In spite of aggressive therapies, the outcome for patients suffering from malignant brain tumors remains uniformly fatal. The failure to accurately predict patterns of tumor growth and invasion in the brain constitutes an important limitation to instituting current and future treatments. The investigators propose that characteristic biological features of these tumors (including growth, necrosis, growth-arrest, and invasion) can be modeled, simulated and predicted as a biosystem using principles developed by complex materials science. To study these nonlinear dynamics, this interdisciplinary group at MGH-Brain Tumor Center and Princeton's Complex Material Theory Laboratory has started to develop a computational tumor growth model. Since a preliminary cellular automaton-model predicts available experimental and clinical data very well, they propose to significantly expand these pilot studies by showing that: 1) solid tumor growth can be modeled using cellular automaton techniques and simulated as an evolving MTS (Specific Aim 1); 2) tumor cell invasion follows the materials science principle of "minimal energy dissipation" (Specific Aim 2); and 3) the combination of the results from Aims 1 and 2 leads to the appropriate model to simulate the growing tumor as a complex dynamic self-organizing system (Specific Aim 3). The advanced computational modeling will draw from various techniques of statistical mechanics, including multidimensional cellular automata, molecular dynamics, fractal analysis and microstructure characterization. The resulting simulation data will be continually compared to available clinical and experimental observations to verify their accuracy. Ultimately, this work will not only significantly alter the understanding of tumorigenesis, but also will inform on the effect of treatments on network patterns, and thus allow the development of novel, more specific therapeutic approaches. Future applications include virtual treatment planning and intraoperative navigation. Moreover, important advances in materials science, statistical mechanics and complex biomedical system simulation in general are expected.