[{"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","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele"}],"publist_id":"5702","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","apa":"De Martino, D. (2016). Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. IOP Publishing Ltd. https://doi.org/10.1088/1478-3975/13/1/016003","ama":"De Martino D. Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis. Physical Biology. 2016;13(1). doi:10.1088/1478-3975/13/1/016003","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).","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.","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."},"project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"article_number":"016003","date_created":"2018-12-11T11:52:18Z","doi":"10.1088/1478-3975/13/1/016003","date_published":"2016-01-29T00:00:00Z","publication":"Physical Biology","day":"29","year":"2016","oa":1,"publisher":"IOP Publishing Ltd.","quality_controlled":"1","department":[{"_id":"GaTk"}],"date_updated":"2021-01-12T06:51:04Z","status":"public","type":"journal_article","_id":"1485","ec_funded":1,"volume":13,"issue":"1","language":[{"iso":"eng"}],"publication_status":"published","intvolume":" 13","month":"01","main_file_link":[{"url":"http://arxiv.org/abs/1505.04613","open_access":"1"}],"scopus_import":1,"oa_version":"Preprint","abstract":[{"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.","lang":"eng"}]},{"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.","quality_controlled":"1","publisher":"Elsevier","day":"01","publication":"Biosystems","year":"2016","date_published":"2016-11-01T00:00:00Z","doi":"10.1016/j.biosystems.2016.07.005","date_created":"2018-12-11T11:50:24Z","page":"15 - 25","project":[{"name":"Quantitative Reactive Modeling","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"name":"Rigorous Systems Engineering","grant_number":"S 11407_N23","call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"name":"The Wittgenstein Prize","grant_number":"Z211","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","apa":"Schilling, C., Bogomolov, S., Henzinger, T. A., Podelski, A., & Ruess, J. (2016). Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. Elsevier. https://doi.org/10.1016/j.biosystems.2016.07.005","ama":"Schilling C, Bogomolov S, Henzinger TA, Podelski A, Ruess J. Adaptive moment closure for parameter inference of biochemical reaction networks. Biosystems. 2016;149:15-25. doi:10.1016/j.biosystems.2016.07.005","ieee":"C. Schilling, S. Bogomolov, T. A. Henzinger, A. Podelski, and J. Ruess, “Adaptive moment closure for parameter inference of biochemical reaction networks,” Biosystems, vol. 149. Elsevier, pp. 15–25, 2016.","short":"C. Schilling, S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, Biosystems 149 (2016) 15–25.","mla":"Schilling, Christian, et al. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Biosystems, vol. 149, Elsevier, 2016, pp. 15–25, doi:10.1016/j.biosystems.2016.07.005."},"title":"Adaptive moment closure for parameter inference of biochemical reaction networks","publist_id":"6210","author":[{"first_name":"Christian","last_name":"Schilling","full_name":"Schilling, Christian"},{"last_name":"Bogomolov","orcid":"0000-0002-0686-0365","full_name":"Bogomolov, Sergiy","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","first_name":"Sergiy"},{"id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A","orcid":"0000−0002−2985−7724","full_name":"Henzinger, Thomas A","last_name":"Henzinger"},{"first_name":"Andreas","full_name":"Podelski, Andreas","last_name":"Podelski"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","last_name":"Ruess","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob"}],"oa_version":"None","abstract":[{"lang":"eng","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"}],"month":"11","intvolume":" 149","scopus_import":1,"language":[{"iso":"eng"}],"publication_status":"published","volume":149,"related_material":{"record":[{"relation":"earlier_version","id":"1658","status":"public"}]},"ec_funded":1,"_id":"1148","status":"public","type":"journal_article","date_updated":"2023-02-23T10:08:46Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}]},{"quality_controlled":"1","publisher":"MIT Press","oa":1,"page":"142-143","date_published":"2016-09-01T00:00:00Z","doi":"10.7551/978-0-262-33936-0-ch029","date_created":"2020-07-05T22:00:47Z","has_accepted_license":"1","year":"2016","day":"01","publication":"Proceedings of the Artificial Life Conference 2016","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","full_name":"Martius, Georg S","last_name":"Martius"},{"full_name":"Hostettler, Rafael","last_name":"Hostettler","first_name":"Rafael"},{"first_name":"Alois","last_name":"Knoll","full_name":"Knoll, Alois"},{"last_name":"Der","full_name":"Der, Ralf","first_name":"Ralf"}],"article_processing_charge":"No","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.","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.","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","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,"month":"09","intvolume":" 28","abstract":[{"lang":"eng","text":"With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with."}],"oa_version":"Published Version","volume":28,"license":"https://creativecommons.org/licenses/by/4.0/","ec_funded":1,"publication_identifier":{"isbn":["9780262339360"]},"publication_status":"published","file":[{"access_level":"open_access","relation":"main_file","content_type":"application/pdf","file_id":"8096","checksum":"cff63e7a4b8ac466ba51a9c84153a940","creator":"cziletti","date_updated":"2020-07-14T12:48:09Z","file_size":678670,"date_created":"2020-07-06T12:59:09Z","file_name":"2016_ProcALIFE_Martius.pdf"}],"language":[{"iso":"eng"}],"type":"conference","conference":{"start_date":"2016-07-04","location":"Cancun, Mexico","end_date":"2016-07-08","name":"ALIFE 2016: 15th International Conference on the Synthesis and Simulation of Living Systems"},"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","_id":"8094","file_date_updated":"2020-07-14T12:48:09Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"date_updated":"2021-01-12T08:16:53Z","ddc":["610"]},{"ddc":["570"],"date_updated":"2023-02-23T14:05:40Z","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:38Z","_id":"1197","status":"public","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":[{"file_id":"5884","checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","relation":"main_file","access_level":"open_access","content_type":"application/pdf","file_name":"2016_PLOS_Prentice.pdf","date_created":"2019-01-25T10:35:00Z","creator":"kschuh","file_size":4492021,"date_updated":"2020-07-14T12:44:38Z"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":12,"related_material":{"record":[{"status":"public","id":"9709","relation":"research_data"}]},"issue":"11","oa_version":"Published Version","abstract":[{"lang":"eng","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."}],"month":"11","intvolume":" 12","scopus_import":1,"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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","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.","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.","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."},"title":"Error-robust modes of the retinal population code","publist_id":"6153","author":[{"full_name":"Prentice, Jason","last_name":"Prentice","first_name":"Jason"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"first_name":"Mark","full_name":"Ioffe, Mark","last_name":"Ioffe"},{"last_name":"Loback","full_name":"Loback, Adrianna","first_name":"Adrianna"},{"last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"article_number":"e1005855","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425"}],"day":"17","publication":"PLoS Computational Biology","has_accepted_license":"1","year":"2016","doi":"10.1371/journal.pcbi.1005148","date_published":"2016-11-17T00:00:00Z","date_created":"2018-12-11T11:50:40Z","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.","quality_controlled":"1","publisher":"Public Library of Science","oa":1},{"abstract":[{"lang":"eng","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."}],"oa_version":"None","main_file_link":[{"url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"alternative_title":["Advances in Neural Information Processing Systems"],"scopus_import":1,"intvolume":" 29","month":"01","publication_status":"published","language":[{"iso":"eng"}],"ec_funded":1,"volume":29,"_id":"948","conference":{"end_date":"2016-12-10","location":"Barcelona, Spaine","start_date":"2016-12-05","name":"NIPS: Neural Information Processing Systems"},"type":"conference","status":"public","date_updated":"2021-01-12T08:22:08Z","department":[{"_id":"GaTk"}],"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","year":"2016","day":"01","page":"4285 - 4293","date_created":"2018-12-11T11:49:21Z","date_published":"2016-01-01T00:00:00Z","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"citation":{"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.","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.","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.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems, 2016, pp. 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.","mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","publist_id":"6469","author":[{"first_name":"Travis","last_name":"Monk","full_name":"Monk, Travis"},{"first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","last_name":"Savin","full_name":"Savin, Cristina"},{"full_name":"Lücke, Jörg","last_name":"Lücke","first_name":"Jörg"}],"title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics"},{"_id":"1270","pubrep_id":"696","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","ddc":["571"],"date_updated":"2023-02-23T14:11:37Z","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:42Z","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."}],"intvolume":" 11","month":"09","scopus_import":1,"language":[{"iso":"eng"}],"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"}],"publication_status":"published","issue":"9","volume":11,"related_material":{"record":[{"id":"9869","status":"public","relation":"research_data"},{"relation":"research_data","status":"public","id":"9870"},{"relation":"research_data","status":"public","id":"9871"}]},"article_number":"e0163628","project":[{"name":"Biophysics of information processing in gene regulation","grant_number":"P28844-B27","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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","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.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, PLoS One 11 (2016).","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."},"title":"Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information","author":[{"last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick","first_name":"Patrick"},{"last_name":"Gerland","full_name":"Gerland, Ulrich","first_name":"Ulrich"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"publist_id":"6050","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).","oa":1,"quality_controlled":"1","publisher":"Public Library of Science","publication":"PLoS One","day":"27","year":"2016","has_accepted_license":"1","date_created":"2018-12-11T11:51:03Z","doi":"10.1371/journal.pone.0163628","date_published":"2016-09-27T00:00:00Z"},{"_id":"9870","status":"public","type":"research_data_reference","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-21T16:56:40Z","citation":{"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.","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.","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.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in an Ising model.” Public Library of Science, 2016.","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","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in an Ising model. 2016. doi:10.1371/journal.pone.0163628.s002"},"title":"Computation of positional information in an Ising model","department":[{"_id":"GaTk"}],"article_processing_charge":"No","author":[{"first_name":"Patrick","full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand"},{"first_name":"Ulrich","full_name":"Gerland, Ulrich","last_name":"Gerland"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper"}],"oa_version":"Published Version","abstract":[{"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.","lang":"eng"}],"month":"09","publisher":"Public Library of Science","day":"27","year":"2016","date_created":"2021-08-10T09:23:45Z","doi":"10.1371/journal.pone.0163628.s002","date_published":"2016-09-27T00:00:00Z","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]}},{"month":"09","publisher":"Public Library of Science","oa_version":"Published Version","abstract":[{"text":"A lower bound on the error of a positional estimator with limited positional information is derived.","lang":"eng"}],"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","date_created":"2021-08-10T08:53:48Z","day":"27","year":"2016","status":"public","type":"research_data_reference","_id":"9869","department":[{"_id":"GaTk"}],"title":"Error bound on an estimator of position","author":[{"first_name":"Patrick","full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand"},{"full_name":"Gerland, Ulrich","last_name":"Gerland","first_name":"Ulrich"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455"}],"article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-21T16:56:40Z","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.","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.","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"}},{"status":"public","type":"research_data_reference","_id":"9871","title":"Computation of positional information in a discrete morphogen field","department":[{"_id":"GaTk"}],"author":[{"last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick","first_name":"Patrick"},{"last_name":"Gerland","full_name":"Gerland, Ulrich","first_name":"Ulrich"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","date_updated":"2023-02-21T16:56:40Z","citation":{"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.","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","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).","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.","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."},"month":"09","publisher":"Public Library of Science","oa_version":"Published Version","abstract":[{"text":"The positional information in a discrete morphogen field with Gaussian noise is computed.","lang":"eng"}],"related_material":{"record":[{"relation":"used_in_publication","id":"1270","status":"public"}]},"doi":"10.1371/journal.pone.0163628.s003","date_created":"2021-08-10T09:27:35Z","day":"27","year":"2016"},{"supervisor":[{"last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"date_updated":"2023-09-07T11:44:34Z","ddc":["570"],"file_date_updated":"2020-09-21T11:30:40Z","department":[{"_id":"GaTk"}],"_id":"1128","type":"dissertation","status":"public","publication_identifier":{"issn":["2663-337X"]},"degree_awarded":"PhD","publication_status":"published","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"},{"file_size":6096178,"date_updated":"2020-09-21T11:30:40Z","creator":"dernst","file_name":"Thesis_Georg_Rieckh.pdf","date_created":"2020-09-21T11:30:40Z","content_type":"application/pdf","relation":"main_file","access_level":"open_access","success":1,"checksum":"51ae398166370d18fd22478b6365c4da","file_id":"8542"}],"language":[{"iso":"eng"}],"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."}],"oa_version":"Published Version","alternative_title":["ISTA Thesis"],"month":"08","citation":{"ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","chicago":"Rieckh, Georg. “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.","short":"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.","mla":"Rieckh, Georg. Studying the Complexities of Transcriptional Regulation. Institute of Science and Technology Austria, 2016."},"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","author":[{"first_name":"Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg","last_name":"Rieckh"}],"publist_id":"6232","article_processing_charge":"No","title":"Studying the complexities of transcriptional regulation","has_accepted_license":"1","year":"2016","day":"01","page":"114","date_published":"2016-08-01T00:00:00Z","date_created":"2018-12-11T11:50:18Z","publisher":"Institute of Science and Technology Austria","oa":1},{"article_number":"12307","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"},{"grant_number":"250152","name":"Limits to selection in biology and in evolutionary computation","_id":"25B07788-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27","name":"Biophysics of information processing in gene regulation"}],"citation":{"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.","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.","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","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"},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","author":[{"full_name":"Friedlander, Tamar","last_name":"Friedlander","first_name":"Tamar","id":"36A5845C-F248-11E8-B48F-1D18A9856A87"},{"id":"4456104E-F248-11E8-B48F-1D18A9856A87","first_name":"Roshan","full_name":"Prizak, Roshan","last_name":"Prizak"},{"orcid":"0000-0001-6220-2052","full_name":"Guet, Calin C","last_name":"Guet","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C"},{"first_name":"Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton","orcid":"0000-0002-8548-5240","full_name":"Barton, Nicholas H"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","orcid":"0000-0002-6699-1455","full_name":"Tkacik, Gasper"}],"publist_id":"5887","title":"Intrinsic limits to gene regulation by global crosstalk","publisher":"Nature Publishing Group","quality_controlled":"1","oa":1,"has_accepted_license":"1","year":"2016","day":"04","publication":"Nature Communications","date_published":"2016-08-04T00:00:00Z","doi":"10.1038/ncomms12307","date_created":"2018-12-11T11:51:34Z","_id":"1358","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","date_updated":"2023-09-07T12:53:49Z","ddc":["576"],"file_date_updated":"2020-07-14T12:44:46Z","department":[{"_id":"GaTk"},{"_id":"NiBa"},{"_id":"CaGu"}],"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","scopus_import":1,"month":"08","intvolume":" 7","publication_status":"published","file":[{"creator":"system","file_size":861805,"date_updated":"2020-07-14T12:44:46Z","file_name":"IST-2016-627-v1+1_ncomms12307.pdf","date_created":"2018-12-12T10:12:01Z","relation":"main_file","access_level":"open_access","content_type":"application/pdf","checksum":"fe3f3a1526d180b29fe691ab11435b78","file_id":"4919"},{"content_type":"application/pdf","access_level":"open_access","relation":"main_file","file_id":"4920","checksum":"164864a1a675f3ad80e9917c27aba07f","date_updated":"2020-07-14T12:44:46Z","file_size":1084703,"creator":"system","date_created":"2018-12-12T10:12:02Z","file_name":"IST-2016-627-v1+2_ncomms12307-s1.pdf"}],"language":[{"iso":"eng"}],"volume":7,"related_material":{"record":[{"relation":"dissertation_contains","status":"public","id":"6071"}]},"ec_funded":1},{"article_number":"42","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734"}],"citation":{"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).","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","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","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"first_name":"John","full_name":"Lygeros, John","last_name":"Lygeros"},{"last_name":"Ruess","orcid":"0000-0003-1615-3282","full_name":"Ruess, Jakob","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"}],"article_processing_charge":"No","title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","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.","quality_controlled":"1","publisher":"Frontiers","oa":1,"has_accepted_license":"1","year":"2015","day":"10","publication":"Frontiers in Environmental Science","doi":"10.3389/fenvs.2015.00042","date_published":"2015-06-10T00:00:00Z","date_created":"2022-02-25T11:42:25Z","_id":"10794","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)"},"status":"public","keyword":["General Environmental Science"],"date_updated":"2022-02-25T11:59:23Z","ddc":["000","570"],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"file_date_updated":"2022-02-25T11:55:26Z","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"}],"oa_version":"Published Version","scopus_import":"1","month":"06","intvolume":" 3","publication_identifier":{"issn":["2296-665X"]},"publication_status":"published","file":[{"checksum":"26c222487564e1be02a11d688d6f769d","file_id":"10795","success":1,"access_level":"open_access","relation":"main_file","content_type":"application/pdf","date_created":"2022-02-25T11:55:26Z","file_name":"2015_FrontiersEnvironmScience_Parise.pdf","creator":"dernst","date_updated":"2022-02-25T11:55:26Z","file_size":1371201}],"language":[{"iso":"eng"}],"volume":3,"ec_funded":1},{"file":[{"date_created":"2018-12-12T10:07:43Z","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf","creator":"system","date_updated":"2020-07-14T12:45:01Z","file_size":605355,"checksum":"838657118ae286463a2b7737319f35ce","file_id":"4641","access_level":"open_access","relation":"main_file","content_type":"application/pdf"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":143,"issue":"24","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","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:45:01Z","_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","publisher":"American Institute of Physics","quality_controlled":"1","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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","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","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."},"title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess"}],"publist_id":"5632","article_number":"244103","project":[{"name":"Quantitative Reactive Modeling","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"name":"Rigorous Systems Engineering","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","name":"The Wittgenstein Prize","grant_number":"Z211"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}]},{"language":[{"iso":"eng"}],"publication_status":"published","issue":"26","volume":112,"ec_funded":1,"oa_version":"Submitted Version","pmid":1,"abstract":[{"lang":"eng","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."}],"month":"06","intvolume":" 112","scopus_import":1,"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/","open_access":"1"}],"date_updated":"2021-01-12T06:51:27Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"_id":"1538","status":"public","type":"journal_article","day":"30","publication":"PNAS","year":"2015","doi":"10.1073/pnas.1423947112","date_published":"2015-06-30T00:00:00Z","date_created":"2018-12-11T11:52:36Z","page":"8148 - 8153","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). ","publisher":"National Academy of Sciences","quality_controlled":"1","oa":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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","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","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.","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."},"title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","author":[{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","last_name":"Ruess","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"},{"full_name":"Parise, Francesca","last_name":"Parise","first_name":"Francesca"},{"full_name":"Milias Argeitis, Andreas","last_name":"Milias Argeitis","first_name":"Andreas"},{"first_name":"Mustafa","full_name":"Khammash, Mustafa","last_name":"Khammash"},{"first_name":"John","last_name":"Lygeros","full_name":"Lygeros, John"}],"publist_id":"5633","external_id":{"pmid":["26085136"]},"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}]},{"_id":"1564","pubrep_id":"479","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","ddc":["570"],"date_updated":"2021-01-12T06:51:37Z","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:45:02Z","oa_version":"Published Version","intvolume":" 9","month":"11","scopus_import":1,"language":[{"iso":"eng"}],"file":[{"file_id":"4927","checksum":"cea73b6d3ef1579f32da10b82f4de4fd","access_level":"open_access","relation":"main_file","content_type":"application/pdf","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}],"publication_status":"published","ec_funded":1,"issue":"11","volume":9,"article_number":"145","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).","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","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","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."},"title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","publist_id":"5607","author":[{"full_name":"Gilson, Matthieu","last_name":"Gilson","first_name":"Matthieu"},{"full_name":"Savin, Cristina","last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Zenke","full_name":"Zenke, Friedemann","first_name":"Friedemann"}],"oa":1,"publisher":"Frontiers Research Foundation","quality_controlled":"1","publication":"Frontiers in Computational Neuroscience","day":"30","year":"2015","has_accepted_license":"1","date_created":"2018-12-11T11:52:45Z","doi":"10.3389/fncom.2015.00145","date_published":"2015-11-30T00:00:00Z"},{"page":"E6224 - E6232","date_published":"2015-11-10T00:00:00Z","doi":"10.1073/pnas.1508400112","date_created":"2018-12-11T11:52:47Z","year":"2015","day":"10","publication":"PNAS","quality_controlled":"1","publisher":"National Academy of Sciences","oa":1,"publist_id":"5601","author":[{"last_name":"Der","full_name":"Der, Ralf","first_name":"Ralf"},{"last_name":"Martius","full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S"}],"external_id":{"pmid":["26504200"]},"title":"Novel plasticity rule can explain the development of sensorimotor intelligence","citation":{"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","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.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","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.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"}],"volume":112,"issue":"45","ec_funded":1,"publication_status":"published","language":[{"iso":"eng"}],"scopus_import":1,"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"month":"11","intvolume":" 112","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."}],"pmid":1,"oa_version":"Submitted Version","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:51:40Z","type":"journal_article","status":"public","_id":"1570"},{"series_title":"Lecture Notes in Computer Science","_id":"1658","type":"conference","conference":{"name":"CMSB: Computational Methods in Systems Biology","start_date":"2015-09-16","end_date":"2015-09-18","location":"Nantes, France"},"status":"public","date_updated":"2023-02-21T16:17:24Z","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"abstract":[{"lang":"eng","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."}],"oa_version":"None","alternative_title":["LNCS"],"scopus_import":1,"month":"09","intvolume":" 9308","publication_status":"published","language":[{"iso":"eng"}],"related_material":{"record":[{"relation":"later_version","id":"1148","status":"public"}]},"volume":9308,"ec_funded":1,"project":[{"name":"Quantitative Reactive Modeling","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","call_identifier":"FP7"},{"call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","name":"The Wittgenstein Prize"},{"grant_number":"S 11407_N23","name":"Rigorous Systems Engineering","_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF"},{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"citation":{"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.","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.","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.","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.","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","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","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"},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publist_id":"5492","author":[{"id":"369D9A44-F248-11E8-B48F-1D18A9856A87","first_name":"Sergiy","orcid":"0000-0002-0686-0365","full_name":"Bogomolov, Sergiy","last_name":"Bogomolov"},{"orcid":"0000−0002−2985−7724","full_name":"Henzinger, Thomas A","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A"},{"full_name":"Podelski, Andreas","last_name":"Podelski","first_name":"Andreas"},{"full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob"},{"first_name":"Christian","last_name":"Schilling","full_name":"Schilling, Christian"}],"title":"Adaptive moment closure for parameter inference of biochemical reaction networks","publisher":"Springer","quality_controlled":"1","year":"2015","day":"01","page":"77 - 89","date_published":"2015-09-01T00:00:00Z","doi":"10.1007/978-3-319-23401-4_8","date_created":"2018-12-11T11:53:18Z"},{"publist_id":"5447","author":[{"last_name":"Marre","full_name":"Marre, Olivier","first_name":"Olivier"},{"last_name":"Botella Soler","full_name":"Botella Soler, Vicente","orcid":"0000-0002-8790-1914","id":"421234E8-F248-11E8-B48F-1D18A9856A87","first_name":"Vicente"},{"first_name":"Kristina","last_name":"Simmons","full_name":"Simmons, Kristina"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"},{"first_name":"Michael","last_name":"Berry","full_name":"Berry, Michael"}],"title":"High accuracy decoding of dynamical motion from a large retinal population","citation":{"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.","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.","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","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","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.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425"}],"article_number":"e1004304","doi":"10.1371/journal.pcbi.1004304","date_published":"2015-07-01T00:00:00Z","date_created":"2018-12-11T11:53:31Z","has_accepted_license":"1","year":"2015","day":"01","publication":"PLoS Computational Biology","publisher":"Public Library of Science","quality_controlled":"1","oa":1,"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).","file_date_updated":"2020-07-14T12:45:12Z","department":[{"_id":"GaTk"}],"date_updated":"2021-01-12T06:52:35Z","ddc":["570"],"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":"455","_id":"1697","volume":11,"issue":"7","publication_status":"published","file":[{"checksum":"472b979f3f1cffb37b3e503f085115ca","file_id":"5212","access_level":"open_access","relation":"main_file","content_type":"application/pdf","date_created":"2018-12-12T10:16:25Z","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","creator":"system","date_updated":"2020-07-14T12:45:12Z","file_size":4673930}],"language":[{"iso":"eng"}],"scopus_import":1,"month":"07","intvolume":" 11","abstract":[{"lang":"eng","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."}],"oa_version":"Published Version"},{"oa_version":"Submitted Version","pmid":1,"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. "}],"month":"09","intvolume":" 112","scopus_import":1,"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/","open_access":"1"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":112,"issue":"37","_id":"1701","status":"public","type":"journal_article","date_updated":"2021-01-12T06:52:37Z","department":[{"_id":"GaTk"}],"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,"day":"15","publication":"PNAS","year":"2015","date_published":"2015-09-15T00:00:00Z","doi":"10.1073/pnas.1514188112","date_created":"2018-12-11T11:53:33Z","page":"11508 - 11513","project":[{"grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513.","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","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","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."},"title":"Thermodynamics and signatures of criticality in a network of neurons","publist_id":"5440","author":[{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"},{"first_name":"Olivier","full_name":"Marre, Olivier","last_name":"Marre"},{"first_name":"Dario","last_name":"Amodei","full_name":"Amodei, 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"]}},{"type":"journal_article","status":"public","_id":"1861","article_number":"8","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","title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_updated":"2021-01-12T06:53:41Z","citation":{"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","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.","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.","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."},"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"ACM","scopus_import":1,"quality_controlled":"1","month":"02","intvolume":" 25","abstract":[{"lang":"eng","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."}],"oa_version":"None","acknowledgement":"HYCON2; EC; European Commission\r\n","volume":25,"date_published":"2015-02-01T00:00:00Z","issue":"2","doi":"10.1145/2688906","date_created":"2018-12-11T11:54:25Z","year":"2015","publication_status":"published","day":"01","language":[{"iso":"eng"}],"publication":"ACM Transactions on Modeling and Computer Simulation"}]