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Inhibiting a signalling pathway concerns controlling the cellular processesof a cancer cell's viability, cell division, and death. Assay protocols createdto see if the molecular structures of the drugs being tested have the desiredinhibition qualities often show great variability across experiments, and it isimperative to diminish the effects of such variability while inferences aredrawn. In this paper we propose the study of experimental data through thelenses of a mathematical model depicting the inhibition mechanism and theactivation-inhibition dynamics. The method is exemplified through assay dataobtained from the study of inhibition of the CXCL12/CXCR4 activation axis forthe melanoma cells. To mitigate the effects of the variability of the data onthe cell viability measurement, the cell viability is theoretically constructedas a function of time depending on several parameters. The values of theseparameters are estimated by using the experimental data. Deriving approximationfor the cell viability in a theoretically pre-determined form has theadvantages of (i) being less sensitive to data variability (ii) the estimatedvalues of the parameters are interpreted directly in the biological processes,(iii) the amount of variability explained via the approximation validates thequality of the model, (iv) with the data integrated into the model one canderive a more complete view over the whole process. These advantages aredemonstrated in the step-by-step implementation of the outlined approach.

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