[{"page":"142-143","file":[{"file_size":678670,"date_updated":"2020-07-14T12:48:09Z","file_id":"8096","file_name":"2016_ProcALIFE_Martius.pdf","date_created":"2020-07-06T12:59:09Z","content_type":"application/pdf","relation":"main_file","access_level":"open_access","creator":"cziletti","checksum":"cff63e7a4b8ac466ba51a9c84153a940"}],"corr_author":"1","title":"Self-organized control of an tendon driven arm by differential extrinsic plasticity","year":"2016","publisher":"MIT Press","doi":"10.7551/978-0-262-33936-0-ch029","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","month":"09","status":"public","author":[{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","last_name":"Martius"},{"first_name":"Rafael","full_name":"Hostettler, Rafael","last_name":"Hostettler"},{"full_name":"Knoll, Alois","first_name":"Alois","last_name":"Knoll"},{"first_name":"Ralf","full_name":"Der, Ralf","last_name":"Der"}],"project":[{"call_identifier":"FP7","grant_number":"291734","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"}],"quality_controlled":"1","publication_status":"published","_id":"8094","file_date_updated":"2020-07-14T12:48:09Z","article_processing_charge":"No","conference":{"name":"ALIFE 2016: Conference on the Synthesis and Simulation of Living Systems","location":"Cancun, Mexico","start_date":"2016-07-04","end_date":"2016-07-08"},"citation":{"apa":"Martius, G. S., Hostettler, R., Knoll, A., &#38; Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In <i>15th International Conference on the Synthesis and Simulation of Living Systems</i> (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press. <a href=\"https://doi.org/10.7551/978-0-262-33936-0-ch029\">https://doi.org/10.7551/978-0-262-33936-0-ch029</a>","chicago":"Martius, Georg S, Rafael Hostettler, Alois Knoll, and Ralf Der. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” In <i>15th International Conference on the Synthesis and Simulation of Living Systems</i>, 28:142–43. MIT Press, 2016. <a href=\"https://doi.org/10.7551/978-0-262-33936-0-ch029\">https://doi.org/10.7551/978-0-262-33936-0-ch029</a>.","mla":"Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.” <i>15th International Conference on the Synthesis and Simulation of Living Systems</i>, vol. 28, MIT Press, 2016, pp. 142–43, doi:<a href=\"https://doi.org/10.7551/978-0-262-33936-0-ch029\">10.7551/978-0-262-33936-0-ch029</a>.","ieee":"G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control of an tendon driven arm by differential extrinsic plasticity,” in <i>15th International Conference on the Synthesis and Simulation of Living Systems</i>, Cancun, Mexico, 2016, vol. 28, pp. 142–143.","ama":"Martius GS, Hostettler R, Knoll A, Der R. Self-organized control of an tendon driven arm by differential extrinsic plasticity. In: <i>15th International Conference on the Synthesis and Simulation of Living Systems</i>. Vol 28. MIT Press; 2016:142-143. doi:<a href=\"https://doi.org/10.7551/978-0-262-33936-0-ch029\">10.7551/978-0-262-33936-0-ch029</a>","ista":"Martius GS, Hostettler R, Knoll A, Der R. 2016. Self-organized control of an tendon driven arm by differential extrinsic plasticity. 15th International Conference on the Synthesis and Simulation of Living Systems. ALIFE 2016: Conference on the Synthesis and Simulation of Living Systems vol. 28, 142–143.","short":"G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, 15th International Conference on the Synthesis and Simulation of Living Systems, MIT Press, 2016, pp. 142–143."},"oa":1,"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2025-07-10T11:55:05Z","date_created":"2020-07-05T22:00:47Z","abstract":[{"lang":"eng","text":"With the accelerated development of robot technologies, optimal control becomes one of the central themes of research. In traditional approaches, the controller, by its internal functionality, finds appropriate actions on the basis of the history of sensor values, guided by the goals, intentions, objectives, learning schemes, and so forth. The idea is that the controller controls the world---the body plus its environment---as reliably as possible. This paper focuses on new lines of self-organization for developmental robotics. We apply the recently developed differential extrinsic synaptic plasticity to a muscle-tendon driven arm-shoulder system from the Myorobotics toolkit. In the experiments, we observe a vast variety of self-organized behavior patterns: when left alone, the arm realizes pseudo-random sequences of different poses. By applying physical forces, the system can be entrained into definite motion patterns like wiping a table. Most interestingly, after attaching an object, the controller gets in a functional resonance with the object's internal dynamics, starting to shake spontaneously bottles half-filled with water or sensitively driving an attached pendulum into a circular mode. When attached to the crank of a wheel the neural system independently discovers how to rotate it. In this way, the robot discovers affordances of objects its body is interacting with."}],"type":"conference","publication_identifier":{"isbn":["9780262339360"]},"scopus_import":"1","oa_version":"Published Version","ddc":["610"],"language":[{"iso":"eng"}],"volume":28,"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"date_published":"2016-09-01T00:00:00Z","has_accepted_license":"1","publication":"15th International Conference on the Synthesis and Simulation of Living Systems","ec_funded":1,"intvolume":"        28","day":"01"},{"abstract":[{"text":"Experience constantly shapes neural circuits through a variety of plasticity mechanisms. While the functional roles of some plasticity mechanisms are well-understood, it remains unclear how changes in neural excitability contribute to learning. Here, we develop a normative interpretation of intrinsic plasticity (IP) as a key component of unsupervised learning. We introduce a novel generative mixture model that accounts for the class-specific statistics of stimulus intensities, and we derive a neural circuit that learns the input classes and their intensities. We will analytically show that inference and learning for our generative model can be achieved by a neural circuit with intensity-sensitive neurons equipped with a specific form of IP. Numerical experiments verify our analytical derivations and show robust behavior for artificial and natural stimuli. Our results link IP to non-trivial input statistics, in particular the statistics of stimulus intensities for classes to which a neuron is sensitive. More generally, our work paves the way toward new classification algorithms that are robust to intensity variations.","lang":"eng"}],"date_created":"2018-12-11T11:49:21Z","type":"conference","citation":{"mla":"Monk, Travis, et al. <i>Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics</i>. Vol. 29, Neural Information Processing Systems Foundation, 2016, pp. 4285–93.","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.","chicago":"Monk, Travis, Cristina Savin, and Jörg Lücke. “Neurons Equipped with Intrinsic Plasticity Learn Stimulus Intensity Statistics,” 29:4285–93. Neural Information Processing Systems Foundation, 2016.","apa":"Monk, T., Savin, C., &#38; 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 Foundation.","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.","short":"T. Monk, C. Savin, J. Lücke, in:, Neural Information Processing Systems Foundation, 2016, 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 Foundation; 2016:4285-4293."},"publist_id":"6469","oa":1,"date_updated":"2025-06-03T11:18:32Z","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)","conference":{"name":"NIPS: Neural Information Processing Systems","location":"Barcelona, Spaine","start_date":"2016-12-05","end_date":"2016-12-10"},"article_processing_charge":"No","oa_version":"None","scopus_import":"1","date_published":"2016-01-01T00:00:00Z","department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"volume":29,"ec_funded":1,"intvolume":"        29","day":"01","alternative_title":["Advances in Neural Information Processing Systems"],"year":"2016","title":"Neurons equipped with intrinsic plasticity learn stimulus intensity statistics","page":"4285 - 4293","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","publisher":"Neural Information Processing Systems Foundation","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"author":[{"last_name":"Monk","first_name":"Travis","full_name":"Monk, Travis"},{"first_name":"Cristina","full_name":"Savin, Cristina","id":"3933349E-F248-11E8-B48F-1D18A9856A87","last_name":"Savin"},{"last_name":"Lücke","full_name":"Lücke, Jörg","first_name":"Jörg"}],"month":"01","status":"public","main_file_link":[{"open_access":"1","url":"https://papers.nips.cc/paper/6582-neurons-equipped-with-intrinsic-plasticity-learn-stimulus-intensity-statistics"}],"_id":"948","quality_controlled":"1","publication_status":"published"},{"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","oa_version":"Published Version","doi":"10.1371/journal.pone.0163628.s001","publisher":"Public Library of Science","date_created":"2021-08-10T08:53:48Z","abstract":[{"lang":"eng","text":"A lower bound on the error of a positional estimator with limited positional information is derived."}],"title":"Error bound on an estimator of position","year":"2016","type":"research_data_reference","citation":{"short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Error bound on an estimator of position, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pone.0163628.s001\">10.1371/journal.pone.0163628.s001</a>.","ama":"Hillenbrand P, Gerland U, Tkačik G. Error bound on an estimator of position. 2016. doi:<a href=\"https://doi.org/10.1371/journal.pone.0163628.s001\">10.1371/journal.pone.0163628.s001</a>","apa":"Hillenbrand, P., Gerland, U., &#38; Tkačik, G. (2016). Error bound on an estimator of position. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0163628.s001\">https://doi.org/10.1371/journal.pone.0163628.s001</a>","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Error Bound on an Estimator of Position.” Public Library of Science, 2016. <a href=\"https://doi.org/10.1371/journal.pone.0163628.s001\">https://doi.org/10.1371/journal.pone.0163628.s001</a>.","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Error bound on an estimator of position.” Public Library of Science, 2016.","mla":"Hillenbrand, Patrick, et al. <i>Error Bound on an Estimator of Position</i>. Public Library of Science, 2016, doi:<a href=\"https://doi.org/10.1371/journal.pone.0163628.s001\">10.1371/journal.pone.0163628.s001</a>."},"date_updated":"2025-09-22T08:46:14Z","article_processing_charge":"No","day":"27","_id":"9869","author":[{"full_name":"Hillenbrand, Patrick","first_name":"Patrick","last_name":"Hillenbrand"},{"last_name":"Gerland","first_name":"Ulrich","full_name":"Gerland, Ulrich"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455"}],"date_published":"2016-09-27T00:00:00Z","department":[{"_id":"GaTk"}],"month":"09","status":"public","related_material":{"record":[{"id":"1270","status":"public","relation":"used_in_publication"}]}},{"day":"27","_id":"9870","date_published":"2016-09-27T00:00:00Z","author":[{"last_name":"Hillenbrand","full_name":"Hillenbrand, Patrick","first_name":"Patrick"},{"last_name":"Gerland","first_name":"Ulrich","full_name":"Gerland, Ulrich"},{"full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","last_name":"Tkačik","orcid":"0000-0002-6699-1455"}],"status":"public","department":[{"_id":"GaTk"}],"month":"09","related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]},"user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf","doi":"10.1371/journal.pone.0163628.s002","oa_version":"Published Version","publisher":"Public Library of Science","type":"research_data_reference","year":"2016","title":"Computation of positional information in an Ising model","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_created":"2021-08-10T09:23:45Z","date_updated":"2025-09-22T08:46:14Z","citation":{"ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in an Ising model.” Public Library of Science, 2016.","mla":"Hillenbrand, Patrick, et al. <i>Computation of Positional Information in an Ising Model</i>. Public Library of Science, 2016, doi:<a href=\"https://doi.org/10.1371/journal.pone.0163628.s002\">10.1371/journal.pone.0163628.s002</a>.","apa":"Hillenbrand, P., Gerland, U., &#38; Tkačik, G. (2016). Computation of positional information in an Ising model. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0163628.s002\">https://doi.org/10.1371/journal.pone.0163628.s002</a>","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in an Ising Model.” Public Library of Science, 2016. <a href=\"https://doi.org/10.1371/journal.pone.0163628.s002\">https://doi.org/10.1371/journal.pone.0163628.s002</a>.","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in an Ising model. 2016. doi:<a href=\"https://doi.org/10.1371/journal.pone.0163628.s002\">10.1371/journal.pone.0163628.s002</a>","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in an Ising model, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pone.0163628.s002\">10.1371/journal.pone.0163628.s002</a>.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016)."},"article_processing_charge":"No"},{"related_material":{"record":[{"relation":"used_in_publication","status":"public","id":"1270"}]},"status":"public","department":[{"_id":"GaTk"}],"month":"09","author":[{"first_name":"Patrick","full_name":"Hillenbrand, Patrick","last_name":"Hillenbrand"},{"first_name":"Ulrich","full_name":"Gerland, Ulrich","last_name":"Gerland"},{"orcid":"0000-0002-6699-1455","first_name":"Gašper","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","last_name":"Tkačik"}],"_id":"9871","day":"27","article_processing_charge":"No","date_updated":"2025-09-22T08:46:14Z","citation":{"apa":"Hillenbrand, P., Gerland, U., &#38; Tkačik, G. (2016). Computation of positional information in a discrete morphogen field. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pone.0163628.s003\">https://doi.org/10.1371/journal.pone.0163628.s003</a>","chicago":"Hillenbrand, Patrick, Ulrich Gerland, and Gašper Tkačik. “Computation of Positional Information in a Discrete Morphogen Field.” Public Library of Science, 2016. <a href=\"https://doi.org/10.1371/journal.pone.0163628.s003\">https://doi.org/10.1371/journal.pone.0163628.s003</a>.","ieee":"P. Hillenbrand, U. Gerland, and G. Tkačik, “Computation of positional information in a discrete morphogen field.” Public Library of Science, 2016.","mla":"Hillenbrand, Patrick, et al. <i>Computation of Positional Information in a Discrete Morphogen Field</i>. Public Library of Science, 2016, doi:<a href=\"https://doi.org/10.1371/journal.pone.0163628.s003\">10.1371/journal.pone.0163628.s003</a>.","short":"P. Hillenbrand, U. Gerland, G. Tkačik, (2016).","ista":"Hillenbrand P, Gerland U, Tkačik G. 2016. Computation of positional information in a discrete morphogen field, Public Library of Science, <a href=\"https://doi.org/10.1371/journal.pone.0163628.s003\">10.1371/journal.pone.0163628.s003</a>.","ama":"Hillenbrand P, Gerland U, Tkačik G. Computation of positional information in a discrete morphogen field. 2016. doi:<a href=\"https://doi.org/10.1371/journal.pone.0163628.s003\">10.1371/journal.pone.0163628.s003</a>"},"title":"Computation of positional information in a discrete morphogen field","year":"2016","type":"research_data_reference","date_created":"2021-08-10T09:27:35Z","abstract":[{"text":"The positional information in a discrete morphogen field with Gaussian noise is computed.","lang":"eng"}],"publisher":"Public Library of Science","doi":"10.1371/journal.pone.0163628.s003","oa_version":"Published Version","user_id":"6785fbc1-c503-11eb-8a32-93094b40e1cf"},{"file_date_updated":"2020-07-14T12:44:46Z","_id":"1358","quality_controlled":"1","publication_status":"published","project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"},{"grant_number":"250152","call_identifier":"FP7","_id":"25B07788-B435-11E9-9278-68D0E5697425","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"}],"author":[{"id":"36A5845C-F248-11E8-B48F-1D18A9856A87","full_name":"Friedlander, Tamar","first_name":"Tamar","last_name":"Friedlander"},{"first_name":"Roshan","full_name":"Prizak, Roshan","id":"4456104E-F248-11E8-B48F-1D18A9856A87","last_name":"Prizak"},{"last_name":"Guet","full_name":"Guet, Calin C","id":"47F8433E-F248-11E8-B48F-1D18A9856A87","first_name":"Calin C","orcid":"0000-0001-6220-2052"},{"orcid":"0000-0002-8548-5240","first_name":"Nicholas H","full_name":"Barton, Nicholas H","id":"4880FE40-F248-11E8-B48F-1D18A9856A87","last_name":"Barton"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455"}],"month":"08","status":"public","related_material":{"record":[{"id":"6071","status":"public","relation":"dissertation_contains"}]},"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1038/ncomms12307","publisher":"Nature Publishing Group","year":"2016","corr_author":"1","title":"Intrinsic limits to gene regulation by global crosstalk","isi":1,"pubrep_id":"627","file":[{"access_level":"open_access","creator":"system","checksum":"fe3f3a1526d180b29fe691ab11435b78","relation":"main_file","content_type":"application/pdf","date_updated":"2020-07-14T12:44:46Z","file_id":"4919","file_name":"IST-2016-627-v1+1_ncomms12307.pdf","date_created":"2018-12-12T10:12:01Z","file_size":861805},{"content_type":"application/pdf","relation":"main_file","checksum":"164864a1a675f3ad80e9917c27aba07f","creator":"system","access_level":"open_access","file_size":1084703,"date_created":"2018-12-12T10:12:02Z","file_name":"IST-2016-627-v1+2_ncomms12307-s1.pdf","file_id":"4920","date_updated":"2020-07-14T12:44:46Z"}],"intvolume":"         7","ec_funded":1,"day":"04","publication":"Nature Communications","external_id":{"isi":["000380858400001"]},"has_accepted_license":"1","date_published":"2016-08-04T00:00:00Z","department":[{"_id":"GaTk"},{"_id":"NiBa"},{"_id":"CaGu"}],"ddc":["576"],"volume":7,"language":[{"iso":"eng"}],"oa_version":"Published Version","scopus_import":"1","abstract":[{"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.","lang":"eng"}],"date_created":"2018-12-11T11:51:34Z","type":"journal_article","citation":{"ista":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. 2016. Intrinsic limits to gene regulation by global crosstalk. Nature Communications. 7, 12307.","short":"T. Friedlander, R. Prizak, C.C. Guet, N.H. Barton, G. Tkačik, Nature Communications 7 (2016).","ama":"Friedlander T, Prizak R, Guet CC, Barton NH, Tkačik G. Intrinsic limits to gene regulation by global crosstalk. <i>Nature Communications</i>. 2016;7. doi:<a href=\"https://doi.org/10.1038/ncomms12307\">10.1038/ncomms12307</a>","chicago":"Friedlander, Tamar, Roshan Prizak, Calin C Guet, Nicholas H Barton, and Gašper Tkačik. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” <i>Nature Communications</i>. Nature Publishing Group, 2016. <a href=\"https://doi.org/10.1038/ncomms12307\">https://doi.org/10.1038/ncomms12307</a>.","apa":"Friedlander, T., Prizak, R., Guet, C. C., Barton, N. H., &#38; Tkačik, G. (2016). Intrinsic limits to gene regulation by global crosstalk. <i>Nature Communications</i>. Nature Publishing Group. <a href=\"https://doi.org/10.1038/ncomms12307\">https://doi.org/10.1038/ncomms12307</a>","mla":"Friedlander, Tamar, et al. “Intrinsic Limits to Gene Regulation by Global Crosstalk.” <i>Nature Communications</i>, vol. 7, 12307, Nature Publishing Group, 2016, doi:<a href=\"https://doi.org/10.1038/ncomms12307\">10.1038/ncomms12307</a>.","ieee":"T. Friedlander, R. Prizak, C. C. Guet, N. H. Barton, and G. Tkačik, “Intrinsic limits to gene regulation by global crosstalk,” <i>Nature Communications</i>, vol. 7. Nature Publishing Group, 2016."},"publist_id":"5887","oa":1,"date_updated":"2026-04-08T13:54:24Z","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"article_number":"12307","article_processing_charge":"No"},{"oa_version":"Published Version","publication_identifier":{"issn":["2663-337X"]},"type":"dissertation","date_created":"2018-12-11T11:50:18Z","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."}],"date_updated":"2026-04-08T14:24:58Z","oa":1,"citation":{"ista":"Rieckh G. 2016. Studying the complexities of transcriptional regulation. Institute of Science and Technology Austria.","short":"G. Rieckh, Studying the Complexities of Transcriptional Regulation, Institute of Science and Technology Austria, 2016.","ama":"Rieckh G. Studying the complexities of transcriptional regulation. 2016.","ieee":"G. Rieckh, “Studying the complexities of transcriptional regulation,” Institute of Science and Technology Austria, 2016.","mla":"Rieckh, Georg. <i>Studying the Complexities of Transcriptional Regulation</i>. Institute of Science and Technology Austria, 2016.","apa":"Rieckh, G. (2016). <i>Studying the complexities of transcriptional regulation</i>. Institute of Science and Technology Austria.","chicago":"Rieckh, Georg. “Studying the Complexities of Transcriptional Regulation.” Institute of Science and Technology Austria, 2016."},"publist_id":"6232","article_processing_charge":"No","day":"01","alternative_title":["ISTA Thesis"],"has_accepted_license":"1","degree_awarded":"PhD","date_published":"2016-08-01T00:00:00Z","department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"ddc":["570"],"user_id":"ba8df636-2132-11f1-aed0-ed93e2281fdd","publisher":"Institute of Science and Technology Austria","corr_author":"1","year":"2016","title":"Studying the complexities of transcriptional regulation","file":[{"access_level":"closed","checksum":"ec453918c3bf8e6f460fd1156ef7b493","creator":"dernst","relation":"main_file","content_type":"application/pdf","file_id":"6815","date_updated":"2019-08-13T11:46:25Z","date_created":"2019-08-13T11:46:25Z","file_name":"Thesis_Georg_Rieckh_w_signature_page.pdf","file_size":2614660},{"file_size":6096178,"file_name":"Thesis_Georg_Rieckh.pdf","date_created":"2020-09-21T11:30:40Z","date_updated":"2020-09-21T11:30:40Z","success":1,"file_id":"8542","content_type":"application/pdf","relation":"main_file","creator":"dernst","checksum":"51ae398166370d18fd22478b6365c4da","access_level":"open_access"}],"page":"114","file_date_updated":"2020-09-21T11:30:40Z","_id":"1128","publication_status":"published","supervisor":[{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gasper"}],"author":[{"id":"34DA8BD6-F248-11E8-B48F-1D18A9856A87","full_name":"Rieckh, Georg","first_name":"Georg","last_name":"Rieckh"}],"status":"public","OA_place":"publisher","month":"08"},{"project":[{"grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"}],"author":[{"last_name":"Friedlander","id":"36A5845C-F248-11E8-B48F-1D18A9856A87","full_name":"Friedlander, Tamar","first_name":"Tamar"},{"full_name":"Mayo, Avraham","first_name":"Avraham","last_name":"Mayo"},{"full_name":"Tlusty, Tsvi","first_name":"Tsvi","last_name":"Tlusty"},{"last_name":"Alon","first_name":"Uri","full_name":"Alon, Uri"}],"status":"public","month":"03","related_material":{"record":[{"status":"public","relation":"research_data","id":"9718"},{"id":"9773","status":"public","relation":"research_data"}]},"issue":"3","file_date_updated":"2020-07-14T12:45:17Z","_id":"1827","quality_controlled":"1","publication_status":"published","year":"2015","isi":1,"title":"Evolution of bow-tie architectures in biology","file":[{"relation":"main_file","content_type":"application/pdf","creator":"system","checksum":"b8aa66f450ff8de393014b87ec7d2efb","access_level":"open_access","file_size":1811647,"file_name":"IST-2016-452-v1+1_journal.pcbi.1004055.pdf","date_created":"2018-12-12T10:15:39Z","date_updated":"2020-07-14T12:45:17Z","file_id":"5161"}],"pubrep_id":"452","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1371/journal.pcbi.1004055","publisher":"Public Library of Science","date_published":"2015-03-23T00:00:00Z","department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"volume":11,"ddc":["576"],"day":"23","intvolume":"        11","ec_funded":1,"publication":"PLoS Computational Biology","has_accepted_license":"1","external_id":{"isi":["000352195700006"]},"type":"journal_article","date_created":"2018-12-11T11:54:14Z","abstract":[{"text":"Bow-tie or hourglass structure is a common architectural feature found in many biological systems. A bow-tie in a multi-layered structure occurs when intermediate layers have much fewer components than the input and output layers. Examples include metabolism where a handful of building blocks mediate between multiple input nutrients and multiple output biomass components, and signaling networks where information from numerous receptor types passes through a small set of signaling pathways to regulate multiple output genes. Little is known, however, about how bow-tie architectures evolve. Here, we address the evolution of bow-tie architectures using simulations of multi-layered systems evolving to fulfill a given input-output goal. We find that bow-ties spontaneously evolve when the information in the evolutionary goal can be compressed. Mathematically speaking, bow-ties evolve when the rank of the input-output matrix describing the evolutionary goal is deficient. The maximal compression possible (the rank of the goal) determines the size of the narrowest part of the network—that is the bow-tie. A further requirement is that a process is active to reduce the number of links in the network, such as product-rule mutations, otherwise a non-bow-tie solution is found in the evolutionary simulations. This offers a mechanism to understand a common architectural principle of biological systems, and a way to quantitate the effective rank of the goals under which they evolved.","lang":"eng"}],"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2025-09-23T08:43:16Z","oa":1,"publist_id":"5278","citation":{"mla":"Friedlander, Tamar, et al. “Evolution of Bow-Tie Architectures in Biology.” <i>PLoS Computational Biology</i>, vol. 11, no. 3, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">10.1371/journal.pcbi.1004055</a>.","ieee":"T. Friedlander, A. Mayo, T. Tlusty, and U. Alon, “Evolution of bow-tie architectures in biology,” <i>PLoS Computational Biology</i>, vol. 11, no. 3. Public Library of Science, 2015.","chicago":"Friedlander, Tamar, Avraham Mayo, Tsvi Tlusty, and Uri Alon. “Evolution of Bow-Tie Architectures in Biology.” <i>PLoS Computational Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">https://doi.org/10.1371/journal.pcbi.1004055</a>.","apa":"Friedlander, T., Mayo, A., Tlusty, T., &#38; Alon, U. (2015). Evolution of bow-tie architectures in biology. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">https://doi.org/10.1371/journal.pcbi.1004055</a>","ama":"Friedlander T, Mayo A, Tlusty T, Alon U. Evolution of bow-tie architectures in biology. <i>PLoS Computational Biology</i>. 2015;11(3). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004055\">10.1371/journal.pcbi.1004055</a>","short":"T. Friedlander, A. Mayo, T. Tlusty, U. Alon, PLoS Computational Biology 11 (2015).","ista":"Friedlander T, Mayo A, Tlusty T, Alon U. 2015. Evolution of bow-tie architectures in biology. PLoS Computational Biology. 11(3)."},"article_processing_charge":"No","scopus_import":"1","oa_version":"Published Version"},{"scopus_import":"1","oa_version":"None","article_processing_charge":"No","article_number":"8","acknowledgement":"HYCON2; EC; European Commission\r\n","date_updated":"2025-09-23T09:36:19Z","publist_id":"5238","citation":{"ieee":"J. Ruess and J. Lygeros, “Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks,” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2. ACM, 2015.","mla":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>, vol. 25, no. 2, 8, ACM, 2015, doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>.","apa":"Ruess, J., &#38; Lygeros, J. (2015). Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>","chicago":"Ruess, Jakob, and John Lygeros. “Moment-Based Methods for Parameter Inference and Experiment Design for Stochastic Biochemical Reaction Networks.” <i>ACM Transactions on Modeling and Computer Simulation</i>. ACM, 2015. <a href=\"https://doi.org/10.1145/2688906\">https://doi.org/10.1145/2688906</a>.","ama":"Ruess J, Lygeros J. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. <i>ACM Transactions on Modeling and Computer Simulation</i>. 2015;25(2). doi:<a href=\"https://doi.org/10.1145/2688906\">10.1145/2688906</a>","short":"J. Ruess, J. Lygeros, ACM Transactions on Modeling and Computer Simulation 25 (2015).","ista":"Ruess J, Lygeros J. 2015. Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks. ACM Transactions on Modeling and Computer Simulation. 25(2), 8."},"type":"journal_article","abstract":[{"text":"Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of themolecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation that governs the time evolution of the probability distribution of the system. This, however, is rarely possible, and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this article is to base methods on only some low-order moments of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.","lang":"eng"}],"date_created":"2018-12-11T11:54:25Z","external_id":{"isi":["000354789200002"]},"publication":"ACM Transactions on Modeling and Computer Simulation","day":"01","intvolume":"        25","language":[{"iso":"eng"}],"volume":25,"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_published":"2015-02-01T00:00:00Z","publisher":"ACM","doi":"10.1145/2688906","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","title":"Moment-based methods for parameter inference and experiment design for stochastic biochemical reaction networks","isi":1,"year":"2015","publication_status":"published","quality_controlled":"1","_id":"1861","issue":"2","status":"public","month":"02","author":[{"orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"},{"last_name":"Lygeros","first_name":"John","full_name":"Lygeros, John"}]},{"doi":"10.1534/genetics.114.171850","publisher":"Genetics Society of America","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","page":"39 - 59","year":"2015","corr_author":"1","title":"Positional information, positional error, and readout precision in morphogenesis: A mathematical framework","isi":1,"quality_controlled":"1","publication_status":"published","issue":"1","_id":"1885","month":"01","status":"public","main_file_link":[{"url":"http://arxiv.org/abs/1404.5599","open_access":"1"}],"author":[{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","first_name":"Gasper","last_name":"Tkacik"},{"full_name":"Dubuis, Julien","first_name":"Julien","last_name":"Dubuis"},{"last_name":"Petkova","full_name":"Petkova, Mariela","first_name":"Mariela"},{"last_name":"Gregor","first_name":"Thomas","full_name":"Gregor, Thomas"}],"scopus_import":"1","oa_version":"Preprint","article_processing_charge":"No","date_created":"2018-12-11T11:54:32Z","abstract":[{"lang":"eng","text":"The concept of positional information is central to our understanding of how cells determine their location in a multicellular structure and thereby their developmental fates. Nevertheless, positional information has neither been defined mathematically nor quantified in a principled way. Here we provide an information-theoretic definition in the context of developmental gene expression patterns and examine the features of expression patterns that affect positional information quantitatively. We connect positional information with the concept of positional error and develop tools to directly measure information and error from experimental data. We illustrate our framework for the case of gap gene expression patterns in the early Drosophila embryo and show how information that is distributed among only four genes is sufficient to determine developmental fates with nearly single-cell resolution. Our approach can be generalized to a variety of different model systems; procedures and examples are discussed in detail. "}],"type":"journal_article","oa":1,"citation":{"mla":"Tkačik, Gašper, et al. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” <i>Genetics</i>, vol. 199, no. 1, Genetics Society of America, 2015, pp. 39–59, doi:<a href=\"https://doi.org/10.1534/genetics.114.171850\">10.1534/genetics.114.171850</a>.","ieee":"G. Tkačik, J. Dubuis, M. Petkova, and T. Gregor, “Positional information, positional error, and readout precision in morphogenesis: A mathematical framework,” <i>Genetics</i>, vol. 199, no. 1. Genetics Society of America, pp. 39–59, 2015.","chicago":"Tkačik, Gašper, Julien Dubuis, Mariela Petkova, and Thomas Gregor. “Positional Information, Positional Error, and Readout Precision in Morphogenesis: A Mathematical Framework.” <i>Genetics</i>. Genetics Society of America, 2015. <a href=\"https://doi.org/10.1534/genetics.114.171850\">https://doi.org/10.1534/genetics.114.171850</a>.","apa":"Tkačik, G., Dubuis, J., Petkova, M., &#38; Gregor, T. (2015). Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. <i>Genetics</i>. Genetics Society of America. <a href=\"https://doi.org/10.1534/genetics.114.171850\">https://doi.org/10.1534/genetics.114.171850</a>","ama":"Tkačik G, Dubuis J, Petkova M, Gregor T. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. <i>Genetics</i>. 2015;199(1):39-59. doi:<a href=\"https://doi.org/10.1534/genetics.114.171850\">10.1534/genetics.114.171850</a>","short":"G. Tkačik, J. Dubuis, M. Petkova, T. Gregor, Genetics 199 (2015) 39–59.","ista":"Tkačik G, Dubuis J, Petkova M, Gregor T. 2015. Positional information, positional error, and readout precision in morphogenesis: A mathematical framework. Genetics. 199(1), 39–59."},"publist_id":"5210","arxiv":1,"date_updated":"2025-09-23T09:53:07Z","publication":"Genetics","external_id":{"isi":["000347712900004"],"arxiv":["1404.5599"]},"intvolume":"       199","day":"01","department":[{"_id":"GaTk"}],"volume":199,"language":[{"iso":"eng"}],"date_published":"2015-01-01T00:00:00Z"},{"date_created":"2018-12-11T11:54:49Z","abstract":[{"text":"We typically think of cells as responding to external signals independently by regulating their gene expression levels, yet they often locally exchange information and coordinate. Can such spatial coupling be of benefit for conveying signals subject to gene regulatory noise? Here we extend our information-theoretic framework for gene regulation to spatially extended systems. As an example, we consider a lattice of nuclei responding to a concentration field of a transcriptional regulator (the &quot;input&quot;) by expressing a single diffusible target gene. When input concentrations are low, diffusive coupling markedly improves information transmission; optimal gene activation functions also systematically change. A qualitatively new regulatory strategy emerges where individual cells respond to the input in a nearly step-like fashion that is subsequently averaged out by strong diffusion. While motivated by early patterning events in the Drosophila embryo, our framework is generically applicable to spatially coupled stochastic gene expression models.","lang":"eng"}],"type":"journal_article","arxiv":1,"publist_id":"5145","oa":1,"citation":{"ama":"Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. 2015;91(6). doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">10.1103/PhysRevE.91.062710</a>","short":"T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (2015).","ista":"Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E Statistical Nonlinear and Soft Matter Physics. 91(6), 062710.","chicago":"Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics, 2015. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>.","apa":"Sokolowski, T. R., &#38; Tkačik, G. (2015). Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>","ieee":"T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 91, no. 6. American Institute of Physics, 2015.","mla":"Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E Statistical Nonlinear and Soft Matter Physics</i>, vol. 91, no. 6, 062710, American Institute of Physics, 2015, doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">10.1103/PhysRevE.91.062710</a>."},"date_updated":"2025-09-23T09:46:10Z","article_number":"062710","article_processing_charge":"No","scopus_import":"1","oa_version":"Preprint","date_published":"2015-06-15T00:00:00Z","department":[{"_id":"GaTk"}],"language":[{"iso":"eng"}],"volume":91,"intvolume":"        91","day":"15","publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","external_id":{"arxiv":["1501.04015"],"isi":["000356131600006"]},"isi":1,"corr_author":"1","title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","year":"2015","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1103/PhysRevE.91.062710","publisher":"American Institute of Physics","author":[{"orcid":"0000-0002-1287-3779","last_name":"Sokolowski","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R"},{"first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik","orcid":"0000-0002-6699-1455"}],"month":"06","status":"public","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1501.04015"}],"issue":"6","_id":"1940","quality_controlled":"1","publication_status":"published"},{"pmid":1,"oa_version":"Submitted Version","scopus_import":"1","oa":1,"publist_id":"5633","citation":{"short":"J. Ruess, F. Parise, A. Milias Argeitis, M. Khammash, J. Lygeros, PNAS 112 (2015) 8148–8153.","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.","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. <i>PNAS</i>. 2015;112(26):8148-8153. doi:<a href=\"https://doi.org/10.1073/pnas.1423947112\">10.1073/pnas.1423947112</a>","apa":"Ruess, J., Parise, F., Milias Argeitis, A., Khammash, M., &#38; Lygeros, J. (2015). Iterative experiment design guides the characterization of a light-inducible gene expression circuit. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1423947112\">https://doi.org/10.1073/pnas.1423947112</a>","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.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1423947112\">https://doi.org/10.1073/pnas.1423947112</a>.","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,” <i>PNAS</i>, vol. 112, no. 26. National Academy of Sciences, pp. 8148–8153, 2015.","mla":"Ruess, Jakob, et al. “Iterative Experiment Design Guides the Characterization of a Light-Inducible Gene Expression Circuit.” <i>PNAS</i>, vol. 112, no. 26, National Academy of Sciences, 2015, pp. 8148–53, doi:<a href=\"https://doi.org/10.1073/pnas.1423947112\">10.1073/pnas.1423947112</a>."},"date_updated":"2025-09-23T09:24:24Z","date_created":"2018-12-11T11:52:36Z","abstract":[{"text":"Systems biology rests on the idea that biological complexity can be better unraveled through the interplay of modeling and experimentation. However, the success of this approach depends critically on the informativeness of the chosen experiments, which is usually unknown a priori. Here, we propose a systematic scheme based on iterations of optimal experiment design, flow cytometry experiments, and Bayesian parameter inference to guide the discovery process in the case of stochastic biochemical reaction networks. To illustrate the benefit of our methodology, we apply it to the characterization of an engineered light-inducible gene expression circuit in yeast and compare the performance of the resulting model with models identified from nonoptimal experiments. In particular, we compare the parameter posterior distributions and the precision to which the outcome of future experiments can be predicted. Moreover, we illustrate how the identified stochastic model can be used to determine light induction patterns that make either the average amount of protein or the variability in a population of cells follow a desired profile. Our results show that optimal experiment design allows one to derive models that are accurate enough to precisely predict and regulate the protein expression in heterogeneous cell populations over extended periods of time.","lang":"eng"}],"type":"journal_article","article_processing_charge":"No","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). ","intvolume":"       112","ec_funded":1,"day":"30","external_id":{"isi":["000357079400070"],"pmid":["26085136"]},"publication":"PNAS","date_published":"2015-06-30T00:00:00Z","language":[{"iso":"eng"}],"volume":112,"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","publisher":"National Academy of Sciences","doi":"10.1073/pnas.1423947112","isi":1,"year":"2015","title":"Iterative experiment design guides the characterization of a light-inducible gene expression circuit","page":"8148 - 8153","_id":"1538","issue":"26","quality_controlled":"1","publication_status":"published","author":[{"last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0003-1615-3282"},{"last_name":"Parise","full_name":"Parise, Francesca","first_name":"Francesca"},{"first_name":"Andreas","full_name":"Milias Argeitis, Andreas","last_name":"Milias Argeitis"},{"first_name":"Mustafa","full_name":"Khammash, Mustafa","last_name":"Khammash"},{"last_name":"Lygeros","full_name":"Lygeros, John","first_name":"John"}],"project":[{"name":"International IST Postdoc Fellowship Programme","grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425"}],"month":"06","main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4491780/"}],"status":"public"},{"month":"12","status":"public","author":[{"orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","full_name":"Ruess, Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87"}],"project":[{"call_identifier":"FP7","grant_number":"267989","_id":"25EE3708-B435-11E9-9278-68D0E5697425","name":"Quantitative Reactive Modeling"},{"name":"Rigorous Systems Engineering","_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23","call_identifier":"FWF"},{"_id":"25F42A32-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"Z211","name":"Formal methods for the design and analysis of complex systems"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"quality_controlled":"1","publication_status":"published","_id":"1539","file_date_updated":"2020-07-14T12:45:01Z","issue":"24","pubrep_id":"593","file":[{"relation":"main_file","content_type":"application/pdf","access_level":"open_access","checksum":"838657118ae286463a2b7737319f35ce","creator":"system","file_size":605355,"file_id":"4641","date_updated":"2020-07-14T12:45:01Z","date_created":"2018-12-12T10:07:43Z","file_name":"IST-2016-593-v1+1_Minimal_moment_equations.pdf"}],"isi":1,"title":"Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space","year":"2015","corr_author":"1","publisher":"American Institute of Physics","doi":"10.1063/1.4937937","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","ddc":["000"],"volume":143,"language":[{"iso":"eng"}],"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"date_published":"2015-12-22T00:00:00Z","external_id":{"isi":["000370412900068"]},"has_accepted_license":"1","publication":"Journal of Chemical Physics","intvolume":"       143","ec_funded":1,"day":"22","article_processing_charge":"No","article_number":"244103","citation":{"ieee":"J. Ruess, “Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space,” <i>Journal of Chemical Physics</i>, vol. 143, no. 24. American Institute of Physics, 2015.","mla":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>, vol. 143, no. 24, 244103, American Institute of Physics, 2015, doi:<a href=\"https://doi.org/10.1063/1.4937937\">10.1063/1.4937937</a>.","chicago":"Ruess, Jakob. “Minimal Moment Equations for Stochastic Models of Biochemical Reaction Networks with Partially Finite State Space.” <i>Journal of Chemical Physics</i>. American Institute of Physics, 2015. <a href=\"https://doi.org/10.1063/1.4937937\">https://doi.org/10.1063/1.4937937</a>.","apa":"Ruess, J. (2015). Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. American Institute of Physics. <a href=\"https://doi.org/10.1063/1.4937937\">https://doi.org/10.1063/1.4937937</a>","ama":"Ruess J. Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space. <i>Journal of Chemical Physics</i>. 2015;143(24). doi:<a href=\"https://doi.org/10.1063/1.4937937\">10.1063/1.4937937</a>","short":"J. Ruess, Journal of Chemical Physics 143 (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."},"oa":1,"publist_id":"5632","date_updated":"2025-09-23T09:34:48Z","date_created":"2018-12-11T11:52:36Z","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. "}],"type":"journal_article","scopus_import":"1","oa_version":"Published Version"},{"doi":"10.3389/fenvs.2015.00042","publisher":"Frontiers","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","file":[{"checksum":"26c222487564e1be02a11d688d6f769d","creator":"dernst","access_level":"open_access","relation":"main_file","content_type":"application/pdf","date_created":"2022-02-25T11:55:26Z","file_name":"2015_FrontiersEnvironmScience_Parise.pdf","file_id":"10795","date_updated":"2022-02-25T11:55:26Z","success":1,"file_size":1371201}],"title":"Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study","year":"2015","corr_author":"1","quality_controlled":"1","publication_status":"published","article_type":"original","file_date_updated":"2022-02-25T11:55:26Z","_id":"10794","status":"public","month":"06","project":[{"grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"}],"author":[{"full_name":"Parise, Francesca","first_name":"Francesca","last_name":"Parise"},{"last_name":"Lygeros","first_name":"John","full_name":"Lygeros, John"},{"last_name":"Ruess","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob","orcid":"0000-0003-1615-3282"}],"scopus_import":"1","oa_version":"Published Version","publication_identifier":{"issn":["2296-665X"]},"article_number":"42","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_processing_charge":"No","type":"journal_article","date_created":"2022-02-25T11:42:25Z","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."}],"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2025-04-15T06:50:01Z","oa":1,"citation":{"ieee":"F. Parise, J. Lygeros, and J. Ruess, “Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study,” <i>Frontiers in Environmental Science</i>, vol. 3. Frontiers, 2015.","mla":"Parise, Francesca, et al. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” <i>Frontiers in Environmental Science</i>, vol. 3, 42, Frontiers, 2015, doi:<a href=\"https://doi.org/10.3389/fenvs.2015.00042\">10.3389/fenvs.2015.00042</a>.","apa":"Parise, F., Lygeros, J., &#38; Ruess, J. (2015). Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. <i>Frontiers in Environmental Science</i>. Frontiers. <a href=\"https://doi.org/10.3389/fenvs.2015.00042\">https://doi.org/10.3389/fenvs.2015.00042</a>","chicago":"Parise, Francesca, John Lygeros, and Jakob Ruess. “Bayesian Inference for Stochastic Individual-Based Models of Ecological Systems: A Pest Control Simulation Study.” <i>Frontiers in Environmental Science</i>. Frontiers, 2015. <a href=\"https://doi.org/10.3389/fenvs.2015.00042\">https://doi.org/10.3389/fenvs.2015.00042</a>.","ama":"Parise F, Lygeros J, Ruess J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. <i>Frontiers in Environmental Science</i>. 2015;3. doi:<a href=\"https://doi.org/10.3389/fenvs.2015.00042\">10.3389/fenvs.2015.00042</a>","short":"F. Parise, J. Lygeros, J. Ruess, Frontiers in Environmental Science 3 (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","has_accepted_license":"1","day":"10","intvolume":"         3","ec_funded":1,"department":[{"_id":"ToHe"},{"_id":"GaTk"}],"volume":3,"language":[{"iso":"eng"}],"ddc":["000","570"],"date_published":"2015-06-10T00:00:00Z","keyword":["General Environmental Science"]},{"article_number":"145","article_processing_charge":"No","type":"journal_article","date_created":"2018-12-11T11:52:45Z","date_updated":"2025-09-23T08:35:56Z","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"publist_id":"5607","oa":1,"citation":{"chicago":"Gilson, Matthieu, Cristina Savin, and Friedemann Zenke. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” <i>Frontiers in Computational Neuroscience</i>. Frontiers Research Foundation, 2015. <a href=\"https://doi.org/10.3389/fncom.2015.00145\">https://doi.org/10.3389/fncom.2015.00145</a>.","apa":"Gilson, M., Savin, C., &#38; Zenke, F. (2015). Editorial: Emergent neural computation from the interaction of different forms of plasticity. <i>Frontiers in Computational Neuroscience</i>. Frontiers Research Foundation. <a href=\"https://doi.org/10.3389/fncom.2015.00145\">https://doi.org/10.3389/fncom.2015.00145</a>","ieee":"M. Gilson, C. Savin, and F. Zenke, “Editorial: Emergent neural computation from the interaction of different forms of plasticity,” <i>Frontiers in Computational Neuroscience</i>, vol. 9, no. 11. Frontiers Research Foundation, 2015.","mla":"Gilson, Matthieu, et al. “Editorial: Emergent Neural Computation from the Interaction of Different Forms of Plasticity.” <i>Frontiers in Computational Neuroscience</i>, vol. 9, no. 11, 145, Frontiers Research Foundation, 2015, doi:<a href=\"https://doi.org/10.3389/fncom.2015.00145\">10.3389/fncom.2015.00145</a>.","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.","ama":"Gilson M, Savin C, Zenke F. Editorial: Emergent neural computation from the interaction of different forms of plasticity. <i>Frontiers in Computational Neuroscience</i>. 2015;9(11). doi:<a href=\"https://doi.org/10.3389/fncom.2015.00145\">10.3389/fncom.2015.00145</a>"},"oa_version":"Published Version","scopus_import":"1","department":[{"_id":"GaTk"}],"volume":9,"language":[{"iso":"eng"}],"ddc":["570"],"date_published":"2015-11-30T00:00:00Z","publication":"Frontiers in Computational Neuroscience","has_accepted_license":"1","external_id":{"isi":["000365824800002"]},"day":"30","ec_funded":1,"intvolume":"         9","file":[{"date_updated":"2020-07-14T12:45:02Z","file_id":"4927","file_name":"IST-2016-479-v1+1_fncom-09-00145.pdf","date_created":"2018-12-12T10:12:09Z","file_size":187038,"access_level":"open_access","creator":"system","checksum":"cea73b6d3ef1579f32da10b82f4de4fd","relation":"main_file","content_type":"application/pdf"}],"pubrep_id":"479","title":"Editorial: Emergent neural computation from the interaction of different forms of plasticity","isi":1,"corr_author":"1","year":"2015","doi":"10.3389/fncom.2015.00145","publisher":"Frontiers Research Foundation","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","status":"public","month":"11","project":[{"grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"}],"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"}],"publication_status":"published","quality_controlled":"1","issue":"11","file_date_updated":"2020-07-14T12:45:02Z","_id":"1564"},{"doi":"10.1073/pnas.1508400112","publisher":"National Academy of Sciences","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","page":"E6224 - E6232","title":"Novel plasticity rule can explain the development of sensorimotor intelligence","year":"2015","corr_author":"1","isi":1,"publication_status":"published","quality_controlled":"1","issue":"45","_id":"1570","month":"11","status":"public","main_file_link":[{"url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4653169/","open_access":"1"}],"project":[{"name":"International IST Postdoc Fellowship Programme","_id":"25681D80-B435-11E9-9278-68D0E5697425","grant_number":"291734","call_identifier":"FP7"}],"author":[{"full_name":"Der, Ralf","first_name":"Ralf","last_name":"Der"},{"full_name":"Martius, Georg S","id":"3A276B68-F248-11E8-B48F-1D18A9856A87","first_name":"Georg S","last_name":"Martius"}],"oa_version":"Submitted Version","scopus_import":"1","pmid":1,"article_processing_charge":"No","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."}],"date_created":"2018-12-11T11:52:47Z","type":"journal_article","publist_id":"5601","oa":1,"citation":{"apa":"Der, R., &#38; Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1508400112\">https://doi.org/10.1073/pnas.1508400112</a>","chicago":"Der, Ralf, and Georg S Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1508400112\">https://doi.org/10.1073/pnas.1508400112</a>.","ieee":"R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” <i>PNAS</i>, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.","mla":"Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.” <i>PNAS</i>, vol. 112, no. 45, National Academy of Sciences, 2015, pp. E6224–32, doi:<a href=\"https://doi.org/10.1073/pnas.1508400112\">10.1073/pnas.1508400112</a>.","short":"R. Der, G.S. Martius, PNAS 112 (2015) E6224–E6232.","ista":"Der R, Martius GS. 2015. Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS. 112(45), E6224–E6232.","ama":"Der R, Martius GS. Novel plasticity rule can explain the development of sensorimotor intelligence. <i>PNAS</i>. 2015;112(45):E6224-E6232. doi:<a href=\"https://doi.org/10.1073/pnas.1508400112\">10.1073/pnas.1508400112</a>"},"date_updated":"2025-09-23T09:41:37Z","publication":"PNAS","external_id":{"pmid":["26504200"],"isi":["000364470300020"]},"intvolume":"       112","ec_funded":1,"day":"10","department":[{"_id":"ChLa"},{"_id":"GaTk"}],"language":[{"iso":"eng"}],"volume":112,"date_published":"2015-11-10T00:00:00Z"},{"article_processing_charge":"No","acknowledgement":"This work was supported by the DFG priority program 1527 (Autonomous Learning) and by the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 318723 (MatheMACS) and from the People Programme (Marie Curie Actions) of the European Union’s Seventh Framework Programme (FP7/2007-2013) under REA grant agreement no. 291734.","citation":{"chicago":"Martius, Georg S, and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” <i>Entropy</i>. MDPI, 2015. <a href=\"https://doi.org/10.3390/e17107266\">https://doi.org/10.3390/e17107266</a>.","apa":"Martius, G. S., &#38; Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. <i>Entropy</i>. MDPI. <a href=\"https://doi.org/10.3390/e17107266\">https://doi.org/10.3390/e17107266</a>","ieee":"G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” <i>Entropy</i>, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.","mla":"Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.” <i>Entropy</i>, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:<a href=\"https://doi.org/10.3390/e17107266\">10.3390/e17107266</a>.","ama":"Martius GS, Olbrich E. Quantifying emergent behavior of autonomous robots. <i>Entropy</i>. 2015;17(10):7266-7297. doi:<a href=\"https://doi.org/10.3390/e17107266\">10.3390/e17107266</a>","short":"G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.","ista":"Martius GS, Olbrich E. 2015. Quantifying emergent behavior of autonomous robots. Entropy. 17(10), 7266–7297."},"oa":1,"publist_id":"5495","tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2025-09-23T09:57:45Z","date_created":"2018-12-11T11:53:17Z","abstract":[{"text":"Quantifying behaviors of robots which were generated autonomously from task-independent objective functions is an important prerequisite for objective comparisons of algorithms and movements of animals. The temporal sequence of such a behavior can be considered as a time series and hence complexity measures developed for time series are natural candidates for its quantification. The predictive information and the excess entropy are such complexity measures. They measure the amount of information the past contains about the future and thus quantify the nonrandom structure in the temporal sequence. However, when using these measures for systems with continuous states one has to deal with the fact that their values will depend on the resolution with which the systems states are observed. For deterministic systems both measures will diverge with increasing resolution. We therefore propose a new decomposition of the excess entropy in resolution dependent and resolution independent parts and discuss how they depend on the dimensionality of the dynamics, correlations and the noise level. For the practical estimation we propose to use estimates based on the correlation integral instead of the direct estimation of the mutual information based on next neighbor statistics because the latter allows less control of the scale dependencies. Using our algorithm we are able to show how autonomous learning generates behavior of increasing complexity with increasing learning duration.","lang":"eng"}],"type":"journal_article","oa_version":"Published Version","scopus_import":"1","ddc":["000"],"volume":17,"language":[{"iso":"eng"}],"department":[{"_id":"ChLa"},{"_id":"GaTk"}],"date_published":"2015-10-23T00:00:00Z","external_id":{"isi":["000364216800039"]},"has_accepted_license":"1","publication":"Entropy","ec_funded":1,"intvolume":"        17","day":"23","pubrep_id":"464","page":"7266 - 7297","file":[{"date_created":"2018-12-12T10:12:25Z","file_name":"IST-2016-464-v1+1_entropy-17-07266.pdf","file_id":"4943","date_updated":"2020-07-14T12:45:08Z","file_size":6455007,"checksum":"945d99631a96e0315acb26dc8541dcf9","creator":"system","access_level":"open_access","relation":"main_file","content_type":"application/pdf"}],"year":"2015","isi":1,"corr_author":"1","title":"Quantifying emergent behavior of autonomous robots","publisher":"MDPI","doi":"10.3390/e17107266","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","month":"10","status":"public","author":[{"id":"3A276B68-F248-11E8-B48F-1D18A9856A87","full_name":"Martius, Georg S","first_name":"Georg S","last_name":"Martius"},{"last_name":"Olbrich","first_name":"Eckehard","full_name":"Olbrich, Eckehard"}],"project":[{"grant_number":"291734","call_identifier":"FP7","_id":"25681D80-B435-11E9-9278-68D0E5697425","name":"International IST Postdoc Fellowship Programme"}],"quality_controlled":"1","publication_status":"published","_id":"1655","file_date_updated":"2020-07-14T12:45:08Z","issue":"10"},{"doi":"10.1007/978-3-319-23401-4_8","publisher":"Springer","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","page":"77 - 89","title":"Adaptive moment closure for parameter inference of biochemical reaction networks","year":"2015","isi":1,"publication_status":"published","quality_controlled":"1","_id":"1658","month":"09","status":"public","related_material":{"record":[{"id":"1148","relation":"later_version","status":"public"}]},"project":[{"name":"Quantitative Reactive Modeling","_id":"25EE3708-B435-11E9-9278-68D0E5697425","grant_number":"267989","call_identifier":"FP7"},{"name":"Formal methods for the design and analysis of complex systems","call_identifier":"FWF","grant_number":"Z211","_id":"25F42A32-B435-11E9-9278-68D0E5697425"},{"name":"Rigorous Systems Engineering","call_identifier":"FWF","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"_id":"25681D80-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"291734","name":"International IST Postdoc Fellowship Programme"}],"author":[{"last_name":"Bogomolov","first_name":"Sergiy","full_name":"Bogomolov, Sergiy","id":"369D9A44-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-0686-0365"},{"orcid":"0000−0002−2985−7724","id":"40876CD8-F248-11E8-B48F-1D18A9856A87","full_name":"Henzinger, Thomas A","first_name":"Thomas A","last_name":"Henzinger"},{"first_name":"Andreas","full_name":"Podelski, Andreas","last_name":"Podelski"},{"orcid":"0000-0003-1615-3282","last_name":"Ruess","first_name":"Jakob","id":"4A245D00-F248-11E8-B48F-1D18A9856A87","full_name":"Ruess, Jakob"},{"full_name":"Schilling, Christian","first_name":"Christian","last_name":"Schilling"}],"oa_version":"None","series_title":"Lecture Notes in Computer Science","scopus_import":"1","conference":{"name":"CMSB: Computational Methods in Systems Biology","end_date":"2015-09-18","location":"Nantes, France","start_date":"2015-09-16"},"article_processing_charge":"No","date_created":"2018-12-11T11:53:18Z","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."}],"type":"conference","citation":{"chicago":"Bogomolov, Sergiy, Thomas A Henzinger, Andreas Podelski, Jakob Ruess, and Christian Schilling. “Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks.” Lecture Notes in Computer Science. Springer, 2015. <a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">https://doi.org/10.1007/978-3-319-23401-4_8</a>.","apa":"Bogomolov, S., Henzinger, T. A., Podelski, A., Ruess, J., &#38; 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. <a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">https://doi.org/10.1007/978-3-319-23401-4_8</a>","mla":"Bogomolov, Sergiy, et al. <i>Adaptive Moment Closure for Parameter Inference of Biochemical Reaction Networks</i>. Vol. 9308, Springer, 2015, pp. 77–89, doi:<a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">10.1007/978-3-319-23401-4_8</a>.","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.","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:<a href=\"https://doi.org/10.1007/978-3-319-23401-4_8\">10.1007/978-3-319-23401-4_8</a>","short":"S. Bogomolov, T.A. Henzinger, A. Podelski, J. Ruess, C. Schilling, 9308 (2015) 77–89.","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."},"publist_id":"5492","date_updated":"2025-09-23T07:44:58Z","alternative_title":["LNCS"],"external_id":{"isi":["000366198300008"]},"intvolume":"      9308","ec_funded":1,"day":"01","department":[{"_id":"ToHe"},{"_id":"GaTk"}],"language":[{"iso":"eng"}],"volume":9308,"date_published":"2015-09-01T00:00:00Z"},{"user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1371/journal.pcbi.1004304","publisher":"Public Library of Science","title":"High accuracy decoding of dynamical motion from a large retinal population","isi":1,"year":"2015","pubrep_id":"455","file":[{"file_size":4673930,"date_updated":"2020-07-14T12:45:12Z","file_id":"5212","file_name":"IST-2016-455-v1+1_journal.pcbi.1004304.pdf","date_created":"2018-12-12T10:16:25Z","content_type":"application/pdf","relation":"main_file","access_level":"open_access","creator":"system","checksum":"472b979f3f1cffb37b3e503f085115ca"}],"file_date_updated":"2020-07-14T12:45:12Z","issue":"7","_id":"1697","publication_status":"published","quality_controlled":"1","project":[{"name":"Sensitivity to higher-order statistics in natural scenes","grant_number":"P 25651-N26","call_identifier":"FWF","_id":"254D1A94-B435-11E9-9278-68D0E5697425"}],"author":[{"last_name":"Marre","first_name":"Olivier","full_name":"Marre, Olivier"},{"orcid":"0000-0002-8790-1914","last_name":"Botella Soler","full_name":"Botella Soler, Vicente","id":"421234E8-F248-11E8-B48F-1D18A9856A87","first_name":"Vicente"},{"last_name":"Simmons","first_name":"Kristina","full_name":"Simmons, Kristina"},{"last_name":"Mora","full_name":"Mora, Thierry","first_name":"Thierry"},{"orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","last_name":"Tkacik"},{"full_name":"Berry, Michael","first_name":"Michael","last_name":"Berry"}],"month":"07","status":"public","oa_version":"Published Version","scopus_import":"1","date_created":"2018-12-11T11:53:31Z","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."}],"type":"journal_article","publist_id":"5447","oa":1,"citation":{"short":"O. Marre, V. Botella Soler, K. Simmons, T. Mora, G. Tkačik, M. Berry, PLoS Computational Biology 11 (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.","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. <i>PLoS Computational Biology</i>. 2015;11(7). doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">10.1371/journal.pcbi.1004304</a>","mla":"Marre, Olivier, et al. “High Accuracy Decoding of Dynamical Motion from a Large Retinal Population.” <i>PLoS Computational Biology</i>, vol. 11, no. 7, e1004304, Public Library of Science, 2015, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">10.1371/journal.pcbi.1004304</a>.","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,” <i>PLoS Computational Biology</i>, vol. 11, no. 7. Public Library of Science, 2015.","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.” <i>PLoS Computational Biology</i>. Public Library of Science, 2015. <a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">https://doi.org/10.1371/journal.pcbi.1004304</a>.","apa":"Marre, O., Botella Soler, V., Simmons, K., Mora, T., Tkačik, G., &#38; Berry, M. (2015). High accuracy decoding of dynamical motion from a large retinal population. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1004304\">https://doi.org/10.1371/journal.pcbi.1004304</a>"},"tmp":{"short":"CC BY (4.0)","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","image":"/images/cc_by.png","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"date_updated":"2025-09-23T09:15:57Z","article_number":"e1004304","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_processing_charge":"No","intvolume":"        11","day":"01","publication":"PLoS Computational Biology","external_id":{"isi":["000360620100016"]},"has_accepted_license":"1","date_published":"2015-07-01T00:00:00Z","department":[{"_id":"GaTk"}],"ddc":["570"],"volume":11,"language":[{"iso":"eng"}]},{"project":[{"name":"Sensitivity to higher-order statistics in natural scenes","_id":"254D1A94-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P 25651-N26"}],"author":[{"orcid":"0000-0002-6699-1455","last_name":"Tkacik","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","first_name":"Gasper"},{"last_name":"Mora","full_name":"Mora, Thierry","first_name":"Thierry"},{"full_name":"Marre, Olivier","first_name":"Olivier","last_name":"Marre"},{"full_name":"Amodei, Dario","first_name":"Dario","last_name":"Amodei"},{"first_name":"Stephanie","full_name":"Palmer, Stephanie","last_name":"Palmer"},{"first_name":"Michael","full_name":"Berry Ii, Michael","last_name":"Berry Ii"},{"last_name":"Bialek","full_name":"Bialek, William","first_name":"William"}],"main_file_link":[{"open_access":"1","url":"http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4577210/"}],"status":"public","month":"09","issue":"37","_id":"1701","quality_controlled":"1","publication_status":"published","title":"Thermodynamics and signatures of criticality in a network of neurons","year":"2015","isi":1,"page":"11508 - 11513","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","doi":"10.1073/pnas.1514188112","publisher":"National Academy of Sciences","date_published":"2015-09-15T00:00:00Z","department":[{"_id":"GaTk"}],"volume":112,"language":[{"iso":"eng"}],"day":"15","intvolume":"       112","publication":"PNAS","external_id":{"pmid":["26330611"],"isi":["000361393700038"]},"type":"journal_article","abstract":[{"lang":"eng","text":"The activity of a neural network is defined by patterns of spiking and silence from the individual neurons. Because spikes are (relatively) sparse, patterns of activity with increasing numbers of spikes are less probable, but, with more spikes, the number of possible patterns increases. This tradeoff between probability and numerosity is mathematically equivalent to the relationship between entropy and energy in statistical physics. We construct this relationship for populations of up to N = 160 neurons in a small patch of the vertebrate retina, using a combination of direct and model-based analyses of experiments on the response of this network to naturalistic movies. We see signs of a thermodynamic limit, where the entropy per neuron approaches a smooth function of the energy per neuron as N increases. The form of this function corresponds to the distribution of activity being poised near an unusual kind of critical point. We suggest further tests of criticality, and give a brief discussion of its functional significance. "}],"date_created":"2018-12-11T11:53:33Z","date_updated":"2025-09-23T14:54:27Z","oa":1,"publist_id":"5440","citation":{"ista":"Tkačik G, Mora T, Marre O, Amodei D, Palmer S, Berry Ii M, Bialek W. 2015. Thermodynamics and signatures of criticality in a network of neurons. PNAS. 112(37), 11508–11513.","short":"G. Tkačik, T. Mora, O. Marre, D. Amodei, S. Palmer, M. Berry Ii, W. Bialek, PNAS 112 (2015) 11508–11513.","ama":"Tkačik G, Mora T, Marre O, et al. Thermodynamics and signatures of criticality in a network of neurons. <i>PNAS</i>. 2015;112(37):11508-11513. doi:<a href=\"https://doi.org/10.1073/pnas.1514188112\">10.1073/pnas.1514188112</a>","mla":"Tkačik, Gašper, et al. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” <i>PNAS</i>, vol. 112, no. 37, National Academy of Sciences, 2015, pp. 11508–13, doi:<a href=\"https://doi.org/10.1073/pnas.1514188112\">10.1073/pnas.1514188112</a>.","ieee":"G. Tkačik <i>et al.</i>, “Thermodynamics and signatures of criticality in a network of neurons,” <i>PNAS</i>, vol. 112, no. 37. National Academy of Sciences, pp. 11508–11513, 2015.","apa":"Tkačik, G., Mora, T., Marre, O., Amodei, D., Palmer, S., Berry Ii, M., &#38; Bialek, W. (2015). Thermodynamics and signatures of criticality in a network of neurons. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1514188112\">https://doi.org/10.1073/pnas.1514188112</a>","chicago":"Tkačik, Gašper, Thierry Mora, Olivier Marre, Dario Amodei, Stephanie Palmer, Michael Berry Ii, and William Bialek. “Thermodynamics and Signatures of Criticality in a Network of Neurons.” <i>PNAS</i>. National Academy of Sciences, 2015. <a href=\"https://doi.org/10.1073/pnas.1514188112\">https://doi.org/10.1073/pnas.1514188112</a>."},"acknowledgement":"Research was supported in part by National Science Foundation Grants PHY-1305525, PHY-1451171, and CCF-0939370, by National Institutes of Health Grant R01 EY14196, and by Austrian Science Foundation Grant FWF P25651. Additional support was provided by the\r\nFannie and John Hertz Foundation, by the Swartz Foundation, by the W. M. Keck Foundation, and by the Simons Foundation.","article_processing_charge":"No","pmid":1,"scopus_import":"1","oa_version":"Submitted Version"}]