Registration and query of biophysical parameters using the MyocyteDB platform

Authors

DOI:

https://doi.org/10.33448/rsd-v11i8.30712

Keywords:

Web plataform; Database; Ventricular myocyte; Mathematical modeling; Biophysical parameters.

Abstract

Currently, the research related to the computational modeling of myocytes has been standing out substantially regarding the knowledge of the complex process of cardiac excitation-contraction. In this context, the availability of a data repository of electrophysiological parameters for the development of mathematical models becomes a growing need. The objective of this article is to present the MyocyteDB web platform that's focused on the inclusion and consultation of biophysical parameters and statistical data for computational modeling of the ventricular myocyte. For the development of this platform, cloud computing technologies (Azure) were used. A careful review of the literature in the search for mathematical models of cardiac electrophysiology was carried out on the conductance values of the main ion channels to compose the initial set of data to be inserted into the platform. From the set of collected values available on this platform, it becomes possible to produce statistical data of a descriptive nature, export data in spreadsheet format, and access the dataset via API. It is expected this platform could be a tool capable of helping future modelers in the research and adjustments of biophysical parameters used in the cardiac electrophysiological modeling process. Thus, providing greater dynamics in the search for values of biophysical parameters used in mathematical modeling of myocytes.

References

Beard, D. A., Neal, M. L., Tabesh-Saleki, N., Thompson, C. T., Bassingtwaighte, J. B., Shimoyama, M., & Carlson, B. E. (2012). Multiscale modeling and data integration in the virtual physiological rat project. Annals of biomedical engineering, 40(11), 2365-2378.

Bondarenko, V. E., Szigeti, G. P., Bett, G. C., Kim, S. J., & Rasmusson, R. L. (2004). Computer model of action potential of mouse ventricular myocytes. American Journal of Physiology-Heart and Circulatory Physiology, 287(3), H1378-H1403.

Chen, P. P. S. (1976). The entity-relationship model—toward a unified view of data. ACM transactions on database systems (TODS), 1(1), 9-36.

Cooper, J., Scharm, M. & Mirams, G. R. (2016). The cardiac electrophysiology web lab. Biophysical journal, 110(2), 292-300.

Coutu, P., & Metzger, J. M. (2005). Genetic manipulation of calcium-handling proteins in cardiac myocytes. II. Mathematical modeling studies. American Journal of Physiology-Heart and Circulatory Physiology, 288(2), H613-H631.

Franck, K. M., Pereira, R. F., & Dantas Filho, J. V. (2021). Ratio-Entity Diagram: a tool for conceptual data modeling in Software Engineering. Research, Society and Development, 10(8), e49510817776-e49510817776. doi: 10.33448/rsd-v10i8.17776.

Gattoni, S., Røe, Å. T., Frisk, M., Louch, W. E., Niederer, S. A., & Smith, N. P. (2016). The calcium–frequency response in the rat ventricular myocyte: an experimental and modelling study. The Journal of physiology, 594(15), 4193-4224.

Grandi, E., Pasqualini, F. S., & Bers, D. M. (2010). A novel computational model of the human ventricular action potential and Ca transient. Journal of molecular and cellular cardiology, 48(1), 112-121.

Goldberger, A. L., Amaral, L. A., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E.; Moody, G. B.; Peng, C-K, & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation, 101(23), e215-e220.

Iyer, V., Mazhari, R., & Winslow, R. L. (2004). A computational model of the human left-ventricular epicardial myocyte. Biophysical journal, 87(3), 1507-1525.

Li, L., Louch, W. E., Niederer, S. A., Andersson, K. B., Christensen, G., Sejersted, O. M., & Smith, N. P. (2011). Calcium dynamics in the ventricular myocytes of SERCA2 knockout mice: a modeling study. Biophysical journal, 100(2), 322-331.

Lloyd, C. M., Halstead, M. D., & Nielsen, P. F. (2004). CellML: its future, present and past. Progress in biophysics and molecular biology, 85(2-3), 433-450.

Mahajan, A., Shiferaw, Y., Sato, D., Baher, A., Olcese, R., Xie, L. H., Yang, M., Chen, P., Restrepo, J. G., Karma, A., Garfinkel, A., Qu, Z., & Weiss, J. N. (2008). A rabbit ventricular action potential model replicating cardiac dynamics at rapid heart rates. Biophysical journal, 94(2), 392-410.

Migliore, M., Morse, T. M., Davison, A. P., Marenco, L., Shepherd, G. M., & Hines, M. L. (2003). ModelDB. Neuroinform, 1, 135-139.

Miguel, G. F. de S., Sá, A. A. R. de, Souza, J. T. de, & Naves, E. L. M. (2021). Home-based telerehabilitation: A review of remote therapy frameworks. Research, Society and Development, 10(6), e4910615489-e4910615489. doi: 10.33448/rsd-v10i6.15489.

Morotti, S., Edwards, A. G., McCulloch, A. D., Bers, D. M., & Grandi, E. (2014). A novel computational model of mouse myocyte electrophysiology to assess the synergy between Na+ loading and CaMKII. The Journal of physiology, 592(6), 1181-1197.

Noble, D., Garny, A., & Noble, P. J. (2012). How the Hodgkin–Huxley equations inspired the cardiac physiome project. The Journal of physiology, 590(11), 2613-2628.

Pandit, S. V., Clark, R. B., Giles, W. R., & Demir, S. S. (2001). A mathematical model of action potential heterogeneity in adult rat left ventricular myocytes. Biophysical journal, 81(6), 3029-3051.

Pásek, M., Šimurda, J., & Christé, G. (2006). The functional role of cardiac T-tubules explored in a model of rat ventricular myocytes. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 364(1842), 1187-1206.

Reenskaug, T. (1979). Models-views-controllers. Xerox PARC technical note.

Rodriguez, B. (2019). The 18th FRAME annual lecture, October 2019: Human in silico trials in pharmacology. Alternatives to Laboratory Animals, 47(5-6), 221-227.

Shabbir, M., Shabbir, A., Iwendi, C., Javed, A. R., Rizwan, M., Herencsar, N., & Lin, J. C. W. (2021). Enhancing security of health information using modular encryption standard in mobile cloud computing. IEEE Access, 9, 8820-8834.

Shannon, T. R., Wang, F., Puglisi, J., Weber, C., & Bers, D. M. (2004). A mathematical treatment of integrated Ca dynamics within the ventricular myocyte. Biophysical journal, 87(5), 3351-3371.

Ten Tusscher, K. H., & Panfilov, A. V. (2006). Alternans and spiral breakup in a human ventricular tissue model. American Journal of Physiology-Heart and Circulatory Physiology, 291(3), H1088-H1100.

Wang, L. J., & Sobie, E. A. (2008). Mathematical model of the neonatal mouse ventricular action potential. American Journal of Physiology-Heart and Circulatory Physiology, 294(6), H2565-H2575.

Downloads

Published

13/06/2022

How to Cite

PLOVAS, R.; PIMENTEL, R.; BISSACO, M. A. S.; GOROSO, D. G.; PUGLISI, J. L.; SILVA, R. R. da. Registration and query of biophysical parameters using the MyocyteDB platform. Research, Society and Development, [S. l.], v. 11, n. 8, p. e14711830712, 2022. DOI: 10.33448/rsd-v11i8.30712. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/30712. Acesso em: 8 oct. 2024.

Issue

Section

Engineerings