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Journal of Computational Neuroscience

, Volume 31, Issue 2, pp 329–346 | Cite as

The use of automated parameter searches to improve ion channel kinetics for neural modeling

  • Eric B. Hendrickson
  • Jeremy R. Edgerton
  • Dieter JaegerEmail author
Article

Abstract

The voltage and time dependence of ion channels can be regulated, notably by phosphorylation, interaction with phospholipids, and binding to auxiliary subunits. Many parameter variation studies have set conductance densities free while leaving kinetic channel properties fixed as the experimental constraints on the latter are usually better than on the former. Because individual cells can tightly regulate their ion channel properties, we suggest that kinetic parameters may be profitably set free during model optimization in order to both improve matches to data and refine kinetic parameters. To this end, we analyzed the parameter optimization of reduced models of three electrophysiologically characterized and morphologically reconstructed globus pallidus neurons. We performed two automated searches with different types of free parameters. First, conductance density parameters were set free. Even the best resulting models exhibited unavoidable problems which were due to limitations in our channel kinetics. We next set channel kinetics free for the optimized density matches and obtained significantly improved model performance. Some kinetic parameters consistently shifted to similar new values in multiple runs across three models, suggesting the possibility for tailored improvements to channel models. These results suggest that optimized channel kinetics can improve model matches to experimental voltage traces, particularly for channels characterized under different experimental conditions than recorded data to be matched by a model. The resulting shifts in channel kinetics from the original template provide valuable guidance for future experimental efforts to determine the detailed kinetics of channel isoforms and possible modulated states in particular types of neurons.

Keywords

Neuron model Computation Globus pallidus Computer simulation Evolutionary algorithms 

Notes

Acknowledgements

This project was supported by National Institute of Neurological Disorders and Stroke (NINDS) grant R01-NS039852 to DJ and National Science Foundation (NSF) DGE-0333411 IGERT fellowship to EBH. Morphological reconstructions and electrophysiological recordings of the 3 neurons modeled in this study were performed by Dr. Jesse Hanson.

Conflict of Interest Notification

To the best of our knowledge, no conflicts of interests exist related to the work we present here.

Supplementary material

10827_2010_312_MOESM1_ESM.pdf (2.6 mb)
ESM 1 (PDF 2657 kb)

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Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Eric B. Hendrickson
    • 1
    • 2
  • Jeremy R. Edgerton
    • 2
  • Dieter Jaeger
    • 2
    Email author
  1. 1.Biomedical Engineering DepartmentGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Biology DepartmentEmory UniversityAtlantaUSA

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