Set of Simulators of the Electrophysiology of the A-Type Potassium Current (IA) in Neurons

María Eugenia Pérez Bonilla; Marleni Reyes Monreal; Miguel Felipe Pérez Escalera; Arturo Reyes Lazalde

doi:10.17488/RMIB.41.3.2

Authors

María Eugenia Pérez Bonilla Benemérita Universidad Autónoma de Puebla, México
Marleni Reyes Monreal Benemérita Universidad Autónoma de Puebla, México
Miguel Felipe Pérez Escalera Benemérita Universidad Autónoma de Puebla, México
Arturo Reyes Lazalde Benemérita Universidad Autónoma de Puebla, México

DOI:

https://doi.org/10.17488/RMIB.41.3.2

Keywords:

A-type potassium current, Simulators, Virtual experiments

Abstract

The A-type potassium current (IA) participates in important brain functions, including neuronal excitability, synaptic integration, and regulation of action potential patterns and fring frequency. Based on the characterization of its electrophysiological properties by current and voltage clamp techniques, mathematical models have been developed that reproduce IA function. For such models, it is necessary to numerically solve equations and utilize hardware with special speed and performance characteristics. Since specifc software for studying IA is not found on the Internet, the aim of this work was to develop a set of simulators grouped into three computer programs: (1) IA Current, (2) IA Constant-V Curves and (3) IA AP Train. These simulators provide a virtual reproduction of experiments on neurons with the possibility of setting the current and voltage, which allows for the study of the electrophysiological and biophysical characteristics of IA and its eﬀect on the train of action potentials. The mathematical models employed were derived from the work of Connor et al., giving rise to Hodgkin-Huxley type models. The programs were developed in Visual Basic® and the diﬀerential equation systems were simultaneously solved numerically. The resulting system represents a breakthrough in the ability to replicate IA activity in neurons.

Downloads

Download data is not yet available.

Author Biographies

María Eugenia Pérez Bonilla, Benemérita Universidad Autónoma de Puebla, México

María Eugenia Pérez Bonilla got her Bachelor of Medicine and Master of Physiology from the Benemérita Universidad Autónoma de Puebla. She has a doctorate in experimental pathology from the Center for Research and Advanced Studies of the Instituto Politecnico Nacional. She is part of a multidisciplinary group with an interest in development of interactive software for education and research in biomedical sciences.

Marleni Reyes Monreal, Benemérita Universidad Autónoma de Puebla, México

Marleni Reyes Monreal is a computer technician, she received her Bachelor Degree in Graphic Design and her Master Degree in Aesthetics and Art from Benemérita Universidad Autónoma de Puebla. She has a Master Degree in Educational Technology (multimedia design and simulators) and she is currently working to obtain her doctorate in Ecoeducation (simulators) and a second one in History (Art). She is part of a multidisciplinary group with an interest in the design and aesthetic development of interactive software for education and research.

Miguel Felipe Pérez Escalera, Benemérita Universidad Autónoma de Puebla, México

Miguel Pérez Escalera got his Bachelor Degree in Computer Science from Benemérita Universidad Autónoma de Puebla, he has a Master Degree in Computer Science from the same institution and he is working for his doctorate, also in Computer Science in Universidad de las Americas Puebla. He is part of a multidisciplinary group with an interest in the development of interactive software and 3D systems for education and research.

Arturo Reyes Lazalde, Benemérita Universidad Autónoma de Puebla, México

Arturo Reyes Lazalde received his Bachelor of Medicine and Master of Physiology from Benemérita Universidad Autónoma de Puebla. He has a doctorate in basic biomedical research (neuroscience) from Universidad Nacional Autónoma de México. He is part of a multidisciplinary group with an interest in the development of interactive software for education and research in biomedical sciences.

References

Randall C, Burkholder T. Hands-on laboratory experience in teaching-learning physiology. Adv Physiol Educ. 1990;259(4):S4–7. https://doi.org/10.1152/advances.1990.259.6.S4

Woodhull-McNeal AP. Project labs in physiology. Adv Physiol Educ. 1992;263(6):S29–32. https://doi.org/10.1152/advances.1992.263.6.S29

Bish JP, Schleidt S. Effective use of computer simulations in an introductory neuroscience laboratory. J Undergrad Neurosci Educ. 2008;6(2):64–7.

Diwakar S, Parasuram H, Medini C, Raman R, Nedungadi P, Wiertelak E, et al. Complementing Neurophysiology Education for Developing Countries via Cost-Effective Virtual Labs: Case studies and Classroom Scenarios. J Undergrad Neurosci Educ. 2014;12(2):130–9.

Maran NJ, Glavin RJ. Low- to high-fidelity simulation - A continuum of medical education? Med Educ Suppl. 2003;37(1):22–8. https://doi.org/10.1046/j.1365-2923.37.s1.9.x

Oriol NE, Hayden EM, Joyal-Mowschenson J, Muret-Wagstaff S, Faux R, Gordon JA. Using immersive healthcare simulation for physiology education: Initial experience in high school, college, and graduate school curricula. Am J Physiol - Adv Physiol Educ. 2011;35(3):252–9. https://doi.org/10.1152/advan.00043.2011

Harris DM, Ryan K, Rabuck C. Using a high-fidelity patient simulator with first-year medical students to facilitate learning of cardiovascular function curves. Am J Physiol - Adv Physiol Educ. 2012;36(3):213–9. https://doi.org/10.1152/advan.00058.2012

Anyanwu GE, Agu AU, Anyaehie UB. Enhancing learning objectives by use of simple virtual microscopic slides in cellular physiology and histology: Impact and attitudes. Am J Physiol - Adv Physiol Educ. 2012;36(2):158–63. https://doi.org/10.1152/advan.00008.2012

Av-Ron E, Byrne JH, Baxter DA. Teaching basic principles of neuroscience with computer simulations. J Undergrad Neurosci Educ. 2006;4(2):40–52.

Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in Nerve. J Physiol. 1952;117(4):500–44. https://doi.org/10.1113/jphysiol.1952.sp004764

Segev I. Temporal Interactions Between Post-Synaptic Potentials. In Bower J, Beeman D (eds.). The book of GENESIS, 2nd ed. New York: Springer-Verlag; 1998. 79–96p. https://doi.org/10.1007/978-1-4612-1634-6_6

Carnevale NT, Hines ML. The NEURON Book. Cambridge: Cambridge University Press; 2006. 480 p.

Izhikevich EM, Edelman GM. Large-scale model of mammalian thalamocortical systems. PNAS. 2008;105(9):3593–8. https://doi.org/10.1073/pnas.0712231105

Hernández OE, Zurek EE. Teaching and learning the Hodgkin-Huxley model based on software developed in NEURON’s programming language hoc. BMC Med Educ. 2013;13(70):1-9. https://doi.org/10.1186/1472-6920-13-70

Reyes-Lazalde A, Reyes-Monreal M, Pérez-Bonilla ME. Desarrollo de un simulador de los experimentos clásicos y actualizados de fijación de Voltaje de Hodgkin y Huxley. Rev Mex Ing Biomed. 2016;37(2):135–48. https://doi.org/10.17488/rmib.37.2.1

Reyes Lazalde A, Pérez-Bonilla ME, Funchs-Gómez OL, Reyes-Monreal M. Interactive simulators to study the passive properties of the axon and the dendritic tree. Rev Mex Ing Biomed [Internet]. 2012;33(1):29–40. Available from: http://www.rmib.mx/index.php/rmib/article/view/226

Goodman D, Brette R. Brian: a simulator for spiking neural networks in Python. Front Neuroinform. 2008;2(NOV):1–10. https://doi.org/10.3389/neuro.11.005.2008

Demir SS. Simulation-Based Training In Electrophysiology By iCELL. In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. Shangai: IEEE-EMBS;2005:851–4. https://doi.org/10.1109/IEMBS.2005.1616549

Ribarič S, Kordaš M. Teaching cardiovascular physiology with equivalent electronic circuits in a practically oriented teaching module. Am J Physiol - Adv Physiol Educ. 2011;35(2):149–60. https://doi.org/10.1152/advan.00072.2010

Reyes Lazalde A, Reyes Monreal M, Pérez Bonilla ME. Experimentación virtual con el simulador dosis-respuesta como herramienta docente en biología. Apertura. 2016;8(2):22–37. http://dx.doi.org/10.32870/Ap.v8n2.855

Vega OA, Londoño-Hincapié SM, Toro-Villa S. Laboratorios virtuales para la enseñanza de las ciencias. Informática. 2016;(35):97–110. https://doi.org/10.30554/ventanainform.35.1849.2016

Hagiwara S, Kusano K, Saito N. Membrane changes of Onchidium nerve cell in potassium‐rich media. J Physiol. 1961;155(3):470–89. https://doi.org/10.1113/jphysiol.1961.sp006640

Cai S-Q, Li W, Sesti F. Multiple modes of A-type potassium current regulation. Curr Pharm Des. 2007;13(31):3178–84. https://doi.org/10.2174/138161207782341286

Connor JA, Walter D, McKown R. Neural repetitive firing: modifications of the Hodgkin-Huxley axon suggested by experimental results from crustacean axons. Biophys J. 1977;18(1):81–102. https://dx.doi.org/10.1016%2FS0006-3495(77)85598-7

Gustafsson B, Galvan M, Grafe P, Wigström H. A transient outward current in a mammalian central neurone blocked by 4-aminopyridine. Nature. 1982;299(5880):252–4. https://doi.org/10.1038/299252a0

Galvan M, Sedlmeir C. Outward currents in voltage‐clamped rat sympathetic neurones. J Physiol. 1984;356(1):115–33. https://doi.org/10.1113/jphysiol.1984.sp015456

Bargas J, Galarraga E, Aceves J. An early outward conductance modulates the firing latency and frequency of neostriatal neurons of the rat brain. Exp Brain Res. 1989;75(1):146–56. https://doi.org/10.1007/BF00248538

Sanchez RM, Surkis A, Leonard CS. Voltage-clamp analysis and computer simulation of a novel cesium- resistant a-current in guinea pig laterodorsal tegmental neurons. J Neurophysiol. 1998;79(6):3111–26. https://doi.org/10.1152/jn.1998.79.6.3111

Fransén E, Tigerholm J. Role of A-type potassium currents in excitability, network synchronicity, and epilepsy. Hippocampus. 2010;20(7):877–87. https://doi.org/10.1002/hipo.20694

Migliore M, Hoffman DA, Magee JC, Johnston D. Role of an A-type K+ conductance in the back-propagation of action potentials in the dendrites of hippocampal pyramidal neurons. J Comput Neurosci. 1999;7(1):5–15. https://doi.org/10.1023/A:1008906225285

Hines ML, Morse T, Migliore M, Carnevale NT, Shepherd GM. ModelDB: A Database to Support Computational Neuroscience. J Comput Neurosci. 2004;17(1):7–11. https://doi.org/10.1023/B:JCNS.0000023869.22017.2e

Lemus-Aguilar I, Bargas J, Tecuapetla F, Galárraga E, Carrillo-Reid L. Diseño modular de instrumentación virtual para la manipulación y el análisis de señales electrofisiológicas. Rev Mex Ing Biomédica [Internet]. 2006;27(2):82–92. Available from: http://www.rmib.mx/index.php/rmib/article/view/359

Cronin J. Mathematical Aspects of Hodgkin-Huxley Neural Theory. Cambridge: Cambridge University Press; 1987. 261p.

Sterratt D, Graham B, Gillies A, Willshaw D. Principles of Computational Modelling in Neuroscience. Cambridge: Cambridge University Press; 2011. 300 p.

Connor JA, Stevens CF. Inward and delayed outward membrane currents in isolated neural somata under voltage clamp. J Physiol. 1971;213(1):1–19. https://doi.org/10.1113/jphysiol.1971.sp009364

Connor JA, Stevens CF. Voltage clamp studies of a transient outward membrane current in gastropod neural somata. J Physiol. 1971;213(1):21–30. https://doi.org/10.1113/jphysiol.1971.sp009365

Connor JA, Stevens CF. Prediction of repetitive firing behaviour from voltage clamp data on an isolated neurone soma. J Physiol. 1971;213(1):31–53. https://doi.org/10.1113/jphysiol.1971.sp009366

Zill DG. Ecuaciones diferenciales con aplicaciones. 2nd Ed. México: Grupo Editorial Iberoamérica; 1988. 516 p.

Rush ME, Rinzel J. The potassium A-current, low firing rates and rebound excitation in Hodgkin-Huxley models. Bull Math Biol. 1995;57(6):899–929. https://doi.org/10.1007/BF02458299

Huguenard JR, McCormick DA. Simulation of the currents involved in rhythmic oscillations in thalamic relay neurons. J Neurophysiol. 1992;68(4):1373–83. https://doi.org/10.1152/jn.1992.68.4.1373