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