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Sensitivity analysis is an effective tool for systematically identifyingspecific perturbations in parameters that have significant effects on thebehavior of a given biosystem, at the scale investigated. In this work, using atwo-dimensional, multiscale non-small cell lung cancer (NSCLC) model, weexamine the effects of perturbations in system parameters which span bothmolecular and cellular levels, i.e. across scales of interest. This is achievedby first linking molecular and cellular activities and then assessing theinfluence of parameters at the molecular level on the tumor's spatio-temporalexpansion rate, which serves as the output behavior at the cellular level.Overall, the algorithm operated reliably over relatively large variations ofmost parameters, hence confirming the robustness of the model. However, threepathway components (proteins PKC, MEK, and ERK) and eleven reaction steps weredetermined to be of critical importance by employing a sensitivity coefficientas an evaluation index. Each of these sensitive parameters exhibited a similarchanging pattern in that a relatively larger increase or decrease in its valueresulted in a lesser influence on the system's cellular performance. This studyprovides a novel cross-scaled approach to analyzing sensitivities ofcomputational model parameters and proposes its application tointerdisciplinary biomarker studies.

Cross-Scale Sensitivity Analysis of a Non-Small Cell Lung Cancer Model: Linking Molecular Signaling Properties to Cellular Behavior