---
_id: '6900'
abstract:
- lang: eng
  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.
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
- first_name: Jakob
  full_name: Ruess, Jakob
  last_name: Ruess
  orcid: 0000-0003-1615-3282
- first_name: Gašper
  full_name: Tkačik, Gašper
  id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
  last_name: Tkačik
  orcid: 0000-0002-6699-1455
citation:
  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>
  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>.
  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.
  ista: Cepeda Humerez SA, Ruess J, Tkačik G. 2019. Estimating information in time-varying
    signals. PLoS computational biology. 15(9), e1007290.
  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>.
  short: S.A. Cepeda Humerez, J. Ruess, G. Tkačik, PLoS Computational Biology 15 (2019)
    e1007290.
date_created: 2019-09-22T22:00:37Z
date_published: 2019-09-03T00:00:00Z
date_updated: 2026-04-08T13:55:45Z
day: '03'
ddc:
- '570'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007290
external_id:
  isi:
  - '000489741800021'
  pmid:
  - '31479447'
file:
- access_level: open_access
  checksum: 81bdce1361c9aa8395d6fa635fb6ab47
  content_type: application/pdf
  creator: kschuh
  date_created: 2019-10-01T10:53:45Z
  date_updated: 2020-07-14T12:47:44Z
  file_id: '6925'
  file_name: 2019_PLoS_Cepeda-Humerez.pdf
  file_size: 3081855
  relation: main_file
file_date_updated: 2020-07-14T12:47:44Z
has_accepted_license: '1'
intvolume: '        15'
isi: 1
issue: '9'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: e1007290
pmid: 1
project:
- _id: 254E9036-B435-11E9-9278-68D0E5697425
  call_identifier: FWF
  grant_number: P28844-B27
  name: Biophysics of information processing in gene regulation
publication: PLoS computational biology
publication_identifier:
  eissn:
  - '15537358'
publication_status: published
publisher: Public Library of Science
quality_controlled: '1'
related_material:
  record:
  - id: '6473'
    relation: part_of_dissertation
    status: public
scopus_import: '1'
status: public
title: Estimating information in time-varying signals
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: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 15
year: '2019'
...