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dc.contributor.authorGatenby, Robert A.en
dc.contributor.authorFrieden, B. Royen
dc.date.accessioned2017-12-05T16:11:37Z
dc.date.available2017-12-05T16:11:37Z
dc.date.issued2017-11-08
dc.identifier.citationCellular information dynamics through transmembrane flow of ions 2017, 7 (1) Scientific Reportsen
dc.identifier.issn2045-2322
dc.identifier.pmid29118414
dc.identifier.doi10.1038/s41598-017-15182-2
dc.identifier.urihttp://hdl.handle.net/10150/626193
dc.description.abstractWe propose cells generate large transmembrane ion gradients to form information circuits that detect, process, and respond to environmental perturbations or signals. In this model, the specialized gates of transmembrane ion channels function as information detectors that communicate to the cell through rapid and (usually) local pulses of ions. Information in the ion "puffs" is received and processed by the cell through resulting changes in charge density and/or mobile cation (and/or anion) concentrations alter the localization and function of peripheral membrane proteins. The subsequent changes in protein binding to the membrane or activation of K+, Ca2+ or Mg2+ -dependent enzymes then constitute a cellular response to the perturbation. To test this hypothesis we analyzed ion-based signal transmission as a communication channel operating with coded inputs and decoded outputs. By minimizing the Kullback-Leibler cross entropy H-KL(p||q) between concentrations of the ion species inside p(i)(t) i = 1,.,N , and outside q(i)(t) the cell membrane, we find signal transmission through transmembrane ion flow forms an optimal Shannon information channel that minimizes information loss and maximizes transmission speed. We demonstrate the ion dynamics in neuronal action potentials described by Hodgkin and Huxley (including the equations themselves) represent a special case of these general information principles.
dc.description.sponsorshipNational Cancer Institute Physical Science Oncology Center [U54 CA143970]; NCI CCSG Support Grant [P30 CA076292]en
dc.language.isoenen
dc.publisherNATURE PUBLISHING GROUPen
dc.relation.urlhttp://www.nature.com/articles/s41598-017-15182-2en
dc.rights© The Author(s) 2017. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License.en
dc.titleCellular information dynamics through transmembrane flow of ionsen
dc.typeArticleen
dc.contributor.departmentUniv Arizona, Coll Opt Scien
dc.identifier.journalScientific Reportsen
dc.description.collectioninformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
refterms.dateFOA2018-08-13T19:56:06Z
html.description.abstractWe propose cells generate large transmembrane ion gradients to form information circuits that detect, process, and respond to environmental perturbations or signals. In this model, the specialized gates of transmembrane ion channels function as information detectors that communicate to the cell through rapid and (usually) local pulses of ions. Information in the ion "puffs" is received and processed by the cell through resulting changes in charge density and/or mobile cation (and/or anion) concentrations alter the localization and function of peripheral membrane proteins. The subsequent changes in protein binding to the membrane or activation of K+, Ca2+ or Mg2+ -dependent enzymes then constitute a cellular response to the perturbation. To test this hypothesis we analyzed ion-based signal transmission as a communication channel operating with coded inputs and decoded outputs. By minimizing the Kullback-Leibler cross entropy H-KL(p||q) between concentrations of the ion species inside p(i)(t) i = 1,.,N , and outside q(i)(t) the cell membrane, we find signal transmission through transmembrane ion flow forms an optimal Shannon information channel that minimizes information loss and maximizes transmission speed. We demonstrate the ion dynamics in neuronal action potentials described by Hodgkin and Huxley (including the equations themselves) represent a special case of these general information principles.


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