMB"],"author":[{"full_name":"Misun, Patrick","first_name":"Patrick","last_name":"Misun"},{"last_name":"Birchler","full_name":"Birchler, Axel","first_name":"Axel"},{"first_name":"Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87","full_name":"Lang, Moritz","last_name":"Lang"},{"first_name":"Andreas","full_name":"Hierlemann, Andreas","last_name":"Hierlemann"},{"last_name":"Frey","first_name":"Olivier","full_name":"Frey, Olivier"}],"publist_id":"7574","publication_status":"published","day":"01","date_created":"2018-12-11T11:45:43Z","publication":"Methods in Molecular Biology","doi":"10.1007/978-1-4939-7792-5_15","date_updated":"2021-01-12T07:40:42Z","publisher":"Springer","quality_controlled":"1","month":"01","volume":1771,"date_published":"2018-01-01T00:00:00Z","type":"journal_article","project":[{"grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"oa_version":"None"},{"issue":"4","scopus_import":1,"file_date_updated":"2020-07-14T12:45:59Z","ec_funded":1,"status":"public","has_accepted_license":"1","citation":{"short":"A. De Martino, D. De Martino, Heliyon 4 (2018).","ieee":"A. De Martino and D. De Martino, “An introduction to the maximum entropy approach and its application to inference problems in biology,” <i>Heliyon</i>, vol. 4, no. 4. Elsevier, 2018.","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.","apa":"De Martino, A., &#38; De Martino, D. (2018). An introduction to the maximum entropy approach and its application to inference problems in biology. <i>Heliyon</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">https://doi.org/10.1016/j.heliyon.2018.e00596</a>","chicago":"De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” <i>Heliyon</i>. Elsevier, 2018. <a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">https://doi.org/10.1016/j.heliyon.2018.e00596</a>.","ama":"De Martino A, De Martino D. An introduction to the maximum entropy approach and its application to inference problems in biology. <i>Heliyon</i>. 2018;4(4). doi:<a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">10.1016/j.heliyon.2018.e00596</a>","mla":"De Martino, Andrea, and Daniele De Martino. “An Introduction to the Maximum Entropy Approach and Its Application to Inference Problems in Biology.” <i>Heliyon</i>, vol. 4, no. 4, e00596, Elsevier, 2018, doi:<a href=\"https://doi.org/10.1016/j.heliyon.2018.e00596\">10.1016/j.heliyon.2018.e00596</a>."},"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"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"306","title":"An introduction to the maximum entropy approach and its application to inference problems in biology","author":[{"first_name":"Andrea","full_name":"De Martino, Andrea","last_name":"De Martino"},{"full_name":"De Martino, Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele","last_name":"De Martino","orcid":"0000-0002-5214-4706"}],"date_created":"2018-12-11T11:45:44Z","publication_status":"published","day":"01","language":[{"iso":"eng"}],"year":"2018","ddc":["530"],"department":[{"_id":"GaTk"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"volume":4,"project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734"}],"type":"journal_article","date_published":"2018-04-01T00:00:00Z","oa_version":"Published Version","publication":"Heliyon","doi":"10.1016/j.heliyon.2018.e00596","publisher":"Elsevier","date_updated":"2021-01-12T07:40:46Z","month":"04","article_number":"e00596","quality_controlled":"1","file":[{"relation":"main_file","creator":"dernst","content_type":"application/pdf","date_created":"2019-02-06T07:36:24Z","file_id":"5929","date_updated":"2020-07-14T12:45:59Z","file_size":994490,"file_name":"2018_Heliyon_DeMartino.pdf","access_level":"open_access","checksum":"67010cf5e3b3e0637c659371714a715a"}],"oa":1},{"date_created":"2018-12-11T11:44:15Z","day":"17","publication_status":"published","publist_id":"8024","author":[{"full_name":"Ferrari, Ulisse","first_name":"Ulisse","last_name":"Ferrari"},{"last_name":"Deny","first_name":"Stephane","full_name":"Deny, Stephane"},{"last_name":"Chalk","full_name":"Chalk, Matthew J","first_name":"Matthew J"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","full_name":"Tkacik, Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"last_name":"Mora","full_name":"Mora, Thierry","first_name":"Thierry"}],"department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"year":"2018","external_id":{"isi":["000447486100004"]},"article_type":"original","oa_version":"Preprint","project":[{"name":"Human Brain Project Specific Grant Agreement 2 (HBP SGA 2)","call_identifier":"H2020","grant_number":"785907","_id":"26436750-B435-11E9-9278-68D0E5697425"}],"date_published":"2018-10-17T00:00:00Z","type":"journal_article","volume":98,"month":"10","quality_controlled":"1","oa":1,"article_number":"042410","article_processing_charge":"No","date_updated":"2023-09-18T09:18:44Z","doi":"10.1103/PhysRevE.98.042410","publisher":"American Physical Society","publication":"Physical Review E","publication_identifier":{"issn":["24700045"]},"issue":"4","scopus_import":"1","isi":1,"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.” <i>Physical Review E</i>. American Physical Society, 2018. <a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">https://doi.org/10.1103/PhysRevE.98.042410</a>.","ama":"Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. <i>Physical Review E</i>. 2018;98(4). doi:<a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">10.1103/PhysRevE.98.042410</a>","mla":"Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” <i>Physical Review E</i>, vol. 98, no. 4, 042410, American Physical Society, 2018, doi:<a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">10.1103/PhysRevE.98.042410</a>.","short":"U. Ferrari, S. Deny, M.J. Chalk, G. Tkačik, O. Marre, T. Mora, Physical Review E 98 (2018).","ieee":"U. Ferrari, S. Deny, M. J. Chalk, G. Tkačik, O. Marre, and T. Mora, “Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons,” <i>Physical Review E</i>, vol. 98, no. 4. American Physical Society, 2018.","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.","apa":"Ferrari, U., Deny, S., Chalk, M. J., Tkačik, G., Marre, O., &#38; Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. <i>Physical Review E</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.98.042410\">https://doi.org/10.1103/PhysRevE.98.042410</a>"},"intvolume":"        98","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/243816v2.full"}],"status":"public","ec_funded":1,"acknowledgement":"This work was supported by ANR Trajectory, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche (LIFESENSES; ANR-10-LABX-65), EC Grant No. H2020-785907 from the Human Brain Project, NIH Grant No. U01NS090501, and an AVIESAN-UNADEV grant to O.M. M.C. was supported by the Agence Nationale de la Recherche Jeune Chercheur/Jeune Chercheuse grant (ANR-17-CE37-0013).","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","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."}],"title":"Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons","_id":"31"},{"quality_controlled":"1","oa":1,"month":"07","publication":"Genetics","publisher":"Genetics Society of America","doi":"10.1534/genetics.118.300748","date_updated":"2025-05-28T11:42:44Z","article_processing_charge":"No","oa_version":"Preprint","article_type":"original","external_id":{"isi":["000437171700017"]},"volume":209,"type":"journal_article","date_published":"2018-07-01T00:00:00Z","project":[{"_id":"25B36484-B435-11E9-9278-68D0E5697425","grant_number":"329960","name":"Mating system and the evolutionary dynamics of hybrid zones","call_identifier":"FP7"},{"_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7"},{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"department":[{"_id":"NiBa"},{"_id":"GaTk"}],"year":"2018","language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"research_data","status":"public","id":"9813"}],"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"}]},"publication_status":"published","day":"01","date_created":"2018-12-11T11:45:47Z","page":"861-883","author":[{"last_name":"Bodova","orcid":"0000-0002-7214-0171","first_name":"Katarina","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bodova, Katarina"},{"id":"3C869AA0-F248-11E8-B48F-1D18A9856A87","first_name":"Tadeas","full_name":"Priklopil, Tadeas","last_name":"Priklopil"},{"first_name":"David","id":"419049E2-F248-11E8-B48F-1D18A9856A87","full_name":"Field, David","last_name":"Field","orcid":"0000-0002-4014-8478"},{"orcid":"0000-0002-8548-5240","last_name":"Barton","full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H"},{"last_name":"Pickup","orcid":"0000-0001-6118-0541","id":"2C78037E-F248-11E8-B48F-1D18A9856A87","first_name":"Melinda","full_name":"Pickup, Melinda"}],"_id":"316","title":"Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system","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."}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","main_file_link":[{"url":"https://www.biorxiv.org/node/80098.abstract","open_access":"1"}],"intvolume":"       209","citation":{"chicago":"Bodova, Katarina, Tadeas Priklopil, David Field, Nicholas H Barton, and Melinda Pickup. “Evolutionary Pathways for the Generation of New Self-Incompatibility Haplotypes in a Non-Self Recognition System.” <i>Genetics</i>. Genetics Society of America, 2018. <a href=\"https://doi.org/10.1534/genetics.118.300748\">https://doi.org/10.1534/genetics.118.300748</a>.","ama":"Bodova K, Priklopil T, Field D, Barton NH, Pickup M. Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. <i>Genetics</i>. 2018;209(3):861-883. doi:<a href=\"https://doi.org/10.1534/genetics.118.300748\">10.1534/genetics.118.300748</a>","mla":"Bodova, Katarina, et al. “Evolutionary Pathways for the Generation of New Self-Incompatibility Haplotypes in a Non-Self Recognition System.” <i>Genetics</i>, vol. 209, no. 3, Genetics Society of America, 2018, pp. 861–83, doi:<a href=\"https://doi.org/10.1534/genetics.118.300748\">10.1534/genetics.118.300748</a>.","short":"K. Bodova, T. Priklopil, D. Field, N.H. Barton, M. Pickup, Genetics 209 (2018) 861–883.","ieee":"K. Bodova, T. Priklopil, D. Field, N. H. Barton, and M. Pickup, “Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system,” <i>Genetics</i>, vol. 209, no. 3. Genetics Society of America, pp. 861–883, 2018.","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.","apa":"Bodova, K., Priklopil, T., Field, D., Barton, N. H., &#38; Pickup, M. (2018). Evolutionary pathways for the generation of new self-incompatibility haplotypes in a non-self recognition system. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.118.300748\">https://doi.org/10.1534/genetics.118.300748</a>"},"ec_funded":1,"status":"public","isi":1,"scopus_import":"1","issue":"3"}]