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The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics

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  • Robert C Cannon
  • Giampaolo D'Alessandro

Abstract

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.

Suggested Citation

  • Robert C Cannon & Giampaolo D'Alessandro, 2006. "The Ion Channel Inverse Problem: Neuroinformatics Meets Biophysics," PLOS Computational Biology, Public Library of Science, vol. 2(8), pages 1-8, August.
  • Handle: RePEc:plo:pcbi00:0020091
    DOI: 10.1371/journal.pcbi.0020091
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    References listed on IDEAS

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    1. Björn Naundorf & Fred Wolf & Maxim Volgushev, 2006. "Unique features of action potential initiation in cortical neurons," Nature, Nature, vol. 440(7087), pages 1060-1063, April.
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    Cited by:

    1. Pietro Balbi & Paolo Massobrio & Jeanette Hellgren Kotaleski, 2017. "A single Markov-type kinetic model accounting for the macroscopic currents of all human voltage-gated sodium channel isoforms," PLOS Computational Biology, Public Library of Science, vol. 13(9), pages 1-29, September.
    2. Meron Gurkiewicz & Alon Korngreen, 2007. "A Numerical Approach to Ion Channel Modelling Using Whole-Cell Voltage-Clamp Recordings and a Genetic Algorithm," PLOS Computational Biology, Public Library of Science, vol. 3(8), pages 1-15, August.
    3. Andrey R Stepanyuk & Anya L Borisyuk & Pavel V Belan, 2011. "Efficient Maximum Likelihood Estimation of Kinetic Rate Constants from Macroscopic Currents," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-18, December.

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