A state-mutating genetic algorithm to design ion-channel models

Edited by Nancy J. Kopell, Boston University, Boston, MA, and approved July 21, 2009
September 29, 2009
106 (39) 16829-16834

Abstract

Realistic computational models of single neurons require component ion channels that reproduce experimental findings. Here, a topology-mutating genetic algorithm that searches for the best state diagram and transition-rate parameters to model macroscopic ion-channel behavior is described. Important features of the algorithm include a topology-altering strategy, automatic satisfaction of equilibrium constraints (microscopic reversibility), and multiple-protocol fitting using sequential goal programming rather than explicit weighting. Application of this genetic algorithm to design a sodium-channel model exhibiting both fast and prolonged inactivation yields a six-state model that produces realistic activity-dependent attenuation of action-potential backpropagation in current-clamp simulations of a CA1 pyramidal neuron.

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Acknowledgments.

We thank A. Korngreen for helpful discussions concerning genetic algorithms. This work was supported by National Institutes of Health Grant NS-046064.

Supporting Information

Supporting Appendix (PDF)
Supporting Information
SM1.mov

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Information & Authors

Information

Published in

Go to Proceedings of the National Academy of Sciences
Go to Proceedings of the National Academy of Sciences
Proceedings of the National Academy of Sciences
Vol. 106 | No. 39
September 29, 2009
PubMed: 19805381

Classifications

Submission history

Received: April 5, 2009
Published online: September 29, 2009
Published in issue: September 29, 2009

Acknowledgments

We thank A. Korngreen for helpful discussions concerning genetic algorithms. This work was supported by National Institutes of Health Grant NS-046064.

Notes

This article is a PNAS Direct Submission.
This article contains supporting information online at www.pnas.org/cgi/content/full/0807786105/DCSupplemental.
*
This formulation is the transpose of standard notation (3); this is done so that the incidence matrices in the graph-theoretic formulation to follow can be used in their standard forms.

Authors

Affiliations

Vilas Menon
Engineering Sciences and Applied Mathematics, McCormick School of Engineering;
Nelson Spruston
Neurobiology and Physiology, Weinberg College of Arts and Sciences; and
William L. Kath1 [email protected]
Engineering Sciences and Applied Mathematics, McCormick School of Engineering;
Neurobiology and Physiology, Weinberg College of Arts and Sciences; and
Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL 60208

Notes

1
To whom correspondence should be addressed. E-mail: [email protected]
Author contributions: V.M., N.S., and W.L.K. designed research; V.M. and W.L.K. performed research; V.M. analyzed data; and V.M., N.S., and W.L.K. wrote the paper.

Competing Interests

The authors declare no conflict of interest.

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    A state-mutating genetic algorithm to design ion-channel models
    Proceedings of the National Academy of Sciences
    • Vol. 106
    • No. 39
    • pp. 16537-16890

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