Quantifying assays: A Modeling tale of variability in cancer therapeutics assessed on cancer cells

By

Roumen Anguelov*, Manjunath Gandhi*, Avulundiah E. Phiri*, Trevor T. Nyakudya#, Priyesh Bipath#, June C. Serem+, and Yvette N. Hlophe#

*Department of Mathematics and Applied Mathematics, +Department of Anatomy, #Department of Physiology University of Pretoria

On

Tuesday, 23 August 2022 14:30 - 16:00

Venue
Plant Science Auditorium, Hatfield Campus, University of Pretoria

Abstract: Inhibiting a signalling pathway concerns controlling the cellular processes of a cancer cell's viability, cell division, and death. Assay protocols created to see if the molecular structures of the drugs being tested have the desired inhibition qualities often show great variability across experiments, and it is imperative to diminish the effects of such variability while inferences are drawn. In this paper we propose the study of experimental data through the lenses of a mathematical model depicting the inhibition mechanism and the activation-inhibition dynamics. The method is exemplified through assay data obtained from the study of inhibition of the CXCL12/CXCR4 activation axis for the melanoma cells. To mitigate the effects of the variability of the data on the cell viability measurement, the cell viability is theoretically constructed as a function of time depending on several parameters. The values of these parameters are estimated by using the experimental data. Deriving approximation for the cell viability in a theoretically pre-determined form has the advantages of (i) being less sensitive to data variability (ii) the estimated values of the parameters are interpreted directly in the biological processes, (iii) the amount of variability explained via the approximation validates the quality of the model, (iv) with the data integrated into the model one can derive a more complete view over the whole process. These advantages are demonstrated in the step-by-step implementation of the outlined approach.

This research project is supported by the DST/NRF SARChI Chair on Mathematical Models and Methods in Bioengineering and Biosciences