[{"type":"research_data_reference","abstract":[{"text":"The positional information in a discrete morphogen field with Gaussian noise is computed.","lang":"eng"}],"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","_id":"9871","year":"2016","status":"public","title":"Computation of positional information in a discrete morphogen field","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","author":[{"first_name":"Patrick","last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick"},{"full_name":"Gerland, Ulrich","last_name":"Gerland","first_name":"Ulrich"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik","first_name":"Gašper","full_name":"Tkačik, Gašper"}],"related_material":{"record":[{"status":"public","relation":"used_in_publication","id":"1270"}]},"date_created":"2021-08-10T09:27:35Z","date_updated":"2023-02-21T16:56:40Z","oa_version":"Published Version","month":"09","day":"27","article_processing_charge":"No","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.","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.","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in a discrete morphogen field. 2016. doi: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.","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","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in a discrete morphogen field.” Public Library of Science, 2016."},"doi":"10.1371/journal.pone.0163628.s003"},{"publication_identifier":{"issn":["2663-337X"]},"month":"08","oa":1,"language":[{"iso":"eng"}],"supervisor":[{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik"}],"degree_awarded":"PhD","publist_id":"6232","file_date_updated":"2020-09-21T11:30:40Z","year":"2016","publisher":"Institute of Science and Technology Austria","department":[{"_id":"GaTk"}],"publication_status":"published","author":[{"full_name":"Rieckh, Georg","id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","first_name":"Georg","last_name":"Rieckh"}],"date_created":"2018-12-11T11:50:18Z","date_updated":"2023-09-07T11:44:34Z","article_processing_charge":"No","has_accepted_license":"1","day":"01","citation":{"chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016.","short":"G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute of Science and Technology Austria, 2016.","mla":"Rieckh, Georg. 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.","ieee":"G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute of Science and Technology Austria, 2016.","ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","ama":"Rieckh G. Studying the complexities of transcriptional regulation. 2016."},"page":"114","date_published":"2016-08-01T00:00:00Z","type":"dissertation","alternative_title":["ISTA Thesis"],"abstract":[{"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.","lang":"eng"}],"_id":"1128","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","ddc":["570"],"status":"public","title":"Studying the complexities of transcriptional regulation","oa_version":"Published Version","file":[{"checksum":"ec453918c3bf8e6f460fd1156ef7b493","date_updated":"2019-08-13T11:46:25Z","date_created":"2019-08-13T11:46:25Z","relation":"main_file","file_id":"6815","content_type":"application/pdf","file_size":2614660,"creator":"dernst","access_level":"closed","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf"},{"file_size":6096178,"content_type":"application/pdf","creator":"dernst","access_level":"open_access","file_name":"Thesis_Georg_Rieckh.pdf","checksum":"51ae398166370d18fd22478b6365c4da","success":1,"date_updated":"2020-09-21T11:30:40Z","date_created":"2020-09-21T11:30:40Z","relation":"main_file","file_id":"8542"}]},{"citation":{"mla":"Friedlander, Tamar, et al. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature Communications, vol. 7, 12307, Nature Publishing Group, 2016, doi:10.1038/ncomms12307.","short":"T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkačik, Nature Communications 7 (2016).","chicago":"Friedlander, Tamar, Roshan Prizak, Calin C Guet, Nicholas H Barton, and Gašper Tkačik. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” Nature Communications. Nature Publishing Group, 2016. https://doi.org/10.1038/ncomms12307.","ama":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 2016;7. doi:10.1038/ncomms12307","ista":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 7, 12307.","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","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."},"publication":"Nature Communications","date_published":"2016-08-04T00:00:00Z","scopus_import":1,"has_accepted_license":"1","day":"04","_id":"1358","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","intvolume":" 7","status":"public","ddc":["576"],"title":"Intrinsic limits to gene regulation by global crosstalk","pubrep_id":"627","oa_version":"Published Version","file":[{"date_created":"2018-12-12T10:12:01Z","date_updated":"2020-07-14T12:44:46Z","checksum":"fe3f3a1526d180b29fe691ab11435b78","relation":"main_file","file_id":"4919","file_size":861805,"content_type":"application/pdf","creator":"system","file_name":"IST-2016-627-v1+1_ncomms12307.pdf","access_level":"open_access"},{"date_created":"2018-12-12T10:12:02Z","date_updated":"2020-07-14T12:44:46Z","checksum":"164864a1a675f3ad80e9917c27aba07f","relation":"main_file","file_id":"4920","content_type":"application/pdf","file_size":1084703,"creator":"system","file_name":"IST-2016-627-v1+2_ncomms12307-s1.pdf","access_level":"open_access"}],"type":"journal_article","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."}],"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"name":"Limits to selection in biology and in evolutionary computation","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","grant_number":"250152"},{"name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27"}],"quality_controlled":"1","doi":"10.1038/ncomms12307","language":[{"iso":"eng"}],"month":"08","year":"2016","publisher":"Nature Publishing Group","department":[{"_id":"GaTk"},{"_id":"NiBa"},{"_id":"CaGu"}],"publication_status":"published","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"6071"}]},"author":[{"id":"36A5845C-F248-11E8-B48F-1D18A9856A87","last_name":"Friedlander","first_name":"Tamar","full_name":"Friedlander, Tamar"},{"id":"4456104E-F248-11E8-B48F-1D18A9856A87","last_name":"Prizak","first_name":"Roshan","full_name":"Prizak, Roshan"},{"orcid":"0000-0001-6220-2052","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","last_name":"Guet","first_name":"Calin C","full_name":"Guet, Calin C"},{"last_name":"Barton","first_name":"Nicholas H","orcid":"0000-0002-8548-5240","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","full_name":"Barton, Nicholas H"},{"full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkacik","first_name":"Gasper"}],"volume":7,"date_created":"2018-12-11T11:51:34Z","date_updated":"2023-09-07T12:53:49Z","article_number":"12307","publist_id":"5887","ec_funded":1,"file_date_updated":"2020-07-14T12:44:46Z","license":"https://creativecommons.org/licenses/by/4.0/"},{"year":"2015","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.","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"Frontiers","publication_status":"published","author":[{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"full_name":"Lygeros, John","first_name":"John","last_name":"Lygeros"},{"last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"}],"volume":3,"date_created":"2022-02-25T11:42:25Z","date_updated":"2022-02-25T11:59:23Z","article_number":"42","ec_funded":1,"file_date_updated":"2022-02-25T11:55:26Z","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","doi":"10.3389/fenvs.2015.00042","language":[{"iso":"eng"}],"publication_identifier":{"issn":["2296-665X"]},"month":"06","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"10794","intvolume":" 3","ddc":["000","570"],"title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","status":"public","oa_version":"Published Version","file":[{"success":1,"checksum":"26c222487564e1be02a11d688d6f769d","date_updated":"2022-02-25T11:55:26Z","date_created":"2022-02-25T11:55:26Z","file_id":"10795","relation":"main_file","creator":"dernst","content_type":"application/pdf","file_size":1371201,"access_level":"open_access","file_name":"2015_FrontiersEnvironmScience_Parise.pdf"}],"type":"journal_article","abstract":[{"lang":"eng","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."}],"citation":{"short":"F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (2015).","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.","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.","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","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.","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."},"publication":"Frontiers in Environmental Science","article_type":"original","date_published":"2015-06-10T00:00:00Z","scopus_import":"1","keyword":["General Environmental Science"],"has_accepted_license":"1","article_processing_charge":"No","day":"10"},{"month":"12","language":[{"iso":"eng"}],"doi":"10.1063/1.4937937","quality_controlled":"1","project":[{"grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","name":"Quantitative Reactive Modeling","call_identifier":"FP7"},{"name":"Rigorous Systems Engineering","call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23"},{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","grant_number":"Z211","call_identifier":"FWF","name":"The Wittgenstein Prize"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"oa":1,"file_date_updated":"2020-07-14T12:45:01Z","ec_funded":1,"publist_id":"5632","article_number":"244103","date_created":"2018-12-11T11:52:36Z","date_updated":"2021-01-12T06:51:28Z","volume":143,"author":[{"full_name":"Ruess, Jakob","last_name":"Ruess","first_name":"Jakob","orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"}],"publication_status":"published","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"American Institute of Physics","year":"2015","day":"22","has_accepted_license":"1","scopus_import":1,"date_published":"2015-12-22T00:00:00Z","publication":"Journal of Chemical Physics","citation":{"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.","short":"J. Ruess, Journal of Chemical Physics 143 (2015).","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","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.","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."},"abstract":[{"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. ","lang":"eng"}],"issue":"24","type":"journal_article","file":[{"creator":"system","file_size":605355,"content_type":"application/pdf","access_level":"open_access","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf","checksum":"838657118ae286463a2b7737319f35ce","date_created":"2018-12-12T10:07:43Z","date_updated":"2020-07-14T12:45:01Z","file_id":"4641","relation":"main_file"}],"oa_version":"Published Version","pubrep_id":"593","title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","ddc":["000"],"status":"public","intvolume":" 143","_id":"1539","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"},{"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."}],"issue":"26","type":"journal_article","oa_version":"Submitted Version","_id":"1538","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","status":"public","intvolume":" 112","day":"30","scopus_import":1,"date_published":"2015-06-30T00:00:00Z","publication":"PNAS","citation":{"short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","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.","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.","ama":"Ruess J, Parise F, Milias Argeitis A, Khammash M, Lygeros J. Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. 2015;112(26):8148-8153. doi:10.1073/pnas.1423947112","apa":"Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., & Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. PNAS. National Academy of Sciences. https://doi.org/10.1073/pnas.1423947112","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.","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."},"page":"8148 - 8153","ec_funded":1,"publist_id":"5633","author":[{"full_name":"Ruess, Jakob","first_name":"Jakob","last_name":"Ruess","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282"},{"first_name":"Francesca","last_name":"Parise","full_name":"Parise, Francesca"},{"last_name":"Milias Argeitis","first_name":"Andreas","full_name":"Milias Argeitis, Andreas"},{"first_name":"Mustafa","last_name":"Khammash","full_name":"Khammash, Mustafa"},{"last_name":"Lygeros","first_name":"John","full_name":"Lygeros, John"}],"date_updated":"2021-01-12T06:51:27Z","date_created":"2018-12-11T11:52:36Z","volume":112,"year":"2015","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). ","pmid":1,"publication_status":"published","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"National Academy of Sciences","month":"06","doi":"10.1073/pnas.1423947112","language":[{"iso":"eng"}],"oa":1,"external_id":{"pmid":["26085136"]},"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/","open_access":"1"}],"quality_controlled":"1","project":[{"call_identifier":"FP7","name":"International IST Postdoc Fellowship Programme","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}]},{"oa_version":"Published Version","file":[{"creator":"system","file_size":187038,"content_type":"application/pdf","file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf","access_level":"open_access","date_created":"2018-12-12T10:12:09Z","date_updated":"2020-07-14T12:45:02Z","checksum":"cea73b6d3ef1579f32da10b82f4de4fd","file_id":"4927","relation":"main_file"}],"pubrep_id":"479","intvolume":" 9","ddc":["570"],"status":"public","title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","_id":"1564","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","issue":"11","type":"journal_article","date_published":"2015-11-30T00:00:00Z","citation":{"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.","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.","short":"M. Gilson, C. Savin, F. Zenke, Frontiers in Computational Neuroscience 9 (2015).","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.","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.","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"},"publication":"Frontiers in Computational Neuroscience","has_accepted_license":"1","day":"30","scopus_import":1,"volume":9,"date_updated":"2021-01-12T06:51:37Z","date_created":"2018-12-11T11:52:45Z","author":[{"full_name":"Gilson, Matthieu","first_name":"Matthieu","last_name":"Gilson"},{"first_name":"Cristina","last_name":"Savin","id":"3933349E-F248-11E8-B48F-1D18A9856A87","full_name":"Savin, Cristina"},{"last_name":"Zenke","first_name":"Friedemann","full_name":"Zenke, Friedemann"}],"department":[{"_id":"GaTk"}],"publisher":"Frontiers Research Foundation","publication_status":"published","year":"2015","ec_funded":1,"publist_id":"5607","file_date_updated":"2020-07-14T12:45:02Z","article_number":"145","language":[{"iso":"eng"}],"doi":"10.3389/fncom.2015.00145","project":[{"name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"quality_controlled":"1","tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"oa":1,"month":"11"},{"month":"11","doi":"10.1073/pnas.1508400112","language":[{"iso":"eng"}],"main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"oa":1,"external_id":{"pmid":["26504200"]},"quality_controlled":"1","project":[{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"publist_id":"5601","ec_funded":1,"author":[{"first_name":"Ralf","last_name":"Der","full_name":"Der, Ralf"},{"full_name":"Martius, Georg S","last_name":"Martius","first_name":"Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87"}],"date_updated":"2021-01-12T06:51:40Z","date_created":"2018-12-11T11:52:47Z","volume":112,"year":"2015","pmid":1,"publication_status":"published","publisher":"National Academy of Sciences","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"day":"10","scopus_import":1,"date_published":"2015-11-10T00:00:00Z","publication":"PNAS","citation":{"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.","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.","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.","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","ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232."},"page":"E6224 - E6232","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."}],"issue":"45","type":"journal_article","oa_version":"Submitted Version","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1570","title":"Novel plasticity rule can explain the development of sensorimotor intelligence","status":"public","intvolume":" 112"},{"day":"01","series_title":"Lecture Notes in Computer Science","scopus_import":1,"date_published":"2015-09-01T00:00:00Z","page":"77 - 89","citation":{"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","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.","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","ieee":"S. Bogomolov, T. A. Henzinger, A. Podelski, J. Ruess, and C. Schilling, “Adaptive moment closure for parameter inference of biochemical reaction networks,” vol. 9308. Springer, pp. 77–89, 2015.","mla":"Bogomolov, Sergiy, et al. Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks. Vol. 9308, Springer, 2015, pp. 77–89, doi:10.1007/978-3-319-23401-4_8.","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","chicago":"Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. https://doi.org/10.1007/978-3-319-23401-4_8."},"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."}],"alternative_title":["LNCS"],"type":"conference","oa_version":"None","intvolume":" 9308","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","status":"public","_id":"1658","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"09","language":[{"iso":"eng"}],"doi":"10.1007/978-3-319-23401-4_8","conference":{"start_date":"2015-09-16","location":"Nantes, France","end_date":"2015-09-18","name":"CMSB: Computational Methods in Systems Biology"},"project":[{"_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989","name":"Quantitative Reactive Modeling","call_identifier":"FP7"},{"name":"The Wittgenstein Prize","call_identifier":"FWF","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23","call_identifier":"FWF","name":"Rigorous Systems Engineering"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","name":"International IST Postdoc Fellowship Programme","call_identifier":"FP7"}],"quality_controlled":"1","ec_funded":1,"publist_id":"5492","volume":9308,"date_updated":"2023-02-21T16:17:24Z","date_created":"2018-12-11T11:53:18Z","related_material":{"record":[{"relation":"later_version","status":"public","id":"1148"}]},"author":[{"last_name":"Bogomolov","first_name":"Sergiy","orcid":"0000-0002-0686-0365","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","full_name":"Bogomolov, Sergiy"},{"full_name":"Henzinger, Thomas A","last_name":"Henzinger","first_name":"Thomas A","orcid":"0000−0002−2985−7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Podelski, Andreas","last_name":"Podelski","first_name":"Andreas"},{"orcid":"0000-0003-1615-3282","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob"},{"full_name":"Schilling, Christian","first_name":"Christian","last_name":"Schilling"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"publisher":"Springer","publication_status":"published","year":"2015"},{"day":"01","has_accepted_license":"1","scopus_import":1,"date_published":"2015-07-01T00:00:00Z","publication":"PLoS Computational Biology","citation":{"short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (2015).","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.","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.","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","ieee":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, and M. Berry, “High accuracy decoding of dynamical motion from a large retinal population,” PLoS Computational Biology, vol. 11, no. 7. Public Library of Science, 2015.","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., & Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. PLoS Computational Biology. Public Library of Science. https://doi.org/10.1371/journal.pcbi.1004304","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."},"abstract":[{"text":"Motion tracking is a challenge the visual system has to solve by reading out the retinal population. It is still unclear how the information from different neurons can be combined together to estimate the position of an object. Here we recorded a large population of ganglion cells in a dense patch of salamander and guinea pig retinas while displaying a bar moving diffusively. We show that the bar’s position can be reconstructed from retinal activity with a precision in the hyperacuity regime using a linear decoder acting on 100+ cells. We then took advantage of this unprecedented precision to explore the spatial structure of the retina’s population code. The classical view would have suggested that the firing rates of the cells form a moving hill of activity tracking the bar’s position. Instead, we found that most ganglion cells in the salamander fired sparsely and idiosyncratically, so that their neural image did not track the bar. Furthermore, ganglion cell activity spanned an area much larger than predicted by their receptive fields, with cells coding for motion far in their surround. As a result, population redundancy was high, and we could find multiple, disjoint subsets of neurons that encoded the trajectory with high precision. This organization allows for diverse collections of ganglion cells to represent high-accuracy motion information in a form easily read out by downstream neural circuits.","lang":"eng"}],"issue":"7","type":"journal_article","file":[{"access_level":"open_access","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","content_type":"application/pdf","file_size":4673930,"creator":"system","relation":"main_file","file_id":"5212","checksum":"472b979f3f1cffb37b3e503f085115ca","date_created":"2018-12-12T10:16:25Z","date_updated":"2020-07-14T12:45:12Z"}],"oa_version":"Published Version","pubrep_id":"455","title":"High accuracy decoding of dynamical motion from a large retinal population","status":"public","ddc":["570"],"intvolume":" 11","_id":"1697","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"07","language":[{"iso":"eng"}],"doi":"10.1371/journal.pcbi.1004304","quality_controlled":"1","project":[{"_id":"254D1A94-B435-11E9-9278-68D0E5697425","grant_number":"P 25651-N26","name":"Sensitivity to higher-order statistics in natural scenes","call_identifier":"FWF"}],"oa":1,"tmp":{"name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode","short":"CC BY (4.0)","image":"/images/cc_by.png"},"file_date_updated":"2020-07-14T12:45:12Z","publist_id":"5447","article_number":"e1004304","date_created":"2018-12-11T11:53:31Z","date_updated":"2021-01-12T06:52:35Z","volume":11,"author":[{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"},{"full_name":"Botella Soler, Vicente","first_name":"Vicente","last_name":"Botella Soler","id":"421234E8-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-8790-1914"},{"full_name":"Simmons, Kristina","first_name":"Kristina","last_name":"Simmons"},{"full_name":"Mora, Thierry","last_name":"Mora","first_name":"Thierry"},{"full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik"},{"first_name":"Michael","last_name":"Berry","full_name":"Berry, Michael"}],"publication_status":"published","department":[{"_id":"GaTk"}],"publisher":"Public Library of Science","year":"2015","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)."}]