[{"ec_funded":1,"related_material":{"record":[{"id":"202","status":"public","relation":"dissertation_contains"}]},"volume":2,"issue":"2","language":[{"iso":"eng"}],"publication_status":"published","intvolume":" 2","month":"02","scopus_import":"1","oa_version":"None","abstract":[{"lang":"eng","text":"Temperate bacteriophages integrate in bacterial genomes as prophages and represent an important source of genetic variation for bacterial evolution, frequently transmitting fitness-augmenting genes such as toxins responsible for virulence of major pathogens. However, only a fraction of bacteriophage infections are lysogenic and lead to prophage acquisition, whereas the majority are lytic and kill the infected bacteria. Unless able to discriminate lytic from lysogenic infections, mechanisms of immunity to bacteriophages are expected to act as a double-edged sword and increase the odds of survival at the cost of depriving bacteria of potentially beneficial prophages. We show that although restriction-modification systems as mechanisms of innate immunity prevent both lytic and lysogenic infections indiscriminately in individual bacteria, they increase the number of prophage-acquiring individuals at the population level. We find that this counterintuitive result is a consequence of phage-host population dynamics, in which restriction-modification systems delay infection onset until bacteria reach densities at which the probability of lysogeny increases. These results underscore the importance of population-level dynamics as a key factor modulating costs and benefits of immunity to temperate bacteriophages"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_updated":"2023-09-15T12:04:57Z","status":"public","type":"journal_article","_id":"457","date_created":"2018-12-11T11:46:35Z","date_published":"2018-02-01T00:00:00Z","doi":"10.1038/s41559-017-0424-z","page":"359 - 366","publication":"Nature Ecology and Evolution","day":"01","year":"2018","isi":1,"quality_controlled":"1","publisher":"Springer Nature","title":"Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity","article_processing_charge":"No","external_id":{"isi":["000426516400027"]},"author":[{"full_name":"Pleska, Maros","orcid":"0000-0001-7460-7479","last_name":"Pleska","first_name":"Maros","id":"4569785E-F248-11E8-B48F-1D18A9856A87"},{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","first_name":"Moritz","full_name":"Lang, Moritz","last_name":"Lang"},{"full_name":"Refardt, Dominik","last_name":"Refardt","first_name":"Dominik"},{"full_name":"Levin, Bruce","last_name":"Levin","first_name":"Bruce"},{"first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0001-6220-2052","full_name":"Guet, Calin C","last_name":"Guet"}],"publist_id":"7364","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"chicago":"Pleska, Maros, Moritz Lang, Dominik Refardt, Bruce Levin, and Calin C Guet. “Phage-Host Population Dynamics Promotes Prophage Acquisition in Bacteria with Innate Immunity.” Nature Ecology and Evolution. Springer Nature, 2018. https://doi.org/10.1038/s41559-017-0424-z.","ista":"Pleska M, Lang M, Refardt D, Levin B, Guet CC. 2018. Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. 2(2), 359–366.","mla":"Pleska, Maros, et al. “Phage-Host Population Dynamics Promotes Prophage Acquisition in Bacteria with Innate Immunity.” Nature Ecology and Evolution, vol. 2, no. 2, Springer Nature, 2018, pp. 359–66, doi:10.1038/s41559-017-0424-z.","ama":"Pleska M, Lang M, Refardt D, Levin B, Guet CC. Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. 2018;2(2):359-366. doi:10.1038/s41559-017-0424-z","apa":"Pleska, M., Lang, M., Refardt, D., Levin, B., & Guet, C. C. (2018). Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity. Nature Ecology and Evolution. Springer Nature. https://doi.org/10.1038/s41559-017-0424-z","ieee":"M. Pleska, M. Lang, D. Refardt, B. Levin, and C. C. Guet, “Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity,” Nature Ecology and Evolution, vol. 2, no. 2. Springer Nature, pp. 359–366, 2018.","short":"M. Pleska, M. Lang, D. Refardt, B. Levin, C.C. Guet, Nature Ecology and Evolution 2 (2018) 359–366."},"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"grant_number":"RGY0079/2011","name":"Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems (HFSP Young investigators' grant)","_id":"251BCBEC-B435-11E9-9278-68D0E5697425"},{"_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 (DOC Fellowship)"}]},{"title":"Implementation of the inference method in Matlab","department":[{"_id":"GaTk"}],"article_processing_charge":"No","author":[{"first_name":"Katarína","last_name":"Bod’Ová","full_name":"Bod’Ová, Katarína"},{"full_name":"Mitchell, Gabriel","last_name":"Mitchell","id":"315BCD80-F248-11E8-B48F-1D18A9856A87","first_name":"Gabriel"},{"first_name":"Roy","last_name":"Harpaz","full_name":"Harpaz, Roy"},{"last_name":"Schneidman","full_name":"Schneidman, Elad","first_name":"Elad"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-09-15T12:06:18Z","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. https://doi.org/10.1371/journal.pone.0193049.s001.","ista":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. 2018. Implementation of the inference method in Matlab, Public Library of Science, 10.1371/journal.pone.0193049.s001.","mla":"Bod’Ová, Katarína, et al. Implementation of the Inference Method in Matlab. Public Library of Science, 2018, doi:10.1371/journal.pone.0193049.s001.","apa":"Bod’Ová, K., Mitchell, G., Harpaz, R., Schneidman, E., & Tkačik, G. (2018). Implementation of the inference method in Matlab. Public Library of Science. https://doi.org/10.1371/journal.pone.0193049.s001","ama":"Bod’Ová K, Mitchell G, Harpaz R, Schneidman E, Tkačik G. Implementation of the inference method in Matlab. 2018. doi:10.1371/journal.pone.0193049.s001","ieee":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, and G. Tkačik, “Implementation of the inference method in Matlab.” Public Library of Science, 2018.","short":"K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, (2018)."},"status":"public","type":"research_data_reference","_id":"9831","date_created":"2021-08-09T07:01:24Z","related_material":{"record":[{"status":"public","id":"406","relation":"used_in_publication"}]},"doi":"10.1371/journal.pone.0193049.s001","date_published":"2018-03-07T00:00:00Z","day":"07","year":"2018","month":"03","publisher":"Public Library of Science","oa_version":"Published Version","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."}]},{"oa_version":"Preprint","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."}],"intvolume":" 98","month":"10","main_file_link":[{"open_access":"1","url":"https://www.biorxiv.org/content/10.1101/243816v2.full"}],"scopus_import":"1","language":[{"iso":"eng"}],"publication_status":"published","publication_identifier":{"issn":["24700045"]},"ec_funded":1,"volume":98,"issue":"4","_id":"31","status":"public","article_type":"original","type":"journal_article","date_updated":"2023-09-18T09:18:44Z","department":[{"_id":"GaTk"}],"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).","oa":1,"quality_controlled":"1","publisher":"American Physical Society","publication":"Physical Review E","day":"17","year":"2018","isi":1,"date_created":"2018-12-11T11:44:15Z","date_published":"2018-10-17T00:00:00Z","doi":"10.1103/PhysRevE.98.042410","article_number":"042410","project":[{"_id":"26436750-B435-11E9-9278-68D0E5697425","call_identifier":"H2020","name":"Human Brain Project Specific Grant Agreement 2 (HBP SGA 2)","grant_number":"785907"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"ama":"Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. 2018;98(4). doi:10.1103/PhysRevE.98.042410","apa":"Ferrari, U., Deny, S., Chalk, M. J., Tkačik, G., Marre, O., & Mora, T. (2018). Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. American Physical Society. https://doi.org/10.1103/PhysRevE.98.042410","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,” Physical Review E, vol. 98, no. 4. American Physical Society, 2018.","mla":"Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” Physical Review E, vol. 98, no. 4, 042410, American Physical Society, 2018, doi:10.1103/PhysRevE.98.042410.","ista":"Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. 2018. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. 98(4), 042410.","chicago":"Ferrari, Ulisse, Stephane Deny, Matthew J Chalk, Gašper Tkačik, Olivier Marre, and Thierry Mora. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” Physical Review E. American Physical Society, 2018. https://doi.org/10.1103/PhysRevE.98.042410."},"title":"Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons","external_id":{"isi":["000447486100004"]},"article_processing_charge":"No","author":[{"full_name":"Ferrari, Ulisse","last_name":"Ferrari","first_name":"Ulisse"},{"last_name":"Deny","full_name":"Deny, Stephane","first_name":"Stephane"},{"first_name":"Matthew J","full_name":"Chalk, Matthew J","last_name":"Chalk"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Marre","full_name":"Marre, Olivier","first_name":"Olivier"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"}],"publist_id":"8024"},{"project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}],"publist_id":"7273","author":[{"full_name":"Chalk, Matthew J","orcid":"0000-0001-7782-4436","last_name":"Chalk","first_name":"Matthew J","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"article_processing_charge":"No","external_id":{"isi":["000419128700049"]},"title":"Toward a unified theory of efficient, predictive, and sparse coding","citation":{"mla":"Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” PNAS, vol. 115, no. 1, National Academy of Sciences, 2018, pp. 186–91, doi:10.1073/pnas.1711114115.","ama":"Chalk MJ, Marre O, Tkačik G. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 2018;115(1):186-191. doi:10.1073/pnas.1711114115","apa":"Chalk, M. J., Marre, O., & Tkačik, G. (2018). Toward a unified theory of efficient, predictive, and sparse coding. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1711114115","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,” PNAS, vol. 115, no. 1. National Academy of Sciences, pp. 186–191, 2018.","chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” PNAS. National Academy of Sciences, 2018. https://doi.org/10.1073/pnas.1711114115.","ista":"Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 115(1), 186–191."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","quality_controlled":"1","publisher":"National Academy of Sciences","oa":1,"page":"186 - 191","date_published":"2018-01-02T00:00:00Z","doi":"10.1073/pnas.1711114115","date_created":"2018-12-11T11:47:04Z","isi":1,"year":"2018","day":"02","publication":"PNAS","type":"journal_article","status":"public","_id":"543","department":[{"_id":"GaTk"}],"date_updated":"2023-09-19T10:16:35Z","scopus_import":"1","main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/152660 "}],"month":"01","intvolume":" 115","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."}],"oa_version":"Submitted Version","volume":115,"issue":"1","publication_status":"published","language":[{"iso":"eng"}]},{"citation":{"mla":"Bodova, Katarina, et al. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” Physica D: Nonlinear Phenomena, vol. 376–377, Elsevier, 2018, pp. 108–20, doi:10.1016/j.physd.2017.10.015.","ieee":"K. Bodova, J. Haskovec, and P. Markowich, “Well posedness and maximum entropy approximation for the dynamics of quantitative traits,” Physica D: Nonlinear Phenomena, vol. 376–377. Elsevier, pp. 108–120, 2018.","short":"K. Bodova, J. Haskovec, P. Markowich, Physica D: Nonlinear Phenomena 376–377 (2018) 108–120.","ama":"Bodova K, Haskovec J, Markowich P. Well posedness and maximum entropy approximation for the dynamics of quantitative traits. Physica D: Nonlinear Phenomena. 2018;376-377:108-120. doi:10.1016/j.physd.2017.10.015","apa":"Bodova, K., Haskovec, J., & Markowich, P. (2018). Well posedness and maximum entropy approximation for the dynamics of quantitative traits. Physica D: Nonlinear Phenomena. Elsevier. https://doi.org/10.1016/j.physd.2017.10.015","chicago":"Bodova, Katarina, Jan Haskovec, and Peter Markowich. “Well Posedness and Maximum Entropy Approximation for the Dynamics of Quantitative Traits.” Physica D: Nonlinear Phenomena. Elsevier, 2018. https://doi.org/10.1016/j.physd.2017.10.015.","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."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","author":[{"last_name":"Bodova","orcid":"0000-0002-7214-0171","full_name":"Bodova, Katarina","first_name":"Katarina","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jan","full_name":"Haskovec, Jan","last_name":"Haskovec"},{"last_name":"Markowich","full_name":"Markowich, Peter","first_name":"Peter"}],"publist_id":"7198","external_id":{"arxiv":["1704.08757"],"isi":["000437962900012"]},"article_processing_charge":"No","title":"Well posedness and maximum entropy approximation for the dynamics of quantitative traits","isi":1,"year":"2018","day":"01","publication":"Physica D: Nonlinear Phenomena","page":"108-120","doi":"10.1016/j.physd.2017.10.015","date_published":"2018-08-01T00:00:00Z","date_created":"2018-12-11T11:47:28Z","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","publisher":"Elsevier","quality_controlled":"1","oa":1,"date_updated":"2023-09-19T10:38:34Z","department":[{"_id":"NiBa"},{"_id":"GaTk"}],"_id":"607","type":"journal_article","status":"public","publication_status":"published","language":[{"iso":"eng"}],"volume":"376-377","abstract":[{"text":"We study the Fokker-Planck equation derived in the large system limit of the Markovian process describing the dynamics of quantitative traits. The Fokker-Planck equation is posed on a bounded domain and its transport and diffusion coefficients vanish on the domain's boundary. We first argue that, despite this degeneracy, the standard no-flux boundary condition is valid. We derive the weak formulation of the problem and prove the existence and uniqueness of its solutions by constructing the corresponding contraction semigroup on a suitable function space. Then, we prove that for the parameter regime with high enough mutation rate the problem exhibits a positive spectral gap, which implies exponential convergence to equilibrium.Next, we provide a simple derivation of the so-called Dynamic Maximum Entropy (DynMaxEnt) method for approximation of observables (moments) of the Fokker-Planck solution, which can be interpreted as a nonlinear Galerkin approximation. The limited applicability of the DynMaxEnt method inspires us to introduce its modified version that is valid for the whole range of admissible parameters. Finally, we present several numerical experiments to demonstrate the performance of both the original and modified DynMaxEnt methods. We observe that in the parameter regimes where both methods are valid, the modified one exhibits slightly better approximation properties compared to the original one.","lang":"eng"}],"oa_version":"Submitted Version","scopus_import":"1","main_file_link":[{"url":"https://arxiv.org/abs/1704.08757","open_access":"1"}],"month":"08"}]