--- _id: '6473' abstract: - lang: eng text: "Single cells are constantly interacting with their environment and each other, more importantly, the accurate perception of environmental cues is crucial for growth, survival, and reproduction. This communication between cells and their environment can be formalized in mathematical terms and be quantified as the information flow between them, as prescribed by information theory. \r\nThe recent availability of real–time dynamical patterns of signaling molecules in single cells has allowed us to identify encoding about the identity of the environment in the time–series. However, efficient estimation of the information transmitted by these signals has been a data–analysis challenge due to the high dimensionality of the trajectories and the limited number of samples. In the first part of this thesis, we develop and evaluate decoding–based estimation methods to lower bound the mutual information and derive model–based precise information estimates for biological reaction networks governed by the chemical master equation. This is followed by applying the decoding-based methods to study the intracellular representation of extracellular changes in budding yeast, by observing the transient dynamics of nuclear translocation of 10 transcription factors in response to 3 stress conditions. Additionally, we apply these estimators to previously published data on ERK and Ca2+ signaling and yeast stress response. We argue that this single cell decoding-based measure of information provides an unbiased, quantitative and interpretable measure for the fidelity of biological signaling processes. \r\nFinally, in the last section, we deal with gene regulation which is primarily controlled by transcription factors (TFs) that bind to the DNA to activate gene expression. The possibility that non-cognate TFs activate transcription diminishes the accuracy of regulation with potentially disastrous effects for the cell. This ’crosstalk’ acts as a previously unexplored source of noise in biochemical networks and puts a strong constraint on their performance. To mitigate erroneous initiation we propose an out of equilibrium scheme that implements kinetic proofreading. We show that such architectures are favored over their equilibrium counterparts for complex organisms despite introducing noise in gene expression. " alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Sarah A full_name: Cepeda Humerez, Sarah A id: 3DEE19A4-F248-11E8-B48F-1D18A9856A87 last_name: Cepeda Humerez citation: ama: Cepeda Humerez SA. Estimating information flow in single cells. 2019. doi:10.15479/AT:ISTA:6473 apa: Cepeda Humerez, S. A. (2019). Estimating information flow in single cells. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:6473 chicago: Cepeda Humerez, Sarah A. “Estimating Information Flow in Single Cells.” Institute of Science and Technology Austria, 2019. https://doi.org/10.15479/AT:ISTA:6473. ieee: S. A. Cepeda Humerez, “Estimating information flow in single cells,” Institute of Science and Technology Austria, 2019. ista: Cepeda Humerez SA. 2019. Estimating information flow in single cells. Institute of Science and Technology Austria. mla: Cepeda Humerez, Sarah A. Estimating Information Flow in Single Cells. Institute of Science and Technology Austria, 2019, doi:10.15479/AT:ISTA:6473. short: S.A. Cepeda Humerez, Estimating Information Flow in Single Cells, Institute of Science and Technology Austria, 2019. date_created: 2019-05-21T00:11:23Z date_published: 2019-05-23T00:00:00Z date_updated: 2023-09-19T15:13:26Z day: '23' ddc: - '004' degree_awarded: PhD department: - _id: GaTk doi: 10.15479/AT:ISTA:6473 file: - access_level: closed checksum: 75f9184c1346e10a5de5f9cc7338309a content_type: application/zip creator: scepeda date_created: 2019-05-23T11:18:16Z date_updated: 2020-07-14T12:47:31Z file_id: '6480' file_name: Thesis_Cepeda.zip file_size: 23937464 relation: source_file - access_level: open_access checksum: afdc0633ddbd71d5b13550d7fb4f4454 content_type: application/pdf creator: scepeda date_created: 2019-05-23T11:18:13Z date_updated: 2020-07-14T12:47:31Z file_id: '6481' file_name: CepedaThesis.pdf file_size: 16646985 relation: main_file file_date_updated: 2020-07-14T12:47:31Z has_accepted_license: '1' keyword: - Information estimation - Time-series - data analysis language: - iso: eng license: https://creativecommons.org/licenses/by/4.0/ month: '05' oa: 1 oa_version: Published Version page: '135' publication_identifier: issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '1576' relation: dissertation_contains status: public - id: '6900' relation: dissertation_contains status: public - id: '281' relation: dissertation_contains status: public - id: '2016' relation: dissertation_contains status: public status: public supervisor: - first_name: Gašper full_name: Tkačik, Gašper id: 3D494DCA-F248-11E8-B48F-1D18A9856A87 last_name: Tkačik orcid: 0000-0002-6699-1455 title: Estimating information flow in single cells tmp: image: /images/cc_by.png legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0) short: CC BY (4.0) type: dissertation user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1 year: '2019' ...