Abstract
Using differential evolution and statistical analysis, this paper investigates a methodology that is capable of determining the ion channels in a neuron from membrane potential data obtained by the current-clamp method. These data provide the aggregated electrical response of the neuron under stimulation by integrating the individual responses of the different ion channels involved. The proposed methodology aims at determining which are these ion channels based on the hypothesis that each ion channel provides a specific signature in the aggregated response that we are able to detect. In order to assess the methodology, we propose a benchmark of synthetic data where the types of ion channels are predefined in advance. Results show that the methodology is able to determine the correct ion channels in three out of the four data sets. Furthermore, we obtain some hints for future enhancements on the method.
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Notes
- 1.
DE Parameters and parameter ranges are in Appendix B.
References
Bargmann, C.I.: Neurobiology of the caenorhabditis elegans genome. Science 282(5396), 2028–2033 (1998)
Buhry, L., Grassia, F., Giremus, A., Grivel, E., Renaud, S., Saïghi, S.: Automated parameter estimation of the hodgkin-huxley model using the differential evolution algorithm: application to neuromimetic analog integrated circuits. Neural Comput. 23(10), 2599–2625 (2011)
Buhry, L., Pace, M., Saïghi, S.: Global parameter estimation of an hodgkin-huxley formalism using membrane voltage recordings: application to neuro-mimetic analog integrated circuits. Neurocomputing 81, 75–85 (2012)
Buhry, L., Saighi, S., Giremus, A., Grivel, E., Renaud, S.: Parameter estimation of the hodgkin-huxley model using metaheuristics: application to neuromimetic analog integrated circuits. In: 2008 IEEE Biomedical Circuits and Systems Conference, pp. 173–176. IEEE (2008)
Chalasani, S.H., et al.: Dissecting a circuit for olfactory behaviour in caenorhabditis elegans. Nature 450(7166), 63 (2007)
Dayan, P., Abbott, L.F.: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press, Cambridge (2001)
Druckmann, S., Banitt, Y., Gidon, A.A., Schürmann, F., Markram, H., Segev, I.: A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front. Neurosci. 1, 1 (2007)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. NCS, Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-44874-8
Emtage, L., Aziz-Zaman, S., Padovan-Merhar, O., Horvitz, H.R., Fang-Yen, C., Ringstad, N.: Irk-1 potassium channels mediate peptidergic inhibition of caenorhabditis elegans serotonin neurons via a go signaling pathway. J. Neurosci. 32(46), 16285–16295 (2012)
Goodman, M.B., Hall, D.H., Avery, L., Lockery, S.R.: Active currents regulate sensitivity and dynamic range in C. elegans neurons. Neuron 20(4), 763–772 (1998)
Gordus, A., Pokala, N., Levy, S., Flavell, S.W., Bargmann, C.I.: Feedback from network states generates variability in a probabilistic olfactory circuit. Cell 161(2), 215–227 (2015)
Hendricks, M., Ha, H., Maffey, N., Zhang, Y.: Compartmentalized calcium dynamics in a C. elegans interneuron encode head movement. Nature 487(7405), 99–103 (2012)
Hodgkin, A.L., Huxley, A.F.: A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117(4), 500–544 (1952)
Hodgkin, A.L., Huxley, A.F., Katz, B.: Measurement of current-voltage relations in the membrane of the giant axon of Loligo. J. Physiol. 116(4), 424–448 (1952)
Hodgkin, A.L., Huxley, A.F.: The components of membrane conductance in the giant axon of Loligo. J. Physiol. 116(4), 473–496 (1952)
Hodgkin, A.L., Huxley, A.F.: Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. J. Physiol. 116(4), 449–472 (1952)
Hodgkin, A.L., Huxley, A.F.: The dual effect of membrane potential on sodium conductance in the giant axon of Loligo. J. Physiol. 116(4), 497–506 (1952)
Holm, S.: A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979)
Iavarone, E., et al.: Experimentally-constrained biophysical models of tonic and burst firing modes in thalamocortical neurons. PLoS Comput. Biol. 15(5), e1006753 (2019)
Izhikevich, E.M.: Dynamical Systems in Neuroscience. MIT Press, Cambridge (2007)
Kuramochi, M., Doi, M.: A computational model based on multi-regional calcium imaging represents the spatio-temporal dynamics in a caenorhabditis elegans sensory neuron. PLoS ONE 12(1), e0168415 (2017)
Liu, Q., Kidd, P.B., Dobosiewicz, M., Bargmann, C.I.: C. elegans awa olfactory neurons fire calcium-mediated all-or-none action potentials. Cell 175(1), 57–70 (2018)
Naudin, L., Corson, N., Aziz-Alaoui, M., Jiménez Laredo, J.L., Démare, T.: On the modeling of the three types of non-spiking neurons of the caenorhabditis elegans. Int. J. Neural Syst. 31, S012906572050063X (2020)
Naudin, L., Laredo, J.L.J., Liu, Q., Corson, N.: Systematic generation of biophysically detailed models with generalization capability for non-spiking neurons. hal-03474984 (2021)
Nguyen, V.K., Hernandez-Vargas, E.A.: Parameter estimation in mathematical models of viral infections using R. In: Yamauchi, Y. (ed.) Influenza Virus. MMB, vol. 1836, pp. 531–549. Springer, New York (2018). https://doi.org/10.1007/978-1-4939-8678-1_25
Nicoletti, M., Loppini, A., Chiodo, L., Folli, V., Ruocco, G., Filippi, S.: Biophysical modeling of C. elegans neurons: single ion currents and whole-cell dynamics of AWCon and RMD. PLoS ONE 14(7), e0218738 (2019)
Piggott, B.J., Liu, J., Feng, Z., Wescott, S.A., Xu, X.S.: The neural circuits and synaptic mechanisms underlying motor initiation in C. elegans. Cell 147(4), 922–933 (2011)
Salkoff, L.B., et al.: Potassium channels in c. elegans. WormBook (2005)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11(4), 341–359 (1997)
Venkadesh, S., et al.: Evolving simple models of diverse intrinsic dynamics in hippocampal neuron types. Front. Neuroinform. 12, 8 (2018)
Wilcoxon, F.: Individual comparisons by ranking methods. In: Kotz, S., Johnson, N.L. (eds.) Breakthroughs in Statistics, pp. 196–202. Springer, New York (1992). https://doi.org/10.1007/978-1-4612-4380-9_16
Wittkowski, K.M.: Friedman-type statistics and consistent multiple comparisons for unbalanced designs with missing data. J. Am. Stat. Assoc. 83(404), 1163–1170 (1988)
Wojtovich, A.P., DiStefano, P., Sherman, T., Brookes, P.S., Nehrke, K.: Mitochondrial ATP-sensitive potassium channel activity and hypoxic preconditioning are independent of an inwardly rectifying potassium channel subunit in caenorhabditis elegans. FEBS Lett. 586(4), 428–434 (2012)
Zheng, M., Cao, P., Yang, J., Xu, X.S., Feng, Z.: Calcium imaging of multiple neurons in freely behaving C. elegans. J. Neurosci. Methods 206(1), 78–82 (2012)
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Appendices
Appendix A Mathematical Description of the Models
Appendix B Experimental Setup and Parameter Ranges
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Jiménez Laredo, J.L., Naudin, L., Corson, N., Fernandes, C.M. (2022). A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings. In: Jiménez Laredo, J.L., Hidalgo, J.I., Babaagba, K.O. (eds) Applications of Evolutionary Computation. EvoApplications 2022. Lecture Notes in Computer Science, vol 13224. Springer, Cham. https://doi.org/10.1007/978-3-031-02462-7_2
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