[{"volume":13,"issue":"1","ec_funded":1,"publication_status":"published","language":[{"iso":"eng"}],"scopus_import":1,"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1505.04613"}],"month":"01","intvolume":" 13","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."}],"oa_version":"Preprint","department":[{"_id":"GaTk"}],"date_updated":"2021-01-12T06:51:04Z","type":"journal_article","status":"public","_id":"1485","date_published":"2016-01-29T00:00:00Z","doi":"10.1088/1478-3975/13/1/016003","date_created":"2018-12-11T11:52:18Z","year":"2016","day":"29","publication":"Physical Biology","publisher":"IOP Publishing Ltd.","quality_controlled":"1","oa":1,"author":[{"last_name":"De Martino","orcid":"0000-0002-5214-4706","full_name":"De Martino, Daniele","id":"3FF5848A-F248-11E8-B48F-1D18A9856A87","first_name":"Daniele"}],"publist_id":"5702","title":"Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis","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.","short":"D. De Martino, Physical Biology 13 (2016).","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.","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","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."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"article_number":"016003"},{"quality_controlled":"1","publisher":"Elsevier","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.","date_created":"2018-12-11T11:50:24Z","doi":"10.1016/j.biosystems.2016.07.005","date_published":"2016-11-01T00:00:00Z","page":"15 - 25","publication":"Biosystems","day":"01","year":"2016","project":[{"grant_number":"267989","name":"Quantitative Reactive Modeling","_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"},{"name":"The Wittgenstein Prize","grant_number":"Z211","call_identifier":"FWF","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"title":"Adaptive moment closure for parameter inference of biochemical reaction networks","publist_id":"6210","author":[{"full_name":"Schilling, Christian","last_name":"Schilling","first_name":"Christian"},{"id":"369D9A44-F248-11E8-B48F-1D18A9856A87","first_name":"Sergiy","full_name":"Bogomolov, Sergiy","orcid":"0000-0002-0686-0365","last_name":"Bogomolov"},{"full_name":"Henzinger, Thomas A","orcid":"0000−0002−2985−7724","last_name":"Henzinger","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas A"},{"first_name":"Andreas","last_name":"Podelski","full_name":"Podelski, Andreas"},{"id":"4A245D00-F248-11E8-B48F-1D18A9856A87","first_name":"Jakob","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282","last_name":"Ruess"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","citation":{"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.","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.","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."},"intvolume":" 149","month":"11","scopus_import":1,"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"}],"ec_funded":1,"volume":149,"related_material":{"record":[{"relation":"earlier_version","status":"public","id":"1658"}]},"language":[{"iso":"eng"}],"publication_status":"published","status":"public","type":"journal_article","_id":"1148","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_updated":"2023-02-23T10:08:46Z"},{"title":"Self-organized control of an tendon driven arm by differential extrinsic plasticity","article_processing_charge":"No","author":[{"full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Rafael","last_name":"Hostettler","full_name":"Hostettler, Rafael"},{"last_name":"Knoll","full_name":"Knoll, Alois","first_name":"Alois"},{"last_name":"Der","full_name":"Der, Ralf","first_name":"Ralf"}],"user_id":"D865714E-FA4E-11E9-B85B-F5C5E5697425","citation":{"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","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","short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 142–143.","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.","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.","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."},"project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"date_created":"2020-07-05T22:00:47Z","doi":"10.7551/978-0-262-33936-0-ch029","date_published":"2016-09-01T00:00:00Z","page":"142-143","publication":"Proceedings of the Artificial Life Conference 2016","day":"01","year":"2016","has_accepted_license":"1","oa":1,"quality_controlled":"1","publisher":"MIT Press","file_date_updated":"2020-07-14T12:48:09Z","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"ddc":["610"],"date_updated":"2021-01-12T08:16:53Z","status":"public","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"},"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":"conference","_id":"8094","ec_funded":1,"volume":28,"language":[{"iso":"eng"}],"file":[{"file_name":"2016_ProcALIFE_Martius.pdf","date_created":"2020-07-06T12:59:09Z","creator":"cziletti","file_size":678670,"date_updated":"2020-07-14T12:48:09Z","checksum":"cff63e7a4b8ac466ba51a9c84153a940","file_id":"8096","relation":"main_file","access_level":"open_access","content_type":"application/pdf"}],"publication_status":"published","publication_identifier":{"isbn":["9780262339360"]},"intvolume":" 28","month":"09","scopus_import":1,"oa_version":"Published Version","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"}]},{"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.","publisher":"Public Library of Science","quality_controlled":"1","oa":1,"day":"17","publication":"PLoS Computational Biology","has_accepted_license":"1","year":"2016","date_published":"2016-11-17T00:00:00Z","doi":"10.1371/journal.pcbi.1005148","date_created":"2018-12-11T11:50:40Z","article_number":"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"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","citation":{"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.","short":"J. Prentice, O. Marre, M. Ioffe, A. Loback, G. Tkačik, M. Berry, PLoS Computational Biology 12 (2016).","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.","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."},"title":"Error-robust modes of the retinal population code","publist_id":"6153","author":[{"full_name":"Prentice, Jason","last_name":"Prentice","first_name":"Jason"},{"full_name":"Marre, Olivier","last_name":"Marre","first_name":"Olivier"},{"first_name":"Mark","full_name":"Ioffe, Mark","last_name":"Ioffe"},{"first_name":"Adrianna","full_name":"Loback, Adrianna","last_name":"Loback"},{"last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Berry, Michael","last_name":"Berry","first_name":"Michael"}],"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"}],"month":"11","intvolume":" 12","scopus_import":1,"file":[{"date_updated":"2020-07-14T12:44:38Z","file_size":4492021,"creator":"kschuh","date_created":"2019-01-25T10:35:00Z","file_name":"2016_PLOS_Prentice.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file","checksum":"47b08cbd4dbf32b25ba161f5f4b262cc","file_id":"5884"}],"language":[{"iso":"eng"}],"publication_status":"published","volume":12,"related_material":{"record":[{"id":"9709","status":"public","relation":"research_data"}]},"issue":"11","_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)"},"ddc":["570"],"date_updated":"2023-02-23T14:05:40Z","department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:38Z"},{"project":[{"call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"author":[{"first_name":"Travis","last_name":"Monk","full_name":"Monk, Travis"},{"full_name":"Savin, Cristina","last_name":"Savin","first_name":"Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Lücke","full_name":"Lücke, Jörg","first_name":"Jörg"}],"publist_id":"6469","title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","citation":{"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.","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.","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.","mla":"Monk, Travis, et al. Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics. Vol. 29, Neural Information Processing Systems, 2016, pp. 4285–93.","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."},"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","quality_controlled":"1","publisher":"Neural Information Processing Systems","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)","page":"4285 - 4293","date_created":"2018-12-11T11:49:21Z","date_published":"2016-01-01T00:00:00Z","year":"2016","day":"01","conference":{"start_date":"2016-12-05","location":"Barcelona, Spaine","end_date":"2016-12-10","name":"NIPS: Neural Information Processing Systems"},"type":"conference","status":"public","_id":"948","department":[{"_id":"GaTk"}],"date_updated":"2021-01-12T08:22:08Z","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"],"intvolume":" 29","month":"01","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","ec_funded":1,"volume":29,"publication_status":"published","language":[{"iso":"eng"}]},{"department":[{"_id":"GaTk"}],"file_date_updated":"2020-07-14T12:44:42Z","date_updated":"2023-02-23T14:11:37Z","ddc":["571"],"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":"696","_id":"1270","related_material":{"record":[{"id":"9869","status":"public","relation":"research_data"},{"relation":"research_data","id":"9870","status":"public"},{"relation":"research_data","id":"9871","status":"public"}]},"volume":11,"issue":"9","publication_status":"published","file":[{"checksum":"3d0d55d373096a033bd9cf79288c8586","file_id":"4837","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2018-12-12T10:10:47Z","file_name":"IST-2016-696-v1+1_journal.pone.0163628.PDF","date_updated":"2020-07-14T12:44:42Z","file_size":4950415,"creator":"system"}],"language":[{"iso":"eng"}],"scopus_import":1,"month":"09","intvolume":" 11","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."}],"oa_version":"Published Version","publist_id":"6050","author":[{"full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand","first_name":"Patrick"},{"last_name":"Gerland","full_name":"Gerland, Ulrich","first_name":"Ulrich"},{"last_name":"Tkacik","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87"}],"title":"Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information","citation":{"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.","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.","apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. (2016). Beyond the French flag model: Exploiting spatial and gene regulatory interactions for positional information. PLoS One. 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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)."},{"month":"09","publisher":"Public Library of Science","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."}],"date_published":"2016-09-27T00:00:00Z","related_material":{"record":[{"relation":"used_in_publication","id":"1270","status":"public"}]},"doi":"10.1371/journal.pone.0163628.s002","date_created":"2021-08-10T09:23:45Z","day":"27","year":"2016","status":"public","type":"research_data_reference","_id":"9870","department":[{"_id":"GaTk"}],"title":"Computation of positional information in an Ising model","author":[{"last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick","first_name":"Patrick"},{"first_name":"Ulrich","full_name":"Gerland, Ulrich","last_name":"Gerland"},{"orcid":"0000-0002-6699-1455","full_name":"Tkačik, Gašper","last_name":"Tkačik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper"}],"article_processing_charge":"No","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","citation":{"apa":"Hillenbrand, P., Gerland, U., & Tkačik, G. 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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","publication_identifier":{"issn":["2663-337X"]},"degree_awarded":"PhD","publication_status":"published","file":[{"checksum":"ec453918c3bf8e6f460fd1156ef7b493","file_id":"6815","access_level":"closed","relation":"main_file","content_type":"application/pdf","date_created":"2019-08-13T11:46:25Z","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf","creator":"dernst","date_updated":"2019-08-13T11:46:25Z","file_size":2614660},{"file_name":"Thesis_Georg_Rieckh.pdf","date_created":"2020-09-21T11:30:40Z","creator":"dernst","file_size":6096178,"date_updated":"2020-09-21T11:30:40Z","success":1,"checksum":"51ae398166370d18fd22478b6365c4da","file_id":"8542","relation":"main_file","access_level":"open_access","content_type":"application/pdf"}],"language":[{"iso":"eng"}],"citation":{"chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016.","ista":"Rieckh G. 2016. 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