{"citation":{"mla":"Cepeda Humerez, Sarah A., et al. “Estimating Information in Time-Varying Signals.” <i>PLoS Computational Biology</i>, vol. 15, no. 9, Public Library of Science, 2019, p. e1007290, doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">10.1371/journal.pcbi.1007290</a>.","ieee":"S. A. Cepeda Humerez, J. Ruess, and G. Tkačik, “Estimating information in time-varying signals,” <i>PLoS computational biology</i>, vol. 15, no. 9. Public Library of Science, p. e1007290, 2019.","ama":"Cepeda Humerez SA, Ruess J, Tkačik G. Estimating information in time-varying signals. <i>PLoS computational biology</i>. 2019;15(9):e1007290. doi:<a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">10.1371/journal.pcbi.1007290</a>","ista":"Cepeda Humerez SA, Ruess J, Tkačik G. 2019. Estimating information in time-varying signals. PLoS computational biology. 15(9), e1007290.","apa":"Cepeda Humerez, S. A., Ruess, J., &#38; Tkačik, G. (2019). Estimating information in time-varying signals. <i>PLoS Computational Biology</i>. Public Library of Science. <a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">https://doi.org/10.1371/journal.pcbi.1007290</a>","chicago":"Cepeda Humerez, Sarah A, Jakob Ruess, and Gašper Tkačik. “Estimating Information in Time-Varying Signals.” <i>PLoS Computational Biology</i>. Public Library of Science, 2019. <a href=\"https://doi.org/10.1371/journal.pcbi.1007290\">https://doi.org/10.1371/journal.pcbi.1007290</a>.","short":"S.A. Cepeda Humerez, J. Ruess, G. Tkačik, PLoS Computational Biology 15 (2019) e1007290."},"language":[{"iso":"eng"}],"_id":"6900","doi":"10.1371/journal.pcbi.1007290","page":"e1007290","author":[{"first_name":"Sarah A","last_name":"Cepeda Humerez","full_name":"Cepeda Humerez, Sarah A","id":"3DEE19A4-F248-11E8-B48F-1D18A9856A87"},{"orcid":"0000-0003-1615-3282","first_name":"Jakob","last_name":"Ruess","full_name":"Ruess, Jakob"},{"orcid":"0000-0002-6699-1455","id":"3D494DCA-F248-11E8-B48F-1D18A9856A87","full_name":"Tkačik, Gašper","last_name":"Tkačik","first_name":"Gašper"}],"has_accepted_license":"1","publication":"PLoS computational biology","publication_identifier":{"eissn":["15537358"]},"external_id":{"pmid":["31479447"],"isi":["000489741800021"]},"isi":1,"file_date_updated":"2020-07-14T12:47:44Z","ddc":["570"],"project":[{"name":"Biophysics of information processing in gene regulation","_id":"254E9036-B435-11E9-9278-68D0E5697425","grant_number":"P28844-B27","call_identifier":"FWF"}],"volume":15,"intvolume":"        15","year":"2019","issue":"9","date_published":"2019-09-03T00:00:00Z","publication_status":"published","abstract":[{"text":"Across diverse biological systems—ranging from neural networks to intracellular signaling and genetic regulatory networks—the information about changes in the environment is frequently encoded in the full temporal dynamics of the network nodes. A pressing data-analysis challenge has thus been to efficiently estimate the amount of information that these dynamics convey from experimental data. Here we develop and evaluate decoding-based estimation methods to lower bound the mutual information about a finite set of inputs, encoded in single-cell high-dimensional time series data. For biological reaction networks governed by the chemical Master equation, we derive model-based information approximations and analytical upper bounds, against which we benchmark our proposed model-free decoding estimators. In contrast to the frequently-used k-nearest-neighbor estimator, decoding-based estimators robustly extract a large fraction of the available information from high-dimensional trajectories with a realistic number of data samples. We apply these estimators to previously published data on Erk and Ca2+ signaling in mammalian cells and to yeast stress-response, and find that substantial amount of information about environmental state can be encoded by non-trivial response statistics even in stationary signals. We argue that these single-cell, decoding-based information estimates, rather than the commonly-used tests for significant differences between selected population response statistics, provide a proper and unbiased measure for the performance of biological signaling networks.","lang":"eng"}],"department":[{"_id":"GaTk"}],"day":"03","file":[{"checksum":"81bdce1361c9aa8395d6fa635fb6ab47","relation":"main_file","content_type":"application/pdf","creator":"kschuh","access_level":"open_access","file_name":"2019_PLoS_Cepeda-Humerez.pdf","date_updated":"2020-07-14T12:47:44Z","file_id":"6925","date_created":"2019-10-01T10:53:45Z","file_size":3081855}],"type":"journal_article","tmp":{"image":"/images/cc_by.png","name":"Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)","short":"CC BY (4.0)","legal_code_url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"pmid":1,"date_updated":"2023-09-07T12:55:21Z","month":"09","article_processing_charge":"No","related_material":{"record":[{"status":"public","relation":"part_of_dissertation","id":"6473"}]},"status":"public","title":"Estimating information in time-varying signals","quality_controlled":"1","date_created":"2019-09-22T22:00:37Z","user_id":"4359f0d1-fa6c-11eb-b949-802e58b17ae8","oa":1,"oa_version":"Published Version","scopus_import":"1","publisher":"Public Library of Science"}