Multi-Scale Study of Cancer

Friday, February 15, 2013: 8:00 AM-9:30 AM
Room 207 (Hynes Convention Center)
Despite the identification of hundreds more potential targets by genome sequencing, the pharmaceutical industry has been faced with a decline in the production of new successful drugs. Mathematical models provide a framework in which to interpret the vast amount of experimental data generated to suggest experiments necessary to test new biological theories leading to development of efficient drugs. Genetic mutations occurring at a subcellular level manifest themselves in a complex, multi-scale cancer process as functional changes at the cellular and tissue scales. Integration of mathematical modeling approaches with statistical analysis of the experimental and clinical data leads to breakthroughs in understanding of the cancer growth, which is translated into novel patient-specific therapies and development of new drugs. When applied to experimental data, statistical techniques can reveal whether a particular intervention produces a significant response or whether a correlation exists between observable phenomena. Establishing why such correlations arise requires verification of a large number of hypotheses using modeling. For example, models offer greater scope for understanding normal and diseased colorectal crypts, for testing and identifying new therapeutic targets in colon cancer, and for predicting their impacts. Speakers have been selected for their contribution to the field and their ability to communicate to audiences with diverse backgrounds and scientific interests.
Mark Alber, University of Notre Dame
Jill P. Mesirov, Broad Institute of Massachusetts Institute of Technology and Harvard University
Mark Alber, University of Notre Dame
Mark Alber, University of Notre Dame
and Jeremy Gunawardena, Harvard Medical School
Philip Maini, University of Oxford
Hybrid Approach to Multi-Scale Modeling of Cancer
Martin Nowak, Harvard University
Dynamics of Targeted Cancer Therapy
Kathleen Wilkie, Tufts University School of Medicine
Modeling in Cancer Immunology
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