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Friedlander, R. Prizak, N.H. Barton, G. Tkačik, Nature Communications 8 (2017).","ista":"Friedlander T, Prizak R, Barton NH, Tkačik G. 2017. Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. 8(1), 216.","ama":"Friedlander T, Prizak R, Barton NH, Tkačik G. Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. 2017;8(1). doi:10.1038/s41467-017-00238-8","apa":"Friedlander, T., Prizak, R., Barton, N. H., & Tkačik, G. (2017). Evolution of new regulatory functions on biophysically realistic fitness landscapes. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/s41467-017-00238-8","ieee":"T. Friedlander, R. Prizak, N. H. Barton, and G. Tkačik, “Evolution of new regulatory functions on biophysically realistic fitness landscapes,” Nature Communications, vol. 8, no. 1. Nature Publishing Group, 2017.","chicago":"Friedlander, Tamar, Roshan Prizak, Nicholas H Barton, and Gašper Tkačik. “Evolution of New Regulatory Functions on Biophysically Realistic Fitness Landscapes.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/s41467-017-00238-8."},"year":"2017","date_updated":"2025-05-28T11:42:50Z","abstract":[{"lang":"eng","text":"Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components."}],"day":"09","doi":"10.1038/s41467-017-00238-8","language":[{"iso":"eng"}],"has_accepted_license":"1","publication":"Nature Communications","article_number":"216","month":"08","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152"},{"name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"oa_version":"Published Version","related_material":{"record":[{"id":"6071","relation":"dissertation_contains","status":"public"}]},"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","file":[{"content_type":"application/pdf","file_name":"IST-2017-864-v1+1_s41467-017-00238-8.pdf","date_updated":"2020-07-14T12:48:16Z","checksum":"29a1b5db458048d3bd5c67e0e2a56818","file_size":998157,"date_created":"2018-12-12T10:14:14Z","creator":"system","file_id":"5064","access_level":"open_access","relation":"main_file"},{"date_created":"2018-12-12T10:14:15Z","checksum":"7b78401e52a576cf3e6bbf8d0abadc17","file_size":9715993,"date_updated":"2020-07-14T12:48:16Z","content_type":"application/pdf","file_name":"IST-2017-864-v1+2_41467_2017_238_MOESM1_ESM.pdf","access_level":"open_access","relation":"main_file","file_id":"5065","creator":"system"}],"type":"journal_article","date_published":"2017-08-09T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publist_id":"6459","oa":1,"publication_identifier":{"issn":["20411723"]}},{"publication":" Physical Review E Statistical Nonlinear and Soft Matter Physics ","month":"06","oa_version":"Submitted Version","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"language":[{"iso":"eng"}],"date_published":"2017-06-28T00:00:00Z","type":"journal_article","publist_id":"6446","oa":1,"publication_identifier":{"issn":["24700045"]},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","main_file_link":[{"open_access":"1","url":"https://arxiv.org/pdf/1703.00853.pdf"}],"author":[{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706","last_name":"De Martino","first_name":"Daniele"}],"issue":"6","_id":"959","scopus_import":"1","title":"Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics","intvolume":" 95","publication_status":"published","date_created":"2018-12-11T11:49:25Z","department":[{"_id":"GaTk"}],"article_processing_charge":"No","page":"062419","quality_controlled":"1","ec_funded":1,"publisher":"American Institute of Physics","isi":1,"external_id":{"isi":["000404546400004"]},"date_updated":"2023-09-22T09:59:01Z","citation":{"chicago":"De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary Metabolic Network Models via Thermodynamics.” Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics, 2017. https://doi.org/10.1103/PhysRevE.95.062419.","ieee":"D. De Martino, “Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics,” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6. American Institute of Physics, p. 062419, 2017.","ama":"De Martino D. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . 2017;95(6):062419. doi:10.1103/PhysRevE.95.062419","apa":"De Martino, D. (2017). Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . American Institute of Physics. https://doi.org/10.1103/PhysRevE.95.062419","ista":"De Martino D. 2017. Scales and multimodal flux distributions in stationary metabolic network models via thermodynamics. Physical Review E Statistical Nonlinear and Soft Matter Physics . 95(6), 062419.","mla":"De Martino, Daniele. “Scales and Multimodal Flux Distributions in Stationary Metabolic Network Models via Thermodynamics.” Physical Review E Statistical Nonlinear and Soft Matter Physics , vol. 95, no. 6, American Institute of Physics, 2017, p. 062419, doi:10.1103/PhysRevE.95.062419.","short":"D. De Martino, Physical Review E Statistical Nonlinear and Soft Matter Physics 95 (2017) 062419."},"year":"2017","abstract":[{"lang":"eng","text":"In this work it is shown that scale-free tails in metabolic flux distributions inferred in stationary models are an artifact due to reactions involved in thermodynamically unfeasible cycles, unbounded by physical constraints and in principle able to perform work without expenditure of free energy. After implementing thermodynamic constraints by removing such loops, metabolic flux distributions scale meaningfully with the physical limiting factors, acquiring in turn a richer multimodal structure potentially leading to symmetry breaking while optimizing for objective functions."}],"doi":"10.1103/PhysRevE.95.062419","day":"28","volume":95},{"scopus_import":"1","_id":"1007","author":[{"full_name":"Lang, Moritz","last_name":"Lang","first_name":"Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Eduardo","last_name":"Sontag","full_name":"Sontag, Eduardo"}],"article_processing_charge":"Yes (in subscription journal)","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:49:39Z","publication_status":"published","title":"Zeros of nonlinear systems with input invariances","pubrep_id":"813","quality_controlled":"1","ec_funded":1,"page":"46 - 55","file_date_updated":"2018-12-12T10:11:29Z","publisher":"International Federation of Automatic Control","year":"2017","citation":{"ista":"Lang M, Sontag E. 2017. Zeros of nonlinear systems with input invariances. Automatica. 81C, 46–55.","short":"M. Lang, E. Sontag, Automatica 81C (2017) 46–55.","mla":"Lang, Moritz, and Eduardo Sontag. “Zeros of Nonlinear Systems with Input Invariances.” Automatica, vol. 81C, International Federation of Automatic Control, 2017, pp. 46–55, doi:10.1016/j.automatica.2017.03.030.","chicago":"Lang, Moritz, and Eduardo Sontag. “Zeros of Nonlinear Systems with Input Invariances.” Automatica. International Federation of Automatic Control, 2017. https://doi.org/10.1016/j.automatica.2017.03.030.","ieee":"M. Lang and E. Sontag, “Zeros of nonlinear systems with input invariances,” Automatica, vol. 81C. International Federation of Automatic Control, pp. 46–55, 2017.","apa":"Lang, M., & Sontag, E. (2017). Zeros of nonlinear systems with input invariances. Automatica. International Federation of Automatic Control. https://doi.org/10.1016/j.automatica.2017.03.030","ama":"Lang M, Sontag E. Zeros of nonlinear systems with input invariances. Automatica. 2017;81C:46-55. doi:10.1016/j.automatica.2017.03.030"},"date_updated":"2023-10-17T08:51:18Z","external_id":{"isi":["000403513900006"]},"isi":1,"day":"01","doi":"10.1016/j.automatica.2017.03.030","abstract":[{"text":"A nonlinear system possesses an invariance with respect to a set of transformations if its output dynamics remain invariant when transforming the input, and adjusting the initial condition accordingly. Most research has focused on invariances with respect to time-independent pointwise transformations like translational-invariance (u(t) -> u(t) + p, p in R) or scale-invariance (u(t) -> pu(t), p in R>0). In this article, we introduce the concept of s0-invariances with respect to continuous input transformations exponentially growing/decaying over time. We show that s0-invariant systems not only encompass linear time-invariant (LTI) systems with transfer functions having an irreducible zero at s0 in R, but also that the input/output relationship of nonlinear s0-invariant systems possesses properties well known from their linear counterparts. Furthermore, we extend the concept of s0-invariances to second- and higher-order s0-invariances, corresponding to invariances with respect to transformations of the time-derivatives of the input, and encompassing LTI systems with zeros of multiplicity two or higher. Finally, we show that nth-order 0-invariant systems realize – under mild conditions – nth-order nonlinear differential operators: when excited by an input of a characteristic functional form, the system’s output converges to a constant value only depending on the nth (nonlinear) derivative of the input.","lang":"eng"}],"volume":"81C","ddc":["000"],"has_accepted_license":"1","publication":"Automatica","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"oa_version":"Published Version","month":"06","language":[{"iso":"eng"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2017-06-01T00:00:00Z","publication_identifier":{"issn":["0005-1098"]},"oa":1,"publist_id":"6391","file":[{"access_level":"open_access","relation":"main_file","creator":"system","file_id":"4884","file_size":1401954,"date_created":"2018-12-12T10:11:29Z","file_name":"IST-2017-813-v1+1_ZerosOfNonlinearSystems.pdf","content_type":"application/pdf","date_updated":"2018-12-12T10:11:29Z"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","status":"public"},{"type":"research_data_reference","date_published":"2017-10-18T00:00:00Z","year":"2017","citation":{"short":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, (2017).","mla":"Prentice, Jason, et al. Data from: Error-Robust Modes of the Retinal Population Code. Dryad, 2017, doi:10.5061/dryad.1f1rc.","ista":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2017. Data from: Error-robust modes of the retinal population code, Dryad, 10.5061/dryad.1f1rc.","apa":"Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M. (2017). Data from: Error-robust modes of the retinal population code. Dryad. https://doi.org/10.5061/dryad.1f1rc","ama":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Data from: Error-robust modes of the retinal population code. 2017. doi:10.5061/dryad.1f1rc","ieee":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Data from: Error-robust modes of the retinal population code.” Dryad, 2017.","chicago":"Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Data from: Error-Robust Modes of the Retinal Population Code.” Dryad, 2017. https://doi.org/10.5061/dryad.1f1rc."},"date_updated":"2023-02-21T16:34:41Z","oa":1,"abstract":[{"text":"Across the nervous system, certain population spiking patterns are observed far more frequently than others. A hypothesis about this structure is that these collective activity patterns function as population codewords–collective modes–carrying information distinct from that of any single cell. We investigate this phenomenon in recordings of ∼150 retinal ganglion cells, the retina’s output. We develop a novel statistical model that decomposes the population response into modes; it predicts the distribution of spiking activity in the ganglion cell population with high accuracy. We found that the modes represent localized features of the visual stimulus that are distinct from the features represented by single neurons. Modes form clusters of activity states that are readily discriminated from one another. When we repeated the same visual stimulus, we found that the same mode was robustly elicited. These results suggest that retinal ganglion cells’ collective signaling is endowed with a form of error-correcting code–a principle that may hold in brain areas beyond retina.","lang":"eng"}],"day":"18","doi":"10.5061/dryad.1f1rc","related_material":{"record":[{"id":"1197","relation":"used_in_publication","status":"public"}]},"status":"public","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","main_file_link":[{"url":"https://doi.org/10.5061/dryad.1f1rc","open_access":"1"}],"author":[{"full_name":"Prentice, Jason","first_name":"Jason","last_name":"Prentice"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"last_name":"Ioffe","first_name":"Mark","full_name":"Ioffe, Mark"},{"full_name":"Loback, Adrianna","last_name":"Loback","first_name":"Adrianna"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper"},{"last_name":"Berry","first_name":"Michael","full_name":"Berry, Michael"}],"_id":"9709","title":"Data from: Error-robust modes of the retinal population code","month":"10","article_processing_charge":"No","date_created":"2021-07-23T11:34:34Z","department":[{"_id":"GaTk"}],"oa_version":"Published Version","publisher":"Dryad"},{"publisher":"Public Library of Science","_id":"9855","author":[{"last_name":"Chalk","first_name":"Matthew J","full_name":"Chalk, Matthew J","orcid":"0000-0001-7782-4436","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Masset, Paul","last_name":"Masset","first_name":"Paul"},{"last_name":"Gutkin","first_name":"Boris","full_name":"Gutkin, Boris"},{"first_name":"Sophie","last_name":"Denève","full_name":"Denève, Sophie"}],"date_created":"2021-08-10T07:05:10Z","article_processing_charge":"No","department":[{"_id":"GaTk"}],"oa_version":"Published Version","title":"Supplementary appendix","month":"06","related_material":{"record":[{"relation":"used_in_publication","id":"680","status":"public"}]},"status":"public","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","year":"2017","citation":{"ieee":"M. J. Chalk, P. Masset, B. Gutkin, and S. Denève, “Supplementary appendix.” Public Library of Science, 2017.","chicago":"Chalk, Matthew J, Paul Masset, Boris Gutkin, and Sophie Denève. “Supplementary Appendix.” Public Library of Science, 2017. https://doi.org/10.1371/journal.pcbi.1005582.s001.","apa":"Chalk, M. J., Masset, P., Gutkin, B., & Denève, S. (2017). Supplementary appendix. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005582.s001","ama":"Chalk MJ, Masset P, Gutkin B, Denève S. Supplementary appendix. 2017. doi:10.1371/journal.pcbi.1005582.s001","ista":"Chalk MJ, Masset P, Gutkin B, Denève S. 2017. Supplementary appendix, Public Library of Science, 10.1371/journal.pcbi.1005582.s001.","mla":"Chalk, Matthew J., et al. Supplementary Appendix. Public Library of Science, 2017, doi:10.1371/journal.pcbi.1005582.s001.","short":"M.J. Chalk, P. Masset, B. Gutkin, S. Denève, (2017)."},"date_updated":"2023-02-23T12:52:17Z","type":"research_data_reference","date_published":"2017-06-01T00:00:00Z","day":"01","doi":"10.1371/journal.pcbi.1005582.s001","abstract":[{"text":"Includes derivation of optimal estimation algorithm, generalisation to non-poisson noise statistics, correlated input noise, and implementation of in a multi-layer neural network.","lang":"eng"}]},{"article_processing_charge":"Yes (in subscription journal)","date_created":"2018-12-11T11:49:35Z","department":[{"_id":"GaTk"},{"_id":"JoCs"}],"publication_status":"published","intvolume":" 8","title":"Subsampling scaling","pubrep_id":"819","scopus_import":"1","_id":"993","author":[{"first_name":"Anna","last_name":"Levina (Martius)","full_name":"Levina (Martius), Anna","id":"35AF8020-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Priesemann, Viola","last_name":"Priesemann","first_name":"Viola"}],"publisher":"Nature Publishing Group","ec_funded":1,"quality_controlled":"1","file_date_updated":"2020-07-14T12:48:19Z","day":"04","doi":"10.1038/ncomms15140","abstract":[{"lang":"eng","text":"In real-world applications, observations are often constrained to a small fraction of a system. Such spatial subsampling can be caused by the inaccessibility or the sheer size of the system, and cannot be overcome by longer sampling. Spatial subsampling can strongly bias inferences about a system’s aggregated properties. To overcome the bias, we derive analytically a subsampling scaling framework that is applicable to different observables, including distributions of neuronal avalanches, of number of people infected during an epidemic outbreak, and of node degrees. We demonstrate how to infer the correct distributions of the underlying full system, how to apply it to distinguish critical from subcritical systems, and how to disentangle subsampling and finite size effects. Lastly, we apply subsampling scaling to neuronal avalanche models and to recordings from developing neural networks. We show that only mature, but not young networks follow power-law scaling, indicating self-organization to criticality during development."}],"year":"2017","citation":{"apa":"Levina (Martius), A., & Priesemann, V. (2017). Subsampling scaling. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms15140","ama":"Levina (Martius) A, Priesemann V. Subsampling scaling. Nature Communications. 2017;8. doi:10.1038/ncomms15140","chicago":"Levina (Martius), Anna, and Viola Priesemann. “Subsampling Scaling.” Nature Communications. Nature Publishing Group, 2017. https://doi.org/10.1038/ncomms15140.","ieee":"A. Levina (Martius) and V. Priesemann, “Subsampling scaling,” Nature Communications, vol. 8. Nature Publishing Group, 2017.","mla":"Levina (Martius), Anna, and Viola Priesemann. “Subsampling Scaling.” Nature Communications, vol. 8, 15140, Nature Publishing Group, 2017, doi:10.1038/ncomms15140.","short":"A. Levina (Martius), V. Priesemann, Nature Communications 8 (2017).","ista":"Levina (Martius) A, Priesemann V. 2017. Subsampling scaling. Nature Communications. 8, 15140."},"date_updated":"2023-09-22T09:54:07Z","external_id":{"isi":["000400560700001"]},"isi":1,"volume":8,"ddc":["005","571"],"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"oa_version":"Published Version","article_number":"15140","month":"05","has_accepted_license":"1","publication":"Nature Communications","language":[{"iso":"eng"}],"publication_identifier":{"issn":["20411723"]},"oa":1,"publist_id":"6406","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2017-05-04T00:00:00Z","file":[{"access_level":"open_access","relation":"main_file","file_id":"5122","creator":"system","date_created":"2018-12-12T10:15:05Z","checksum":"9880212f8c4c53404c7c6fbf9023c53a","file_size":746224,"date_updated":"2020-07-14T12:48:19Z","file_name":"IST-2017-819-v1+1_2017_Levina_SubsamplingScaling.pdf","content_type":"application/pdf"}],"status":"public","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1"},{"month":"12","alternative_title":["Advances in Neural Information Processing Systems"],"title":"Relevant sparse codes with variational information bottleneck","intvolume":" 29","oa_version":"Preprint","publication_status":"published","date_created":"2018-12-11T11:50:03Z","department":[{"_id":"GaTk"}],"author":[{"last_name":"Chalk","first_name":"Matthew J","full_name":"Chalk, Matthew J","orcid":"0000-0001-7782-4436","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"_id":"1082","scopus_import":1,"conference":{"end_date":"2016-12-10","location":"Barcelona, Spain","start_date":"2016-12-05","name":"NIPS: Neural Information Processing Systems"},"publisher":"Neural Information Processing Systems","language":[{"iso":"eng"}],"page":"1965-1973","quality_controlled":"1","abstract":[{"text":"In many applications, it is desirable to extract only the relevant aspects of data. A principled way to do this is the information bottleneck (IB) method, where one seeks a code that maximises information about a relevance variable, Y, while constraining the information encoded about the original data, X. Unfortunately however, the IB method is computationally demanding when data are high-dimensional and/or non-gaussian. Here we propose an approximate variational scheme for maximising a lower bound on the IB objective, analogous to variational EM. Using this method, we derive an IB algorithm to recover features that are both relevant and sparse. Finally, we demonstrate how kernelised versions of the algorithm can be used to address a broad range of problems with non-linear relation between X and Y.","lang":"eng"}],"oa":1,"publist_id":"6298","day":"01","date_published":"2016-12-01T00:00:00Z","type":"conference","date_updated":"2021-01-12T06:48:09Z","citation":{"ista":"Chalk MJ, Marre O, Tkačik G. 2016. Relevant sparse codes with variational information bottleneck. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 1965–1973.","short":"M.J. Chalk, O. Marre, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp. 1965–1973.","mla":"Chalk, Matthew J., et al. Relevant Sparse Codes with Variational Information Bottleneck. Vol. 29, Neural Information Processing Systems, 2016, pp. 1965–73.","ieee":"M. J. Chalk, O. Marre, and G. Tkačik, “Relevant sparse codes with variational information bottleneck,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain, 2016, vol. 29, pp. 1965–1973.","chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Relevant Sparse Codes with Variational Information Bottleneck,” 29:1965–73. Neural Information Processing Systems, 2016.","apa":"Chalk, M. J., Marre, O., & Tkačik, G. (2016). Relevant sparse codes with variational information bottleneck (Vol. 29, pp. 1965–1973). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information Processing Systems.","ama":"Chalk MJ, Marre O, Tkačik G. Relevant sparse codes with variational information bottleneck. In: Vol 29. Neural Information Processing Systems; 2016:1965-1973."},"year":"2016","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","related_material":{"link":[{"url":"https://papers.nips.cc/paper/6101-relevant-sparse-codes-with-variational-information-bottleneck","relation":"other"}]},"volume":29,"main_file_link":[{"url":"https://arxiv.org/abs/1605.07332","open_access":"1"}]},{"_id":"1105","scopus_import":1,"author":[{"full_name":"Savin, Cristina","first_name":"Cristina","last_name":"Savin","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"publication_status":"published","date_created":"2018-12-11T11:50:10Z","department":[{"_id":"GaTk"}],"title":"Estimating nonlinear neural response functions using GP priors and Kronecker methods","alternative_title":["Advances in Neural Information Processing Systems"],"intvolume":" 29","page":"3610-3618","quality_controlled":"1","ec_funded":1,"publisher":"Neural Information Processing Systems","date_updated":"2021-01-12T06:48:19Z","year":"2016","citation":{"ama":"Savin C, Tkačik G. Estimating nonlinear neural response functions using GP priors and Kronecker methods. In: Vol 29. Neural Information Processing Systems; 2016:3610-3618.","apa":"Savin, C., & Tkačik, G. (2016). Estimating nonlinear neural response functions using GP priors and Kronecker methods (Vol. 29, pp. 3610–3618). Presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain: Neural Information Processing Systems.","chicago":"Savin, Cristina, and Gašper Tkačik. “Estimating Nonlinear Neural Response Functions Using GP Priors and Kronecker Methods,” 29:3610–18. Neural Information Processing Systems, 2016.","ieee":"C. Savin and G. Tkačik, “Estimating nonlinear neural response functions using GP priors and Kronecker methods,” presented at the NIPS: Neural Information Processing Systems, Barcelona; Spain, 2016, vol. 29, pp. 3610–3618.","mla":"Savin, Cristina, and Gašper Tkačik. Estimating Nonlinear Neural Response Functions Using GP Priors and Kronecker Methods. Vol. 29, Neural Information Processing Systems, 2016, pp. 3610–18.","short":"C. Savin, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp. 3610–3618.","ista":"Savin C, Tkačik G. 2016. Estimating nonlinear neural response functions using GP priors and Kronecker methods. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 3610–3618."},"day":"01","abstract":[{"lang":"eng","text":"Jointly characterizing neural responses in terms of several external variables promises novel insights into circuit function, but remains computationally prohibitive in practice. Here we use gaussian process (GP) priors and exploit recent advances in fast GP inference and learning based on Kronecker methods, to efficiently estimate multidimensional nonlinear tuning functions. Our estimator require considerably less data than traditional methods and further provides principled uncertainty estimates. We apply these tools to hippocampal recordings during open field exploration and use them to characterize the joint dependence of CA1 responses on the position of the animal and several other variables, including the animal\\'s speed, direction of motion, and network oscillations.Our results provide an unprecedentedly detailed quantification of the tuning of hippocampal neurons. The model\\'s generality suggests that our approach can be used to estimate neural response properties in other brain regions."}],"acknowledgement":"We thank Jozsef Csicsvari for kindly sharing the CA1 data.\r\nThis work was supported by the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme(FP7/2007-2013) under REA grant agreement no. 291734.","volume":29,"oa_version":"None","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"month":"12","language":[{"iso":"eng"}],"conference":{"start_date":"2016-12-05","name":"NIPS: Neural Information Processing Systems","location":"Barcelona; Spain","end_date":"2016-12-10"},"date_published":"2016-12-01T00:00:00Z","type":"conference","publist_id":"6265","main_file_link":[{"url":"http://papers.nips.cc/paper/6153-estimating-nonlinear-neural-response-functions-using-gp-priors-and-kronecker-methods"}],"status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87"},{"language":[{"iso":"eng"}],"has_accepted_license":"1","oa_version":"Published Version","month":"08","file":[{"access_level":"closed","relation":"main_file","creator":"dernst","file_id":"6815","file_size":2614660,"checksum":"ec453918c3bf8e6f460fd1156ef7b493","date_created":"2019-08-13T11:46:25Z","content_type":"application/pdf","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf","date_updated":"2019-08-13T11:46:25Z"},{"relation":"main_file","success":1,"access_level":"open_access","creator":"dernst","file_id":"8542","checksum":"51ae398166370d18fd22478b6365c4da","file_size":6096178,"date_created":"2020-09-21T11:30:40Z","file_name":"Thesis_Georg_Rieckh.pdf","content_type":"application/pdf","date_updated":"2020-09-21T11:30:40Z"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","status":"public","type":"dissertation","date_published":"2016-08-01T00:00:00Z","publication_identifier":{"issn":["2663-337X"]},"publist_id":"6232","oa":1,"supervisor":[{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik"}],"page":"114","file_date_updated":"2020-09-21T11:30:40Z","publisher":"Institute of Science and Technology Austria","_id":"1128","author":[{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","last_name":"Rieckh","first_name":"Georg","full_name":"Rieckh, Georg"}],"article_processing_charge":"No","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:50:18Z","publication_status":"published","alternative_title":["ISTA Thesis"],"title":"Studying the complexities of transcriptional regulation","ddc":["570"],"year":"2016","citation":{"ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","mla":"Rieckh, Georg. Studying the Complexities of Transcriptional Regulation. Institute of Science and Technology Austria, 2016.","short":"G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute of Science and Technology Austria, 2016.","ieee":"G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute of Science and Technology Austria, 2016.","chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016.","ama":"Rieckh G. Studying the complexities of transcriptional regulation. 2016.","apa":"Rieckh, G. (2016). Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria."},"date_updated":"2023-09-07T11:44:34Z","day":"01","degree_awarded":"PhD","abstract":[{"lang":"eng","text":"The process of gene expression is central to the modern understanding of how cellular systems\r\nfunction. In this process, a special kind of regulatory proteins, called transcription factors,\r\nare important to determine how much protein is produced from a given gene. As biological\r\ninformation is transmitted from transcription factor concentration to mRNA levels to amounts of\r\nprotein, various sources of noise arise and pose limits to the fidelity of intracellular signaling.\r\nThis thesis concerns itself with several aspects of stochastic gene expression: (i) the mathematical\r\ndescription of complex promoters responsible for the stochastic production of biomolecules,\r\n(ii) fundamental limits to information processing the cell faces due to the interference from multiple\r\nfluctuating signals, (iii) how the presence of gene expression noise influences the evolution\r\nof regulatory sequences, (iv) and tools for the experimental study of origins and consequences\r\nof cell-cell heterogeneity, including an application to bacterial stress response systems."}]},{"language":[{"iso":"eng"}],"project":[{"name":"Quantitative Reactive Modeling","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"name":"Rigorous Systems Engineering","grant_number":"S 11407_N23","call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"oa_version":"None","month":"11","publication":"Biosystems","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","related_material":{"record":[{"status":"public","id":"1658","relation":"earlier_version"}]},"status":"public","publist_id":"6210","type":"journal_article","date_published":"2016-11-01T00:00:00Z","publisher":"Elsevier","quality_controlled":"1","ec_funded":1,"page":"15 - 25","date_created":"2018-12-11T11:50:24Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publication_status":"published","intvolume":" 149","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","scopus_import":1,"_id":"1148","author":[{"full_name":"Schilling, Christian","last_name":"Schilling","first_name":"Christian"},{"id":"369D9A44-F248-11E8-B48F-1D18A9856A87","first_name":"Sergiy","last_name":"Bogomolov","orcid":"0000-0002-0686-0365","full_name":"Bogomolov, Sergiy"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","first_name":"Thomas A","full_name":"Henzinger, Thomas A","orcid":"0000−0002−2985−7724"},{"full_name":"Podelski, Andreas","last_name":"Podelski","first_name":"Andreas"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"}],"acknowledgement":"This work is based on the CMSB 2015 paper “Adaptive moment closure for parameter inference of biochemical reaction networks” (Bogomolov et al., 2015). The work was partly supported by the German Research Foundation (DFG) as part of the Transregional Collaborative Research Center “Automatic Verification and Analysis of Complex Systems” (SFB/TR 14 AVACS1), by the European Research Council (ERC) under grant 267989 (QUAREM) and by the Austrian Science Fund (FWF) under grants S11402-N23 (RiSE) and Z211-N23 (Wittgenstein Award). J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734.","volume":149,"day":"01","doi":"10.1016/j.biosystems.2016.07.005","abstract":[{"text":"Continuous-time Markov chain (CTMC) models have become a central tool for understanding the dynamics of complex reaction networks and the importance of stochasticity in the underlying biochemical processes. When such models are employed to answer questions in applications, in order to ensure that the model provides a sufficiently accurate representation of the real system, it is of vital importance that the model parameters are inferred from real measured data. This, however, is often a formidable task and all of the existing methods fail in one case or the other, usually because the underlying CTMC model is high-dimensional and computationally difficult to analyze. The parameter inference methods that tend to scale best in the dimension of the CTMC are based on so-called moment closure approximations. However, there exists a large number of different moment closure approximations and it is typically hard to say a priori which of the approximations is the most suitable for the inference procedure. Here, we propose a moment-based parameter inference method that automatically chooses the most appropriate moment closure method. Accordingly, contrary to existing methods, the user is not required to be experienced in moment closure techniques. In addition to that, our method adaptively changes the approximation during the parameter inference to ensure that always the best approximation is used, even in cases where different approximations are best in different regions of the parameter space. © 2016 Elsevier Ireland Ltd","lang":"eng"}],"citation":{"apa":"Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., & Ruess, J. (2016). Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. Elsevier. https://doi.org/10.1016/j.biosystems.2016.07.005","ama":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 2016;149:15-25. doi:10.1016/j.biosystems.2016.07.005","ieee":"C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive moment closure for parameter inference of biochemical reaction networks,” Biosystems, vol. 149. Elsevier, pp. 15–25, 2016.","chicago":"Schilling, Christian, Sergiy Bogomolov, Thomas A Henzinger, Andreas Podelski, and Jakob Ruess. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems. Elsevier, 2016. https://doi.org/10.1016/j.biosystems.2016.07.005.","short":"C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25.","mla":"Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems, vol. 149, Elsevier, 2016, pp. 15–25, doi:10.1016/j.biosystems.2016.07.005.","ista":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. 2016. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 149, 15–25."},"year":"2016","date_updated":"2023-02-23T10:08:46Z"},{"publist_id":"6186","date_published":"2016-11-15T00:00:00Z","type":"journal_article","status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","file":[{"creator":"system","file_id":"5095","access_level":"local","relation":"main_file","content_type":"application/pdf","file_name":"IST-2017-811-v1+1_modular_parameter_identification.pdf","date_updated":"2020-07-14T12:44:37Z","checksum":"781bc3ffd30b2dd65b7727c5a285fc78","file_size":871964,"date_created":"2018-12-12T10:14:41Z"}],"month":"11","oa_version":"Submitted Version","publication":"SIAM Journal on Scientific Computing","has_accepted_license":"1","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"The increasing complexity of dynamic models in systems and synthetic biology poses computational challenges especially for the identification of model parameters. While modularization of the corresponding optimization problems could help reduce the “curse of dimensionality,” abundant feedback and crosstalk mechanisms prohibit a simple decomposition of most biomolecular networks into subnetworks, or modules. Drawing on ideas from network modularization and multiple-shooting optimization, we present here a modular parameter identification approach that explicitly allows for such interdependencies. Interfaces between our modules are given by the experimentally measured molecular species. This definition allows deriving good (initial) estimates for the inter-module communication directly from the experimental data. Given these estimates, the states and parameter sensitivities of different modules can be integrated independently. To achieve consistency between modules, we iteratively adjust the estimates for inter-module communication while optimizing the parameters. After convergence to an optimal parameter set---but not during earlier iterations---the intermodule communication as well as the individual modules\\' state dynamics agree with the dynamics of the nonmodularized network. Our modular parameter identification approach allows for easy parallelization; it can reduce the computational complexity for larger networks and decrease the probability to converge to suboptimal local minima. We demonstrate the algorithm\\'s performance in parameter estimation for two biomolecular networks, a synthetic genetic oscillator and a mammalian signaling pathway."}],"doi":"10.1137/15M103306X","day":"15","date_updated":"2021-01-12T06:48:49Z","year":"2016","citation":{"ama":"Lang M, Stelling J. Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. 2016;38(6):B988-B1008. doi:10.1137/15M103306X","apa":"Lang, M., & Stelling, J. (2016). Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. Society for Industrial and Applied Mathematics . https://doi.org/10.1137/15M103306X","chicago":"Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular Networks.” SIAM Journal on Scientific Computing. Society for Industrial and Applied Mathematics , 2016. https://doi.org/10.1137/15M103306X.","ieee":"M. Lang and J. Stelling, “Modular parameter identification of biomolecular networks,” SIAM Journal on Scientific Computing, vol. 38, no. 6. Society for Industrial and Applied Mathematics , pp. B988–B1008, 2016.","mla":"Lang, Moritz, and Jörg Stelling. “Modular Parameter Identification of Biomolecular Networks.” SIAM Journal on Scientific Computing, vol. 38, no. 6, Society for Industrial and Applied Mathematics , 2016, pp. B988–1008, doi:10.1137/15M103306X.","short":"M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) B988–B1008.","ista":"Lang M, Stelling J. 2016. Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. 38(6), B988–B1008."},"ddc":["003","518","570","621"],"volume":38,"title":"Modular parameter identification of biomolecular networks","pubrep_id":"811","intvolume":" 38","publication_status":"published","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_created":"2018-12-11T11:50:31Z","author":[{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","first_name":"Moritz","last_name":"Lang","full_name":"Lang, Moritz"},{"full_name":"Stelling, Jörg","last_name":"Stelling","first_name":"Jörg"}],"issue":"6","_id":"1170","scopus_import":1,"publisher":"Society for Industrial and Applied Mathematics ","file_date_updated":"2020-07-14T12:44:37Z","page":"B988 - B1008","quality_controlled":"1"},{"intvolume":" 17","month":"07","title":"Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al.","date_created":"2018-12-11T11:50:32Z","department":[{"_id":"GaTk"}],"publication_status":"published","oa_version":"None","author":[{"last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"scopus_import":1,"_id":"1171","publication":"Physics of Life Reviews","publisher":"Elsevier","language":[{"iso":"eng"}],"quality_controlled":"1","page":"166 - 167","publist_id":"6185","day":"01","doi":"10.1016/j.plrev.2016.06.005","type":"journal_article","date_published":"2016-07-01T00:00:00Z","citation":{"short":"G. Tkačik, Physics of Life Reviews 17 (2016) 166–167.","mla":"Tkačik, Gašper. “Understanding Regulatory Networks Requires More than Computing a Multitude of Graph Statistics: Comment on "Drivers of Structural Features in Gene Regulatory Networks: From Biophysical Constraints to Biological Function" by O. C. Martin et Al.” Physics of Life Reviews, vol. 17, Elsevier, 2016, pp. 166–67, doi:10.1016/j.plrev.2016.06.005.","ista":"Tkačik G. 2016. Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. 17, 166–167.","ama":"Tkačik G. Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. 2016;17:166-167. doi:10.1016/j.plrev.2016.06.005","apa":"Tkačik, G. (2016). Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al. Physics of Life Reviews. Elsevier. https://doi.org/10.1016/j.plrev.2016.06.005","chicago":"Tkačik, Gašper. “Understanding Regulatory Networks Requires More than Computing a Multitude of Graph Statistics: Comment on "Drivers of Structural Features in Gene Regulatory Networks: From Biophysical Constraints to Biological Function" by O. C. Martin et Al.” Physics of Life Reviews. Elsevier, 2016. https://doi.org/10.1016/j.plrev.2016.06.005.","ieee":"G. Tkačik, “Understanding regulatory networks requires more than computing a multitude of graph statistics: Comment on "Drivers of structural features in gene regulatory networks: From biophysical constraints to biological function" by O. C. Martin et al.,” Physics of Life Reviews, vol. 17. Elsevier, pp. 166–167, 2016."},"year":"2016","date_updated":"2021-01-12T06:48:50Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","volume":17},{"type":"conference","date_published":"2016-09-01T00:00:00Z","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"oa":1,"publication_identifier":{"isbn":["9780262339360"]},"status":"public","user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","file":[{"creator":"cziletti","file_id":"8096","relation":"main_file","access_level":"open_access","file_name":"2016_ProcALIFE_Martius.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:48:09Z","file_size":678670,"checksum":"cff63e7a4b8ac466ba51a9c84153a940","date_created":"2020-07-06T12:59:09Z"}],"has_accepted_license":"1","publication":"Proceedings of the Artificial Life Conference 2016","month":"09","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"oa_version":"Published Version","language":[{"iso":"eng"}],"conference":{"end_date":"2016-07-08","location":"Cancun, Mexico","name":"ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems","start_date":"2016-07-04"},"citation":{"ista":"Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of an tendon driven arm by differential extrinsic plasticity. Proceedings of the Artificial Life Conference 2016. ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems vol. 28, 142–143.","mla":"Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” Proceedings of the Artificial Life Conference 2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.","short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 142–143.","chicago":"Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In Proceedings of the Artificial Life Conference 2016, 28:142–43. MIT Press, 2016. https://doi.org/10.7551/978-0-262-33936-0-ch029.","ieee":"G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control of an tendon driven arm by differential extrinsic plasticity,” in Proceedings of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp. 142–143.","apa":"Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In Proceedings of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029","ama":"Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon driven arm by differential extrinsic plasticity. In: Proceedings of the Artificial Life Conference 2016. Vol 28. MIT Press; 2016:142-143. doi:10.7551/978-0-262-33936-0-ch029"},"year":"2016","date_updated":"2021-01-12T08:16:53Z","abstract":[{"lang":"eng","text":"With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with."}],"day":"01","doi":"10.7551/978-0-262-33936-0-ch029","ddc":["610"],"volume":28,"author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S"},{"first_name":"Rafael","last_name":"Hostettler","full_name":"Hostettler, Rafael"},{"first_name":"Alois","last_name":"Knoll","full_name":"Knoll, Alois"},{"first_name":"Ralf","last_name":"Der","full_name":"Der, Ralf"}],"scopus_import":1,"_id":"8094","intvolume":" 28","title":"Self-organized control of an tendon driven arm by differential extrinsic plasticity","date_created":"2020-07-05T22:00:47Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"article_processing_charge":"No","publication_status":"published","file_date_updated":"2020-07-14T12:48:09Z","quality_controlled":"1","ec_funded":1,"page":"142-143","publisher":"MIT Press"},{"quality_controlled":"1","ec_funded":1,"publisher":"IOP Publishing Ltd.","_id":"1485","scopus_import":1,"author":[{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele","last_name":"De Martino","orcid":"0000-0002-5214-4706","full_name":"De Martino, Daniele"}],"issue":"1","publication_status":"published","date_created":"2018-12-11T11:52:18Z","department":[{"_id":"GaTk"}],"title":"Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis","intvolume":" 13","volume":13,"date_updated":"2021-01-12T06:51:04Z","citation":{"ieee":"D. De Martino, “Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis,” Physical Biology, vol. 13, no. 1. IOP Publishing Ltd., 2016.","chicago":"De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of E. Coli from the Energy Balance Analysis.” Physical Biology. IOP Publishing Ltd., 2016. https://doi.org/10.1088/1478-3975/13/1/016003.","apa":"De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/1/016003","ama":"De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003","ista":"De Martino D. 2016. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 13(1), 016003.","mla":"De Martino, Daniele. “Genome-Scale Estimate of the Metabolic Turnover of E. Coli from the Energy Balance Analysis.” Physical Biology, vol. 13, no. 1, 016003, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/1/016003.","short":"D. De Martino, Physical Biology 13 (2016)."},"year":"2016","doi":"10.1088/1478-3975/13/1/016003","day":"29","abstract":[{"lang":"eng","text":"In this article the notion of metabolic turnover is revisited in the light of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo methods we perform an exact sampling of the enzymatic fluxes in a genome scale metabolic network of E. Coli in stationary growth conditions from which we infer the metabolites turnover times. However the latter are inferred from net fluxes, and we argue that this approximation is not valid for enzymes working nearby thermodynamic equilibrium. We recalculate turnover times from total fluxes by performing an energy balance analysis of the network and recurring to the fluctuation theorem. We find in many cases values one of order of magnitude lower, implying a faster picture of intermediate metabolism."}],"language":[{"iso":"eng"}],"publication":"Physical Biology","oa_version":"Preprint","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"month":"01","article_number":"016003","main_file_link":[{"url":"http://arxiv.org/abs/1505.04613","open_access":"1"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","date_published":"2016-01-29T00:00:00Z","type":"journal_article","publist_id":"5702","oa":1},{"language":[{"iso":"eng"}],"conference":{"location":"Boston, MA, USA","end_date":"2016-07-08","name":"ACC: American Control Conference","start_date":"2016-07-06"},"has_accepted_license":"1","month":"07","article_number":"7526722","oa_version":"Preprint","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","file":[{"content_type":"application/pdf","file_name":"IST-2017-810-v1+1_root.pdf","date_updated":"2020-07-14T12:44:43Z","checksum":"7219432b43defc62a0d45f48d4ce6a19","file_size":539166,"date_created":"2018-12-12T10:16:17Z","creator":"system","file_id":"5203","access_level":"local","relation":"main_file"}],"date_published":"2016-07-28T00:00:00Z","type":"conference","publist_id":"5950","file_date_updated":"2020-07-14T12:44:43Z","ec_funded":1,"quality_controlled":"1","publisher":"IEEE","author":[{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","first_name":"Moritz","last_name":"Lang","full_name":"Lang, Moritz"},{"last_name":"Sontag","first_name":"Eduardo","full_name":"Sontag, Eduardo"}],"_id":"1320","scopus_import":1,"title":"Scale-invariant systems realize nonlinear differential operators","pubrep_id":"810","publication_status":"published","date_created":"2018-12-11T11:51:21Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"ddc":["003","621"],"volume":"2016-July","acknowledgement":"The research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement n° [291734]. Work supported in part by grants AFOSR FA9550-14-1-0060 and NIH 1R01GM100473.","date_updated":"2021-01-12T06:49:51Z","year":"2016","citation":{"mla":"Lang, Moritz, and Eduardo Sontag. Scale-Invariant Systems Realize Nonlinear Differential Operators. Vol. 2016–July, 7526722, IEEE, 2016, doi:10.1109/ACC.2016.7526722.","short":"M. Lang, E. Sontag, in:, IEEE, 2016.","ista":"Lang M, Sontag E. 2016. Scale-invariant systems realize nonlinear differential operators. ACC: American Control Conference vol. 2016–July, 7526722.","apa":"Lang, M., & Sontag, E. (2016). Scale-invariant systems realize nonlinear differential operators (Vol. 2016–July). Presented at the ACC: American Control Conference, Boston, MA, USA: IEEE. https://doi.org/10.1109/ACC.2016.7526722","ama":"Lang M, Sontag E. Scale-invariant systems realize nonlinear differential operators. In: Vol 2016-July. IEEE; 2016. doi:10.1109/ACC.2016.7526722","ieee":"M. Lang and E. Sontag, “Scale-invariant systems realize nonlinear differential operators,” presented at the ACC: American Control Conference, Boston, MA, USA, 2016, vol. 2016–July.","chicago":"Lang, Moritz, and Eduardo Sontag. “Scale-Invariant Systems Realize Nonlinear Differential Operators,” Vol. 2016–July. IEEE, 2016. https://doi.org/10.1109/ACC.2016.7526722."},"abstract":[{"lang":"eng","text":"In recent years, several biomolecular systems have been shown to be scale-invariant (SI), i.e. to show the same output dynamics when exposed to geometrically scaled input signals (u → pu, p > 0) after pre-adaptation to accordingly scaled constant inputs. In this article, we show that SI systems-as well as systems invariant with respect to other input transformations-can realize nonlinear differential operators: when excited by inputs obeying functional forms characteristic for a given class of invariant systems, the systems' outputs converge to constant values directly quantifying the speed of the input."}],"doi":"10.1109/ACC.2016.7526722","day":"28"},{"day":"20","doi":"10.1038/ncomms10333","abstract":[{"lang":"eng","text":"Antibiotic-sensitive and -resistant bacteria coexist in natural environments with low, if detectable, antibiotic concentrations. Except possibly around localized antibiotic sources, where resistance can provide a strong advantage, bacterial fitness is dominated by stresses unaffected by resistance to the antibiotic. How do such mixed and heterogeneous conditions influence the selective advantage or disadvantage of antibiotic resistance? Here we find that sub-inhibitory levels of tetracyclines potentiate selection for or against tetracycline resistance around localized sources of almost any toxin or stress. Furthermore, certain stresses generate alternating rings of selection for and against resistance around a localized source of the antibiotic. In these conditions, localized antibiotic sources, even at high strengths, can actually produce a net selection against resistance to the antibiotic. Our results show that interactions between the effects of an antibiotic and other stresses in inhomogeneous environments can generate pervasive, complex patterns of selection both for and against antibiotic resistance."}],"citation":{"ieee":"R. P. Chait, A. Palmer, I. Yelin, and R. Kishony, “Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments,” Nature Communications, vol. 7. Nature Publishing Group, 2016.","chicago":"Chait, Remy P, Adam Palmer, Idan Yelin, and Roy Kishony. “Pervasive Selection for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.” Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms10333.","apa":"Chait, R. P., Palmer, A., Yelin, I., & Kishony, R. (2016). Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms10333","ama":"Chait RP, Palmer A, Yelin I, Kishony R. Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. 2016;7. doi:10.1038/ncomms10333","ista":"Chait RP, Palmer A, Yelin I, Kishony R. 2016. Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments. Nature Communications. 7, 10333.","mla":"Chait, Remy P., et al. “Pervasive Selection for and against Antibiotic Resistance in Inhomogeneous Multistress Environments.” Nature Communications, vol. 7, 10333, Nature Publishing Group, 2016, doi:10.1038/ncomms10333.","short":"R.P. Chait, A. Palmer, I. Yelin, R. Kishony, Nature Communications 7 (2016)."},"year":"2016","date_updated":"2021-01-12T06:49:57Z","acknowledgement":"This work was partially supported by US National Institutes of Health grant R01-GM081617, Israeli Centers of Research Excellence I-CORE Program ISF Grant No. 152/11, and the European Research Council FP7 ERC Grant 281891.","volume":7,"ddc":["570","579"],"date_created":"2018-12-11T11:51:25Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publication_status":"published","intvolume":" 7","pubrep_id":"662","title":"Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments","scopus_import":1,"_id":"1332","author":[{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","first_name":"Remy P","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187"},{"first_name":"Adam","last_name":"Palmer","full_name":"Palmer, Adam"},{"full_name":"Yelin, Idan","first_name":"Idan","last_name":"Yelin"},{"full_name":"Kishony, Roy","last_name":"Kishony","first_name":"Roy"}],"publisher":"Nature Publishing Group","quality_controlled":"1","file_date_updated":"2020-07-14T12:44:44Z","oa":1,"publist_id":"5936","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"type":"journal_article","date_published":"2016-01-20T00:00:00Z","file":[{"file_name":"IST-2016-662-v1+1_ncomms10333.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:44:44Z","file_size":1844107,"checksum":"ef147bcbb8bd37e9079cf3ce06f5815d","date_created":"2018-12-12T10:13:52Z","creator":"system","file_id":"5039","access_level":"open_access","relation":"main_file"}],"status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa_version":"Published Version","article_number":"10333","month":"01","has_accepted_license":"1","publication":"Nature Communications","language":[{"iso":"eng"}]},{"scopus_import":1,"publication":"Science","_id":"1342","issue":"6304","author":[{"full_name":"Baym, Michael","first_name":"Michael","last_name":"Baym"},{"last_name":"Lieberman","first_name":"Tami","full_name":"Lieberman, Tami"},{"last_name":"Kelsic","first_name":"Eric","full_name":"Kelsic, Eric"},{"orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P","first_name":"Remy P","last_name":"Chait","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Rotem","last_name":"Gross","full_name":"Gross, Rotem"},{"full_name":"Yelin, Idan","first_name":"Idan","last_name":"Yelin"},{"first_name":"Roy","last_name":"Kishony","full_name":"Kishony, Roy"}],"date_created":"2018-12-11T11:51:29Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"publication_status":"published","oa_version":"Preprint","intvolume":" 353","month":"09","title":"Spatiotemporal microbial evolution on antibiotic landscapes","quality_controlled":"1","page":"1147 - 1151","language":[{"iso":"eng"}],"publisher":"American Association for the Advancement of Science","citation":{"mla":"Baym, Michael, et al. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.” Science, vol. 353, no. 6304, American Association for the Advancement of Science, 2016, pp. 1147–51, doi:10.1126/science.aag0822.","short":"M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony, Science 353 (2016) 1147–1151.","ista":"Baym M, Lieberman T, Kelsic E, Chait RP, Gross R, Yelin I, Kishony R. 2016. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 353(6304), 1147–1151.","apa":"Baym, M., Lieberman, T., Kelsic, E., Chait, R. P., Gross, R., Yelin, I., & Kishony, R. (2016). Spatiotemporal microbial evolution on antibiotic landscapes. Science. American Association for the Advancement of Science. https://doi.org/10.1126/science.aag0822","ama":"Baym M, Lieberman T, Kelsic E, et al. Spatiotemporal microbial evolution on antibiotic landscapes. Science. 2016;353(6304):1147-1151. doi:10.1126/science.aag0822","ieee":"M. Baym et al., “Spatiotemporal microbial evolution on antibiotic landscapes,” Science, vol. 353, no. 6304. American Association for the Advancement of Science, pp. 1147–1151, 2016.","chicago":"Baym, Michael, Tami Lieberman, Eric Kelsic, Remy P Chait, Rotem Gross, Idan Yelin, and Roy Kishony. “Spatiotemporal Microbial Evolution on Antibiotic Landscapes.” Science. American Association for the Advancement of Science, 2016. https://doi.org/10.1126/science.aag0822."},"year":"2016","date_updated":"2021-01-12T06:50:01Z","type":"journal_article","date_published":"2016-09-09T00:00:00Z","day":"09","doi":"10.1126/science.aag0822","publist_id":"5911","oa":1,"abstract":[{"text":"A key aspect of bacterial survival is the ability to evolve while migrating across spatially varying environmental challenges. Laboratory experiments, however, often study evolution in well-mixed systems. Here, we introduce an experimental device, the microbial evolution and growth arena (MEGA)-plate, in which bacteria spread and evolved on a large antibiotic landscape (120 × 60 centimeters) that allowed visual observation of mutation and selection in a migrating bacterial front.While resistance increased consistently, multiple coexisting lineages diversified both phenotypically and genotypically. Analyzing mutants at and behind the propagating front,we found that evolution is not always led by the most resistant mutants; highly resistant mutants may be trapped behindmore sensitive lineages.TheMEGA-plate provides a versatile platformfor studying microbial adaption and directly visualizing evolutionary dynamics.","lang":"eng"}],"main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/","open_access":"1"}],"volume":353,"status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87"},{"date_published":"2016-08-04T00:00:00Z","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)"},"publist_id":"5887","oa":1,"related_material":{"record":[{"id":"6071","relation":"dissertation_contains","status":"public"}]},"status":"public","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","file":[{"content_type":"application/pdf","file_name":"IST-2016-627-v1+1_ncomms12307.pdf","date_updated":"2020-07-14T12:44:46Z","file_size":861805,"checksum":"fe3f3a1526d180b29fe691ab11435b78","date_created":"2018-12-12T10:12:01Z","creator":"system","file_id":"4919","relation":"main_file","access_level":"open_access"},{"creator":"system","file_id":"4920","relation":"main_file","access_level":"open_access","file_name":"IST-2016-627-v1+2_ncomms12307-s1.pdf","content_type":"application/pdf","date_updated":"2020-07-14T12:44:46Z","checksum":"164864a1a675f3ad80e9917c27aba07f","file_size":1084703,"date_created":"2018-12-12T10:12:02Z"}],"publication":"Nature Communications","has_accepted_license":"1","month":"08","article_number":"12307","oa_version":"Published Version","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"language":[{"iso":"eng"}],"date_updated":"2023-09-07T12:53:49Z","year":"2016","citation":{"ieee":"T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton, and G. Tkačik, “Intrinsic limits to gene regulation by global crosstalk,” Nature Communications, vol. 7. Nature Publishing Group, 2016.","chicago":"Friedlander, Tamar, Roshan Prizak, Calin C Guet, Nicholas H Barton, and Gašper Tkačik. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms12307.","ama":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 2016;7. doi:10.1038/ncomms12307","apa":"Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., & Tkačik, G. (2016). Intrinsic limits to gene regulation by global crosstalk. Nature Communications. Nature Publishing Group. https://doi.org/10.1038/ncomms12307","ista":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 7, 12307.","mla":"Friedlander, Tamar, et al. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature Communications, vol. 7, 12307, Nature Publishing Group, 2016, doi:10.1038/ncomms12307.","short":"T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkačik, Nature Communications 7 (2016)."},"abstract":[{"text":"Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements.","lang":"eng"}],"doi":"10.1038/ncomms12307","day":"04","ddc":["576"],"volume":7,"author":[{"full_name":"Friedlander, Tamar","first_name":"Tamar","last_name":"Friedlander","id":"36A5845C-F248-11E8-B48F-1D18A9856A87"},{"id":"4456104E-F248-11E8-B48F-1D18A9856A87","last_name":"Prizak","first_name":"Roshan","full_name":"Prizak, Roshan"},{"id":"47F8433E-F248-11E8-B48F-1D18A9856A87","last_name":"Guet","first_name":"Calin C","full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052"},{"orcid":"0000-0002-8548-5240","full_name":"Barton, Nicholas H","first_name":"Nicholas H","last_name":"Barton","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"}],"_id":"1358","scopus_import":1,"pubrep_id":"627","title":"Intrinsic limits to gene regulation by global crosstalk","intvolume":" 7","publication_status":"published","date_created":"2018-12-11T11:51:34Z","department":[{"_id":"GaTk"},{"_id":"NiBa"},{"_id":"CaGu"}],"file_date_updated":"2020-07-14T12:44:46Z","ec_funded":1,"quality_controlled":"1","publisher":"Nature Publishing Group"},{"publisher":"IOP Publishing Ltd.","quality_controlled":"1","ec_funded":1,"intvolume":" 13","title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","department":[{"_id":"GaTk"}],"date_created":"2018-12-11T11:51:46Z","publication_status":"published","issue":"3","author":[{"orcid":"0000-0002-5214-4706","full_name":"De Martino, Daniele","first_name":"Daniele","last_name":"De Martino","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Fabrizio","last_name":"Capuani","full_name":"Capuani, Fabrizio"},{"full_name":"De Martino, Andrea","first_name":"Andrea","last_name":"De Martino"}],"scopus_import":1,"_id":"1394","volume":13,"acknowledgement":"The research leading to these results has received funding from the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038.","abstract":[{"text":"The solution space of genome-scale models of cellular metabolism provides a map between physically\r\nviable flux configurations and cellular metabolic phenotypes described, at the most basic level, by the\r\ncorresponding growth rates. By sampling the solution space of E. coliʼs metabolic network, we show\r\nthat empirical growth rate distributions recently obtained in experiments at single-cell resolution can\r\nbe explained in terms of a trade-off between the higher fitness of fast-growing phenotypes and the\r\nhigher entropy of slow-growing ones. Based on this, we propose a minimal model for the evolution of\r\na large bacterial population that captures this trade-off. The scaling relationships observed in\r\nexperiments encode, in such frameworks, for the same distance from the maximum achievable growth\r\nrate, the same degree of growth rate maximization, and/or the same rate of phenotypic change. Being\r\ngrounded on genome-scale metabolic network reconstructions, these results allow for multiple\r\nimplications and extensions in spite of the underlying conceptual simplicity.","lang":"eng"}],"day":"27","doi":"10.1088/1478-3975/13/3/036005","year":"2016","citation":{"ieee":"D. De Martino, F. Capuani, and A. De Martino, “Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli,” Physical Biology, vol. 13, no. 3. IOP Publishing Ltd., 2016.","chicago":"De Martino, Daniele, Fabrizio Capuani, and Andrea De Martino. “Growth against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.” Physical Biology. IOP Publishing Ltd., 2016. https://doi.org/10.1088/1478-3975/13/3/036005.","ama":"De Martino D, Capuani F, De Martino A. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 2016;13(3). doi:10.1088/1478-3975/13/3/036005","apa":"De Martino, D., Capuani, F., & De Martino, A. (2016). Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/3/036005","ista":"De Martino D, Capuani F, De Martino A. 2016. Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli. Physical Biology. 13(3), 036005.","short":"D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).","mla":"De Martino, Daniele, et al. “Growth against Entropy in Bacterial Metabolism: The Phenotypic Trade-off behind Empirical Growth Rate Distributions in E. Coli.” Physical Biology, vol. 13, no. 3, 036005, IOP Publishing Ltd., 2016, doi:10.1088/1478-3975/13/3/036005."},"date_updated":"2021-01-12T06:50:23Z","language":[{"iso":"eng"}],"article_number":"036005","month":"05","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"oa_version":"Preprint","publication":"Physical Biology","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","status":"public","main_file_link":[{"url":"https://arxiv.org/abs/1601.03243","open_access":"1"}],"oa":1,"publist_id":"5815","type":"journal_article","date_published":"2016-05-27T00:00:00Z"},{"publist_id":"5787","oa":1,"date_published":"2016-04-06T00:00:00Z","type":"journal_article","status":"public","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"http://arxiv.org/abs/1510.08344","open_access":"1"}],"month":"04","oa_version":"Preprint","project":[{"name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425"},{"name":"Information processing and computation in fish groups","grant_number":"RGP0065/2012","_id":"255008E4-B435-11E9-9278-68D0E5697425"}],"publication":"Genetics","language":[{"iso":"eng"}],"abstract":[{"text":"Selection, mutation, and random drift affect the dynamics of allele frequencies and consequently of quantitative traits. While the macroscopic dynamics of quantitative traits can be measured, the underlying allele frequencies are typically unobserved. Can we understand how the macroscopic observables evolve without following these microscopic processes? This problem has been studied previously by analogy with statistical mechanics: the allele frequency distribution at each time point is approximated by the stationary form, which maximizes entropy. We explore the limitations of this method when mutation is small (4Nμ < 1) so that populations are typically close to fixation, and we extend the theory in this regime to account for changes in mutation strength. We consider a single diallelic locus either under directional selection or with overdominance and then generalize to multiple unlinked biallelic loci with unequal effects. We find that the maximum-entropy approximation is remarkably accurate, even when mutation and selection change rapidly. ","lang":"eng"}],"doi":"10.1534/genetics.115.184127","arxiv":1,"day":"06","external_id":{"arxiv":["1510.08344"]},"date_updated":"2025-05-28T11:42:47Z","citation":{"short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548.","mla":"Bodova, Katarina, et al. “A General Approximation for the Dynamics of Quantitative Traits.” Genetics, vol. 202, no. 4, Genetics Society of America, 2016, pp. 1523–48, doi:10.1534/genetics.115.184127.","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 1523–1548.","ama":"Bodova K, Tkačik G, Barton NH. A general approximation for the dynamics of quantitative traits. Genetics. 2016;202(4):1523-1548. doi:10.1534/genetics.115.184127","apa":"Bodova, K., Tkačik, G., & Barton, N. H. (2016). A general approximation for the dynamics of quantitative traits. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.115.184127","ieee":"K. Bodova, G. Tkačik, and N. H. Barton, “A general approximation for the dynamics of quantitative traits,” Genetics, vol. 202, no. 4. Genetics Society of America, pp. 1523–1548, 2016.","chicago":"Bodova, Katarina, Gašper Tkačik, and Nicholas H Barton. “A General Approximation for the Dynamics of Quantitative Traits.” Genetics. Genetics Society of America, 2016. https://doi.org/10.1534/genetics.115.184127."},"year":"2016","volume":202,"title":"A general approximation for the dynamics of quantitative traits","intvolume":" 202","publication_status":"published","department":[{"_id":"GaTk"},{"_id":"NiBa"}],"article_processing_charge":"No","date_created":"2018-12-11T11:51:55Z","author":[{"orcid":"0000-0002-7214-0171","full_name":"Bod'ová, Katarína","first_name":"Katarína","last_name":"Bod'ová","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","last_name":"Barton","first_name":"Nicholas H"}],"issue":"4","_id":"1420","scopus_import":"1","publisher":"Genetics Society of America","page":"1523 - 1548","quality_controlled":"1","ec_funded":1}]