The ion channel inverse problem: neuroinformatics meets biophysics

Cannon, Robert C. and D'Alessandro, Giampaolo (2006) The ion channel inverse problem: neuroinformatics meets biophysics PLoS Computational Biology, 2, (8), 0862-0869. (doi:10.1371/journal.pcbi.0020091).


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Ion channels are the building blocks of the information processing capability of neurons: any realistic computational model of a neuron must include reliable and effective ion channel components. Sophisticated statistical and computational tools have been developed to study the ion channel structure-function relationship, but this work is rarely incorporated into the models used for single neurons or small networks. The disjunction is partly a matter of convention. Structure-function studies typically use a single Markov model for the whole channel whereas until recently whole-cell modeling software has focused on serial, independent, two-state subunits that can be represented by the Hodgkin-Huxley equations. More fundamentally, there is a difference in purpose that prevents models being easily reused. Biophysical models are typically developed to study one particular aspect of channel gating in detail, whereas neural modelers require broad coverage of the entire range of channel behavior that is often best achieved with approximate representations that omit structural features that cannot be adequately constrained. To bridge the gap so that more recent channel data can be used in neural models requires new computational infrastructure for bringing together diverse sources of data to arrive at best-fit models for whole-cell modeling. We review the current state of channel modeling and explore the developments needed for its conclusions to be integrated into whole-cell modeling.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1371/journal.pcbi.0020091
ISSNs: 1553-734X (print)
Related URLs:
ePrint ID: 45413
Date :
Date Event
25 August 2006Published
Date Deposited: 28 Mar 2007
Last Modified: 16 Apr 2017 18:41
Further Information:Google Scholar

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