---
OA_place: publisher
_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:<a
    href="https://doi.org/10.15479/AT:ISTA:6473">10.15479/AT:ISTA:6473</a>
  apa: Cepeda Humerez, S. A. (2019). <i>Estimating information flow in single cells</i>.
    Institute of Science and Technology Austria. <a href="https://doi.org/10.15479/AT:ISTA:6473">https://doi.org/10.15479/AT:ISTA:6473</a>
  chicago: Cepeda Humerez, Sarah A. “Estimating Information Flow in Single Cells.”
    Institute of Science and Technology Austria, 2019. <a href="https://doi.org/10.15479/AT:ISTA:6473">https://doi.org/10.15479/AT:ISTA:6473</a>.
  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. <i>Estimating Information Flow in Single Cells</i>.
    Institute of Science and Technology Austria, 2019, doi:<a href="https://doi.org/10.15479/AT:ISTA:6473">10.15479/AT:ISTA:6473</a>.
  short: S.A. Cepeda Humerez, Estimating Information Flow in Single Cells, Institute
    of Science and Technology Austria, 2019.
corr_author: '1'
date_created: 2019-05-21T00:11:23Z
date_published: 2019-05-23T00:00:00Z
date_updated: 2026-04-16T08:37:38Z
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: '2016'
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    status: public
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    status: public
  - id: '1576'
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  - id: '6900'
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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: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2019'
...