Maximal information transmission is compatible with ultrasensitive biological pathways.


Journal

Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288

Informations de publication

Date de publication:
15 11 2019
Historique:
received: 24 09 2018
accepted: 29 10 2019
entrez: 16 11 2019
pubmed: 16 11 2019
medline: 13 11 2020
Statut: epublish

Résumé

Cells are often considered input-output devices that maximize the transmission of information by converting extracellular stimuli (input) via signaling pathways (communication channel) to cell behavior (output). However, in biological systems outputs might feed back into inputs due to cell motility, and the biological channel can change by mutations during evolution. Here, we show that the conventional channel capacity obtained by optimizing the input distribution for a fixed channel may not reflect the global optimum. In a new approach we analytically identify both input distributions and input-output curves that optimally transmit information, given constraints from noise and the dynamic range of the channel. We find a universal optimal input distribution only depending on the input noise, and we generalize our formalism to multiple outputs (or inputs). Applying our formalism to Escherichia coli chemotaxis, we find that its pathway is compatible with optimal information transmission despite the ultrasensitive rotary motors.

Identifiants

pubmed: 31729454
doi: 10.1038/s41598-019-53273-4
pii: 10.1038/s41598-019-53273-4
pmc: PMC6858467
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

16898

Subventions

Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/C519670/1
Pays : United Kingdom
Organisme : Biotechnology and Biological Sciences Research Council
ID : BB/G000131/1
Pays : United Kingdom

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Auteurs

Gabriele Micali (G)

Department of Life Sciences, Imperial College, London, UK.
Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London, UK.
Department of Environmental Microbiology, Eawag, Dübendorf, Switzerland.
Department of Environmental Systems Science, ETH Zürich, Zürich, Switzerland.

Robert G Endres (RG)

Department of Life Sciences, Imperial College, London, UK. r.endres@imperial.ac.uk.
Centre for Integrative Systems Biology and Bioinformatics, Imperial College, London, UK. r.endres@imperial.ac.uk.

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