Mathematical modeling of cancer: the future of prognosis and treatment.

BACKGROUND Cancer research has undergone radical changes in the past few years. Producing information both at the basic and clinical levels is no longer the issue. Rather, how to handle this information has become the major obstacle to progress. Intuitive approaches are no longer feasible. The next big step will be to implement mathematical modeling approaches to interrogate the enormous amount of data being produced and extract useful answers (a "top-down" approach to biology and medicine). METHODS Quantitative simulation of clinically relevant cancer situations-based on experimentally validated mathematical modeling-provides an opportunity for the researcher, and eventually the clinician, to address data and information in the context of well-formulated questions and "what if" scenarios. RESULTS AND CONCLUSIONS At the Vanderbilt Integrative Cancer Biology Center (VICBC), we are integrating cancer researchers, oncologists, chemical and biological engineers, computational biologists, computer modelers, theoretical and applied mathematicians, and imaging scientists, in order to implement a vision for a combined web site and computational server that will be a home for our mathematical modeling of cancer invasion. The web site (www.vanderbilt.edu/VICBC/) will serve as a portal to our code, which simulates tumor growth by calculating the dynamics of individual cancer cells (an experimental "bottom-up" approach to complement the top-down model). Eventually, cancer researchers outside of Vanderbilt will be able to initiate a simulation based on providing individual cell data through a web page. We envision placing the web site and computer cluster directly in the hands of biological researchers involved in data mining and mathematical modeling. Furthermore, the web site will also contain teaching props for a new generation of biomedical researchers fluent in both mathematics and biology. This is unconventional bioinformatics: We will be incorporating biological data and functional information into a unified community-based mathematical framework. The result will be a tool for cancer modeling that will ultimately have basic research, therapeutic and educational value.