Abstract
Abstract WHO Grade IV astrocytoma, also known as glioblastoma, is an invasive disease where cell migration drives dispersion of tumor cells throughout the brain. To predict cell migration speeds in complex mechanochemical environments, we developed a cell migration simulator based on our published motor-clutch framework [1-3]. One of the key predictions of our previous modeling is that cell traction force is biphasic with respect to environmental stiffness, i.e. there is an optimal stiffness at which cell traction force is maximal. To test whether such optimality exists within a whole cell migration simulator, we extended our model to allow for multiple cellular protrusions in 1D, 2D, or 3D environments. The cell migration simulator imposes that forces balance and that masses of key elements- actin, adhesions, and myosin motors- are conserved. Using the Gillespie stochastic simulation algorithm implemented in matlab, we simulated cell traction force, F-actin retrograde flow, cell aspect ratio, and cell migration as a function of cell stiffness. We found that all four variables depends biphasically on simulated environmental stiffness with approximately overlapping optima. In addition, the model predicts that the optimal stiffness can be shifted by coordinate shifting of motor and adhesive clutch numbers, a prediction that we are now testing experimentally in vitro. Overall, our modeling results predict that effective anti-migratory treatment of patients will require knowledge of the cell migration speed landscape within parameter space. In particular, we predict that simultaneously inhibiting motors and clutches will be beneficial for patients whose tumors operate near the optimum, largely ineffective for those below the optimum, and potentially detrimental to those above the optimum. Thus, our cell migration simulator provides dynamic physical biomarkers that can potentially be used to classify glioblastoma patients for more precise treatment.
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