[{"has_accepted_license":"1","intvolume":"       122","OA_type":"hybrid","quality_controlled":"1","publication":"Proceedings of the National Academy of Sciences","project":[{"call_identifier":"FWF","grant_number":"P28844-B27","_id":"254E9036-B435-11E9-9278-68D0E5697425","name":"Biophysics of information processing in gene regulation"},{"grant_number":"101118866","name":"Transcription in 4D: the dynamic interplay between chromatin architecture and gene expression in developing pseudo-embryos","_id":"7bfe6a29-9f16-11ee-852c-c0da5e2045d9"},{"_id":"2665AAFE-B435-11E9-9278-68D0E5697425","name":"Can evolution minimize spurious signaling crosstalk to reach optimal performance?","grant_number":"RGP0034/2018"}],"issue":"1","scopus_import":"1","type":"journal_article","isi":1,"status":"public","citation":{"short":"T.R. Sokolowski, T. Gregor, W. Bialek, G. Tkačik, Proceedings of the National Academy of Sciences 122 (2025).","chicago":"Sokolowski, Thomas R, Thomas Gregor, William Bialek, and Gašper Tkačik. “Deriving a Genetic Regulatory Network from an Optimization Principle.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2025. <a href=\"https://doi.org/10.1073/pnas.2402925121\">https://doi.org/10.1073/pnas.2402925121</a>.","apa":"Sokolowski, T. R., Gregor, T., Bialek, W., &#38; Tkačik, G. (2025). Deriving a genetic regulatory network from an optimization principle. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2402925121\">https://doi.org/10.1073/pnas.2402925121</a>","ama":"Sokolowski TR, Gregor T, Bialek W, Tkačik G. Deriving a genetic regulatory network from an optimization principle. <i>Proceedings of the National Academy of Sciences</i>. 2025;122(1). doi:<a href=\"https://doi.org/10.1073/pnas.2402925121\">10.1073/pnas.2402925121</a>","ieee":"T. R. Sokolowski, T. Gregor, W. Bialek, and G. Tkačik, “Deriving a genetic regulatory network from an optimization principle,” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 1. National Academy of Sciences, 2025.","mla":"Sokolowski, Thomas R., et al. “Deriving a Genetic Regulatory Network from an Optimization Principle.” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 1, e2402925121, National Academy of Sciences, 2025, doi:<a href=\"https://doi.org/10.1073/pnas.2402925121\">10.1073/pnas.2402925121</a>.","ista":"Sokolowski TR, Gregor T, Bialek W, Tkačik G. 2025. Deriving a genetic regulatory network from an optimization principle. Proceedings of the National Academy of Sciences. 122(1), e2402925121."},"month":"01","date_published":"2025-01-07T00:00:00Z","department":[{"_id":"GaTk"}],"acknowledgement":"We thank Nicholas H. Barton for his comments on the manuscript, Benjamin Zoller for helpful discussions, and Aleksandra Walczak and Curtis Callan for early collaborations that shaped this work. Special thanks to Eric F. Wieschaus for many persistently inspiring conversations. This work was supported in part by the Human Frontiers Science Program; the Austrian Science Fund (FWF P28844); by the European Research Council grant DynaTrans (101118866); by U.S. NSF, through the Center for the Physics of Biological Function (PHY–1734030); by NIH Grants R01GM097275, U01DA047730, and U01DK127429; by the John Simon Guggenheim Memorial Foundation; and by the LOEWE priority program “Center for Multiscale Modeling in Life Sciences” (CMMS), sponsored by the Hessian Ministry for Science and Research, Arts and Culture (HMWK).","ddc":["570"],"title":"Deriving a genetic regulatory network from an optimization principle","file_date_updated":"2025-01-20T10:10:04Z","corr_author":"1","article_type":"original","year":"2025","article_processing_charge":"Yes (in subscription journal)","pmid":1,"date_created":"2025-01-19T23:01:50Z","publication_status":"published","file":[{"file_size":19073585,"content_type":"application/pdf","date_updated":"2025-01-20T10:10:04Z","date_created":"2025-01-20T10:10:04Z","access_level":"open_access","file_name":"2025_PNAS_Sokolowski.pdf","relation":"main_file","checksum":"8dbfc7d495413340225ebfae69b0cf9a","file_id":"18862","success":1,"creator":"dernst"}],"publication_identifier":{"issn":["0027-8424"],"eissn":["1091-6490"]},"fulldoi":"https://doi.org/10.1073/pnas.2402925121","publisher":"National Academy of Sciences","oa_version":"Published Version","day":"07","volume":122,"oa":1,"external_id":{"pmid":["39752518"],"isi":["001392772400001"]},"doi":"10.1073/pnas.2402925121","article_number":"e2402925121","language":[{"iso":"eng"}],"abstract":[{"lang":"eng","text":"Many biological systems operate near the physical limits to their performance, suggesting that aspects of their behavior and underlying mechanisms could be derived from optimization principles. However, such principles have often been applied only in simplified models. Here, we explore a detailed mechanistic model of the gap gene network in the Drosophila embryo, optimizing its 50+ parameters to maximize the information that gene expression levels provide about nuclear positions. This optimization is conducted under realistic constraints, such as limits on the number of available molecules. Remarkably, the optimal networks we derive closely match the architecture and spatial gene expression profiles observed in the real organism. Our framework quantifies the tradeoffs involved in maximizing functional performance and allows for the exploration of alternative network configurations, addressing the question of which features are necessary and which are contingent. Our results suggest that multiple solutions to the optimization problem might exist across closely related organisms, offering insights into the evolution of gene regulatory networks."}],"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","OA_place":"publisher","tmp":{"name":"Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)","legal_code_url":"https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode","image":"/images/cc_by_nc_nd.png","short":"CC BY-NC-ND (4.0)"},"author":[{"first_name":"Thomas R","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","last_name":"Sokolowski"},{"last_name":"Gregor","first_name":"Thomas","full_name":"Gregor, Thomas"},{"full_name":"Bialek, William","first_name":"William","last_name":"Bialek"},{"last_name":"Tkačik","full_name":"Tkačik, Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gašper"}],"_id":"18849","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_updated":"2026-02-16T12:26:51Z"},{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.1101/848374"}],"article_processing_charge":"No","corr_author":"1","year":"2021","pmid":1,"related_material":{"link":[{"relation":"press_release","description":"News on IST Homepage","url":"https://ist.ac.at/en/news/can-evolution-be-predicted/"}],"record":[{"status":"public","relation":"dissertation_contains","id":"15020"}]},"date_created":"2020-02-28T11:00:12Z","publication_status":"published","month":"04","acknowledgement":"The authors thank Dario Ringach for providing the V1 receptive fields and Olivier Marre for providing the retinal receptive fields. W.M. was funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 754411. M.H. was funded in part by Human Frontiers Science grant no. HFSP RGP0032/2018.","department":[{"_id":"GaTk"}],"title":"Statistical analysis and optimality of neural systems","date_published":"2021-04-07T00:00:00Z","isi":1,"type":"journal_article","citation":{"ista":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. 2021. Statistical analysis and optimality of neural systems. Neuron. 109(7), 1227–1241.e5.","mla":"Mlynarski, Wiktor F., et al. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>, vol. 109, no. 7, Cell Press, 2021, p. 1227–1241.e5, doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>.","ieee":"W. F. Mlynarski, M. Hledik, T. R. Sokolowski, and G. Tkačik, “Statistical analysis and optimality of neural systems,” <i>Neuron</i>, vol. 109, no. 7. Cell Press, p. 1227–1241.e5, 2021.","ama":"Mlynarski WF, Hledik M, Sokolowski TR, Tkačik G. Statistical analysis and optimality of neural systems. <i>Neuron</i>. 2021;109(7):1227-1241.e5. doi:<a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">10.1016/j.neuron.2021.01.020</a>","apa":"Mlynarski, W. F., Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2021). Statistical analysis and optimality of neural systems. <i>Neuron</i>. Cell Press. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>","chicago":"Mlynarski, Wiktor F, Michal Hledik, Thomas R Sokolowski, and Gašper Tkačik. “Statistical Analysis and Optimality of Neural Systems.” <i>Neuron</i>. Cell Press, 2021. <a href=\"https://doi.org/10.1016/j.neuron.2021.01.020\">https://doi.org/10.1016/j.neuron.2021.01.020</a>.","short":"W.F. Mlynarski, M. Hledik, T.R. Sokolowski, G. Tkačik, Neuron 109 (2021) 1227–1241.e5."},"status":"public","intvolume":"       109","publication":"Neuron","project":[{"_id":"260C2330-B435-11E9-9278-68D0E5697425","name":"ISTplus - Postdoctoral Fellowships","call_identifier":"H2020","grant_number":"754411"}],"quality_controlled":"1","scopus_import":"1","issue":"7","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"7553","author":[{"id":"358A453A-F248-11E8-B48F-1D18A9856A87","full_name":"Mlynarski, Wiktor F","first_name":"Wiktor F","last_name":"Mlynarski"},{"full_name":"Hledik, Michal","id":"4171253A-F248-11E8-B48F-1D18A9856A87","first_name":"Michal","last_name":"Hledik"},{"orcid":"0000-0002-1287-3779","id":"3E999752-F248-11E8-B48F-1D18A9856A87","full_name":"Sokolowski, Thomas R","first_name":"Thomas R","last_name":"Sokolowski"},{"last_name":"Tkačik","first_name":"Gašper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","orcid":"0000-0002-6699-1455"}],"date_updated":"2026-04-07T12:59:24Z","doi":"10.1016/j.neuron.2021.01.020","ec_funded":1,"language":[{"iso":"eng"}],"abstract":[{"text":"Normative theories and statistical inference provide complementary approaches for the study of biological systems. A normative theory postulates that organisms have adapted to efficiently solve essential tasks, and proceeds to mathematically work out testable consequences of such optimality; parameters that maximize the hypothesized organismal function can be derived ab initio, without reference to experimental data. In contrast, statistical inference focuses on efficient utilization of data to learn model parameters, without reference to any a priori notion of biological function, utility, or fitness. Traditionally, these two approaches were developed independently and applied separately. Here we unify them in a coherent Bayesian framework that embeds a normative theory into a family of maximum-entropy “optimization priors.” This family defines a smooth interpolation between a data-rich inference regime (characteristic of “bottom-up” statistical models), and a data-limited ab inito prediction regime (characteristic of “top-down” normative theory). We demonstrate the applicability of our framework using data from the visual cortex, and argue that the flexibility it affords is essential to address a number of fundamental challenges relating to inference and prediction in complex, high-dimensional biological problems.","lang":"eng"}],"volume":109,"external_id":{"pmid":["33592180"],"isi":["000637809600006"]},"page":"1227-1241.e5","oa":1,"publisher":"Cell Press","fulldoi":"https://doi.org/10.1016/j.neuron.2021.01.020","day":"07","oa_version":"Preprint"},{"date_published":"2019-08-01T00:00:00Z","title":"A tight upper bound on mutual information","department":[{"_id":"GaTk"}],"month":"08","publication_identifier":{"isbn":["9781538669006"]},"publication_status":"published","date_created":"2020-03-22T23:00:47Z","related_material":{"record":[{"status":"public","relation":"dissertation_contains","id":"15020"}]},"year":"2019","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1812.01475"}],"article_processing_charge":"No","scopus_import":"1","quality_controlled":"1","project":[{"call_identifier":"H2020","grant_number":"665385","_id":"2564DBCA-B435-11E9-9278-68D0E5697425","name":"International IST Doctoral Program"}],"publication":"IEEE Information Theory Workshop, ITW 2019","status":"public","citation":{"ieee":"M. Hledik, T. R. Sokolowski, and G. Tkačik, “A tight upper bound on mutual information,” in <i>IEEE Information Theory Workshop, ITW 2019</i>, Visby, Sweden, 2019.","mla":"Hledik, Michal, et al. “A Tight Upper Bound on Mutual Information.” <i>IEEE Information Theory Workshop, ITW 2019</i>, 8989292, IEEE, 2019, doi:<a href=\"https://doi.org/10.1109/ITW44776.2019.8989292\">10.1109/ITW44776.2019.8989292</a>.","ista":"Hledik M, Sokolowski TR, Tkačik G. 2019. A tight upper bound on mutual information. IEEE Information Theory Workshop, ITW 2019. Information Theory Workshop, 8989292.","apa":"Hledik, M., Sokolowski, T. R., &#38; Tkačik, G. (2019). A tight upper bound on mutual information. In <i>IEEE Information Theory Workshop, ITW 2019</i>. Visby, Sweden: IEEE. <a href=\"https://doi.org/10.1109/ITW44776.2019.8989292\">https://doi.org/10.1109/ITW44776.2019.8989292</a>","ama":"Hledik M, Sokolowski TR, Tkačik G. A tight upper bound on mutual information. In: <i>IEEE Information Theory Workshop, ITW 2019</i>. IEEE; 2019. doi:<a href=\"https://doi.org/10.1109/ITW44776.2019.8989292\">10.1109/ITW44776.2019.8989292</a>","short":"M. Hledik, T.R. Sokolowski, G. Tkačik, in:, IEEE Information Theory Workshop, ITW 2019, IEEE, 2019.","chicago":"Hledik, Michal, Thomas R Sokolowski, and Gašper Tkačik. “A Tight Upper Bound on Mutual Information.” In <i>IEEE Information Theory Workshop, ITW 2019</i>. IEEE, 2019. <a href=\"https://doi.org/10.1109/ITW44776.2019.8989292\">https://doi.org/10.1109/ITW44776.2019.8989292</a>."},"type":"conference","isi":1,"abstract":[{"text":"We derive a tight lower bound on equivocation (conditional entropy), or equivalently a tight upper bound on mutual information between a signal variable and channel outputs. The bound is in terms of the joint distribution of the signals and maximum a posteriori decodes (most probable signals given channel output). As part of our derivation, we describe the key properties of the distribution of signals, channel outputs and decodes, that minimizes equivocation and maximizes mutual information. This work addresses a problem in data analysis, where mutual information between signals and decodes is sometimes used to lower bound the mutual information between signals and channel outputs. Our result provides a corresponding upper bound.","lang":"eng"}],"language":[{"iso":"eng"}],"article_number":"8989292","ec_funded":1,"conference":{"name":"Information Theory Workshop","start_date":"2019-08-25","end_date":"2019-08-28","location":"Visby, Sweden"},"doi":"10.1109/ITW44776.2019.8989292","date_updated":"2026-04-07T12:59:24Z","arxiv":1,"_id":"7606","author":[{"last_name":"Hledik","first_name":"Michal","full_name":"Hledik, Michal","id":"4171253A-F248-11E8-B48F-1D18A9856A87"},{"id":"3E999752-F248-11E8-B48F-1D18A9856A87","full_name":"Sokolowski, Thomas R","orcid":"0000-0002-1287-3779","first_name":"Thomas R","last_name":"Sokolowski"},{"last_name":"Tkačik","orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","first_name":"Gašper"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","oa_version":"Preprint","day":"01","fulldoi":"https://doi.org/10.1109/ITW44776.2019.8989292","publisher":"IEEE","oa":1,"external_id":{"arxiv":["1812.01475"],"isi":["000540384500015"]}},{"day":"07","oa_version":"Preprint","fulldoi":"https://doi.org/10.1063/1.5064867","publisher":"AIP Publishing","oa":1,"external_id":{"arxiv":["1708.09364"],"isi":["000458109300009"]},"volume":150,"language":[{"iso":"eng"}],"article_number":"054108","abstract":[{"lang":"eng","text":"Biochemical reactions often occur at low copy numbers but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. While particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them typically imply trade-offs between computational efficiency and biochemical accuracy. eGFRD (enhanced Green’s Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M ≤ N analytically tractable one- and two-particle systems; the analytical solutions (Green’s functions) then are used to implement an event-driven particle-based scheme that allows particles to make large jumps in time and space while retaining access to their state variables at arbitrary simulation times. Here we present “eGFRD2,” a new eGFRD version that implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on 2D planes representing membranes, and on 1D elongated cylinders representative of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion used to model active transport. We find that, for low particle densities, eGFRD2 is up to 6 orders of magnitude faster than conventional Brownian dynamics. We exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1 gradient formation, which involves 3D diffusion, active transport on microtubules, and autophosphorylation on the membrane, confirming recent experimental and theoretical results on this system to hold under genuinely stochastic conditions."}],"doi":"10.1063/1.5064867","arxiv":1,"date_updated":"2023-09-06T14:59:28Z","author":[{"last_name":"Sokolowski","orcid":"0000-0002-1287-3779","id":"3E999752-F248-11E8-B48F-1D18A9856A87","full_name":"Sokolowski, Thomas R","first_name":"Thomas R"},{"first_name":"Joris","full_name":"Paijmans, Joris","last_name":"Paijmans"},{"last_name":"Bossen","first_name":"Laurens","full_name":"Bossen, Laurens"},{"full_name":"Miedema, Thomas","first_name":"Thomas","last_name":"Miedema"},{"first_name":"Martijn","full_name":"Wehrens, Martijn","last_name":"Wehrens"},{"full_name":"Becker, Nils B.","first_name":"Nils B.","last_name":"Becker"},{"last_name":"Kaizu","full_name":"Kaizu, Kazunari","first_name":"Kazunari"},{"full_name":"Takahashi, Koichi","first_name":"Koichi","last_name":"Takahashi"},{"last_name":"Dogterom","first_name":"Marileen","full_name":"Dogterom, Marileen"},{"last_name":"ten Wolde","first_name":"Pieter Rein","full_name":"ten Wolde, Pieter Rein"}],"_id":"7422","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","quality_controlled":"1","publication":"The Journal of Chemical Physics","issue":"5","intvolume":"       150","status":"public","citation":{"short":"T.R. Sokolowski, J. Paijmans, L. Bossen, T. Miedema, M. Wehrens, N.B. Becker, K. Kaizu, K. Takahashi, M. Dogterom, P.R. ten Wolde, The Journal of Chemical Physics 150 (2019).","chicago":"Sokolowski, Thomas R, Joris Paijmans, Laurens Bossen, Thomas Miedema, Martijn Wehrens, Nils B. Becker, Kazunari Kaizu, Koichi Takahashi, Marileen Dogterom, and Pieter Rein ten Wolde. “EGFRD in All Dimensions.” <i>The Journal of Chemical Physics</i>. AIP Publishing, 2019. <a href=\"https://doi.org/10.1063/1.5064867\">https://doi.org/10.1063/1.5064867</a>.","apa":"Sokolowski, T. R., Paijmans, J., Bossen, L., Miedema, T., Wehrens, M., Becker, N. B., … ten Wolde, P. R. (2019). eGFRD in all dimensions. <i>The Journal of Chemical Physics</i>. AIP Publishing. <a href=\"https://doi.org/10.1063/1.5064867\">https://doi.org/10.1063/1.5064867</a>","ama":"Sokolowski TR, Paijmans J, Bossen L, et al. eGFRD in all dimensions. <i>The Journal of Chemical Physics</i>. 2019;150(5). doi:<a href=\"https://doi.org/10.1063/1.5064867\">10.1063/1.5064867</a>","ieee":"T. R. Sokolowski <i>et al.</i>, “eGFRD in all dimensions,” <i>The Journal of Chemical Physics</i>, vol. 150, no. 5. AIP Publishing, 2019.","mla":"Sokolowski, Thomas R., et al. “EGFRD in All Dimensions.” <i>The Journal of Chemical Physics</i>, vol. 150, no. 5, 054108, AIP Publishing, 2019, doi:<a href=\"https://doi.org/10.1063/1.5064867\">10.1063/1.5064867</a>.","ista":"Sokolowski TR, Paijmans J, Bossen L, Miedema T, Wehrens M, Becker NB, Kaizu K, Takahashi K, Dogterom M, ten Wolde PR. 2019. eGFRD in all dimensions. The Journal of Chemical Physics. 150(5), 054108."},"type":"journal_article","isi":1,"date_published":"2019-02-07T00:00:00Z","department":[{"_id":"GaTk"}],"title":"eGFRD in all dimensions","month":"02","date_created":"2020-01-30T10:34:36Z","publication_identifier":{"eissn":["1089-7690"],"issn":["0021-9606"]},"publication_status":"published","article_type":"original","year":"2019","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1708.09364"}]},{"date_published":"2016-02-16T00:00:00Z","department":[{"_id":"GaTk"}],"acknowledgement":"We thank Sophie Martin, Ken Sawin, Stephen Huisman,\r\nand Damian Brunner for strains; Julianne\r\nTeapal, Marcel Janson, Sergio Rincon,\r\nand Phong Tran for technical assistance; Andrew Mugler and Bela Mulder for\r\ndiscussions; and Sander Tans, Phong Tran,\r\nand Anne Paoletti for critical reading\r\nof the manuscript. This work is part of the research program of the\r\n“\r\nStichting\r\nvoor Fundamenteel Onderzoek de Materie,\r\n”\r\nwhich is financially supported by\r\nthe\r\n“\r\nNederlandse organisatie voor Wete\r\nnschappelijk Onderzoek (NWO).\r\n”","title":"Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells","month":"02","date_created":"2018-12-11T11:50:55Z","publication_status":"published","year":"2016","main_file_link":[{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4763754/","open_access":"1"}],"article_processing_charge":"No","quality_controlled":"1","publication":"PNAS","issue":"7","scopus_import":"1","intvolume":"       113","status":"public","citation":{"apa":"Recouvreux, P., Sokolowski, T. R., Grammoustianou, A., Tenwolde, P., &#38; Dogterom, M. (2016). Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. <i>PNAS</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.1419248113\">https://doi.org/10.1073/pnas.1419248113</a>","ama":"Recouvreux P, Sokolowski TR, Grammoustianou A, Tenwolde P, Dogterom M. Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. <i>PNAS</i>. 2016;113(7):1811-1816. doi:<a href=\"https://doi.org/10.1073/pnas.1419248113\">10.1073/pnas.1419248113</a>","ieee":"P. Recouvreux, T. R. Sokolowski, A. Grammoustianou, P. Tenwolde, and M. Dogterom, “Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells,” <i>PNAS</i>, vol. 113, no. 7. National Academy of Sciences, pp. 1811–1816, 2016.","mla":"Recouvreux, Pierre, et al. “Chimera Proteins with Affinity for Membranes and Microtubule Tips Polarize in the Membrane of Fission Yeast Cells.” <i>PNAS</i>, vol. 113, no. 7, National Academy of Sciences, 2016, pp. 1811–16, doi:<a href=\"https://doi.org/10.1073/pnas.1419248113\">10.1073/pnas.1419248113</a>.","ista":"Recouvreux P, Sokolowski TR, Grammoustianou A, Tenwolde P, Dogterom M. 2016. Chimera proteins with affinity for membranes and microtubule tips polarize in the membrane of fission yeast cells. PNAS. 113(7), 1811–1816.","chicago":"Recouvreux, Pierre, Thomas R Sokolowski, Aristea Grammoustianou, Pieter Tenwolde, and Marileen Dogterom. “Chimera Proteins with Affinity for Membranes and Microtubule Tips Polarize in the Membrane of Fission Yeast Cells.” <i>PNAS</i>. National Academy of Sciences, 2016. <a href=\"https://doi.org/10.1073/pnas.1419248113\">https://doi.org/10.1073/pnas.1419248113</a>.","short":"P. Recouvreux, T.R. Sokolowski, A. Grammoustianou, P. Tenwolde, M. Dogterom, PNAS 113 (2016) 1811–1816."},"type":"journal_article","isi":1,"language":[{"iso":"eng"}],"abstract":[{"text":"Cell polarity refers to a functional spatial organization of proteins that is crucial for the control of essential cellular processes such as growth and division. To establish polarity, cells rely on elaborate regulation networks that control the distribution of proteins at the cell membrane. In fission yeast cells, a microtubule-dependent network has been identified that polarizes the distribution of signaling proteins that restricts growth to cell ends and targets the cytokinetic machinery to the middle of the cell. Although many molecular components have been shown to play a role in this network, it remains unknown which molecular functionalities are minimally required to establish a polarized protein distribution in this system. Here we show that a membrane-binding protein fragment, which distributes homogeneously in wild-type fission yeast cells, can be made to concentrate at cell ends by attaching it to a cytoplasmic microtubule end-binding protein. This concentration results in a polarized pattern of chimera proteins with a spatial extension that is very reminiscent of natural polarity patterns in fission yeast. However, chimera levels fluctuate in response to microtubule dynamics, and disruption of microtubules leads to disappearance of the pattern. Numerical simulations confirm that the combined functionality of membrane anchoring and microtubule tip affinity is in principle sufficient to create polarized patterns. Our chimera protein may thus represent a simple molecular functionality that is able to polarize the membrane, onto which additional layers of molecular complexity may be built to provide the temporal robustness that is typical of natural polarity patterns.","lang":"eng"}],"doi":"10.1073/pnas.1419248113","date_updated":"2025-09-22T09:15:17Z","author":[{"first_name":"Pierre","full_name":"Recouvreux, Pierre","last_name":"Recouvreux"},{"last_name":"Sokolowski","orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R"},{"first_name":"Aristea","full_name":"Grammoustianou, Aristea","last_name":"Grammoustianou"},{"last_name":"Tenwolde","full_name":"Tenwolde, Pieter","first_name":"Pieter"},{"first_name":"Marileen","full_name":"Dogterom, Marileen","last_name":"Dogterom"}],"_id":"1244","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","day":"16","oa_version":"Submitted Version","publist_id":"6085","publisher":"National Academy of Sciences","fulldoi":"https://doi.org/10.1073/pnas.1419248113","page":"1811 - 1816","oa":1,"external_id":{"isi":["000370220000046"]},"volume":113},{"date_created":"2018-12-11T11:50:54Z","publication_status":"published","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"https://arxiv.org/abs/1507.02562"}],"year":"2016","department":[{"_id":"GaTk"}],"acknowledgement":"We thank T. Gregor, A. Prochaintz, and others for\r\nhelpful discussions. This work was supported in part by\r\nGrants No. PHY-1305525 and No. CCF-0939370 from the\r\nUS National Science Foundation and by the W.M. Keck\r\nFoundation. A.M.W. acknowledges the support by European\r\nResearch Council (ERC) Grant No. MCCIG PCIG10–GA-\r\n2011–303561. G.T. and T.R.S. were supported by Austrian\r\nScience Fund (FWF) Grant No. P28844S.","title":"Extending the dynamic range of transcription factor action by translational regulation","date_published":"2016-02-04T00:00:00Z","month":"02","citation":{"ieee":"T. R. Sokolowski, A. Walczak, W. Bialek, and G. Tkačik, “Extending the dynamic range of transcription factor action by translational regulation,” <i>Physical Review E</i>, vol. 93, no. 2. American Physical Society, 2016.","mla":"Sokolowski, Thomas R., et al. “Extending the Dynamic Range of Transcription Factor Action by Translational Regulation.” <i>Physical Review E</i>, vol. 93, no. 2, 022404, American Physical Society, 2016, doi:<a href=\"https://doi.org/10.1103/PhysRevE.93.022404\">10.1103/PhysRevE.93.022404</a>.","ista":"Sokolowski TR, Walczak A, Bialek W, Tkačik G. 2016. Extending the dynamic range of transcription factor action by translational regulation. Physical Review E. 93(2), 022404.","apa":"Sokolowski, T. R., Walczak, A., Bialek, W., &#38; Tkačik, G. (2016). Extending the dynamic range of transcription factor action by translational regulation. <i>Physical Review E</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.93.022404\">https://doi.org/10.1103/PhysRevE.93.022404</a>","ama":"Sokolowski TR, Walczak A, Bialek W, Tkačik G. Extending the dynamic range of transcription factor action by translational regulation. <i>Physical Review E</i>. 2016;93(2). doi:<a href=\"https://doi.org/10.1103/PhysRevE.93.022404\">10.1103/PhysRevE.93.022404</a>","short":"T.R. Sokolowski, A. Walczak, W. Bialek, G. Tkačik, Physical Review E 93 (2016).","chicago":"Sokolowski, Thomas R, Aleksandra Walczak, William Bialek, and Gašper Tkačik. “Extending the Dynamic Range of Transcription Factor Action by Translational Regulation.” <i>Physical Review E</i>. American Physical Society, 2016. <a href=\"https://doi.org/10.1103/PhysRevE.93.022404\">https://doi.org/10.1103/PhysRevE.93.022404</a>."},"status":"public","isi":1,"type":"journal_article","project":[{"_id":"254E9036-B435-11E9-9278-68D0E5697425","name":"Biophysics of information processing in gene regulation","call_identifier":"FWF","grant_number":"P28844-B27"}],"quality_controlled":"1","publication":"Physical Review E","issue":"2","scopus_import":"1","intvolume":"        93","date_updated":"2026-08-12T14:28:33Z","arxiv":1,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","author":[{"last_name":"Sokolowski","id":"3E999752-F248-11E8-B48F-1D18A9856A87","full_name":"Sokolowski, Thomas R","orcid":"0000-0002-1287-3779","first_name":"Thomas R"},{"last_name":"Walczak","first_name":"Aleksandra","full_name":"Walczak, Aleksandra"},{"last_name":"Bialek","full_name":"Bialek, William","first_name":"William"},{"id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper","orcid":"0000-0002-6699-1455","first_name":"Gasper","last_name":"Tkacik"}],"_id":"1242","language":[{"iso":"eng"}],"article_number":"022404","abstract":[{"lang":"eng","text":"A crucial step in the regulation of gene expression is binding of transcription factor (TF) proteins to regulatory sites along the DNA. But transcription factors act at nanomolar concentrations, and noise due to random arrival of these molecules at their binding sites can severely limit the precision of regulation. Recent work on the optimization of information flow through regulatory networks indicates that the lower end of the dynamic range of concentrations is simply inaccessible, overwhelmed by the impact of this noise. Motivated by the behavior of homeodomain proteins, such as the maternal morphogen Bicoid in the fruit fly embryo, we suggest a scheme in which transcription factors also act as indirect translational regulators, binding to the mRNA of other regulatory proteins. Intuitively, each mRNA molecule acts as an independent sensor of the input concentration, and averaging over these multiple sensors reduces the noise. We analyze information flow through this scheme and identify conditions under which it outperforms direct transcriptional regulation. Our results suggest that the dual role of homeodomain proteins is not just a historical accident, but a solution to a crucial physics problem in the regulation of gene expression."}],"doi":"10.1103/PhysRevE.93.022404","external_id":{"isi":["000369439100005"],"arxiv":["1507.02562"]},"oa":1,"volume":93,"publist_id":"6088","oa_version":"Preprint","day":"04","publisher":"American Physical Society","fulldoi":"https://doi.org/10.1103/PhysRevE.93.022404"},{"month":"06","department":[{"_id":"GaTk"}],"title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","date_published":"2015-06-15T00:00:00Z","article_processing_charge":"No","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1501.04015"}],"corr_author":"1","year":"2015","date_created":"2018-12-11T11:54:49Z","publication_status":"published","intvolume":"        91","publication":"Physical Review E","quality_controlled":"1","issue":"6","scopus_import":"1","isi":1,"type":"journal_article","citation":{"ieee":"T. R. Sokolowski and G. Tkačik, “Optimizing information flow in small genetic networks. IV. Spatial coupling,” <i>Physical Review E</i>, vol. 91, no. 6. American Physical Society, 2015.","ista":"Sokolowski TR, Tkačik G. 2015. Optimizing information flow in small genetic networks. IV. Spatial coupling. Physical Review E. 91(6), 062710.","mla":"Sokolowski, Thomas R., and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E</i>, vol. 91, no. 6, 062710, American Physical Society, 2015, doi:<a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">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</i>. American Physical Society. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>","ama":"Sokolowski TR, Tkačik G. Optimizing information flow in small genetic networks. IV. Spatial coupling. <i>Physical Review E</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 91 (2015).","chicago":"Sokolowski, Thomas R, and Gašper Tkačik. “Optimizing Information Flow in Small Genetic Networks. IV. Spatial Coupling.” <i>Physical Review E</i>. American Physical Society, 2015. <a href=\"https://doi.org/10.1103/PhysRevE.91.062710\">https://doi.org/10.1103/PhysRevE.91.062710</a>."},"status":"public","doi":"10.1103/PhysRevE.91.062710","language":[{"iso":"eng"}],"article_number":"062710","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"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","_id":"1940","author":[{"orcid":"0000-0002-1287-3779","full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","first_name":"Thomas R","last_name":"Sokolowski"},{"last_name":"Tkacik","full_name":"Tkacik, Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-6699-1455","first_name":"Gasper"}],"date_updated":"2026-08-12T14:28:08Z","arxiv":1,"fulldoi":"https://doi.org/10.1103/PhysRevE.91.062710","publisher":"American Physical Society","publist_id":"5145","oa_version":"Preprint","day":"15","volume":91,"external_id":{"isi":["000356131600006"],"arxiv":["1501.04015"]},"oa":1}]