In order to study cancer at a system-level, engineering and physical sciences approaches tightly integrated with experimental data and clinical observations are necessary. The aim of this work is to predict tumor growth and invasion from the molecular and cellular scale events, with the ultimate goal to help analyze tumors of specific patients. The work begins with model development that describes tumor behavior in the language of mathematics and physics. Model parameter values are calibrated from experimental data. The experiments include culturing tumor cells in 2-D monolayers and as 3-D spheroids, analysis of histopathology of tumor biopsy specimens, and measurements and observations from previous work in the literature. The effects of varying the model parameters are then studied to predict the tumor behavior and to design optimal therapy. This process is iterative, with the findings used to refine the underlying model as well as to guide the experiments. This bioengineering work provides interdisciplinary exposure to the latest research and technologies in the exciting fields of cancer biology, scientific computing, data visualization, mathematical biology, and physical oncology.
There are big challenges with this research. A major difficulty is that the biological processes that need to be modeled may not be well understood. Cells are complex, and tissues magnify this complexity in unexpected ways. Furthermore, it may be challenging to determine values for the model parameters from the biological information, and many of the relevant parameters may not even be known before a model is developed. Another challenge is that the modeling work is iterative in nature. Hypothesis formulation enables simulation; comparison to experimental data may then lead to reformulation of the hypothesis. In this case, the model may have to undergo refinement or perhaps be completely reformulated. The biggest challenge of all, however, is to properly translate this work so that it can be applied to help patients who suffer from cancer.