[{"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"mla":"Barone, Vanessa, et al. “An Effective Feedback Loop between Cell-Cell Contact Duration and Morphogen Signaling Determines Cell Fate.” Developmental Cell, vol. 43, no. 2, Cell Press, 2017, pp. 198–211, doi:10.1016/j.devcel.2017.09.014.","ama":"Barone V, Lang M, Krens G, et al. An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate. Developmental Cell. 2017;43(2):198-211. doi:10.1016/j.devcel.2017.09.014","apa":"Barone, V., Lang, M., Krens, G., Pradhan, S., Shamipour, S., Sako, K., … Heisenberg, C.-P. J. (2017). An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate. Developmental Cell. Cell Press. https://doi.org/10.1016/j.devcel.2017.09.014","short":"V. Barone, M. Lang, G. Krens, S. Pradhan, S. Shamipour, K. Sako, M.K. Sikora, C.C. Guet, C.-P.J. Heisenberg, Developmental Cell 43 (2017) 198–211.","ieee":"V. Barone et al., “An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate,” Developmental Cell, vol. 43, no. 2. Cell Press, pp. 198–211, 2017.","chicago":"Barone, Vanessa, Moritz Lang, Gabriel Krens, Saurabh Pradhan, Shayan Shamipour, Keisuke Sako, Mateusz K Sikora, Calin C Guet, and Carl-Philipp J Heisenberg. “An Effective Feedback Loop between Cell-Cell Contact Duration and Morphogen Signaling Determines Cell Fate.” Developmental Cell. Cell Press, 2017. https://doi.org/10.1016/j.devcel.2017.09.014.","ista":"Barone V, Lang M, Krens G, Pradhan S, Shamipour S, Sako K, Sikora MK, Guet CC, Heisenberg C-PJ. 2017. An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate. Developmental Cell. 43(2), 198–211."},"title":"An effective feedback loop between cell-cell contact duration and morphogen signaling determines cell fate","author":[{"full_name":"Barone, Vanessa","orcid":"0000-0003-2676-3367","last_name":"Barone","id":"419EECCC-F248-11E8-B48F-1D18A9856A87","first_name":"Vanessa"},{"id":"29E0800A-F248-11E8-B48F-1D18A9856A87","first_name":"Moritz","full_name":"Lang, Moritz","last_name":"Lang"},{"first_name":"Gabriel","id":"2B819732-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-4761-5996","full_name":"Krens, Gabriel","last_name":"Krens"},{"last_name":"Pradhan","full_name":"Pradhan, Saurabh","first_name":"Saurabh"},{"last_name":"Shamipour","full_name":"Shamipour, Shayan","id":"40B34FE2-F248-11E8-B48F-1D18A9856A87","first_name":"Shayan"},{"id":"3BED66BE-F248-11E8-B48F-1D18A9856A87","first_name":"Keisuke","last_name":"Sako","full_name":"Sako, Keisuke","orcid":"0000-0002-6453-8075"},{"full_name":"Sikora, Mateusz K","last_name":"Sikora","first_name":"Mateusz K","id":"2F74BCDE-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","full_name":"Guet, Calin C","orcid":"0000-0001-6220-2052","last_name":"Guet"},{"last_name":"Heisenberg","full_name":"Heisenberg, Carl-Philipp J","orcid":"0000-0002-0912-4566","first_name":"Carl-Philipp J","id":"39427864-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"6934","article_processing_charge":"No","external_id":{"isi":["000413443700011"]},"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"},{"name":"Cell segregation in gastrulation: the role of cell fate specification","grant_number":"I2058","_id":"252DD2A6-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"day":"23","publication":"Developmental Cell","isi":1,"year":"2017","date_published":"2017-10-23T00:00:00Z","doi":"10.1016/j.devcel.2017.09.014","date_created":"2018-12-11T11:48:13Z","page":"198 - 211","publisher":"Cell Press","quality_controlled":"1","date_updated":"2024-03-27T23:30:38Z","department":[{"_id":"CaHe"},{"_id":"CaGu"},{"_id":"GaTk"}],"_id":"735","status":"public","type":"journal_article","language":[{"iso":"eng"}],"publication_identifier":{"issn":["15345807"]},"publication_status":"published","issue":"2","related_material":{"record":[{"relation":"dissertation_contains","id":"961","status":"public"},{"id":"8350","status":"public","relation":"dissertation_contains"}]},"volume":43,"ec_funded":1,"oa_version":"None","abstract":[{"text":"Cell-cell contact formation constitutes an essential step in evolution, leading to the differentiation of specialized cell types. However, remarkably little is known about whether and how the interplay between contact formation and fate specification affects development. Here, we identify a positive feedback loop between cell-cell contact duration, morphogen signaling, and mesendoderm cell-fate specification during zebrafish gastrulation. We show that long-lasting cell-cell contacts enhance the competence of prechordal plate (ppl) progenitor cells to respond to Nodal signaling, required for ppl cell-fate specification. We further show that Nodal signaling promotes ppl cell-cell contact duration, generating a positive feedback loop between ppl cell-cell contact duration and cell-fate specification. Finally, by combining mathematical modeling and experimentation, we show that this feedback determines whether anterior axial mesendoderm cells become ppl or, instead, turn into endoderm. Thus, the interdependent activities of cell-cell signaling and contact formation control fate diversification within the developing embryo.","lang":"eng"}],"month":"10","intvolume":" 43","scopus_import":"1"},{"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:48:09Z","citation":{"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.","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.","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.","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.","chicago":"Chalk, Matthew J, Olivier Marre, and Gašper Tkačik. “Relevant Sparse Codes with Variational Information Bottleneck,” 29:1965–73. Neural Information Processing Systems, 2016."},"title":"Relevant sparse codes with variational information bottleneck","department":[{"_id":"GaTk"}],"author":[{"last_name":"Chalk","orcid":"0000-0001-7782-4436","full_name":"Chalk, Matthew J","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","first_name":"Matthew J"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"publist_id":"6298","_id":"1082","status":"public","conference":{"name":"NIPS: Neural Information Processing Systems","location":"Barcelona, Spain","end_date":"2016-12-10","start_date":"2016-12-05"},"type":"conference","language":[{"iso":"eng"}],"day":"01","publication_status":"published","year":"2016","date_created":"2018-12-11T11:50:03Z","volume":29,"related_material":{"link":[{"url":"https://papers.nips.cc/paper/6101-relevant-sparse-codes-with-variational-information-bottleneck","relation":"other"}]},"date_published":"2016-12-01T00:00:00Z","page":"1965-1973","oa_version":"Preprint","abstract":[{"lang":"eng","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."}],"intvolume":" 29","month":"12","oa":1,"main_file_link":[{"url":"https://arxiv.org/abs/1605.07332","open_access":"1"}],"publisher":"Neural Information Processing Systems","quality_controlled":"1","scopus_import":1,"alternative_title":["Advances in Neural Information Processing Systems"]},{"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","short":"C. Savin, G. Tkačik, in:, Neural Information Processing Systems, 2016, pp. 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.","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.","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.","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.","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."},"title":"Estimating nonlinear neural response functions using GP priors and Kronecker methods","publist_id":"6265","author":[{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","last_name":"Savin","full_name":"Savin, Cristina"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik"}],"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"day":"01","year":"2016","date_created":"2018-12-11T11:50:10Z","date_published":"2016-12-01T00:00:00Z","page":"3610-3618","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.","publisher":"Neural Information Processing Systems","quality_controlled":"1","date_updated":"2021-01-12T06:48:19Z","department":[{"_id":"GaTk"}],"_id":"1105","status":"public","conference":{"start_date":"2016-12-05","end_date":"2016-12-10","location":"Barcelona; Spain","name":"NIPS: Neural Information Processing Systems"},"type":"conference","language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"volume":29,"oa_version":"None","abstract":[{"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.","lang":"eng"}],"intvolume":" 29","month":"12","main_file_link":[{"url":"http://papers.nips.cc/paper/6153-estimating-nonlinear-neural-response-functions-using-gp-priors-and-kronecker-methods"}],"alternative_title":["Advances in Neural Information Processing Systems"],"scopus_import":1},{"type":"journal_article","status":"public","pubrep_id":"811","_id":"1170","file_date_updated":"2020-07-14T12:44:37Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:48:49Z","ddc":["003","518","570","621"],"scopus_import":1,"month":"11","intvolume":" 38","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."}],"oa_version":"Submitted Version","issue":"6","volume":38,"publication_status":"published","file":[{"content_type":"application/pdf","access_level":"local","relation":"main_file","checksum":"781bc3ffd30b2dd65b7727c5a285fc78","file_id":"5095","date_updated":"2020-07-14T12:44:37Z","file_size":871964,"creator":"system","date_created":"2018-12-12T10:14:41Z","file_name":"IST-2017-811-v1+1_modular_parameter_identification.pdf"}],"language":[{"iso":"eng"}],"author":[{"full_name":"Lang, Moritz","last_name":"Lang","first_name":"Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Jörg","full_name":"Stelling, Jörg","last_name":"Stelling"}],"publist_id":"6186","title":"Modular parameter identification of biomolecular networks","citation":{"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.","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","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","short":"M. Lang, J. Stelling, SIAM Journal on Scientific Computing 38 (2016) B988–B1008.","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.","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.","ista":"Lang M, Stelling J. 2016. Modular parameter identification of biomolecular networks. SIAM Journal on Scientific Computing. 38(6), B988–B1008."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","publisher":"Society for Industrial and Applied Mathematics ","page":"B988 - B1008","doi":"10.1137/15M103306X","date_published":"2016-11-15T00:00:00Z","date_created":"2018-12-11T11:50:31Z","has_accepted_license":"1","year":"2016","day":"15","publication":"SIAM Journal on Scientific Computing"},{"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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","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.","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."},"date_updated":"2021-01-12T06:48:50Z","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.","department":[{"_id":"GaTk"}],"publist_id":"6185","author":[{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"}],"_id":"1171","status":"public","type":"journal_article","day":"01","publication":"Physics of Life Reviews","language":[{"iso":"eng"}],"year":"2016","publication_status":"published","volume":17,"doi":"10.1016/j.plrev.2016.06.005","date_published":"2016-07-01T00:00:00Z","date_created":"2018-12-11T11:50:32Z","page":"166 - 167","oa_version":"None","month":"07","intvolume":" 17","quality_controlled":"1","scopus_import":1,"publisher":"Elsevier"},{"oa_version":"Preprint","abstract":[{"text":"We consider a population dynamics model coupling cell growth to a diffusion in the space of metabolic phenotypes as it can be obtained from realistic constraints-based modelling. \r\nIn the asymptotic regime of slow\r\ndiffusion, that coincides with the relevant experimental range, the resulting\r\nnon-linear Fokker–Planck equation is solved for the steady state in the WKB\r\napproximation that maps it into the ground state of a quantum particle in an\r\nAiry potential plus a centrifugal term. We retrieve scaling laws for growth rate\r\nfluctuations and time response with respect to the distance from the maximum\r\ngrowth rate suggesting that suboptimal populations can have a faster response\r\nto perturbations.","lang":"eng"}],"intvolume":" 2016","month":"12","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1606.09048"}],"scopus_import":1,"language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"issue":"12","volume":2016,"_id":"1188","status":"public","type":"journal_article","date_updated":"2021-01-12T06:48:57Z","department":[{"_id":"GaTk"}],"acknowledgement":"D De Martino is 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]. D Masoero is supported by the FCT scholarship, number SFRH/BPD/75908/2011. D De Martino thanks the Grupo de Física Matemática of the Universidade de Lisboa for the kind hospitality. We also wish to thank Matteo Osella, Vincenzo Vitagliano and Vera Luz Masoero for useful discussions, also late at night.","oa":1,"publisher":"IOPscience","quality_controlled":"1","publication":" Journal of Statistical Mechanics: Theory and Experiment","day":"30","year":"2016","date_created":"2018-12-11T11:50:37Z","doi":"10.1088/1742-5468/aa4e8f","date_published":"2016-12-30T00:00:00Z","article_number":"123502","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"De Martino D, Masoero D. 2016. Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. 2016(12), 123502.","chicago":"De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy Fitness Maximization, Applied to Metabolism & Growth.” Journal of Statistical Mechanics: Theory and Experiment. IOPscience, 2016. https://doi.org/10.1088/1742-5468/aa4e8f.","short":"D. De Martino, D. Masoero, Journal of Statistical Mechanics: Theory and Experiment 2016 (2016).","ieee":"D. De Martino and D. Masoero, “Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth,” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12. IOPscience, 2016.","apa":"De Martino, D., & Masoero, D. (2016). Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. IOPscience. https://doi.org/10.1088/1742-5468/aa4e8f","ama":"De Martino D, Masoero D. Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth. Journal of Statistical Mechanics: Theory and Experiment. 2016;2016(12). doi:10.1088/1742-5468/aa4e8f","mla":"De Martino, Daniele, and Davide Masoero. “Asymptotic Analysis of Noisy Fitness Maximization, Applied to Metabolism & Growth.” Journal of Statistical Mechanics: Theory and Experiment, vol. 2016, no. 12, 123502, IOPscience, 2016, doi:10.1088/1742-5468/aa4e8f."},"title":"Asymptotic analysis of noisy fitness maximization, applied to metabolism & growth","publist_id":"6165","author":[{"last_name":"De Martino","full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706","first_name":"Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Davide","full_name":"Masoero, Davide","last_name":"Masoero"}]},{"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Hu, Fang, Lavanya Rishishwar, Ambily Sivadas, Gabriel Mitchell, Jordan King, Timothy Murphy, Janet Gilsdorf, Leonard Mayer, and Xin Wang. “Comparative Genomic Analysis of Haemophilus Haemolyticus and Nontypeable Haemophilus Influenzae and a New Testing Scheme for Their Discrimination.” Journal of Clinical Microbiology. American Society for Microbiology, 2016. https://doi.org/10.1128/JCM.01511-16.","ista":"Hu F, Rishishwar L, Sivadas A, Mitchell G, King J, Murphy T, Gilsdorf J, Mayer L, Wang X. 2016. Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination. Journal of Clinical Microbiology. 54(12), 3010–3017.","mla":"Hu, Fang, et al. “Comparative Genomic Analysis of Haemophilus Haemolyticus and Nontypeable Haemophilus Influenzae and a New Testing Scheme for Their Discrimination.” Journal of Clinical Microbiology, vol. 54, no. 12, American Society for Microbiology, 2016, pp. 3010–17, doi:10.1128/JCM.01511-16.","short":"F. Hu, L. Rishishwar, A. Sivadas, G. Mitchell, J. King, T. Murphy, J. Gilsdorf, L. Mayer, X. Wang, Journal of Clinical Microbiology 54 (2016) 3010–3017.","ieee":"F. Hu et al., “Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination,” Journal of Clinical Microbiology, vol. 54, no. 12. American Society for Microbiology, pp. 3010–3017, 2016.","ama":"Hu F, Rishishwar L, Sivadas A, et al. Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination. Journal of Clinical Microbiology. 2016;54(12):3010-3017. doi:10.1128/JCM.01511-16","apa":"Hu, F., Rishishwar, L., Sivadas, A., Mitchell, G., King, J., Murphy, T., … Wang, X. (2016). Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination. Journal of Clinical Microbiology. American Society for Microbiology. https://doi.org/10.1128/JCM.01511-16"},"title":"Comparative genomic analysis of Haemophilus haemolyticus and nontypeable Haemophilus influenzae and a new testing scheme for their discrimination","publist_id":"6146","author":[{"first_name":"Fang","last_name":"Hu","full_name":"Hu, Fang"},{"last_name":"Rishishwar","full_name":"Rishishwar, Lavanya","first_name":"Lavanya"},{"last_name":"Sivadas","full_name":"Sivadas, Ambily","first_name":"Ambily"},{"id":"315BCD80-F248-11E8-B48F-1D18A9856A87","first_name":"Gabriel","full_name":"Mitchell, Gabriel","last_name":"Mitchell"},{"last_name":"King","full_name":"King, Jordan","first_name":"Jordan"},{"first_name":"Timothy","full_name":"Murphy, Timothy","last_name":"Murphy"},{"first_name":"Janet","last_name":"Gilsdorf","full_name":"Gilsdorf, Janet"},{"first_name":"Leonard","last_name":"Mayer","full_name":"Mayer, Leonard"},{"last_name":"Wang","full_name":"Wang, Xin","first_name":"Xin"}],"acknowledgement":"We are grateful to ABCs for providing strains and the Bacterial Meningitis Laboratory for technical support.","oa":1,"quality_controlled":"1","publisher":"American Society for Microbiology","publication":"Journal of Clinical Microbiology","day":"01","year":"2016","date_created":"2018-12-11T11:50:41Z","date_published":"2016-12-01T00:00:00Z","doi":"10.1128/JCM.01511-16","page":"3010 - 3017","_id":"1203","status":"public","type":"journal_article","date_updated":"2021-01-12T06:49:04Z","department":[{"_id":"GaTk"}],"oa_version":"Submitted Version","abstract":[{"text":"Haemophilus haemolyticus has been recently discovered to have the potential to cause invasive disease. It is closely related to nontypeable Haemophilus influenzae (NT H. influenzae). NT H. influenzae and H. haemolyticus are often misidentified because none of the existing tests targeting the known phenotypes of H. haemolyticus are able to specifically identify H. haemolyticus. Through comparative genomic analysis of H. haemolyticus and NT H. influenzae, we identified genes unique to H. haemolyticus that can be used as targets for the identification of H. haemolyticus. A real-time PCR targeting purT (encoding phosphoribosylglycinamide formyltransferase 2 in the purine synthesis pathway) was developed and evaluated. The lower limit of detection was 40 genomes/PCR; the sensitivity and specificity in detecting H. haemolyticus were 98.9% and 97%, respectively. To improve the discrimination of H. haemolyticus and NT H. influenzae, a testing scheme combining two targets (H. haemolyticus purT and H. influenzae hpd, encoding protein D lipoprotein) was also evaluated and showed 96.7% sensitivity and 98.2% specificity for the identification of H. haemolyticus and 92.8% sensitivity and 100% specificity for the identification of H. influenzae, respectively. The dual-target testing scheme can be used for the diagnosis and surveillance of infection and disease caused by H. haemolyticus and NT H. influenzae.","lang":"eng"}],"intvolume":" 54","month":"12","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5121393/","open_access":"1"}],"scopus_import":1,"language":[{"iso":"eng"}],"publication_status":"published","issue":"12","volume":54},{"status":"public","conference":{"name":"IEEE RSJ International Conference on Intelligent Robots and Systems IROS ","start_date":"2016-09-09","location":"Daejeon, Korea","end_date":"2016-09-14"},"type":"conference","article_number":"7759138","_id":"1214","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"title":"Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm","publist_id":"6121","author":[{"first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","last_name":"Martius","full_name":"Martius, Georg S"},{"full_name":"Hostettler, Raphael","last_name":"Hostettler","first_name":"Raphael"},{"last_name":"Knoll","full_name":"Knoll, Alois","first_name":"Alois"},{"first_name":"Ralf","full_name":"Der, Ralf","last_name":"Der"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Martius GS, Hostettler R, Knoll A, Der R. 2016. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. IEEE RSJ International Conference on Intelligent Robots and Systems IROS vol. 2016–November, 7759138.","chicago":"Martius, Georg S, Raphael Hostettler, Alois Knoll, and Ralf Der. “Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm,” Vol. 2016–November. IEEE, 2016. https://doi.org/10.1109/IROS.2016.7759138.","ieee":"G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm,” presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea, 2016, vol. 2016–November.","short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, IEEE, 2016.","ama":"Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November. IEEE; 2016. doi:10.1109/IROS.2016.7759138","apa":"Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138","mla":"Martius, Georg S., et al. Compliant Control for Soft Robots: Emergent Behavior of a Tendon Driven Anthropomorphic Arm. Vol. 2016–November, 7759138, IEEE, 2016, doi:10.1109/IROS.2016.7759138."},"date_updated":"2021-01-12T06:49:08Z","month":"11","scopus_import":1,"quality_controlled":"1","publisher":"IEEE","acknowledgement":"RD thanks for the hospitality at the Max-Planck-Institute and for helpful discussions with Nihat Ay and Keyan Zahedi.","oa_version":"None","abstract":[{"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. While very successful with classical robots, these methods run into severe difficulties when applied to soft robots, a new field of robotics with large interest for human-robot interaction. We claim that a novel controller paradigm opens new perspective for this field. This paper applies a recently developed neuro controller with 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 develops to rotate it. In this way, the robot discovers affordances of objects its body is interacting with.","lang":"eng"}],"date_created":"2018-12-11T11:50:45Z","doi":"10.1109/IROS.2016.7759138","date_published":"2016-11-28T00:00:00Z","volume":"2016-November","language":[{"iso":"eng"}],"day":"28","year":"2016","publication_status":"published"},{"_id":"1220","type":"conference","conference":{"name":"AIAA: Aviation Technology, Integration, and Operations Conference","location":"Washington, D.C., USA","end_date":"2016-06-17","start_date":"2016-06-13"},"status":"public","citation":{"ista":"Mikić G, Stoll A, Bevirt J, Grah R, Moore M. 2016. Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency. AIAA: Aviation Technology, Integration, and Operations Conference, 1–19.","chicago":"Mikić, Gregor, Alex Stoll, Joe Bevirt, Rok Grah, and Mark Moore. “Fuselage Boundary Layer Ingestion Propulsion Applied to a Thin Haul Commuter Aircraft for Optimal Efficiency,” 1–19. AIAA, 2016. https://doi.org/10.2514/6.2016-3764.","short":"G. Mikić, A. Stoll, J. Bevirt, R. Grah, M. Moore, in:, AIAA, 2016, pp. 1–19.","ieee":"G. Mikić, A. Stoll, J. Bevirt, R. Grah, and M. Moore, “Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency,” presented at the AIAA: Aviation Technology, Integration, and Operations Conference, Washington, D.C., USA, 2016, pp. 1–19.","ama":"Mikić G, Stoll A, Bevirt J, Grah R, Moore M. Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency. In: AIAA; 2016:1-19. doi:10.2514/6.2016-3764","apa":"Mikić, G., Stoll, A., Bevirt, J., Grah, R., & Moore, M. (2016). Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency (pp. 1–19). Presented at the AIAA: Aviation Technology, Integration, and Operations Conference, Washington, D.C., USA: AIAA. https://doi.org/10.2514/6.2016-3764","mla":"Mikić, Gregor, et al. Fuselage Boundary Layer Ingestion Propulsion Applied to a Thin Haul Commuter Aircraft for Optimal Efficiency. AIAA, 2016, pp. 1–19, doi:10.2514/6.2016-3764."},"date_updated":"2023-02-21T10:17:50Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Mikić","full_name":"Mikić, Gregor","first_name":"Gregor"},{"last_name":"Stoll","full_name":"Stoll, Alex","first_name":"Alex"},{"full_name":"Bevirt, Joe","last_name":"Bevirt","first_name":"Joe"},{"last_name":"Grah","full_name":"Grah, Rok","orcid":"0000-0003-2539-3560","id":"483E70DE-F248-11E8-B48F-1D18A9856A87","first_name":"Rok"},{"first_name":"Mark","full_name":"Moore, Mark","last_name":"Moore"}],"publist_id":"6114","title":"Fuselage boundary layer ingestion propulsion applied to a thin haul commuter aircraft for optimal efficiency","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"abstract":[{"text":"Theoretical and numerical aspects of aerodynamic efficiency of propulsion systems coupled to the boundary layer of a fuselage are studied. We discuss the effects of local flow fields, which are affected both by conservative flow acceleration as well as total pressure losses, on the efficiency of boundary layer immersed propulsion devices. We introduce the concept of a boundary layer retardation turbine that helps reduce skin friction over the fuselage. We numerically investigate efficiency gains offered by boundary layer and wake interacting devices. We discuss the results in terms of a total energy consumption framework and show that efficiency gains of any device depend on all the other elements of the propulsion system.","lang":"eng"}],"oa_version":"Preprint","scopus_import":1,"publisher":"AIAA","quality_controlled":"1","main_file_link":[{"url":"https://ntrs.nasa.gov/search.jsp?R=20160010167&hterms=Fuselage+boundary+layer+ingestion+propulsion+applied+thin+haul+commuter+aircraft+optimal+efficiency&qs=N%3D0%26Ntk%3DAll%26Ntt%3DFuselage%2520boundary%2520layer%2520ingestion%2520propulsion%2520applied%2520to%2520a%2520thin%2520haul%2520commuter%2520aircraft%2520for%2520optimal%2520efficiency%26Ntx%3Dmode%2520matchallpartial%26Nm%3D123%7CCollection%7CNASA%2520STI%7C%7C17%7CCollection%7CNACA","open_access":"1"}],"oa":1,"month":"06","publication_status":"published","year":"2016","day":"01","language":[{"iso":"eng"}],"page":"1 - 19","date_published":"2016-06-01T00:00:00Z","doi":"10.2514/6.2016-3764","date_created":"2018-12-11T11:50:47Z"},{"department":[{"_id":"GaTk"}],"date_updated":"2021-01-12T06:49:20Z","type":"journal_article","status":"public","_id":"1242","issue":"2","volume":93,"publication_status":"published","language":[{"iso":"eng"}],"scopus_import":1,"main_file_link":[{"url":"https://arxiv.org/abs/1507.02562","open_access":"1"}],"month":"02","intvolume":" 93","abstract":[{"lang":"eng","text":"A crucial step in the regulation of gene expression is binding of transcription factor (TF) proteins to regulatory sites along the DNA. But transcription factors act at nanomolar concentrations, and noise due to random arrival of these molecules at their binding sites can severely limit the precision of regulation. Recent work on the optimization of information flow through regulatory networks indicates that the lower end of the dynamic range of concentrations is simply inaccessible, overwhelmed by the impact of this noise. Motivated by the behavior of homeodomain proteins, such as the maternal morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which transcription factors also act as indirect translational regulators, binding to the mRNA of other regulatory proteins. Intuitively, each mRNA molecule acts as an independent sensor of the input concentration, and averaging over these multiple sensors reduces the noise. We analyze information flow through this scheme and identify conditions under which it outperforms direct transcriptional regulation. Our results suggest that the dual role of homeodomain proteins is not just a historical accident, but a solution to a crucial physics problem in the regulation of gene expression."}],"oa_version":"Preprint","author":[{"id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","last_name":"Sokolowski"},{"full_name":"Walczak, Aleksandra","last_name":"Walczak","first_name":"Aleksandra"},{"last_name":"Bialek","full_name":"Bialek, William","first_name":"William"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"publist_id":"6088","title":"Extending the dynamic range of transcription factor action by translational regulation","citation":{"chicago":"Sokolowski, Thomas R, Aleksandra Walczak, William Bialek, and Gašper Tkačik. “Extending the Dynamic Range of Transcription Factor Action by Translational Regulation.” Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics, 2016. https://doi.org/10.1103/PhysRevE.93.022404.","ista":"Sokolowski TR, Walczak A, Bialek W, Tkačik G. 2016. Extending the dynamic range of transcription factor action by translational regulation. Physical Review E Statistical Nonlinear and Soft Matter Physics. 93(2), 022404.","mla":"Sokolowski, Thomas R., et al. “Extending the Dynamic Range of Transcription Factor Action by Translational Regulation.” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 93, no. 2, 022404, American Institute of Physics, 2016, doi:10.1103/PhysRevE.93.022404.","apa":"Sokolowski, T. R., Walczak, A., Bialek, W., & Tkačik, G. (2016). Extending the dynamic range of transcription factor action by translational regulation. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.93.022404","ama":"Sokolowski TR, Walczak A, Bialek W, Tkačik G. Extending the dynamic range of transcription factor action by translational regulation. Physical Review E Statistical Nonlinear and Soft Matter Physics. 2016;93(2). doi:10.1103/PhysRevE.93.022404","ieee":"T. R. Sokolowski, A. Walczak, W. Bialek, and G. Tkačik, “Extending the dynamic range of transcription factor action by translational regulation,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 93, no. 2. American Institute of Physics, 2016.","short":"T.R. Sokolowski, A. Walczak, W. Bialek, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 93 (2016)."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","project":[{"_id":"254E9036-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation"}],"article_number":"022404","date_published":"2016-02-04T00:00:00Z","doi":"10.1103/PhysRevE.93.022404","date_created":"2018-12-11T11:50:54Z","year":"2016","day":"04","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","quality_controlled":"1","publisher":"American Institute of Physics","oa":1,"acknowledgement":"We thank T. Gregor, A. Prochaintz, and others for\r\nhelpful discussions. This work was supported in part by\r\nGrants No. PHY-1305525 and No. CCF-0939370 from the\r\nUS National Science Foundation and by the W.M. Keck\r\nFoundation. A.M.W. acknowledges the support by European\r\nResearch Council (ERC) Grant No. MCCIG PCIG10–GA-\r\n2011–303561. G.T. and T.R.S. were supported by Austrian\r\nScience Fund (FWF) Grant No. P28844S."},{"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ama":"Recouvreux P, Sokolowski TR, Grammoustianou A, Tenwolde P, Dogterom M. Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. PNAS. 2016;113(7):1811-1816. doi:10.1073/pnas.1419248113","apa":"Recouvreux, P., Sokolowski, T. R., Grammoustianou, A., Tenwolde, P., & Dogterom, M. (2016). Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1419248113","short":"P. Recouvreux, T.R. Sokolowski, A. Grammoustianou, P. Tenwolde, M. Dogterom, PNAS 113 (2016) 1811–1816.","ieee":"P. Recouvreux, T. R. Sokolowski, A. Grammoustianou, P. Tenwolde, and M. Dogterom, “Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells,” PNAS, vol. 113, no. 7. National Academy of Sciences, pp. 1811–1816, 2016.","mla":"Recouvreux, Pierre, et al. “Chimera Proteins with Affinity for Membranes and Microtubule Tips Polarize in the Membrane of Fission Yeast Cells.” PNAS, vol. 113, no. 7, National Academy of Sciences, 2016, pp. 1811–16, doi:10.1073/pnas.1419248113.","ista":"Recouvreux P, Sokolowski TR, Grammoustianou A, Tenwolde P, Dogterom M. 2016. Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. PNAS. 113(7), 1811–1816.","chicago":"Recouvreux, Pierre, Thomas R Sokolowski, Aristea Grammoustianou, Pieter Tenwolde, and Marileen Dogterom. “Chimera Proteins with Affinity for Membranes and Microtubule Tips Polarize in the Membrane of Fission Yeast Cells.” PNAS. National Academy of Sciences, 2016. https://doi.org/10.1073/pnas.1419248113."},"title":"Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells","publist_id":"6085","author":[{"first_name":"Pierre","last_name":"Recouvreux","full_name":"Recouvreux, Pierre"},{"last_name":"Sokolowski","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","first_name":"Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Grammoustianou, Aristea","last_name":"Grammoustianou","first_name":"Aristea"},{"first_name":"Pieter","last_name":"Tenwolde","full_name":"Tenwolde, Pieter"},{"first_name":"Marileen","last_name":"Dogterom","full_name":"Dogterom, Marileen"}],"day":"16","publication":"PNAS","year":"2016","doi":"10.1073/pnas.1419248113","date_published":"2016-02-16T00:00:00Z","date_created":"2018-12-11T11:50:55Z","page":"1811 - 1816","acknowledgement":"We thank Sophie Martin, Ken Sawin, Stephen Huisman,\r\nand Damian Brunner for strains; Julianne\r\nTeapal, Marcel Janson, Sergio Rincon,\r\nand Phong Tran for technical assistance; Andrew Mugler and Bela Mulder for\r\ndiscussions; and Sander Tans, Phong Tran,\r\nand Anne Paoletti for critical reading\r\nof the manuscript. This work is part of the research program of the\r\n“\r\nStichting\r\nvoor Fundamenteel Onderzoek de Materie,\r\n”\r\nwhich is financially supported by\r\nthe\r\n“\r\nNederlandse organisatie voor Wete\r\nnschappelijk Onderzoek (NWO).\r\n”","publisher":"National Academy of Sciences","quality_controlled":"1","oa":1,"date_updated":"2021-01-12T06:49:21Z","department":[{"_id":"GaTk"}],"_id":"1244","status":"public","type":"journal_article","language":[{"iso":"eng"}],"publication_status":"published","volume":113,"issue":"7","oa_version":"Submitted Version","abstract":[{"text":"Cell polarity refers to a functional spatial organization of proteins that is crucial for the control of essential cellular processes such as growth and division. To establish polarity, cells rely on elaborate regulation networks that control the distribution of proteins at the cell membrane. In fission yeast cells, a microtubule-dependent network has been identified that polarizes the distribution of signaling proteins that restricts growth to cell ends and targets the cytokinetic machinery to the middle of the cell. Although many molecular components have been shown to play a role in this network, it remains unknown which molecular functionalities are minimally required to establish a polarized protein distribution in this system. Here we show that a membrane-binding protein fragment, which distributes homogeneously in wild-type fission yeast cells, can be made to concentrate at cell ends by attaching it to a cytoplasmic microtubule end-binding protein. This concentration results in a polarized pattern of chimera proteins with a spatial extension that is very reminiscent of natural polarity patterns in fission yeast. However, chimera levels fluctuate in response to microtubule dynamics, and disruption of microtubules leads to disappearance of the pattern. Numerical simulations confirm that the combined functionality of membrane anchoring and microtubule tip affinity is in principle sufficient to create polarized patterns. Our chimera protein may thus represent a simple molecular functionality that is able to polarize the membrane, onto which additional layers of molecular complexity may be built to provide the temporal robustness that is typical of natural polarity patterns.","lang":"eng"}],"month":"02","intvolume":" 113","scopus_import":1,"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763754/"}]},{"oa_version":"Preprint","abstract":[{"lang":"eng","text":"Life depends as much on the flow of information as on the flow of energy. Here we review the many efforts to make this intuition precise. Starting with the building blocks of information theory, we explore examples where it has been possible to measure, directly, the flow of information in biological networks, or more generally where information-theoretic ideas have been used to guide the analysis of experiments. Systems of interest range from single molecules (the sequence diversity in families of proteins) to groups of organisms (the distribution of velocities in flocks of birds), and all scales in between. Many of these analyses are motivated by the idea that biological systems may have evolved to optimize the gathering and representation of information, and we review the experimental evidence for this optimization, again across a wide range of scales."}],"month":"03","intvolume":" 7","scopus_import":1,"main_file_link":[{"url":"https://arxiv.org/abs/1412.8752","open_access":"1"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":7,"_id":"1248","status":"public","type":"journal_article","date_updated":"2021-01-12T06:49:23Z","department":[{"_id":"GaTk"}],"acknowledgement":"Our work was supported in part by the US\r\nNational Science Foundation (PHY–1305525 and CCF–\r\n0939370), by the Austrian Science Foundation (FWF\r\nP25651), by the Human Frontiers Science Program, and\r\nby the Simons and Swartz Foundations.","publisher":"Annual Reviews","quality_controlled":"1","oa":1,"day":"10","publication":"Annual Review of Condensed Matter Physics","year":"2016","doi":"10.1146/annurev-conmatphys-031214-014803","date_published":"2016-03-10T00:00:00Z","date_created":"2018-12-11T11:50:56Z","page":"89 - 117","project":[{"call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425","name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Tkačik G, Bialek W. 2016. Information processing in living systems. Annual Review of Condensed Matter Physics. 7, 89–117.","chicago":"Tkačik, Gašper, and William Bialek. “Information Processing in Living Systems.” Annual Review of Condensed Matter Physics. Annual Reviews, 2016. https://doi.org/10.1146/annurev-conmatphys-031214-014803.","short":"G. Tkačik, W. Bialek, Annual Review of Condensed Matter Physics 7 (2016) 89–117.","ieee":"G. Tkačik and W. Bialek, “Information processing in living systems,” Annual Review of Condensed Matter Physics, vol. 7. Annual Reviews, pp. 89–117, 2016.","apa":"Tkačik, G., & Bialek, W. (2016). Information processing in living systems. Annual Review of Condensed Matter Physics. Annual Reviews. https://doi.org/10.1146/annurev-conmatphys-031214-014803","ama":"Tkačik G, Bialek W. Information processing in living systems. Annual Review of Condensed Matter Physics. 2016;7:89-117. doi:10.1146/annurev-conmatphys-031214-014803","mla":"Tkačik, Gašper, and William Bialek. “Information Processing in Living Systems.” Annual Review of Condensed Matter Physics, vol. 7, Annual Reviews, 2016, pp. 89–117, doi:10.1146/annurev-conmatphys-031214-014803."},"title":"Information processing in living systems","publist_id":"6080","author":[{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Bialek, William","last_name":"Bialek","first_name":"William"}]},{"publication_status":"published","language":[{"iso":"eng"}],"volume":27,"issue":"6","abstract":[{"lang":"eng","text":"In this work, the Gardner problem of inferring interactions and fields for an Ising neural network from given patterns under a local stability hypothesis is addressed under a dual perspective. By means of duality arguments, an integer linear system is defined whose solution space is the dual of the Gardner space and whose solutions represent mutually unstable patterns. We propose and discuss Monte Carlo methods in order to find and remove unstable patterns and uniformly sample the space of interactions thereafter. We illustrate the problem on a set of real data and perform ensemble calculation that shows how the emergence of phase dominated by unstable patterns can be triggered in a nonlinear discontinuous way."}],"oa_version":"Preprint","scopus_import":1,"main_file_link":[{"url":"https://arxiv.org/abs/1505.02963","open_access":"1"}],"month":"06","intvolume":" 27","date_updated":"2021-01-12T06:49:28Z","department":[{"_id":"GaTk"}],"_id":"1260","type":"journal_article","article_type":"original","status":"public","year":"2016","day":"01","publication":"International Journal of Modern Physics C","date_published":"2016-06-01T00:00:00Z","doi":"10.1142/S0129183116500674","date_created":"2018-12-11T11:51:00Z","quality_controlled":"1","publisher":"World Scientific Publishing","oa":1,"citation":{"ista":"De Martino D. 2016. The dual of the space of interactions in neural network models. International Journal of Modern Physics C. 27(6), 1650067.","chicago":"De Martino, Daniele. “The Dual of the Space of Interactions in Neural Network Models.” International Journal of Modern Physics C. World Scientific Publishing, 2016. https://doi.org/10.1142/S0129183116500674.","short":"D. De Martino, International Journal of Modern Physics C 27 (2016).","ieee":"D. De Martino, “The dual of the space of interactions in neural network models,” International Journal of Modern Physics C, vol. 27, no. 6. World Scientific Publishing, 2016.","apa":"De Martino, D. (2016). The dual of the space of interactions in neural network models. International Journal of Modern Physics C. World Scientific Publishing. https://doi.org/10.1142/S0129183116500674","ama":"De Martino D. The dual of the space of interactions in neural network models. International Journal of Modern Physics C. 2016;27(6). doi:10.1142/S0129183116500674","mla":"De Martino, Daniele. “The Dual of the Space of Interactions in Neural Network Models.” International Journal of Modern Physics C, vol. 27, no. 6, 1650067, World Scientific Publishing, 2016, doi:10.1142/S0129183116500674."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","last_name":"De Martino","full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706"}],"publist_id":"6065","external_id":{"arxiv":["1505.02963"]},"article_processing_charge":"No","title":"The dual of the space of interactions in neural network models","article_number":"1650067"},{"pubrep_id":"700","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"1266","file_date_updated":"2020-07-14T12:44:42Z","department":[{"_id":"GaTk"}],"ddc":["571"],"date_updated":"2021-01-12T06:49:30Z","intvolume":" 5","month":"07","scopus_import":1,"oa_version":"Published Version","abstract":[{"text":"Cortical networks exhibit ‘global oscillations’, in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a ‘prediction error’ while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code.","lang":"eng"}],"volume":5,"issue":"2016JULY","language":[{"iso":"eng"}],"file":[{"checksum":"dc52d967dc76174477bb258d84be2899","file_id":"4874","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2018-12-12T10:11:20Z","file_name":"IST-2016-700-v1+1_e13824-download.pdf","date_updated":"2020-07-14T12:44:42Z","file_size":2819055,"creator":"system"}],"publication_status":"published","article_number":"e13824","title":"Neural oscillations as a signature of efficient coding in the presence of synaptic delays","publist_id":"6056","author":[{"orcid":"0000-0001-7782-4436","full_name":"Chalk, Matthew J","last_name":"Chalk","id":"2BAAC544-F248-11E8-B48F-1D18A9856A87","first_name":"Matthew J"},{"last_name":"Gutkin","full_name":"Gutkin, Boris","first_name":"Boris"},{"full_name":"Denève, Sophie","last_name":"Denève","first_name":"Sophie"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Chalk, Matthew J., et al. “Neural Oscillations as a Signature of Efficient Coding in the Presence of Synaptic Delays.” ELife, vol. 5, no. 2016JULY, e13824, eLife Sciences Publications, 2016, doi:10.7554/eLife.13824.","ama":"Chalk MJ, Gutkin B, Denève S. Neural oscillations as a signature of efficient coding in the presence of synaptic delays. eLife. 2016;5(2016JULY). doi:10.7554/eLife.13824","apa":"Chalk, M. J., Gutkin, B., & Denève, S. (2016). Neural oscillations as a signature of efficient coding in the presence of synaptic delays. ELife. eLife Sciences Publications. https://doi.org/10.7554/eLife.13824","ieee":"M. J. Chalk, B. Gutkin, and S. Denève, “Neural oscillations as a signature of efficient coding in the presence of synaptic delays,” eLife, vol. 5, no. 2016JULY. eLife Sciences Publications, 2016.","short":"M.J. Chalk, B. Gutkin, S. Denève, ELife 5 (2016).","chicago":"Chalk, Matthew J, Boris Gutkin, and Sophie Denève. “Neural Oscillations as a Signature of Efficient Coding in the Presence of Synaptic Delays.” ELife. eLife Sciences Publications, 2016. https://doi.org/10.7554/eLife.13824.","ista":"Chalk MJ, Gutkin B, Denève S. 2016. Neural oscillations as a signature of efficient coding in the presence of synaptic delays. eLife. 5(2016JULY), e13824."},"oa":1,"quality_controlled":"1","publisher":"eLife Sciences Publications","acknowledgement":"Boris Gutkin acknowledges funding by the Russian Academic Excellence Project '5-100’.","date_created":"2018-12-11T11:51:02Z","doi":"10.7554/eLife.13824","date_published":"2016-07-01T00:00:00Z","publication":"eLife","day":"01","year":"2016","has_accepted_license":"1"},{"year":"2016","day":"01","publication":"Nature Chemical Biology","page":"902 - 904","date_published":"2016-11-01T00:00:00Z","doi":"10.1038/nchembio.2176","date_created":"2018-12-11T11:51:10Z","acknowledgement":"This work was supported in part by National Institute of Allergy and Infectious Diseases grant U54 AI057159, US National Institutes of Health grants R01 GM081617 (to R.K.) and GM086258 (to J.C.), European Research Council FP7 ERC grant 281891 (to R.K.) and a National Science Foundation Graduate Fellowship (to L.K.S.).\r\n","quality_controlled":"1","publisher":"Nature Publishing Group","oa":1,"citation":{"chicago":"Stone, Laura, Michael Baym, Tami Lieberman, Remy P Chait, Jon Clardy, and Roy Kishony. “Compounds That Select against the Tetracycline-Resistance Efflux Pump.” Nature Chemical Biology. Nature Publishing Group, 2016. https://doi.org/10.1038/nchembio.2176.","ista":"Stone L, Baym M, Lieberman T, Chait RP, Clardy J, Kishony R. 2016. Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. 12(11), 902–904.","mla":"Stone, Laura, et al. “Compounds That Select against the Tetracycline-Resistance Efflux Pump.” Nature Chemical Biology, vol. 12, no. 11, Nature Publishing Group, 2016, pp. 902–04, doi:10.1038/nchembio.2176.","ieee":"L. Stone, M. Baym, T. Lieberman, R. P. Chait, J. Clardy, and R. Kishony, “Compounds that select against the tetracycline-resistance efflux pump,” Nature Chemical Biology, vol. 12, no. 11. Nature Publishing Group, pp. 902–904, 2016.","short":"L. Stone, M. Baym, T. Lieberman, R.P. Chait, J. Clardy, R. Kishony, Nature Chemical Biology 12 (2016) 902–904.","ama":"Stone L, Baym M, Lieberman T, Chait RP, Clardy J, Kishony R. Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. 2016;12(11):902-904. doi:10.1038/nchembio.2176","apa":"Stone, L., Baym, M., Lieberman, T., Chait, R. P., Clardy, J., & Kishony, R. (2016). Compounds that select against the tetracycline-resistance efflux pump. Nature Chemical Biology. Nature Publishing Group. https://doi.org/10.1038/nchembio.2176"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publist_id":"6026","author":[{"full_name":"Stone, Laura","last_name":"Stone","first_name":"Laura"},{"first_name":"Michael","full_name":"Baym, Michael","last_name":"Baym"},{"first_name":"Tami","last_name":"Lieberman","full_name":"Lieberman, Tami"},{"id":"3464AE84-F248-11E8-B48F-1D18A9856A87","first_name":"Remy P","full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","last_name":"Chait"},{"full_name":"Clardy, Jon","last_name":"Clardy","first_name":"Jon"},{"last_name":"Kishony","full_name":"Kishony, Roy","first_name":"Roy"}],"title":"Compounds that select against the tetracycline-resistance efflux pump","publication_status":"published","language":[{"iso":"eng"}],"issue":"11","volume":12,"abstract":[{"lang":"eng","text":"We developed a competition-based screening strategy to identify compounds that invert the selective advantage of antibiotic resistance. Using our assay, we screened over 19,000 compounds for the ability to select against the TetA tetracycline-resistance efflux pump in Escherichia coli and identified two hits, β-thujaplicin and disulfiram. Treating a tetracycline-resistant population with β-thujaplicin selects for loss of the resistance gene, enabling an effective second-phase treatment with doxycycline."}],"oa_version":"Preprint","scopus_import":1,"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069154/"}],"month":"11","intvolume":" 12","date_updated":"2021-01-12T06:49:39Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"_id":"1290","type":"journal_article","status":"public"},{"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"article_number":"7526722","author":[{"first_name":"Moritz","id":"29E0800A-F248-11E8-B48F-1D18A9856A87","full_name":"Lang, Moritz","last_name":"Lang"},{"full_name":"Sontag, Eduardo","last_name":"Sontag","first_name":"Eduardo"}],"publist_id":"5950","title":"Scale-invariant systems realize nonlinear differential operators","citation":{"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.","ista":"Lang M, Sontag E. 2016. Scale-invariant systems realize nonlinear differential operators. ACC: American Control Conference vol. 2016–July, 7526722.","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.","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","short":"M. Lang, E. Sontag, in:, IEEE, 2016.","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."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","publisher":"IEEE","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.","doi":"10.1109/ACC.2016.7526722","date_published":"2016-07-28T00:00:00Z","date_created":"2018-12-11T11:51:21Z","has_accepted_license":"1","year":"2016","day":"28","type":"conference","conference":{"location":"Boston, MA, USA","end_date":"2016-07-08","start_date":"2016-07-06","name":"ACC: American Control Conference"},"status":"public","pubrep_id":"810","_id":"1320","file_date_updated":"2020-07-14T12:44:43Z","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:49:51Z","ddc":["003","621"],"scopus_import":1,"month":"07","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."}],"oa_version":"Preprint","volume":"2016-July","ec_funded":1,"publication_status":"published","file":[{"access_level":"local","relation":"main_file","content_type":"application/pdf","checksum":"7219432b43defc62a0d45f48d4ce6a19","file_id":"5203","creator":"system","date_updated":"2020-07-14T12:44:43Z","file_size":539166,"date_created":"2018-12-12T10:16:17Z","file_name":"IST-2017-810-v1+1_root.pdf"}],"language":[{"iso":"eng"}]},{"pubrep_id":"662","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"1332","department":[{"_id":"CaGu"},{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:44Z","ddc":["570","579"],"date_updated":"2021-01-12T06:49:57Z","intvolume":" 7","month":"01","scopus_import":1,"oa_version":"Published Version","abstract":[{"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.","lang":"eng"}],"volume":7,"language":[{"iso":"eng"}],"file":[{"file_id":"5039","checksum":"ef147bcbb8bd37e9079cf3ce06f5815d","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2018-12-12T10:13:52Z","file_name":"IST-2016-662-v1+1_ncomms10333.pdf","date_updated":"2020-07-14T12:44:44Z","file_size":1844107,"creator":"system"}],"publication_status":"published","article_number":"10333","title":"Pervasive selection for and against antibiotic resistance in inhomogeneous multistress environments","publist_id":"5936","author":[{"full_name":"Chait, Remy P","orcid":"0000-0003-0876-3187","last_name":"Chait","first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Adam","last_name":"Palmer","full_name":"Palmer, Adam"},{"full_name":"Yelin, Idan","last_name":"Yelin","first_name":"Idan"},{"first_name":"Roy","full_name":"Kishony, Roy","last_name":"Kishony"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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).","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.","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"},"oa":1,"quality_controlled":"1","publisher":"Nature Publishing Group","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.","date_created":"2018-12-11T11:51:25Z","doi":"10.1038/ncomms10333","date_published":"2016-01-20T00:00:00Z","publication":"Nature Communications","day":"20","year":"2016","has_accepted_license":"1"},{"type":"journal_article","status":"public","_id":"1342","publist_id":"5911","author":[{"first_name":"Michael","full_name":"Baym, Michael","last_name":"Baym"},{"first_name":"Tami","last_name":"Lieberman","full_name":"Lieberman, Tami"},{"first_name":"Eric","full_name":"Kelsic, Eric","last_name":"Kelsic"},{"first_name":"Remy P","id":"3464AE84-F248-11E8-B48F-1D18A9856A87","last_name":"Chait","orcid":"0000-0003-0876-3187","full_name":"Chait, Remy P"},{"last_name":"Gross","full_name":"Gross, Rotem","first_name":"Rotem"},{"first_name":"Idan","full_name":"Yelin, Idan","last_name":"Yelin"},{"first_name":"Roy","full_name":"Kishony, Roy","last_name":"Kishony"}],"department":[{"_id":"CaGu"},{"_id":"GaTk"}],"title":"Spatiotemporal microbial evolution on antibiotic landscapes","citation":{"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.","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.","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.","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","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","short":"M. Baym, T. Lieberman, E. Kelsic, R.P. Chait, R. Gross, I. Yelin, R. Kishony, Science 353 (2016) 1147–1151.","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."},"date_updated":"2021-01-12T06:50:01Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa":1,"main_file_link":[{"open_access":"1","url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5534434/"}],"quality_controlled":"1","publisher":"American Association for the Advancement of Science","scopus_import":1,"intvolume":" 353","month":"09","abstract":[{"lang":"eng","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."}],"oa_version":"Preprint","page":"1147 - 1151","date_created":"2018-12-11T11:51:29Z","volume":353,"doi":"10.1126/science.aag0822","issue":"6304","date_published":"2016-09-09T00:00:00Z","year":"2016","publication_status":"published","publication":"Science","language":[{"iso":"eng"}],"day":"09"},{"article_number":"036005","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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","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","short":"D. De Martino, F. Capuani, A. De Martino, Physical Biology 13 (2016).","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.","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.","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.","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."},"title":"Growth against entropy in bacterial metabolism: the phenotypic trade-off behind empirical growth rate distributions in E. coli","publist_id":"5815","author":[{"id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele","last_name":"De Martino","full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706"},{"first_name":"Fabrizio","last_name":"Capuani","full_name":"Capuani, Fabrizio"},{"full_name":"De Martino, Andrea","last_name":"De Martino","first_name":"Andrea"}],"acknowledgement":"The research leading to these results has received funding from the from the Marie\r\nCurie Action ITN NETADIS, grant agreement no. 290038.","oa":1,"publisher":"IOP Publishing Ltd.","quality_controlled":"1","publication":"Physical Biology","day":"27","year":"2016","date_created":"2018-12-11T11:51:46Z","doi":"10.1088/1478-3975/13/3/036005","date_published":"2016-05-27T00:00:00Z","_id":"1394","status":"public","type":"journal_article","date_updated":"2021-01-12T06:50:23Z","department":[{"_id":"GaTk"}],"oa_version":"Preprint","abstract":[{"lang":"eng","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."}],"intvolume":" 13","month":"05","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1601.03243"}],"scopus_import":1,"language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"volume":13,"issue":"3"},{"publication_status":"published","language":[{"iso":"eng"}],"ec_funded":1,"issue":"4","volume":202,"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"}],"oa_version":"Preprint","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1510.08344"}],"scopus_import":"1","intvolume":" 202","month":"04","date_updated":"2022-08-01T10:49:55Z","department":[{"_id":"GaTk"},{"_id":"NiBa"}],"_id":"1420","type":"journal_article","status":"public","year":"2016","publication":"Genetics","day":"06","page":"1523 - 1548","date_created":"2018-12-11T11:51:55Z","date_published":"2016-04-06T00:00:00Z","doi":"10.1534/genetics.115.184127","oa":1,"publisher":"Genetics Society of America","quality_controlled":"1","citation":{"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.","ista":"Bodova K, Tkačik G, Barton NH. 2016. A general approximation for the dynamics of quantitative traits. Genetics. 202(4), 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.","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.","short":"K. Bodova, G. Tkačik, N.H. Barton, Genetics 202 (2016) 1523–1548."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","external_id":{"arxiv":["1510.08344"]},"article_processing_charge":"No","publist_id":"5787","author":[{"first_name":"Katarína","id":"2BA24EA0-F248-11E8-B48F-1D18A9856A87","full_name":"Bod'ová, Katarína","orcid":"0000-0002-7214-0171","last_name":"Bod'ová"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H","last_name":"Barton","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240"}],"title":"A general approximation for the dynamics of quantitative traits","project":[{"_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152"},{"_id":"255008E4-B435-11E9-9278-68D0E5697425","grant_number":"RGP0065/2012","name":"Information processing and computation in fish groups"}]},{"oa_version":"Preprint","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."}],"intvolume":" 13","month":"01","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1505.04613"}],"scopus_import":1,"language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"volume":13,"issue":"1","_id":"1485","status":"public","type":"journal_article","date_updated":"2021-01-12T06:51:04Z","department":[{"_id":"GaTk"}],"oa":1,"quality_controlled":"1","publisher":"IOP Publishing Ltd.","publication":"Physical Biology","day":"29","year":"2016","date_created":"2018-12-11T11:52:18Z","doi":"10.1088/1478-3975/13/1/016003","date_published":"2016-01-29T00:00:00Z","article_number":"016003","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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","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","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.","short":"D. De Martino, Physical Biology 13 (2016).","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."},"title":"Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis","author":[{"full_name":"De Martino, Daniele","orcid":"0000-0002-5214-4706","last_name":"De Martino","first_name":"Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5702"},{"doi":"10.1016/j.biosystems.2016.07.005","date_published":"2016-11-01T00:00:00Z","date_created":"2018-12-11T11:50:24Z","page":"15 - 25","day":"01","publication":"Biosystems","year":"2016","publisher":"Elsevier","quality_controlled":"1","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.","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","author":[{"first_name":"Christian","full_name":"Schilling, Christian","last_name":"Schilling"},{"orcid":"0000-0002-0686-0365","full_name":"Bogomolov, Sergiy","last_name":"Bogomolov","first_name":"Sergiy","id":"369D9A44-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Henzinger","full_name":"Henzinger, Thomas A","orcid":"0000−0002−2985−7724","first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Andreas","last_name":"Podelski","full_name":"Podelski, Andreas"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","last_name":"Ruess","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"}],"publist_id":"6210","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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.","short":"C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25.","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.","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"},"project":[{"call_identifier":"FP7","_id":"25EE3708-B435-11E9-9278-68D0E5697425","name":"Quantitative Reactive Modeling","grant_number":"267989"},{"_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Rigorous Systems Engineering","grant_number":"S 11407_N23"},{"name":"The Wittgenstein Prize","grant_number":"Z211","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"volume":149,"related_material":{"record":[{"id":"1658","status":"public","relation":"earlier_version"}]},"ec_funded":1,"language":[{"iso":"eng"}],"publication_status":"published","month":"11","intvolume":" 149","scopus_import":1,"oa_version":"None","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"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_updated":"2023-02-23T10:08:46Z","status":"public","type":"journal_article","_id":"1148"},{"oa":1,"quality_controlled":"1","publisher":"MIT Press","page":"142-143","date_created":"2020-07-05T22:00:47Z","doi":"10.7551/978-0-262-33936-0-ch029","date_published":"2016-09-01T00:00:00Z","year":"2016","has_accepted_license":"1","publication":"Proceedings of the Artificial Life Conference 2016","day":"01","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"article_processing_charge":"No","author":[{"full_name":"Martius, Georg S","last_name":"Martius","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S"},{"first_name":"Rafael","full_name":"Hostettler, Rafael","last_name":"Hostettler"},{"first_name":"Alois","full_name":"Knoll, Alois","last_name":"Knoll"},{"full_name":"Der, Ralf","last_name":"Der","first_name":"Ralf"}],"title":"Self-organized control of an tendon driven arm by differential extrinsic plasticity","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.","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.","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","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.","short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 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."},"user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","scopus_import":1,"intvolume":" 28","month":"09","abstract":[{"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.","lang":"eng"}],"oa_version":"Published Version","ec_funded":1,"volume":28,"publication_status":"published","publication_identifier":{"isbn":["9780262339360"]},"language":[{"iso":"eng"}],"file":[{"creator":"cziletti","file_size":678670,"date_updated":"2020-07-14T12:48:09Z","file_name":"2016_ProcALIFE_Martius.pdf","date_created":"2020-07-06T12:59:09Z","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_id":"8096","checksum":"cff63e7a4b8ac466ba51a9c84153a940"}],"tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"conference":{"name":"ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems","location":"Cancun, Mexico","end_date":"2016-07-08","start_date":"2016-07-04"},"type":"conference","status":"public","_id":"8094","file_date_updated":"2020-07-14T12:48:09Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"date_updated":"2021-01-12T08:16:53Z","ddc":["610"]},{"title":"Error-robust modes of the retinal population code","author":[{"first_name":"Jason","last_name":"Prentice","full_name":"Prentice, Jason"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"last_name":"Ioffe","full_name":"Ioffe, Mark","first_name":"Mark"},{"first_name":"Adrianna","last_name":"Loback","full_name":"Loback, Adrianna"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Michael","full_name":"Berry, Michael","last_name":"Berry"}],"publist_id":"6153","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Prentice, Jason, et al. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology, vol. 12, no. 11, e1005855, Public Library of Science, 2016, doi:10.1371/journal.pcbi.1005148.","ieee":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, and M. Berry, “Error-robust modes of the retinal population code,” PLoS Computational Biology, vol. 12, no. 11. Public Library of Science, 2016.","short":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational Biology 12 (2016).","apa":"Prentice, J., Marre, O., Ioffe, M., Loback, A., Tkačik, G., & Berry, M. (2016). Error-robust modes of the retinal population code. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1005148","ama":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. Error-robust modes of the retinal population code. PLoS Computational Biology. 2016;12(11). doi:10.1371/journal.pcbi.1005148","chicago":"Prentice, Jason, Olivier Marre, Mark Ioffe, Adrianna Loback, Gašper Tkačik, and Michael Berry. “Error-Robust Modes of the Retinal Population Code.” PLoS Computational Biology. Public Library of Science, 2016. https://doi.org/10.1371/journal.pcbi.1005148.","ista":"Prentice J, Marre O, Ioffe M, Loback A, Tkačik G, Berry M. 2016. Error-robust modes of the retinal population code. PLoS Computational Biology. 12(11), e1005855."},"project":[{"grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"}],"article_number":"e1005855","date_created":"2018-12-11T11:50:40Z","doi":"10.1371/journal.pcbi.1005148","date_published":"2016-11-17T00:00:00Z","publication":"PLoS Computational Biology","day":"17","year":"2016","has_accepted_license":"1","oa":1,"quality_controlled":"1","publisher":"Public Library of Science","acknowledgement":"JSP was supported by a C.V. Starr Fellowship from the Starr Foundation (http://www.starrfoundation.org/). GT was supported by Austrian Research Foundation (https://www.fwf.ac.at/en/) grant FWF P25651. MJB received support from National Eye Institute (https://nei.nih.gov/) grant EY 14196 and from the National Science Foundation grant 1504977. The authors thank Cristina Savin and Vicent Botella-Soler for helpful comments on the manuscript.","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:38Z","ddc":["570"],"date_updated":"2023-02-23T14:05:40Z","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"1197","volume":12,"related_material":{"record":[{"id":"9709","status":"public","relation":"research_data"}]},"issue":"11","language":[{"iso":"eng"}],"file":[{"creator":"kschuh","file_size":4492021,"date_updated":"2020-07-14T12:44:38Z","file_name":"2016_PLOS_Prentice.pdf","date_created":"2019-01-25T10:35:00Z","relation":"main_file","access_level":"open_access","content_type":"application/pdf","checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","file_id":"5884"}],"publication_status":"published","intvolume":" 12","month":"11","scopus_import":1,"oa_version":"Published Version","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"}]},{"date_updated":"2021-01-12T08:22:08Z","department":[{"_id":"GaTk"}],"_id":"948","status":"public","conference":{"name":"NIPS: Neural Information Processing Systems","start_date":"2016-12-05","end_date":"2016-12-10","location":"Barcelona, Spaine"},"type":"conference","language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"volume":29,"oa_version":"None","abstract":[{"text":"Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations.","lang":"eng"}],"intvolume":" 29","month":"01","main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"scopus_import":1,"alternative_title":["Advances in Neural Information Processing Systems"],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems, 2016.","ista":"Monk T, Savin C, Lücke J. 2016. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. NIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 29, 4285–4293.","mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93.","apa":"Monk, T., Savin, C., & Lücke, J. (2016). Neurons equipped with intrinsic plasticity learn stimulus intensity statistics (Vol. 29, pp. 4285–4293). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine: Neural Information Processing Systems.","ama":"Monk T, Savin C, Lücke J. Neurons equipped with intrinsic plasticity learn stimulus intensity statistics. In: Vol 29. Neural Information Processing Systems; 2016:4285-4293.","ieee":"T. Monk, C. Savin, and J. Lücke, “Neurons equipped with intrinsic plasticity learn stimulus intensity statistics,” presented at the NIPS: Neural Information Processing Systems, Barcelona, Spaine, 2016, vol. 29, pp. 4285–4293.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 4285–4293."},"title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","author":[{"first_name":"Travis","last_name":"Monk","full_name":"Monk, Travis"},{"id":"3933349E-F248-11E8-B48F-1D18A9856A87","first_name":"Cristina","full_name":"Savin, Cristina","last_name":"Savin"},{"first_name":"Jörg","full_name":"Lücke, Jörg","last_name":"Lücke"}],"publist_id":"6469","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"day":"01","year":"2016","date_created":"2018-12-11T11:49:21Z","date_published":"2016-01-01T00:00:00Z","page":"4285 - 4293","acknowledgement":"DFG Cluster of Excellence EXC 1077/1 (Hearing4all) and LU 1196/5-1 (JL and TM), People Programme (Marie Curie Actions) FP7/2007-2013 grant agreement no. 291734 (CS)","publisher":"Neural Information Processing Systems","quality_controlled":"1"},{"date_published":"2016-09-27T00:00:00Z","doi":"10.1371/journal.pone.0163628","date_created":"2018-12-11T11:51:03Z","day":"27","publication":"PLoS One","has_accepted_license":"1","year":"2016","quality_controlled":"1","publisher":"Public Library of Science","oa":1,"acknowledgement":"The authors would like to thank Thomas Sokolowski and Filipe Tostevin for helpful discussions. PH and UG were funded by the German Excellence Initiative via the program \"Nanosystems Initiative Munich\" (https://www.nano-initiative-munich.de) and the German Research Foundation via the SFB 1032 \"Nanoagents for Spatiotemporal Control of Molecular and Cellular Reactions\" (http://www.sfb1032.physik.uni-muenchen.de). GT was funded by the Austrian Science Fund (FWF P 28844) (http://www.fwf.ac.at).","title":"Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information","author":[{"first_name":"Patrick","full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand"},{"full_name":"Gerland, Ulrich","last_name":"Gerland","first_name":"Ulrich"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"publist_id":"6050","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Hillenbrand, Patrick, et al. “Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information.” PLoS One, vol. 11, no. 9, e0163628, Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, PLoS One 11 (2016).","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information,” PLoS One, vol. 11, no. 9. Public Library of Science, 2016.","ama":"Hillenbrand P, Gerland U, Tkačik G. Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 2016;11(9). doi:10.1371/journal.pone.0163628","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Beyond the French Flag Model: Exploiting Spatial and Gene Regulatory Interactions for Positional Information.” PLoS One. Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 11(9), e0163628."},"project":[{"_id":"254E9036-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27"}],"article_number":"e0163628","related_material":{"record":[{"relation":"research_data","status":"public","id":"9869"},{"id":"9870","status":"public","relation":"research_data"},{"id":"9871","status":"public","relation":"research_data"}]},"volume":11,"issue":"9","file":[{"creator":"system","date_updated":"2020-07-14T12:44:42Z","file_size":4950415,"date_created":"2018-12-12T10:10:47Z","file_name":"IST-2016-696-v1+1_journal.pone.0163628.PDF","access_level":"open_access","relation":"main_file","content_type":"application/pdf","checksum":"3d0d55d373096a033bd9cf79288c8586","file_id":"4837"}],"language":[{"iso":"eng"}],"publication_status":"published","month":"09","intvolume":" 11","scopus_import":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"A crucial step in the early development of multicellular organisms involves the establishment of spatial patterns of gene expression which later direct proliferating cells to take on different cell fates. These patterns enable the cells to infer their global position within a tissue or an organism by reading out local gene expression levels. The patterning system is thus said to encode positional information, a concept that was formalized recently in the framework of information theory. Here we introduce a toy model of patterning in one spatial dimension, which can be seen as an extension of Wolpert's paradigmatic "French Flag" model, to patterning by several interacting, spatially coupled genes subject to intrinsic and extrinsic noise. Our model, a variant of an Ising spin system, allows us to systematically explore expression patterns that optimally encode positional information. We find that optimal patterning systems use positional cues, as in the French Flag model, together with gene-gene interactions to generate combinatorial codes for position which we call "Counter" patterns. Counter patterns can also be stabilized against noise and variations in system size or morphogen dosage by longer-range spatial interactions of the type invoked in the Turing model. The simple setup proposed here qualitatively captures many of the experimentally observed properties of biological patterning systems and allows them to be studied in a single, theoretically consistent framework."}],"department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:42Z","ddc":["571"],"date_updated":"2023-02-23T14:11:37Z","status":"public","pubrep_id":"696","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"_id":"1270"},{"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-21T16:56:40Z","citation":{"ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in an Ising model.” Public Library of Science, 2016.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in an Ising model. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s002","mla":"Hillenbrand, Patrick, et al. Computation of Positional Information in an Ising Model. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s002.","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in an Ising model, Public Library of Science, 10.1371/journal.pone.0163628.s002.","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in an Ising Model.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s002."},"title":"Computation of positional information in an Ising model","department":[{"_id":"GaTk"}],"author":[{"first_name":"Patrick","last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick"},{"first_name":"Ulrich","full_name":"Gerland, Ulrich","last_name":"Gerland"},{"first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper"}],"article_processing_charge":"No","_id":"9870","status":"public","type":"research_data_reference","day":"27","year":"2016","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]},"date_published":"2016-09-27T00:00:00Z","doi":"10.1371/journal.pone.0163628.s002","date_created":"2021-08-10T09:23:45Z","oa_version":"Published Version","abstract":[{"lang":"eng","text":"The effect of noise in the input field on an Ising model is approximated. Furthermore, methods to compute positional information in an Ising model by transfer matrices and Monte Carlo sampling are outlined."}],"month":"09","publisher":"Public Library of Science"},{"type":"research_data_reference","status":"public","_id":"9869","article_processing_charge":"No","author":[{"last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick","first_name":"Patrick"},{"first_name":"Ulrich","last_name":"Gerland","full_name":"Gerland, Ulrich"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","last_name":"Tkačik"}],"title":"Error bound on an estimator of position","department":[{"_id":"GaTk"}],"citation":{"chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on an Estimator of Position.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s001.","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position, Public Library of Science, 10.1371/journal.pone.0163628.s001.","mla":"Hillenbrand, Patrick, et al. Error Bound on an Estimator of Position. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s001.","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Error bound on an estimator of position. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s001","ama":"Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position. 2016. doi:10.1371/journal.pone.0163628.s001","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of position.” Public Library of Science, 2016."},"date_updated":"2023-02-21T16:56:40Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","publisher":"Public Library of Science","month":"09","abstract":[{"text":"A lower bound on the error of a positional estimator with limited positional information is derived.","lang":"eng"}],"oa_version":"Published Version","date_created":"2021-08-10T08:53:48Z","date_published":"2016-09-27T00:00:00Z","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]},"doi":"10.1371/journal.pone.0163628.s001","year":"2016","day":"27"},{"article_processing_charge":"No","author":[{"full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand","first_name":"Patrick"},{"full_name":"Gerland, Ulrich","last_name":"Gerland","first_name":"Ulrich"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper"}],"department":[{"_id":"GaTk"}],"title":"Computation of positional information in a discrete morphogen field","citation":{"ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in a discrete morphogen field, Public Library of Science, 10.1371/journal.pone.0163628.s003.","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in a Discrete Morphogen Field.” Public Library of Science, 2016. https://doi.org/10.1371/journal.pone.0163628.s003.","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in a discrete morphogen field.” Public Library of Science, 2016.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Computation of positional information in a discrete morphogen field. Public Library of Science. https://doi.org/10.1371/journal.pone.0163628.s003","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in a discrete morphogen field. 2016. doi:10.1371/journal.pone.0163628.s003","mla":"Hillenbrand, Patrick, et al. Computation of Positional Information in a Discrete Morphogen Field. Public Library of Science, 2016, doi:10.1371/journal.pone.0163628.s003."},"date_updated":"2023-02-21T16:56:40Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","type":"research_data_reference","status":"public","_id":"9871","date_created":"2021-08-10T09:27:35Z","related_material":{"record":[{"id":"1270","status":"public","relation":"used_in_publication"}]},"doi":"10.1371/journal.pone.0163628.s003","year":"2016","day":"27","publisher":"Public Library of Science","month":"09","abstract":[{"lang":"eng","text":"The positional information in a discrete morphogen field with Gaussian noise is computed."}],"oa_version":"Published Version"},{"month":"08","alternative_title":["ISTA Thesis"],"oa_version":"Published Version","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."}],"file":[{"relation":"main_file","access_level":"closed","content_type":"application/pdf","file_id":"6815","checksum":"ec453918c3bf8e6f460fd1156ef7b493","creator":"dernst","file_size":2614660,"date_updated":"2019-08-13T11:46:25Z","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf","date_created":"2019-08-13T11:46:25Z"},{"date_created":"2020-09-21T11:30:40Z","file_name":"Thesis_Georg_Rieckh.pdf","creator":"dernst","date_updated":"2020-09-21T11:30:40Z","file_size":6096178,"checksum":"51ae398166370d18fd22478b6365c4da","file_id":"8542","success":1,"access_level":"open_access","relation":"main_file","content_type":"application/pdf"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["2663-337X"]},"publication_status":"published","degree_awarded":"PhD","status":"public","type":"dissertation","_id":"1128","department":[{"_id":"GaTk"}],"file_date_updated":"2020-09-21T11:30:40Z","ddc":["570"],"supervisor":[{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"date_updated":"2023-09-07T11:44:34Z","publisher":"Institute of Science and Technology Austria","oa":1,"date_published":"2016-08-01T00:00:00Z","date_created":"2018-12-11T11:50:18Z","page":"114","day":"01","has_accepted_license":"1","year":"2016","title":"Studying the complexities of transcriptional regulation","author":[{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","first_name":"Georg","full_name":"Rieckh, Georg","last_name":"Rieckh"}],"publist_id":"6232","article_processing_charge":"No","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","citation":{"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.","apa":"Rieckh, G. (2016). Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","ama":"Rieckh G. Studying the complexities of transcriptional regulation. 2016.","chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016.","ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria."}},{"author":[{"first_name":"Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","last_name":"Friedlander","full_name":"Friedlander, Tamar"},{"full_name":"Prizak, Roshan","last_name":"Prizak","id":"4456104E-F248-11E8-B48F-1D18A9856A87","first_name":"Roshan"},{"last_name":"Guet","orcid":"0000-0001-6220-2052","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C"},{"first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8548-5240","full_name":"Barton, Nicholas H","last_name":"Barton"},{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5887","title":"Intrinsic limits to gene regulation by global crosstalk","citation":{"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).","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.","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","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.","ista":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 7, 12307."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"name":"Limits to selection in biology and in evolutionary computation","grant_number":"250152","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425"},{"name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"article_number":"12307","date_published":"2016-08-04T00:00:00Z","doi":"10.1038/ncomms12307","date_created":"2018-12-11T11:51:34Z","has_accepted_license":"1","year":"2016","day":"04","publication":"Nature Communications","quality_controlled":"1","publisher":"Nature Publishing Group","oa":1,"department":[{"_id":"GaTk"},{"_id":"NiBa"},{"_id":"CaGu"}],"file_date_updated":"2020-07-14T12:44:46Z","date_updated":"2023-09-07T12:53:49Z","ddc":["576"],"type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"status":"public","pubrep_id":"627","_id":"1358","volume":7,"related_material":{"record":[{"status":"public","id":"6071","relation":"dissertation_contains"}]},"ec_funded":1,"publication_status":"published","file":[{"file_id":"4919","checksum":"fe3f3a1526d180b29fe691ab11435b78","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"IST-2016-627-v1+1_ncomms12307.pdf","date_created":"2018-12-12T10:12:01Z","file_size":861805,"date_updated":"2020-07-14T12:44:46Z","creator":"system"},{"checksum":"164864a1a675f3ad80e9917c27aba07f","file_id":"4920","access_level":"open_access","relation":"main_file","content_type":"application/pdf","date_created":"2018-12-12T10:12:02Z","file_name":"IST-2016-627-v1+2_ncomms12307-s1.pdf","creator":"system","date_updated":"2020-07-14T12:44:46Z","file_size":1084703}],"language":[{"iso":"eng"}],"scopus_import":1,"month":"08","intvolume":" 7","abstract":[{"lang":"eng","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."}],"oa_version":"Published Version"},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Parise, Francesca, John Lygeros, and Jakob Ruess. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” Frontiers in Environmental Science. Frontiers, 2015. https://doi.org/10.3389/fenvs.2015.00042.","ista":"Parise F, Lygeros J, Ruess J. 2015. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. 3, 42.","mla":"Parise, Francesca, et al. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” Frontiers in Environmental Science, vol. 3, 42, Frontiers, 2015, doi:10.3389/fenvs.2015.00042.","ieee":"F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study,” Frontiers in Environmental Science, vol. 3. Frontiers, 2015.","short":"F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).","apa":"Parise, F., Lygeros, J., & Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. Frontiers. https://doi.org/10.3389/fenvs.2015.00042","ama":"Parise F, Lygeros J, Ruess J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Frontiers in Environmental Science. 2015;3. doi:10.3389/fenvs.2015.00042"},"title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","author":[{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"full_name":"Lygeros, John","last_name":"Lygeros","first_name":"John"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess"}],"article_processing_charge":"No","article_number":"42","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"day":"10","publication":"Frontiers in Environmental Science","has_accepted_license":"1","year":"2015","date_published":"2015-06-10T00:00:00Z","doi":"10.3389/fenvs.2015.00042","date_created":"2022-02-25T11:42:25Z","acknowledgement":"The authors would like to acknowledge contributions from Baptiste Mottet who performed preliminary analysis regarding parameter inference for the considered case study in a student project (Mottet, 2014/2015).\r\nThe research leading to these results has received funding from the People Programme (Marie Curie Actions) of the European Union's Seventh Framework Programme (FP7/2007-2013) under REA grant agreement No. [291734] and from SystemsX under the project SignalX.","publisher":"Frontiers","quality_controlled":"1","oa":1,"ddc":["000","570"],"date_updated":"2022-02-25T11:59:23Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"file_date_updated":"2022-02-25T11:55:26Z","_id":"10794","status":"public","keyword":["General Environmental Science"],"article_type":"original","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"file":[{"date_created":"2022-02-25T11:55:26Z","file_name":"2015_FrontiersEnvironmScience_Parise.pdf","date_updated":"2022-02-25T11:55:26Z","file_size":1371201,"creator":"dernst","file_id":"10795","checksum":"26c222487564e1be02a11d688d6f769d","success":1,"content_type":"application/pdf","access_level":"open_access","relation":"main_file"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["2296-665X"]},"publication_status":"published","volume":3,"ec_funded":1,"oa_version":"Published Version","abstract":[{"text":"Mathematical models are of fundamental importance in the understanding of complex population dynamics. For instance, they can be used to predict the population evolution starting from different initial conditions or to test how a system responds to external perturbations. For this analysis to be meaningful in real applications, however, it is of paramount importance to choose an appropriate model structure and to infer the model parameters from measured data. While many parameter inference methods are available for models based on deterministic ordinary differential equations, the same does not hold for more detailed individual-based models. Here we consider, in particular, stochastic models in which the time evolution of the species abundances is described by a continuous-time Markov chain. These models are governed by a master equation that is typically difficult to solve. Consequently, traditional inference methods that rely on iterative evaluation of parameter likelihoods are computationally intractable. The aim of this paper is to present recent advances in parameter inference for continuous-time Markov chain models, based on a moment closure approximation of the parameter likelihood, and to investigate how these results can help in understanding, and ultimately controlling, complex systems in ecology. Specifically, we illustrate through an agricultural pest case study how parameters of a stochastic individual-based model can be identified from measured data and how the resulting model can be used to solve an optimal control problem in a stochastic setting. In particular, we show how the matter of determining the optimal combination of two different pest control methods can be formulated as a chance constrained optimization problem where the control action is modeled as a state reset, leading to a hybrid system formulation.","lang":"eng"}],"month":"06","intvolume":" 3","scopus_import":"1"},{"file":[{"relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_id":"4641","checksum":"838657118ae286463a2b7737319f35ce","creator":"system","file_size":605355,"date_updated":"2020-07-14T12:45:01Z","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf","date_created":"2018-12-12T10:07:43Z"}],"language":[{"iso":"eng"}],"publication_status":"published","issue":"24","volume":143,"ec_funded":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Many stochastic models of biochemical reaction networks contain some chemical species for which the number of molecules that are present in the system can only be finite (for instance due to conservation laws), but also other species that can be present in arbitrarily large amounts. The prime example of such networks are models of gene expression, which typically contain a small and finite number of possible states for the promoter but an infinite number of possible states for the amount of mRNA and protein. One of the main approaches to analyze such models is through the use of equations for the time evolution of moments of the chemical species. Recently, a new approach based on conditional moments of the species with infinite state space given all the different possible states of the finite species has been proposed. It was argued that this approach allows one to capture more details about the full underlying probability distribution with a smaller number of equations. Here, I show that the result that less moments provide more information can only stem from an unnecessarily complicated description of the system in the classical formulation. The foundation of this argument will be the derivation of moment equations that describe the complete probability distribution over the finite state space but only low-order moments over the infinite state space. I will show that the number of equations that is needed is always less than what was previously claimed and always less than the number of conditional moment equations up to the same order. To support these arguments, a symbolic algorithm is provided that can be used to derive minimal systems of unconditional moment equations for models with partially finite state space. "}],"month":"12","intvolume":" 143","scopus_import":1,"ddc":["000"],"date_updated":"2021-01-12T06:51:28Z","file_date_updated":"2020-07-14T12:45:01Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"_id":"1539","status":"public","pubrep_id":"593","type":"journal_article","day":"22","publication":"Journal of Chemical Physics","has_accepted_license":"1","year":"2015","date_published":"2015-12-22T00:00:00Z","doi":"10.1063/1.4937937","date_created":"2018-12-11T11:52:36Z","quality_controlled":"1","publisher":"American Institute of Physics","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"apa":"Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. American Institute of Physics. https://doi.org/10.1063/1.4937937","ama":"Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 2015;143(24). doi:10.1063/1.4937937","ieee":"J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” Journal of Chemical Physics, vol. 143, no. 24. American Institute of Physics, 2015.","short":"J. Ruess, Journal of Chemical Physics 143 (2015).","mla":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:10.1063/1.4937937.","ista":"Ruess J. 2015. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. Journal of Chemical Physics. 143(24), 244103.","chicago":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” Journal of Chemical Physics. American Institute of Physics, 2015. https://doi.org/10.1063/1.4937937."},"title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","publist_id":"5632","author":[{"first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob","last_name":"Ruess"}],"article_number":"244103","project":[{"_id":"25EE3708-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"267989","name":"Quantitative Reactive Modeling"},{"_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"S 11407_N23","name":"Rigorous Systems Engineering"},{"grant_number":"Z211","name":"The Wittgenstein Prize","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}]},{"publisher":"National Academy of Sciences","quality_controlled":"1","oa":1,"acknowledgement":"J.R., F.P., and J.L. acknowledge support from the European Commission under the Network of Excellence HYCON2 (highly-complex and networked control systems) and SystemsX.ch under the SignalX Project. J.R. acknowledges support from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme FP7/2007-2013 under REA (Research Executive Agency) Grant 291734. M.K. acknowledges support from Human Frontier Science Program Grant RP0061/2011 (www.hfsp.org). ","page":"8148 - 8153","date_published":"2015-06-30T00:00:00Z","doi":"10.1073/pnas.1423947112","date_created":"2018-12-11T11:52:36Z","year":"2015","day":"30","publication":"PNAS","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","last_name":"Ruess","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob"},{"full_name":"Parise, Francesca","last_name":"Parise","first_name":"Francesca"},{"first_name":"Andreas","last_name":"Milias Argeitis","full_name":"Milias Argeitis, Andreas"},{"full_name":"Khammash, Mustafa","last_name":"Khammash","first_name":"Mustafa"},{"full_name":"Lygeros, John","last_name":"Lygeros","first_name":"John"}],"publist_id":"5633","external_id":{"pmid":["26085136"]},"title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","citation":{"ista":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. 2015. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 112(26), 8148–8153.","chicago":"Ruess, Jakob, Francesca Parise, Andreas Milias Argeitis, Mustafa Khammash, and John Lygeros. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1423947112.","ieee":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, and J. Lygeros, “Iterative experiment design guides the characterization of a light-inducible gene expression circuit,” PNAS, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.","short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","ama":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112","apa":"Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112","mla":"Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” PNAS, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:10.1073/pnas.1423947112."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/"}],"month":"06","intvolume":" 112","abstract":[{"text":"Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.","lang":"eng"}],"pmid":1,"oa_version":"Submitted Version","issue":"26","volume":112,"ec_funded":1,"publication_status":"published","language":[{"iso":"eng"}],"type":"journal_article","status":"public","_id":"1538","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:51:27Z"},{"file_date_updated":"2020-07-14T12:45:02Z","department":[{"_id":"GaTk"}],"ddc":["570"],"date_updated":"2021-01-12T06:51:37Z","status":"public","pubrep_id":"479","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"_id":"1564","issue":"11","volume":9,"ec_funded":1,"file":[{"date_created":"2018-12-12T10:12:09Z","file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf","creator":"system","date_updated":"2020-07-14T12:45:02Z","file_size":187038,"file_id":"4927","checksum":"cea73b6d3ef1579f32da10b82f4de4fd","access_level":"open_access","relation":"main_file","content_type":"application/pdf"}],"language":[{"iso":"eng"}],"publication_status":"published","month":"11","intvolume":" 9","scopus_import":1,"oa_version":"Published Version","title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","author":[{"full_name":"Gilson, Matthieu","last_name":"Gilson","first_name":"Matthieu"},{"last_name":"Savin","full_name":"Savin, Cristina","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Zenke","full_name":"Zenke, Friedemann","first_name":"Friedemann"}],"publist_id":"5607","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).","ieee":"M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” Frontiers in Computational Neuroscience, vol. 9, no. 11. Frontiers Research Foundation, 2015.","ama":"Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 2015;9(11). doi:10.3389/fncom.2015.00145","apa":"Gilson, M., Savin, C., & Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. Frontiers Research Foundation. https://doi.org/10.3389/fncom.2015.00145","mla":"Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:10.3389/fncom.2015.00145.","ista":"Gilson M, Savin C, Zenke F. 2015. Editorial: Emergent neural computation from the interaction of different forms of plasticity. Frontiers in Computational Neuroscience. 9(11), 145.","chicago":"Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” Frontiers in Computational Neuroscience. Frontiers Research Foundation, 2015. https://doi.org/10.3389/fncom.2015.00145."},"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"article_number":"145","doi":"10.3389/fncom.2015.00145","date_published":"2015-11-30T00:00:00Z","date_created":"2018-12-11T11:52:45Z","day":"30","publication":"Frontiers in Computational Neuroscience","has_accepted_license":"1","year":"2015","publisher":"Frontiers Research Foundation","quality_controlled":"1","oa":1},{"publication":"PNAS","day":"10","year":"2015","date_created":"2018-12-11T11:52:47Z","date_published":"2015-11-10T00:00:00Z","doi":"10.1073/pnas.1508400112","page":"E6224 - E6232","oa":1,"publisher":"National Academy of Sciences","quality_controlled":"1","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.","chicago":"Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1508400112.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","ieee":"R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.","apa":"Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1508400112","ama":"Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 2015;112(45):E6224-E6232. doi:10.1073/pnas.1508400112","mla":"Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:10.1073/pnas.1508400112."},"title":"Novel plasticity rule can explain the development of sensorimotor intelligence","external_id":{"pmid":["26504200"]},"author":[{"full_name":"Der, Ralf","last_name":"Der","first_name":"Ralf"},{"full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5601","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"issue":"45","volume":112,"oa_version":"Submitted Version","pmid":1,"abstract":[{"lang":"eng","text":"Grounding autonomous behavior in the nervous system is a fundamental challenge for neuroscience. In particular, self-organized behavioral development provides more questions than answers. Are there special functional units for curiosity, motivation, and creativity? This paper argues that these features can be grounded in synaptic plasticity itself, without requiring any higher-level constructs. We propose differential extrinsic plasticity (DEP) as a new synaptic rule for self-learning systems and apply it to a number of complex robotic systems as a test case. Without specifying any purpose or goal, seemingly purposeful and adaptive rhythmic behavior is developed, displaying a certain level of sensorimotor intelligence. These surprising results require no systemspecific modifications of the DEP rule. They rather arise from the underlying mechanism of spontaneous symmetry breaking,which is due to the tight brain body environment coupling. The new synaptic rule is biologically plausible and would be an interesting target for neurobiological investigation. We also argue that this neuronal mechanism may have been a catalyst in natural evolution."}],"intvolume":" 112","month":"11","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"scopus_import":1,"date_updated":"2021-01-12T06:51:40Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"_id":"1570","status":"public","type":"journal_article"},{"_id":"1658","series_title":"Lecture Notes in Computer Science","status":"public","conference":{"end_date":"2015-09-18","location":"Nantes, France","start_date":"2015-09-16","name":"CMSB: Computational Methods in Systems Biology"},"type":"conference","date_updated":"2023-02-21T16:17:24Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"oa_version":"None","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.","lang":"eng"}],"intvolume":" 9308","month":"09","alternative_title":["LNCS"],"scopus_import":1,"language":[{"iso":"eng"}],"publication_status":"published","ec_funded":1,"related_material":{"record":[{"id":"1148","status":"public","relation":"later_version"}]},"volume":9308,"project":[{"call_identifier":"FP7","_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989","name":"Quantitative Reactive Modeling"},{"name":"The Wittgenstein Prize","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23","name":"Rigorous Systems Engineering"},{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"ista":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. 2015. Adaptive moment closure for parameter inference of biochemical reaction networks. 9308, 77–89.","chicago":"Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8.","ama":"Bogomolov S, Henzinger TA, Podelski A, Ruess J, Schilling C. Adaptive moment closure for parameter inference of biochemical reaction networks. 2015;9308:77-89. doi:10.1007/978-3-319-23401-4_8","apa":"Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., & Schilling, C. (2015). Adaptive moment closure for parameter inference of biochemical reaction networks. Presented at the CMSB: Computational Methods in Systems Biology, Nantes, France: Springer. https://doi.org/10.1007/978-3-319-23401-4_8","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","ieee":"S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015.","mla":"Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8."},"title":"Adaptive moment closure for parameter inference of biochemical reaction networks","author":[{"first_name":"Sergiy","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","last_name":"Bogomolov","orcid":"0000-0002-0686-0365","full_name":"Bogomolov, Sergiy"},{"first_name":"Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","last_name":"Henzinger","orcid":"0000−0002−2985−7724","full_name":"Henzinger, Thomas A"},{"first_name":"Andreas","full_name":"Podelski, Andreas","last_name":"Podelski"},{"first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob","last_name":"Ruess"},{"first_name":"Christian","full_name":"Schilling, Christian","last_name":"Schilling"}],"publist_id":"5492","publisher":"Springer","quality_controlled":"1","day":"01","year":"2015","date_created":"2018-12-11T11:53:18Z","doi":"10.1007/978-3-319-23401-4_8","date_published":"2015-09-01T00:00:00Z","page":"77 - 89"},{"article_number":"e1004304","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425"}],"citation":{"short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015).","ieee":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” PLoS Computational Biology, vol. 11, no. 7. Public Library of Science, 2015.","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304","ama":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 2015;11(7). doi:10.1371/journal.pcbi.1004304","mla":"Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004304.","ista":"Marre O, Botella Soler V, Simmons K, Mora T, Tkačik G, Berry M. 2015. High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. 11(7), e1004304.","chicago":"Marre, Olivier, Vicente Botella Soler, Kristina Simmons, Thierry Mora, Gašper Tkačik, and Michael Berry. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” PLoS Computational Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004304."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5447","author":[{"last_name":"Marre","full_name":"Marre, Olivier","first_name":"Olivier"},{"last_name":"Botella Soler","orcid":"0000-0002-8790-1914","full_name":"Botella Soler, Vicente","id":"421234E8-F248-11E8-B48F-1D18A9856A87","first_name":"Vicente"},{"first_name":"Kristina","full_name":"Simmons, Kristina","last_name":"Simmons"},{"first_name":"Thierry","full_name":"Mora, Thierry","last_name":"Mora"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"title":"High accuracy decoding of dynamical motion from a large retinal population","acknowledgement":"This work was supported by grants EY 014196 and EY 017934 to MJB, ANR OPTIMA, the French State program Investissements d’Avenir managed by the Agence Nationale de la Recherche [LIFESENSES: ANR-10-LABX-65], and by a EC grant from the Human Brain Project (CLAP) to OM, the Austrian Research Foundation FWF P25651 to VBS and GT. VBS is partially supported by contracts MEC, Spain (Grant No. AYA2010- 22111-C03-02, Grant No. AYA2013-48623-C2-2 and FEDER Funds).","oa":1,"publisher":"Public Library of Science","quality_controlled":"1","year":"2015","has_accepted_license":"1","publication":"PLoS Computational Biology","day":"01","date_created":"2018-12-11T11:53:31Z","date_published":"2015-07-01T00:00:00Z","doi":"10.1371/journal.pcbi.1004304","_id":"1697","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","pubrep_id":"455","status":"public","date_updated":"2021-01-12T06:52:35Z","ddc":["570"],"file_date_updated":"2020-07-14T12:45:12Z","department":[{"_id":"GaTk"}],"abstract":[{"text":"Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.","lang":"eng"}],"oa_version":"Published Version","scopus_import":1,"intvolume":" 11","month":"07","publication_status":"published","language":[{"iso":"eng"}],"file":[{"content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"472b979f3f1cffb37b3e503f085115ca","file_id":"5212","date_updated":"2020-07-14T12:45:12Z","file_size":4673930,"creator":"system","date_created":"2018-12-12T10:16:25Z","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf"}],"issue":"7","volume":11},{"publication_status":"published","language":[{"iso":"eng"}],"issue":"37","volume":112,"abstract":[{"lang":"eng","text":"The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. "}],"pmid":1,"oa_version":"Submitted Version","scopus_import":1,"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/"}],"month":"09","intvolume":" 112","date_updated":"2021-01-12T06:52:37Z","department":[{"_id":"GaTk"}],"_id":"1701","type":"journal_article","status":"public","year":"2015","day":"15","publication":"PNAS","page":"11508 - 11513","doi":"10.1073/pnas.1514188112","date_published":"2015-09-15T00:00:00Z","date_created":"2018-12-11T11:53:33Z","acknowledgement":"Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation.","publisher":"National Academy of Sciences","quality_controlled":"1","oa":1,"citation":{"apa":"Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., & Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1514188112","ama":"Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 2015;112(37):11508-11513. doi:10.1073/pnas.1514188112","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513.","ieee":"G. Tkačik et al., “Thermodynamics and signatures of criticality in a network of neurons,” PNAS, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015.","mla":"Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:10.1073/pnas.1514188112.","ista":"Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513.","chicago":"Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” PNAS. National Academy of Sciences, 2015. https://doi.org/10.1073/pnas.1514188112."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5440","author":[{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"last_name":"Mora","full_name":"Mora, Thierry","first_name":"Thierry"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"last_name":"Amodei","full_name":"Amodei, Dario","first_name":"Dario"},{"first_name":"Stephanie","last_name":"Palmer","full_name":"Palmer, Stephanie"},{"first_name":"Michael","last_name":"Berry Ii","full_name":"Berry Ii, Michael"},{"last_name":"Bialek","full_name":"Bialek, William","first_name":"William"}],"external_id":{"pmid":["26330611"]},"title":"Thermodynamics and signatures of criticality in a network of neurons","project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes"}]},{"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:53:41Z","citation":{"mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2, 8, ACM, 2015, doi:10.1145/2688906.","ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” ACM Transactions on Modeling and Computer Simulation, vol. 25, no. 2. ACM, 2015.","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 2015;25(2). doi:10.1145/2688906","apa":"Ruess, J., & Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. ACM. https://doi.org/10.1145/2688906","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” ACM Transactions on Modeling and Computer Simulation. ACM, 2015. https://doi.org/10.1145/2688906.","ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8."},"title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"author":[{"full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"},{"first_name":"John","last_name":"Lygeros","full_name":"Lygeros, John"}],"publist_id":"5238","article_number":"8","_id":"1861","status":"public","type":"journal_article","publication":"ACM Transactions on Modeling and Computer Simulation","language":[{"iso":"eng"}],"day":"01","publication_status":"published","year":"2015","date_created":"2018-12-11T11:54:25Z","volume":25,"date_published":"2015-02-01T00:00:00Z","doi":"10.1145/2688906","issue":"2","acknowledgement":"HYCON2; EC; European Commission\r\n","oa_version":"None","abstract":[{"text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.","lang":"eng"}],"intvolume":" 25","month":"02","scopus_import":1,"quality_controlled":"1","publisher":"ACM"},{"abstract":[{"text":"The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. ","lang":"eng"}],"oa_version":"Preprint","publisher":"Genetics Society of America","quality_controlled":"1","scopus_import":1,"oa":1,"main_file_link":[{"url":"http://arxiv.org/abs/1404.5599","open_access":"1"}],"month":"01","intvolume":" 199","year":"2015","publication_status":"published","day":"01","language":[{"iso":"eng"}],"publication":"Genetics","page":"39 - 59","volume":199,"date_published":"2015-01-01T00:00:00Z","issue":"1","doi":"10.1534/genetics.114.171850","date_created":"2018-12-11T11:54:32Z","_id":"1885","type":"journal_article","status":"public","citation":{"ista":"Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 199(1), 39–59.","chicago":"Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” Genetics. Genetics Society of America, 2015. https://doi.org/10.1534/genetics.114.171850.","ieee":"G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” Genetics, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.","short":"G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59.","apa":"Tkačik, G., Dubuis, J., Petkova, M., & Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. Genetics Society of America. https://doi.org/10.1534/genetics.114.171850","ama":"Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 2015;199(1):39-59. doi:10.1534/genetics.114.171850","mla":"Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” Genetics, vol. 199, no. 1, Genetics Society of America, 2015, pp. 39–59, doi:10.1534/genetics.114.171850."},"date_updated":"2021-01-12T06:53:50Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5210","author":[{"orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"last_name":"Dubuis","full_name":"Dubuis, Julien","first_name":"Julien"},{"first_name":"Mariela","last_name":"Petkova","full_name":"Petkova, Mariela"},{"full_name":"Gregor, Thomas","last_name":"Gregor","first_name":"Thomas"}],"department":[{"_id":"GaTk"}],"title":"Positional information, positional error, and readout precision in morphogenesis: A mathematical framework"},{"department":[{"_id":"GaTk"}],"title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","author":[{"id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","last_name":"Sokolowski"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"publist_id":"5145","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2021-01-12T06:54:13Z","citation":{"short":"T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (2015).","ieee":"T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6. American Institute of Physics, 2015.","ama":"Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 2015;91(6). doi:10.1103/PhysRevE.91.062710","apa":"Sokolowski, T. R., & Tkačik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics. https://doi.org/10.1103/PhysRevE.91.062710","mla":"Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear and Soft Matter Physics, vol. 91, no. 6, 062710, American Institute of Physics, 2015, doi:10.1103/PhysRevE.91.062710.","ista":"Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 91(6), 062710.","chicago":"Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” Physical Review E Statistical Nonlinear and Soft Matter Physics. American Institute of Physics, 2015. https://doi.org/10.1103/PhysRevE.91.062710."},"status":"public","type":"journal_article","article_number":"062710","_id":"1940","date_published":"2015-06-15T00:00:00Z","volume":91,"issue":"6","doi":"10.1103/PhysRevE.91.062710","date_created":"2018-12-11T11:54:49Z","day":"15","language":[{"iso":"eng"}],"publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","publication_status":"published","year":"2015","month":"06","intvolume":" 91","publisher":"American Institute of Physics","scopus_import":1,"quality_controlled":"1","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1501.04015"}],"oa":1,"oa_version":"Preprint","abstract":[{"text":"We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the "input") by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.","lang":"eng"}]},{"author":[{"last_name":"Friedlander","full_name":"Friedlander, Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","first_name":"Tamar"},{"first_name":"Avraham E.","full_name":"Mayo, Avraham E.","last_name":"Mayo"},{"first_name":"Tsvi","last_name":"Tlusty","full_name":"Tlusty, Tsvi"},{"last_name":"Alon","full_name":"Alon, Uri","first_name":"Uri"}],"article_processing_charge":"No","department":[{"_id":"GaTk"}],"title":"Supporting information text","citation":{"chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Supporting Information Text.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s001.","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Supporting information text, Public Library of Science, 10.1371/journal.pcbi.1004055.s001.","mla":"Friedlander, Tamar, et al. Supporting Information Text. Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.s001.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Supporting information text.” Public Library of Science, 2015.","apa":"Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Supporting information text. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s001","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Supporting information text. 2015. doi:10.1371/journal.pcbi.1004055.s001"},"date_updated":"2023-02-23T10:16:13Z","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","type":"research_data_reference","status":"public","_id":"9718","related_material":{"record":[{"id":"1827","status":"public","relation":"used_in_publication"}]},"doi":"10.1371/journal.pcbi.1004055.s001","date_published":"2015-03-23T00:00:00Z","date_created":"2021-07-26T08:35:23Z","year":"2015","day":"23","publisher":"Public Library of Science","month":"03","oa_version":"Published Version"},{"intvolume":" 11","month":"03","scopus_import":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved."}],"ec_funded":1,"volume":11,"issue":"3","related_material":{"record":[{"relation":"research_data","id":"9718","status":"public"},{"id":"9773","status":"public","relation":"research_data"}]},"language":[{"iso":"eng"}],"file":[{"checksum":"b8aa66f450ff8de393014b87ec7d2efb","file_id":"5161","content_type":"application/pdf","relation":"main_file","access_level":"open_access","file_name":"IST-2016-452-v1+1_journal.pcbi.1004055.pdf","date_created":"2018-12-12T10:15:39Z","file_size":1811647,"date_updated":"2020-07-14T12:45:17Z","creator":"system"}],"publication_status":"published","pubrep_id":"452","status":"public","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","_id":"1827","file_date_updated":"2020-07-14T12:45:17Z","department":[{"_id":"GaTk"}],"ddc":["576"],"date_updated":"2023-02-23T14:07:51Z","oa":1,"publisher":"Public Library of Science","quality_controlled":"1","date_created":"2018-12-11T11:54:14Z","doi":"10.1371/journal.pcbi.1004055","date_published":"2015-03-23T00:00:00Z","publication":"PLoS Computational Biology","day":"23","year":"2015","has_accepted_license":"1","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"title":"Evolution of bow-tie architectures in biology","article_processing_charge":"No","author":[{"full_name":"Friedlander, Tamar","last_name":"Friedlander","first_name":"Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Mayo, Avraham","last_name":"Mayo","first_name":"Avraham"},{"first_name":"Tsvi","last_name":"Tlusty","full_name":"Tlusty, Tsvi"},{"first_name":"Uri","full_name":"Alon, Uri","last_name":"Alon"}],"publist_id":"5278","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"chicago":"Friedlander, Tamar, Avraham Mayo, Tsvi Tlusty, and Uri Alon. “Evolution of Bow-Tie Architectures in Biology.” PLoS Computational Biology. Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.","ista":"Friedlander T, Mayo A, Tlusty T, Alon U. 2015. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 11(3).","mla":"Friedlander, Tamar, et al. “Evolution of Bow-Tie Architectures in Biology.” PLoS Computational Biology, vol. 11, no. 3, Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.","ieee":"T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures in biology,” PLoS Computational Biology, vol. 11, no. 3. Public Library of Science, 2015.","short":"T. Friedlander, A. Mayo, T. Tlusty, U. Alon, PLoS Computational Biology 11 (2015).","ama":"Friedlander T, Mayo A, Tlusty T, Alon U. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 2015;11(3). doi:10.1371/journal.pcbi.1004055","apa":"Friedlander, T., Mayo, A., Tlusty, T., & Alon, U. (2015). Evolution of bow-tie architectures in biology. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055"}},{"status":"public","type":"research_data_reference","_id":"9773","department":[{"_id":"GaTk"}],"title":"Evolutionary simulation code","author":[{"last_name":"Friedlander","full_name":"Friedlander, Tamar","first_name":"Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Avraham E.","last_name":"Mayo","full_name":"Mayo, Avraham E."},{"first_name":"Tsvi","last_name":"Tlusty","full_name":"Tlusty, Tsvi"},{"full_name":"Alon, Uri","last_name":"Alon","first_name":"Uri"}],"article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"apa":"Friedlander, T., Mayo, A. E., Tlusty, T., & Alon, U. (2015). Evolutionary simulation code. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004055.s002","ama":"Friedlander T, Mayo AE, Tlusty T, Alon U. Evolutionary simulation code. 2015. doi:10.1371/journal.pcbi.1004055.s002","ieee":"T. Friedlander, A. E. Mayo, T. Tlusty, and U. Alon, “Evolutionary simulation code.” Public Library of Science, 2015.","short":"T. Friedlander, A.E. Mayo, T. Tlusty, U. Alon, (2015).","mla":"Friedlander, Tamar, et al. Evolutionary Simulation Code. Public Library of Science, 2015, doi:10.1371/journal.pcbi.1004055.s002.","ista":"Friedlander T, Mayo AE, Tlusty T, Alon U. 2015. Evolutionary simulation code, Public Library of Science, 10.1371/journal.pcbi.1004055.s002.","chicago":"Friedlander, Tamar, Avraham E. Mayo, Tsvi Tlusty, and Uri Alon. “Evolutionary Simulation Code.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pcbi.1004055.s002."},"date_updated":"2023-02-23T10:16:13Z","month":"03","publisher":"Public Library of Science","oa_version":"Published Version","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1827"}]},"date_published":"2015-03-23T00:00:00Z","doi":"10.1371/journal.pcbi.1004055.s002","date_created":"2021-08-05T12:58:07Z","day":"23","year":"2015"},{"date_created":"2021-07-23T12:00:37Z","date_published":"2015-11-06T00:00:00Z","doi":"10.1371/journal.pgen.1005639.s001","related_material":{"record":[{"id":"1666","status":"public","relation":"used_in_publication"}]},"year":"2015","day":"06","publisher":"Public Library of Science","month":"11","oa_version":"Published Version","article_processing_charge":"No","author":[{"orcid":"0000-0002-8523-0758","full_name":"Tugrul, Murat","last_name":"Tugrul","first_name":"Murat","id":"37C323C6-F248-11E8-B48F-1D18A9856A87"},{"id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago","last_name":"Paixao","full_name":"Paixao, Tiago","orcid":"0000-0003-2361-3953"},{"orcid":"0000-0002-8548-5240","full_name":"Barton, Nicholas H","last_name":"Barton","first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","last_name":"Tkačik","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"title":"Other fitness models for comparison & for interacting TFBSs","date_updated":"2023-02-23T10:09:08Z","citation":{"ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Other fitness models for comparison & for interacting TFBSs.” Public Library of Science, 2015.","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, (2015).","apa":"Tugrul, M., Paixao, T., Barton, N. H., & Tkačik, G. (2015). Other fitness models for comparison & for interacting TFBSs. Public Library of Science. https://doi.org/10.1371/journal.pgen.1005639.s001","ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Other fitness models for comparison & for interacting TFBSs. 2015. doi:10.1371/journal.pgen.1005639.s001","mla":"Tugrul, Murat, et al. Other Fitness Models for Comparison & for Interacting TFBSs. Public Library of Science, 2015, doi:10.1371/journal.pgen.1005639.s001.","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Other fitness models for comparison & for interacting TFBSs, Public Library of Science, 10.1371/journal.pgen.1005639.s001.","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Other Fitness Models for Comparison & for Interacting TFBSs.” Public Library of Science, 2015. https://doi.org/10.1371/journal.pgen.1005639.s001."},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","type":"research_data_reference","status":"public","_id":"9712"},{"volume":11,"issue":"11","related_material":{"record":[{"relation":"research_data","id":"9712","status":"public"},{"relation":"dissertation_contains","id":"1131","status":"public"}]},"ec_funded":1,"file":[{"date_updated":"2020-07-14T12:45:10Z","file_size":2580778,"creator":"system","date_created":"2018-12-12T10:07:58Z","file_name":"IST-2016-463-v1+1_journal.pgen.1005639.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"a4e72fca5ccf40ddacf4d08c8e46b554","file_id":"4657"}],"language":[{"iso":"eng"}],"publication_status":"published","month":"11","intvolume":" 11","scopus_import":1,"oa_version":"Published Version","abstract":[{"lang":"eng","text":"Evolution of gene regulation is crucial for our understanding of the phenotypic differences between species, populations and individuals. Sequence-specific binding of transcription factors to the regulatory regions on the DNA is a key regulatory mechanism that determines gene expression and hence heritable phenotypic variation. We use a biophysical model for directional selection on gene expression to estimate the rates of gain and loss of transcription factor binding sites (TFBS) in finite populations under both point and insertion/deletion mutations. Our results show that these rates are typically slow for a single TFBS in an isolated DNA region, unless the selection is extremely strong. These rates decrease drastically with increasing TFBS length or increasingly specific protein-DNA interactions, making the evolution of sites longer than ∼ 10 bp unlikely on typical eukaryotic speciation timescales. Similarly, evolution converges to the stationary distribution of binding sequences very slowly, making the equilibrium assumption questionable. The availability of longer regulatory sequences in which multiple binding sites can evolve simultaneously, the presence of “pre-sites” or partially decayed old sites in the initial sequence, and biophysical cooperativity between transcription factors, can all facilitate gain of TFBS and reconcile theoretical calculations with timescales inferred from comparative genomics."}],"file_date_updated":"2020-07-14T12:45:10Z","department":[{"_id":"NiBa"},{"_id":"CaGu"},{"_id":"GaTk"}],"ddc":["576"],"date_updated":"2023-09-07T11:53:49Z","status":"public","pubrep_id":"463","type":"journal_article","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"_id":"1666","date_published":"2015-11-06T00:00:00Z","doi":"10.1371/journal.pgen.1005639","date_created":"2018-12-11T11:53:21Z","day":"06","publication":"PLoS Genetics","has_accepted_license":"1","year":"2015","publisher":"Public Library of Science","quality_controlled":"1","oa":1,"title":"Dynamics of transcription factor binding site evolution","publist_id":"5483","author":[{"last_name":"Tugrul","orcid":"0000-0002-8523-0758","full_name":"Tugrul, Murat","id":"37C323C6-F248-11E8-B48F-1D18A9856A87","first_name":"Murat"},{"last_name":"Paixao","orcid":"0000-0003-2361-3953","full_name":"Paixao, Tiago","id":"2C5658E6-F248-11E8-B48F-1D18A9856A87","first_name":"Tiago"},{"last_name":"Barton","full_name":"Barton, Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","first_name":"Nicholas H"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"mla":"Tugrul, Murat, et al. “Dynamics of Transcription Factor Binding Site Evolution.” PLoS Genetics, vol. 11, no. 11, Public Library of Science, 2015, doi:10.1371/journal.pgen.1005639.","apa":"Tugrul, M., Paixao, T., Barton, N. H., & Tkačik, G. (2015). Dynamics of transcription factor binding site evolution. PLoS Genetics. Public Library of Science. https://doi.org/10.1371/journal.pgen.1005639","ama":"Tugrul M, Paixao T, Barton NH, Tkačik G. Dynamics of transcription factor binding site evolution. PLoS Genetics. 2015;11(11). doi:10.1371/journal.pgen.1005639","short":"M. Tugrul, T. Paixao, N.H. Barton, G. Tkačik, PLoS Genetics 11 (2015).","ieee":"M. Tugrul, T. Paixao, N. H. Barton, and G. Tkačik, “Dynamics of transcription factor binding site evolution,” PLoS Genetics, vol. 11, no. 11. Public Library of Science, 2015.","chicago":"Tugrul, Murat, Tiago Paixao, Nicholas H Barton, and Gašper Tkačik. “Dynamics of Transcription Factor Binding Site Evolution.” PLoS Genetics. Public Library of Science, 2015. https://doi.org/10.1371/journal.pgen.1005639.","ista":"Tugrul M, Paixao T, Barton NH, Tkačik G. 2015. Dynamics of transcription factor binding site evolution. PLoS Genetics. 11(11)."},"project":[{"_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation"}]},{"type":"journal_article","status":"public","_id":"1576","department":[{"_id":"GaTk"}],"date_updated":"2023-09-07T12:55:21Z","scopus_import":1,"main_file_link":[{"url":"http://arxiv.org/abs/1504.05716","open_access":"1"}],"month":"12","intvolume":" 115","abstract":[{"text":"Gene expression is controlled primarily by interactions between transcription factor proteins (TFs) and the regulatory DNA sequence, a process that can be captured well by thermodynamic models of regulation. These models, however, neglect regulatory crosstalk: the possibility that noncognate TFs could initiate transcription, with potentially disastrous effects for the cell. Here, we estimate the importance of crosstalk, suggest that its avoidance strongly constrains equilibrium models of TF binding, and propose an alternative nonequilibrium scheme that implements kinetic proofreading to suppress erroneous initiation. This proposal is consistent with the observed covalent modifications of the transcriptional apparatus and predicts increased noise in gene expression as a trade-off for improved specificity. Using information theory, we quantify this trade-off to find when optimal proofreading architectures are favored over their equilibrium counterparts. Such architectures exhibit significant super-Poisson noise at low expression in steady state.","lang":"eng"}],"oa_version":"Preprint","related_material":{"record":[{"status":"public","id":"6473","relation":"part_of_dissertation"}]},"volume":115,"issue":"24","ec_funded":1,"publication_status":"published","language":[{"iso":"eng"}],"project":[{"call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation"}],"article_number":"248101","author":[{"last_name":"Cepeda Humerez","full_name":"Cepeda Humerez, Sarah A","first_name":"Sarah A","id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87"},{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","first_name":"Georg","full_name":"Rieckh, Georg","last_name":"Rieckh"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"publist_id":"5595","title":"Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation","citation":{"mla":"Cepeda Humerez, Sarah A., et al. “Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation.” Physical Review Letters, vol. 115, no. 24, 248101, American Physical Society, 2015, doi:10.1103/PhysRevLett.115.248101.","apa":"Cepeda Humerez, S. A., Rieckh, G., & Tkačik, G. (2015). Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. American Physical Society. https://doi.org/10.1103/PhysRevLett.115.248101","ama":"Cepeda Humerez SA, Rieckh G, Tkačik G. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. 2015;115(24). doi:10.1103/PhysRevLett.115.248101","short":"S.A. Cepeda Humerez, G. Rieckh, G. Tkačik, Physical Review Letters 115 (2015).","ieee":"S. A. Cepeda Humerez, G. Rieckh, and G. Tkačik, “Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation,” Physical Review Letters, vol. 115, no. 24. American Physical Society, 2015.","chicago":"Cepeda Humerez, Sarah A, Georg Rieckh, and Gašper Tkačik. “Stochastic Proofreading Mechanism Alleviates Crosstalk in Transcriptional Regulation.” Physical Review Letters. American Physical Society, 2015. https://doi.org/10.1103/PhysRevLett.115.248101.","ista":"Cepeda Humerez SA, Rieckh G, Tkačik G. 2015. Stochastic proofreading mechanism alleviates crosstalk in transcriptional regulation. Physical Review Letters. 115(24), 248101."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","publisher":"American Physical Society","oa":1,"doi":"10.1103/PhysRevLett.115.248101","date_published":"2015-12-08T00:00:00Z","date_created":"2018-12-11T11:52:49Z","year":"2015","day":"08","publication":"Physical Review Letters"},{"year":"2015","has_accepted_license":"1","publication":"Entropy","day":"23","page":"7266 - 7297","date_created":"2018-12-11T11:53:17Z","doi":"10.3390/e17107266","date_published":"2015-10-23T00:00:00Z","acknowledgement":"This work was supported by the DFG priority program 1527 (Autonomous Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734.","oa":1,"quality_controlled":"1","publisher":"MDPI","citation":{"mla":"Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266.","ama":"Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. Entropy. 2015;17(10):7266-7297. doi:10.3390/e17107266","apa":"Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy. MDPI. https://doi.org/10.3390/e17107266","short":"G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.","ieee":"G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.","chicago":"Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” Entropy. MDPI, 2015. https://doi.org/10.3390/e17107266.","ista":"Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots. Entropy. 17(10), 7266–7297."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"No","author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","last_name":"Martius","full_name":"Martius, Georg S"},{"first_name":"Eckehard","full_name":"Olbrich, Eckehard","last_name":"Olbrich"}],"publist_id":"5495","title":"Quantifying emergent behavior of autonomous robots","project":[{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"publication_status":"published","language":[{"iso":"eng"}],"file":[{"checksum":"945d99631a96e0315acb26dc8541dcf9","file_id":"4943","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2018-12-12T10:12:25Z","file_name":"IST-2016-464-v1+1_entropy-17-07266.pdf","date_updated":"2020-07-14T12:45:08Z","file_size":6455007,"creator":"system"}],"ec_funded":1,"volume":17,"issue":"10","abstract":[{"lang":"eng","text":"Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration."}],"oa_version":"Published Version","scopus_import":"1","intvolume":" 17","month":"10","date_updated":"2023-10-17T11:42:00Z","ddc":["000"],"file_date_updated":"2020-07-14T12:45:08Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"_id":"1655","tmp":{"legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)"},"type":"journal_article","pubrep_id":"464","status":"public"},{"conference":{"start_date":"2014-12-08","end_date":"2014-12-13","location":"Montreal, Canada","name":"NIPS: Neural Information Processing Systems"},"type":"conference","status":"public","_id":"1708","publist_id":"5427","author":[{"last_name":"Savin","full_name":"Savin, Cristina","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Sophie","last_name":"Denève","full_name":"Denève, Sophie"}],"department":[{"_id":"GaTk"}],"title":"Spatio-temporal representations of uncertainty in spiking neural networks","date_updated":"2021-01-12T06:52:40Z","citation":{"ista":"Savin C, Denève S. 2014. Spatio-temporal representations of uncertainty in spiking neural networks. NIPS: Neural Information Processing Systems vol. 3, 2024–2032.","chicago":"Savin, Cristina, and Sophie Denève. “Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks,” 3:2024–32. Neural Information Processing Systems, 2014.","ama":"Savin C, Denève S. Spatio-temporal representations of uncertainty in spiking neural networks. In: Vol 3. Neural Information Processing Systems; 2014:2024-2032.","apa":"Savin, C., & Denève, S. (2014). Spatio-temporal representations of uncertainty in spiking neural networks (Vol. 3, pp. 2024–2032). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.","ieee":"C. Savin and S. Denève, “Spatio-temporal representations of uncertainty in spiking neural networks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2014, vol. 3, no. January, pp. 2024–2032.","short":"C. Savin, S. Denève, in:, Neural Information Processing Systems, 2014, pp. 2024–2032.","mla":"Savin, Cristina, and Sophie Denève. Spatio-Temporal Representations of Uncertainty in Spiking Neural Networks. Vol. 3, no. January, Neural Information Processing Systems, 2014, pp. 2024–32."},"user_id":"4435EBFC-F248-11E8-B48F-1D18A9856A87","main_file_link":[{"url":"http://papers.nips.cc/paper/5343-spatio-temporal-representations-of-uncertainty-in-spiking-neural-networks.pdf"}],"publisher":"Neural Information Processing Systems","scopus_import":1,"quality_controlled":"1","intvolume":" 3","month":"01","abstract":[{"text":"It has been long argued that, because of inherent ambiguity and noise, the brain needs to represent uncertainty in the form of probability distributions. The neural encoding of such distributions remains however highly controversial. Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. Furthermore, the model highlights the challenges associated with interpreting neural activity in relation to behavioral uncertainty and points to alternative population-level approaches for the experimental validation of distributed representations.","lang":"eng"}],"oa_version":"None","page":"2024 - 2032","date_created":"2018-12-11T11:53:35Z","issue":"January","volume":3,"date_published":"2014-01-01T00:00:00Z","year":"2014","publication_status":"published","language":[{"iso":"eng"}],"day":"01"}]