Modeling of Action Potential Generation in NG108-15 Cells

  • Peter MolnarEmail author
  • James J. Hickman
Part of the Methods in Molecular Biology book series (MIMB, volume 1183)


In order to explore the possibility of identifying toxins based on their effect on the shape of action potentials, we created a computer model of the action potential generation in NG108-15 cells (a neuroblastoma/glioma hybrid cell line). To generate the experimental data for model validation, voltage-dependent sodium, potassium and high-threshold calcium currents, as well as action potentials, were recorded from NG108-15 cells with conventional whole-cell patch-clamp methods. Based on the classic Hodgkin–Huxley formalism and the linear thermodynamic description of the rate constants, ion-channel parameters were estimated using an automatic fitting method. Utilizing the established parameters, action potentials were generated using the Hodgkin–Huxley formalism and were fitted to the recorded action potentials. To demonstrate the applicability of the method for toxin detection and discrimination, the effect of tetrodotoxin (a sodium channel blocker) and tefluthrin (a pyrethroid that is a sodium channel opener) were studied. The two toxins affected the shape of the action potentials differently, and their respective effects were identified based on the predicted changes in the fitted parameters.

Key words

Action potential Computer modeling Hodgkin–Huxley Linear thermodynamic description Toxin detection NG108-15 Parameter fitting 



This work was supported by NIH Career Award K01 EB03465 and DOE grant DE-FG02-04ER46171.


  1. 1.
    Gross GW, Harsch A, Rhoades BK et al (1997) Odor, drug and toxin analysis with neuronal networks in vitro: extracellular array recording of network responses. Biosens Bioelectron 12:373–393PubMedCrossRefGoogle Scholar
  2. 2.
    Gross GW, Rhoades BK, Azzazy HM et al (1995) The use of neuronal networks on multielectrode arrays as biosensors. Biosens Bioelectron 10:553–567PubMedCrossRefGoogle Scholar
  3. 3.
    Morefield SI, Keefer EW, Chapman KD et al (2000) Drug evaluations using neuronal networks cultured on microelectrode arrays. Biosens Bioelectron 15:383–396PubMedCrossRefGoogle Scholar
  4. 4.
    Akanda N, Molnar P, Stancescu M et al (2009) Analysis of toxin-induced changes in action potential shape for drug development. J Biomol Screen 14:1228–1235PubMedCrossRefGoogle Scholar
  5. 5.
    Amigo JM, Szczepański J, Wajnryb E et al (2003) On the number of states of the neuronal sources. Biosystems 68:57–66PubMedCrossRefGoogle Scholar
  6. 6.
    Chiappalone M, Vato A, Tedesco MB et al (2003) Networks of neurons coupled to microelectrode arrays: a neuronal sensory system for pharmacological applications. Biosens Bioelectron 18:627–634PubMedCrossRefGoogle Scholar
  7. 7.
    Xia Y, Gopal KV, Gross GW (2003) Differential acute effects of fluoxetine on frontal and auditory cortex networks in vitro. Brain Res 973:151–160PubMedCrossRefGoogle Scholar
  8. 8.
    Akay M, Mazza E, Neubauer JA (1998) Non-linear dynamic analysis of hypoxia-induced changes in action potential shape in neurons cultured from the rostral ventrolateral medulla (RVLM). FASEB J 12:2881Google Scholar
  9. 9.
    Clark RB, Bouchard RA, Salinas-Stefanon E et al (1993) Heterogeneity of action-potential wave-forms and potassium currents in rat ventricle. Cardiovasc Res 27:1795–1799PubMedCrossRefGoogle Scholar
  10. 10.
    Djouhri L, Lawson SN (1999) Changes in somatic action potential shape in guinea-pig nociceptive primary afferent neurones during inflammation in vivo. J Physiol 520:565–576PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Mohan DK, Molnar P, Hickman JJ (2006) Toxin detection based on action potential shape analysis using a realistic mathematical model of differentiated NG108-15 cells. Biosens Bioelectron 21:1804–1811PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Muraki K, Imaizumi Y, Watanabe M (1994) Effects of noradrenaline on membrane currents and action-potential shape in smooth-muscle cells from guinea-pig ureter. J Physiol 481:617–627PubMedCentralPubMedGoogle Scholar
  13. 13.
    Nygren A, Fiset C, Firek L et al (1998) Mathematical model of an adult human atrial cell: the role of K+ currents in repolarization. Circ Res 82:63–81PubMedCrossRefGoogle Scholar
  14. 14.
    Bernus O, Wilders R, Zemlin CW et al (2002) A computationally efficient electrophysiological model of human ventricular cells. Am J Physiol Heart Circ Physiol 282:H2296–H2308PubMedGoogle Scholar
  15. 15.
    Nygren A, Leon LJ, Giles WR (2001) Simulations of the human atrial action potential. Philos Trans R Soc Lond Ser A 359(1783):1111–1125CrossRefGoogle Scholar
  16. 16.
    Winslow RL et al (2001) Computational models of the failing myocyte: relating altered gene expression to cellular function. Philos Trans R Soc Lond Ser A 359:1187–1200CrossRefGoogle Scholar
  17. 17.
    Winslow RL, Cortassa S, Greenstein JL (2005) Using models of the myocyte for functional interpretation of cardiac proteomic data. J Physiol 563:73–81PubMedCentralPubMedCrossRefGoogle Scholar
  18. 18.
    Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544PubMedCentralPubMedGoogle Scholar
  19. 19.
    Destexhe A, Huguenard JR (2000) Nonlinear thermodynamic models of voltage-dependent currents. J Comput Neurosci 9:259–270PubMedCrossRefGoogle Scholar
  20. 20.
    Weiss TF (1996) Cellular biophysics. MIT Press, Cambridge, MAGoogle Scholar
  21. 21.
    Kowtha VC, Quong JN, Bryant HJ et al (1993) Comparative electrophysiological properties of NG108-15 cells in serum-containing and serum-free media. Neurosci Lett 164:129–133PubMedCrossRefGoogle Scholar
  22. 22.
    Ma W, Pancrazio JJ, Coulombe M et al (1998) Neuronal and glial epitopes and transmitter-synthesizing enzymes appear in parallel with membrane excitability during neuroblastoma x glioma hybrid differentiation. Dev Brain Res 106:155–163CrossRefGoogle Scholar
  23. 23.
    Molnar P, Kang J-F, Bhargava N, Das M, Hickman JJ (2007) Synaptic connectivity in engineered neuronal networks. Patch-clamp methods and protocols. Humana, Totowa, NJ, pp 165–173Google Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Faculty of Natural SciencesUniversity of West HungarySzombathelyHungary
  2. 2.Nano Science Technology CenterUniversity of Central FloridaOrlandoUSA

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