cco landing

The Center for Computational Oncology's vision is to develop biophysical models of tumor initiation, growth, invasion and metastasis to establish a sound theoretical framework for describing the hallmarks of cancer and to use this knowledge to discover fundamental cancer biology and develop tumor-forecasting methods to optimize treatment and outcomes for the individual patient. 

The last half decade has seen an explosion in literature on mathematical and computational models of invasion of growth of tumors in living tissue. Particularly intriguing is the progress toward patient-specific treatments made possible by new predictive computer simulations. 

The reasons for this new potential in tumor growth models include: 

An increasing consensus in the medical science community on the principal mechanisms leading to various cancer types. 

New families of models based on a better understanding of the role of genetics in encoding proteins that form phenotypes and molecular alterations at the gene, cell and tissue level. These models could greatly increase our understanding of the origins and growth of cancer and new therapies to combat it. These advances have led to a flurry of new multiscale computational models that depict events at many spatial and temporal scales, from sub-cellular to cellular to tissue and organ levels. 

The gradual emergence of predictive medical sciences, which addresses in depth the actual validity and, equivalently, the predictability of various models in the presence of uncertainties. This vital discipline has come to the forefront because the indispensable data needed to calibrate and validate tumor growth models have only recently become available. 

The enormous advances in high-performance computing have brought into play an arsenal of new tools with great potential for developing realistic high-fidelity simulations of cancer cell behavior. 

The Center for Computational Oncology is involved in active research in many of the foundations of modeling tumor growth and in accessing and employing relevant in vitro and in vivo data to calibrate and validate predictive models. 

Center for Computational Oncology