{"month":"06","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","year":"2015","day":"15","publisher":"American Institute of Physics","department":[{"_id":"GaTk"}],"quality_controlled":"1","title":"Optimizing information flow in small genetic networks. IV. Spatial coupling","main_file_link":[{"url":"http://arxiv.org/abs/1501.04015","open_access":"1"}],"intvolume":"        91","citation":{"short":"T.R. Sokolowski, G. Tkačik, Physical Review E Statistical Nonlinear and Soft Matter Physics 91 (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>.","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>","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.","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>","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."},"date_published":"2015-06-15T00:00:00Z","oa":1,"_id":"1940","scopus_import":1,"doi":"10.1103/PhysRevE.91.062710","type":"journal_article","issue":"6","publication_status":"published","article_number":"062710","language":[{"iso":"eng"}],"publication":"Physical Review E Statistical Nonlinear and Soft Matter Physics","date_created":"2018-12-11T11:54:49Z","status":"public","author":[{"full_name":"Sokolowski, Thomas R","id":"3E999752-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-1287-3779","first_name":"Thomas R","last_name":"Sokolowski"},{"last_name":"Tkacik","orcid":"0000-0002-6699-1455","first_name":"Gasper","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkacik, Gasper"}],"oa_version":"Preprint","publist_id":"5145","date_updated":"2021-01-12T06:54:13Z","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"}],"volume":91}