Computational model to simulate tumorous cell cycle dependent ion current modulation
2021
Graz University of Technology, Graz, Austria
The A549 cell line, derived from non-small cell lung cancer (NSCLC), is a widely used model for the study of lung cancer and the development of anticancer drugs. In this work, the authors present for the first time an electrophysiological model of the A549 human lung adenocarcinoma cell line. The model accounts for the kinetics of the major ion channels contributing to the total membrane current and the resting membrane potential of the cells. Based on experimental data using the whole-cell patch-clamp technique and an extensive literature review, the kinetics of each channel was modelled using a hidden Markov model, and the number of ion channels represented was estimated by fitting the macroscopic currents to the recorded whole-cell currents. The model was parameterized taking into account the specific ion channel activities of the A549 cells obtained from literature data and includes the major functionally expressed ion channels in the plasma membrane of the A549 cells known to date, and also takes into account the respective voltage and calcium dependencies. This approach now allows, for the first time, the simulation of channel interaction, activation and inhibition and, most importantly, the prediction of membrane potential changes for parts of the cell cycle. The availability of this first A549 in silico model 1.0 provides a deeper understanding of the potential roles and interactions of ion channels in tumor development and progression and may aid in the testing, verification, and validation of research hypotheses in lung cancer electrophysiology.
A549 in-silico 1.0: A first computational model to simulate cell cycle dependent ion current modulation in the human lung adenocarcinoma
Theresa Rienmüller, Sonja Langthaler, Christian Baumgartner
Added on: 07-29-2021
[1] https://journals.plos.org/ploscompbiol/article?id=10.1371%2fjournal.pcbi.1009091&emci=b4e6395d-d4e4-eb11-a7ad-501ac57b8fa7&emdi=44a13ddc-eee4-eb11-a7ad-501ac57b8fa7&ceid=2015591
