--- _id: '457' 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 article_processing_charge: No author: - first_name: Maros full_name: Pleska, Maros id: 4569785E-F248-11E8-B48F-1D18A9856A87 last_name: Pleska orcid: 0000-0001-7460-7479 - first_name: Moritz full_name: Lang, Moritz id: 29E0800A-F248-11E8-B48F-1D18A9856A87 last_name: Lang - first_name: Dominik full_name: Refardt, Dominik last_name: Refardt - first_name: Bruce full_name: Levin, Bruce last_name: Levin - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 citation: 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 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. 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. 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. short: M. Pleska, M. Lang, D. Refardt, B. Levin, C.C. Guet, Nature Ecology and Evolution 2 (2018) 359–366. date_created: 2018-12-11T11:46:35Z date_published: 2018-02-01T00:00:00Z date_updated: 2023-09-15T12:04:57Z day: '01' department: - _id: CaGu - _id: GaTk doi: 10.1038/s41559-017-0424-z ec_funded: 1 external_id: isi: - '000426516400027' intvolume: ' 2' isi: 1 issue: '2' language: - iso: eng month: '02' oa_version: None page: 359 - 366 project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 251BCBEC-B435-11E9-9278-68D0E5697425 grant_number: RGY0079/2011 name: Multi-Level Conflicts in Evolutionary Dynamics of Restriction-Modification Systems (HFSP Young investigators' grant) - _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) publication: Nature Ecology and Evolution publication_status: published publisher: Springer Nature publist_id: '7364' quality_controlled: '1' related_material: record: - id: '202' relation: dissertation_contains status: public scopus_import: '1' status: public title: Phage-host population dynamics promotes prophage acquisition in bacteria with innate immunity type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2 year: '2018' ... --- _id: '9831' 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.' article_processing_charge: No author: - first_name: Katarína full_name: Bod’Ová, Katarína last_name: Bod’Ová - first_name: Gabriel full_name: Mitchell, Gabriel id: 315BCD80-F248-11E8-B48F-1D18A9856A87 last_name: Mitchell - first_name: Roy full_name: Harpaz, Roy last_name: Harpaz - first_name: Elad full_name: Schneidman, Elad last_name: Schneidman - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 citation: 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 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 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. 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. 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. short: K. Bod’Ová, G. Mitchell, R. Harpaz, E. Schneidman, G. Tkačik, (2018). date_created: 2021-08-09T07:01:24Z date_published: 2018-03-07T00:00:00Z date_updated: 2023-09-15T12:06:18Z day: '07' department: - _id: GaTk doi: 10.1371/journal.pone.0193049.s001 month: '03' oa_version: Published Version publisher: Public Library of Science related_material: record: - id: '406' relation: used_in_publication status: public status: public title: Implementation of the inference method in Matlab type: research_data_reference user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf year: '2018' ... --- _id: '31' 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. 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). article_number: '042410' article_processing_charge: No article_type: original author: - first_name: Ulisse full_name: Ferrari, Ulisse last_name: Ferrari - first_name: Stephane full_name: Deny, Stephane last_name: Deny - first_name: Matthew J full_name: Chalk, Matthew J last_name: Chalk - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Thierry full_name: Mora, Thierry last_name: Mora 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 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. 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. ista: Ferrari U, Deny S, Chalk MJ, Tkačik G, Marre O, Mora T. 2018. Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons. Physical Review E. 98(4), 042410. mla: Ferrari, Ulisse, et al. “Separating Intrinsic Interactions from Extrinsic Correlations in a Network of Sensory Neurons.” Physical Review E, vol. 98, no. 4, 042410, American Physical Society, 2018, doi:10.1103/PhysRevE.98.042410. short: U. Ferrari, S. Deny, M.J. Chalk, G. Tkačik, O. Marre, T. Mora, Physical Review E 98 (2018). date_created: 2018-12-11T11:44:15Z date_published: 2018-10-17T00:00:00Z date_updated: 2023-09-18T09:18:44Z day: '17' department: - _id: GaTk doi: 10.1103/PhysRevE.98.042410 ec_funded: 1 external_id: isi: - '000447486100004' intvolume: ' 98' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://www.biorxiv.org/content/10.1101/243816v2.full month: '10' oa: 1 oa_version: Preprint project: - _id: 26436750-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '785907' name: Human Brain Project Specific Grant Agreement 2 (HBP SGA 2) publication: Physical Review E publication_identifier: issn: - '24700045' publication_status: published publisher: American Physical Society publist_id: '8024' quality_controlled: '1' scopus_import: '1' status: public title: Separating intrinsic interactions from extrinsic correlations in a network of sensory neurons type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 98 year: '2018' ... --- _id: '543' 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. article_processing_charge: No author: - first_name: Matthew J full_name: Chalk, Matthew J id: 2BAAC544-F248-11E8-B48F-1D18A9856A87 last_name: Chalk orcid: 0000-0001-7782-4436 - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: 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 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. 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. ista: Chalk MJ, Marre O, Tkačik G. 2018. Toward a unified theory of efficient, predictive, and sparse coding. PNAS. 115(1), 186–191. mla: Chalk, Matthew J., et al. “Toward a Unified Theory of Efficient, Predictive, and Sparse Coding.” PNAS, vol. 115, no. 1, National Academy of Sciences, 2018, pp. 186–91, doi:10.1073/pnas.1711114115. short: M.J. Chalk, O. Marre, G. Tkačik, PNAS 115 (2018) 186–191. date_created: 2018-12-11T11:47:04Z date_published: 2018-01-02T00:00:00Z date_updated: 2023-09-19T10:16:35Z day: '02' department: - _id: GaTk doi: 10.1073/pnas.1711114115 external_id: isi: - '000419128700049' intvolume: ' 115' isi: 1 issue: '1' language: - iso: eng main_file_link: - open_access: '1' url: 'https://doi.org/10.1101/152660 ' month: '01' oa: 1 oa_version: Submitted Version page: 186 - 191 project: - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PNAS publication_status: published publisher: National Academy of Sciences publist_id: '7273' quality_controlled: '1' scopus_import: '1' status: public title: Toward a unified theory of efficient, predictive, and sparse coding type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 115 year: '2018' ... --- _id: '607' abstract: - lang: eng 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. 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" article_processing_charge: No author: - first_name: Katarina full_name: Bodova, Katarina id: 2BA24EA0-F248-11E8-B48F-1D18A9856A87 last_name: Bodova orcid: 0000-0002-7214-0171 - first_name: Jan full_name: Haskovec, Jan last_name: Haskovec - first_name: Peter full_name: Markowich, Peter last_name: Markowich citation: 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.' 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.' 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.' 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.' short: 'K. Bodova, J. Haskovec, P. Markowich, Physica D: Nonlinear Phenomena 376–377 (2018) 108–120.' date_created: 2018-12-11T11:47:28Z date_published: 2018-08-01T00:00:00Z date_updated: 2023-09-19T10:38:34Z day: '01' department: - _id: NiBa - _id: GaTk doi: 10.1016/j.physd.2017.10.015 external_id: arxiv: - '1704.08757' isi: - '000437962900012' isi: 1 language: - iso: eng main_file_link: - open_access: '1' url: https://arxiv.org/abs/1704.08757 month: '08' oa: 1 oa_version: Submitted Version page: 108-120 publication: 'Physica D: Nonlinear Phenomena' publication_status: published publisher: Elsevier publist_id: '7198' quality_controlled: '1' scopus_import: '1' status: public title: Well posedness and maximum entropy approximation for the dynamics of quantitative traits type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 376-377 year: '2018' ... --- _id: '19' 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. article_processing_charge: No article_type: original author: - first_name: Adam full_name: Palmer, Adam last_name: Palmer - first_name: Remy P full_name: Chait, Remy P id: 3464AE84-F248-11E8-B48F-1D18A9856A87 last_name: Chait orcid: 0000-0003-0876-3187 - first_name: Roy full_name: Kishony, Roy last_name: Kishony citation: ama: Palmer A, Chait RP, Kishony R. Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. 2018;35(11):2669-2684. doi:10.1093/molbev/msy163 apa: Palmer, A., Chait, R. P., & Kishony, R. (2018). Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. Oxford University Press. https://doi.org/10.1093/molbev/msy163 chicago: Palmer, Adam, Remy P Chait, and Roy Kishony. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” Molecular Biology and Evolution. Oxford University Press, 2018. https://doi.org/10.1093/molbev/msy163. ieee: A. Palmer, R. P. Chait, and R. Kishony, “Nonoptimal gene expression creates latent potential for antibiotic resistance,” Molecular Biology and Evolution, vol. 35, no. 11. Oxford University Press, pp. 2669–2684, 2018. ista: Palmer A, Chait RP, Kishony R. 2018. Nonoptimal gene expression creates latent potential for antibiotic resistance. Molecular Biology and Evolution. 35(11), 2669–2684. mla: Palmer, Adam, et al. “Nonoptimal Gene Expression Creates Latent Potential for Antibiotic Resistance.” Molecular Biology and Evolution, vol. 35, no. 11, Oxford University Press, 2018, pp. 2669–84, doi:10.1093/molbev/msy163. short: A. Palmer, R.P. Chait, R. Kishony, Molecular Biology and Evolution 35 (2018) 2669–2684. date_created: 2018-12-11T11:44:11Z date_published: 2018-08-28T00:00:00Z date_updated: 2023-10-17T11:51:06Z day: '28' department: - _id: CaGu - _id: GaTk doi: 10.1093/molbev/msy163 external_id: isi: - '000452567200006' pmid: - '30169679' intvolume: ' 35' isi: 1 issue: '11' language: - iso: eng main_file_link: - open_access: '1' url: https://www.ncbi.nlm.nih.gov/pubmed/30169679 month: '08' oa: 1 oa_version: Submitted Version page: 2669 - 2684 pmid: 1 publication: Molecular Biology and Evolution publication_identifier: issn: - 0737-4038 publication_status: published publisher: Oxford University Press publist_id: '8036' quality_controlled: '1' scopus_import: '1' status: public title: Nonoptimal gene expression creates latent potential for antibiotic resistance type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 35 year: '2018' ... --- _id: '292' 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.' article_number: e1006057 article_processing_charge: Yes article_type: original author: - first_name: Vicent full_name: Botella Soler, Vicent id: 421234E8-F248-11E8-B48F-1D18A9856A87 last_name: Botella Soler orcid: 0000-0002-8790-1914 - first_name: Stephane full_name: Deny, Stephane last_name: Deny - first_name: Georg S full_name: Martius, Georg S last_name: Martius - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Botella Soler V, Deny S, Martius GS, Marre O, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. 2018;14(5). doi:10.1371/journal.pcbi.1006057 apa: Botella Soler, V., Deny, S., Martius, G. S., Marre, O., & Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1006057 chicago: Botella Soler, Vicente, Stephane Deny, Georg S Martius, Olivier Marre, and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” PLoS Computational Biology. Public Library of Science, 2018. https://doi.org/10.1371/journal.pcbi.1006057. ieee: V. Botella Soler, S. Deny, G. S. Martius, O. Marre, and G. Tkačik, “Nonlinear decoding of a complex movie from the mammalian retina,” PLoS Computational Biology, vol. 14, no. 5. Public Library of Science, 2018. 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. mla: Botella Soler, Vicente, et al. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.” PLoS Computational Biology, vol. 14, no. 5, e1006057, Public Library of Science, 2018, doi:10.1371/journal.pcbi.1006057. short: V. Botella Soler, S. Deny, G.S. Martius, O. Marre, G. Tkačik, PLoS Computational Biology 14 (2018). date_created: 2018-12-11T11:45:39Z date_published: 2018-05-10T00:00:00Z date_updated: 2024-02-21T13:45:25Z day: '10' ddc: - '570' department: - _id: GaTk doi: 10.1371/journal.pcbi.1006057 ec_funded: 1 external_id: isi: - '000434012100002' file: - access_level: open_access checksum: 3026f94d235219e15514505fdbadf34e content_type: application/pdf creator: dernst date_created: 2019-02-13T11:07:15Z date_updated: 2020-07-14T12:45:53Z file_id: '5974' file_name: 2018_Plos_Botella_Soler.pdf file_size: 3460786 relation: main_file file_date_updated: 2020-07-14T12:45:53Z has_accepted_license: '1' intvolume: ' 14' isi: 1 issue: '5' language: - iso: eng month: '05' oa: 1 oa_version: Published Version project: - _id: 25CBA828-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '720270' name: Human Brain Project Specific Grant Agreement 1 (HBP SGA 1) - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publication: PLoS Computational Biology publication_status: published publisher: Public Library of Science quality_controlled: '1' related_material: link: - description: News on IST Homepage relation: press_release url: https://ist.ac.at/en/news/video-of-moving-discs-reconstructed-from-rat-retinal-neuron-signals/ record: - id: '5584' relation: research_data status: public scopus_import: '1' status: public title: Nonlinear decoding of a complex movie from the mammalian retina tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 14 year: '2018' ... --- _id: '5584' abstract: - lang: eng 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. " article_processing_charge: No author: - first_name: Stephane full_name: Deny, Stephane last_name: Deny - first_name: Olivier full_name: Marre, Olivier last_name: Marre - first_name: Vicente full_name: Botella-Soler, Vicente last_name: Botella-Soler - first_name: Georg S full_name: Martius, Georg S id: 3A276B68-F248-11E8-B48F-1D18A9856A87 last_name: Martius - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98 apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:98 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. https://doi.org/10.15479/AT:ISTA:98. 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. 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, 10.15479/AT:ISTA:98. mla: Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98. short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018). datarep_id: '98' date_created: 2018-12-12T12:31:39Z date_published: 2018-03-29T00:00:00Z date_updated: 2024-02-21T13:45:26Z day: '29' ddc: - '570' department: - _id: ChLa - _id: GaTk doi: 10.15479/AT:ISTA:98 file: - access_level: open_access checksum: 6808748837b9afbbbabc2a356ca2b88a content_type: application/octet-stream creator: system date_created: 2018-12-12T13:02:24Z date_updated: 2020-07-14T12:47:07Z file_id: '5590' file_name: IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat file_size: 1142543971 relation: main_file - access_level: open_access checksum: d6d6cd07743038fe3a12352983fcf9dd content_type: application/pdf creator: system date_created: 2018-12-12T13:02:25Z date_updated: 2020-07-14T12:47:07Z file_id: '5591' file_name: IST-2018-98-v1+2_ExperimentStructure.pdf file_size: 702336 relation: main_file - access_level: open_access checksum: 0c9cfb4dab35bb3dc25a04395600b1c8 content_type: application/octet-stream creator: system date_created: 2018-12-12T13:02:26Z date_updated: 2020-07-14T12:47:07Z file_id: '5592' file_name: IST-2018-98-v1+3_GoodLocations_area2_20x20.mat file_size: 432 relation: main_file - access_level: open_access checksum: 2a83b011012e21e934b4596285b1a183 content_type: text/plain creator: system date_created: 2018-12-12T13:02:26Z date_updated: 2020-07-14T12:47:07Z file_id: '5593' file_name: IST-2018-98-v1+4_README.txt file_size: 986 relation: main_file file_date_updated: 2020-07-14T12:47:07Z has_accepted_license: '1' keyword: - retina - decoding - regression - neural networks - complex stimulus license: https://creativecommons.org/publicdomain/zero/1.0/ month: '03' oa: 1 oa_version: Published Version project: - _id: 254D1A94-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P 25651-N26 name: Sensitivity to higher-order statistics in natural scenes publisher: Institute of Science and Technology Austria related_material: record: - id: '292' relation: used_in_publication status: public status: public title: Nonlinear decoding of a complex movie from the mammalian retina tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ... --- _id: '161' abstract: - lang: eng text: 'Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.' article_number: '2988' article_processing_charge: No author: - first_name: Daniele full_name: De Martino, Daniele id: 3FF5848A-F248-11E8-B48F-1D18A9856A87 last_name: De Martino orcid: 0000-0002-5214-4706 - first_name: Andersson Anna full_name: Mc, Andersson Anna last_name: Mc - first_name: Tobias full_name: Bergmiller, Tobias id: 2C471CFA-F248-11E8-B48F-1D18A9856A87 last_name: Bergmiller orcid: 0000-0001-5396-4346 - first_name: Calin C full_name: Guet, Calin C id: 47F8433E-F248-11E8-B48F-1D18A9856A87 last_name: Guet orcid: 0000-0001-6220-2052 - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: De Martino D, Mc AA, Bergmiller T, Guet CC, Tkačik G. Statistical mechanics for metabolic networks during steady state growth. Nature Communications. 2018;9(1). doi:10.1038/s41467-018-05417-9 apa: De Martino, D., Mc, A. A., Bergmiller, T., Guet, C. C., & Tkačik, G. (2018). Statistical mechanics for metabolic networks during steady state growth. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-018-05417-9 chicago: De Martino, Daniele, Andersson Anna Mc, Tobias Bergmiller, Calin C Guet, and Gašper Tkačik. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” Nature Communications. Springer Nature, 2018. https://doi.org/10.1038/s41467-018-05417-9. ieee: D. De Martino, A. A. Mc, T. Bergmiller, C. C. Guet, and G. Tkačik, “Statistical mechanics for metabolic networks during steady state growth,” Nature Communications, vol. 9, no. 1. Springer Nature, 2018. 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. mla: De Martino, Daniele, et al. “Statistical Mechanics for Metabolic Networks during Steady State Growth.” Nature Communications, vol. 9, no. 1, 2988, Springer Nature, 2018, doi:10.1038/s41467-018-05417-9. short: D. De Martino, A.A. Mc, T. Bergmiller, C.C. Guet, G. Tkačik, Nature Communications 9 (2018). date_created: 2018-12-11T11:44:57Z date_published: 2018-07-30T00:00:00Z date_updated: 2024-02-21T13:45:39Z day: '30' ddc: - '570' department: - _id: GaTk - _id: CaGu doi: 10.1038/s41467-018-05417-9 ec_funded: 1 external_id: isi: - '000440149300021' file: - access_level: open_access checksum: 3ba7ab27b27723c7dcf633e8fc1f8f18 content_type: application/pdf creator: dernst date_created: 2018-12-17T16:44:28Z date_updated: 2020-07-14T12:45:06Z file_id: '5728' file_name: 2018_NatureComm_DeMartino.pdf file_size: 1043205 relation: main_file file_date_updated: 2020-07-14T12:45:06Z has_accepted_license: '1' intvolume: ' 9' isi: 1 issue: '1' language: - iso: eng month: '07' oa: 1 oa_version: Published Version project: - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme publication: Nature Communications publication_status: published publisher: Springer Nature publist_id: '7760' quality_controlled: '1' related_material: record: - id: '5587' relation: popular_science status: public scopus_import: '1' status: public title: Statistical mechanics for metabolic networks during steady state growth tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: journal_article user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 9 year: '2018' ... --- _id: '5587' abstract: - lang: eng text: "Supporting material to the article \r\nSTATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH\r\n\r\nboundscoli.dat\r\nFlux Bounds of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium. \r\n\r\npolcoli.dat\r\nMatrix enconding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium, \r\nobtained from the soichiometric matrix by standard linear algebra (reduced row echelon form).\r\n\r\nellis.dat\r\nApproximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\npoint0.dat\r\nCenter of the approximate Lowner-John ellipsoid rounding the polytope of the E. coli catabolic core model iAF1260 in a glucose limited minimal medium\r\nobtained with the Lovasz method.\r\n\r\nlovasz.cpp \r\nThis c++ code file receives in input the polytope of the feasible steady states of a metabolic network, \r\n(matrix and bounds), and it gives in output an approximate Lowner-John ellipsoid rounding the polytope\r\nwith the Lovasz method \r\nNB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we refer to PLoS ONE 10.4 e0122670 (2015).\r\n\r\nsampleHRnew.cpp \r\nThis c++ code file receives in input the polytope of the feasible steady states of a metabolic network, \r\n(matrix and bounds), the ellipsoid rounding the polytope, a point inside and \r\nit gives in output a max entropy sampling at fixed average growth rate \r\nof the steady states by performing an Hit-and-Run Monte Carlo Markov chain.\r\nNB inputs are referred by defaults to the catabolic core of the E.Coli network iAF1260. \r\nFor further details we refer to PLoS ONE 10.4 e0122670 (2015)." article_processing_charge: No author: - first_name: Daniele full_name: De Martino, Daniele id: 3FF5848A-F248-11E8-B48F-1D18A9856A87 last_name: De Martino orcid: 0000-0002-5214-4706 - first_name: Gasper full_name: Tkacik, Gasper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkacik orcid: 0000-0002-6699-1455 citation: ama: De Martino D, Tkačik G. Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” 2018. doi:10.15479/AT:ISTA:62 apa: De Martino, D., & Tkačik, G. (2018). Supporting materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:62 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. https://doi.org/10.15479/AT:ISTA:62. 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, 10.15479/AT:ISTA:62. mla: De Martino, Daniele, and Gašper Tkačik. Supporting Materials “STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH.” Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:62. short: D. De Martino, G. Tkačik, (2018). datarep_id: '111' date_created: 2018-12-12T12:31:41Z date_published: 2018-09-21T00:00:00Z date_updated: 2024-02-21T13:45:39Z day: '21' ddc: - '530' department: - _id: GaTk doi: 10.15479/AT:ISTA:62 ec_funded: 1 file: - access_level: open_access checksum: 97992e3e8cf8544ec985a48971708726 content_type: application/zip creator: system date_created: 2018-12-12T13:05:13Z date_updated: 2020-07-14T12:47:08Z file_id: '5641' file_name: IST-2018-111-v1+1_CODES.zip file_size: 14376 relation: main_file file_date_updated: 2020-07-14T12:47:08Z has_accepted_license: '1' keyword: - metabolic networks - e.coli core - maximum entropy - monte carlo markov chain sampling - ellipsoidal rounding month: '09' oa: 1 oa_version: Published Version project: - _id: 25681D80-B435-11E9-9278-68D0E5697425 call_identifier: FP7 grant_number: '291734' name: International IST Postdoc Fellowship Programme - _id: 254E9036-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: P28844-B27 name: Biophysics of information processing in gene regulation publisher: Institute of Science and Technology Austria related_material: record: - id: '161' relation: research_paper status: public status: public title: Supporting materials "STATISTICAL MECHANICS FOR METABOLIC NETWORKS IN STEADY-STATE GROWTH" tmp: image: /images/cc_0.png legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode name: Creative Commons Public Domain Dedication (CC0 1.0) short: CC0 (1.0) type: research_data user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 year: '2018' ...