Supermodeling in Simulation of Melanoma Progression

Supermodeling is an interesting and non-standard concept used recently for simulation of complex and non-linear systems such as climate and weather dynamics. It consists in coupling of a few imperfect sub-models to create the superior supermodel. We discuss here the supermodeling strategy in the context of tumor growth simulation. To check its adaptive flexibility we have developed a basic, but still computationally complex, 3-D modeling framework of melanoma growth. The supermodel of melanoma consists of a few coupled sub-models, which differ in values of a parameter responsible for tumor cells and extracellular matrix interactions. We demonstrate that due to synchronization of submodels, the supermodel is able to simulate qualitatively different scenarios of cancer growth than those observed for sub-models when run separately. These scenarios correspond to the basic types of melanoma cancer. This property makes the supermodel very flexible to follow and to predict real cases of melanoma development through learning the coupling coefficients between sub-models from real data. On the basis of preliminary simulation results, we discuss the prospects of supermodeling strategy as a promising coupling factor between formal and data-based models of tumor.