{"month":"01","intvolume":"       122","project":[{"grant_number":"P28844-B27","call_identifier":"FWF","name":"Biophysics of information processing in gene regulation","_id":"254E9036-B435-11E9-9278-68D0E5697425"},{"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"}],"file":[{"file_id":"18862","checksum":"8dbfc7d495413340225ebfae69b0cf9a","file_name":"2025_PNAS_Sokolowski.pdf","file_size":19073585,"creator":"dernst","success":1,"date_created":"2025-01-20T10:10:04Z","access_level":"open_access","content_type":"application/pdf","date_updated":"2025-01-20T10:10:04Z","relation":"main_file"}],"corr_author":"1","file_date_updated":"2025-01-20T10:10:04Z","article_number":"e2402925121","scopus_import":"1","pmid":1,"year":"2025","status":"public","citation":{"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 of the United States of America. 122(1), e2402925121.","mla":"Sokolowski, Thomas R., et al. “Deriving a Genetic Regulatory Network from an Optimization Principle.” <i>Proceedings of the National Academy of Sciences of the United States of America</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>.","short":"T.R. Sokolowski, T. Gregor, W. Bialek, G. Tkačik, Proceedings of the National Academy of Sciences of the United States of America 122 (2025).","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 of the United States of America</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 of the United States of America</i>, vol. 122, no. 1. National Academy of Sciences, 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 of the United States of America</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 of the United States of America</i>. National Academy of Sciences. <a href=\"https://doi.org/10.1073/pnas.2402925121\">https://doi.org/10.1073/pnas.2402925121</a>"},"tmp":{"short":"CC BY-NC-ND (4.0)","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"},"publisher":"National Academy of Sciences","date_created":"2025-01-19T23:01:50Z","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"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","article_processing_charge":"Yes (in subscription journal)","doi":"10.1073/pnas.2402925121","external_id":{"pmid":["39752518"]},"oa_version":"Published Version","language":[{"iso":"eng"}],"OA_type":"hybrid","author":[{"full_name":"Sokolowski, Thomas R","last_name":"Sokolowski","id":"3E999752-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-1287-3779","first_name":"Thomas R"},{"last_name":"Gregor","full_name":"Gregor, Thomas","first_name":"Thomas"},{"first_name":"William","last_name":"Bialek","full_name":"Bialek, William"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","first_name":"Gašper","full_name":"Tkačik, Gašper","last_name":"Tkačik"}],"_id":"18849","date_updated":"2025-01-20T10:13:39Z","publication":"Proceedings of the National Academy of Sciences of the United States of America","issue":"1","article_type":"original","type":"journal_article","has_accepted_license":"1","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."}],"date_published":"2025-01-07T00:00:00Z","publication_identifier":{"eissn":["1091-6490"],"issn":["0027-8424"]},"license":"https://creativecommons.org/licenses/by-nc-nd/4.0/","volume":122,"department":[{"_id":"GaTk"}],"day":"07","quality_controlled":"1","OA_place":"publisher","oa":1,"publication_status":"published","title":"Deriving a genetic regulatory network from an optimization principle"}