Title |
Tailoring Mathematical Models to Stem-Cell Derived Cardiomyocyte Lines Can Improve Predictions of Drug-Induced Changes to Their Electrophysiology
|
---|---|
Published in |
Frontiers in Physiology, December 2017
|
DOI | 10.3389/fphys.2017.00986 |
Pubmed ID | |
Authors |
Chon Lok Lei, Ken Wang, Michael Clerx, Ross H. Johnstone, Maria P. Hortigon-Vinagre, Victor Zamora, Andrew Allan, Godfrey L. Smith, David J. Gavaghan, Gary R. Mirams, Liudmila Polonchuk |
Abstract |
Human induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) have applications in disease modeling, cell therapy, drug screening and personalized medicine. Computational models can be used to interpret experimental findings in iPSC-CMs, provide mechanistic insights, and translate these findings to adult cardiomyocyte (CM) electrophysiology. However, different cell lines display different expression of ion channels, pumps and receptors, and show differences in electrophysiology. In this exploratory study, we use a mathematical model based on iPSC-CMs from Cellular Dynamic International (CDI, iCell), and compare its predictions to novel experimental recordings made with the Axiogenesis Cor.4U line. We show that tailoring this model to the specific cell line, even using limited data and a relatively simple approach, leads to improved predictions of baseline behavior and response to drugs. This demonstrates the need and the feasibility to tailor models to individual cell lines, although a more refined approach will be needed to characterize individual currents, address differences in ion current kinetics, and further improve these results. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 4 | 40% |
United States | 2 | 20% |
Macao | 1 | 10% |
Australia | 1 | 10% |
Switzerland | 1 | 10% |
Unknown | 1 | 10% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 8 | 80% |
Members of the public | 2 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 42 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 11 | 26% |
Student > Ph. D. Student | 9 | 21% |
Student > Bachelor | 5 | 12% |
Student > Doctoral Student | 4 | 10% |
Lecturer | 3 | 7% |
Other | 3 | 7% |
Unknown | 7 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Biochemistry, Genetics and Molecular Biology | 6 | 14% |
Engineering | 6 | 14% |
Agricultural and Biological Sciences | 5 | 12% |
Computer Science | 4 | 10% |
Mathematics | 4 | 10% |
Other | 7 | 17% |
Unknown | 10 | 24% |