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2019 AAAS Annual Meeting

CQM faculty member Dr. Reinhard Laubenbacher, together with Dr. Mark Alber from UC Riverside, organizes scientific symposium on "Mathematical Modeling of Diseases: Translational Approaches" at the 2019 AAAS Annual Meeting in Washington, DC., February 14-17, 2019.


Progress in understanding and modeling disease processes based on the availability of data about individual patients has the potential to greatly improve medical treatments by personalizing them, thus leading to what is known as precision medicine. Precision medicine’s promise is to customize treatments by making use of patient-specific data, such as genomic, physiological, or lifestyle information. The overarching challenge then is to connect individual features of a patient’s data to the outcome of certain interventions. Computational models are used that range from data-driven and correlation-based associations to mechanistic predictive models, often integrating processes that span several spatial and temporal scales. This session presents three examples of personalized models. The first is called an artificial pancreas, which is used to treat patients with type I diabetes. The second model provides a guide to surgical treatment of tumors. The third is used to improve outcomes of coronary bypass graft surgeries. These translational modeling projects integrate an understanding of the relevant individual variability of physiology or biochemistry with fundamental insights into disease dynamics. All of these projects are based on close collaborations between mathematical and computational scientists and clinicians. The result is a very timely and important bench-to-bedside paradigm.


Boris Kovatchev, University of Virginia, Charlottesville, VA

The Artificial Pancreas: Models, Signals, and Control in Diabetes

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Kristin Swanson, Mayo Clinic, Scottsdale, AZ

Each Cancer Patient Deserves Their Own Equation: Patient-Specific Neuro-Oncology


Andrew D. McCulloch, University of California San Diego, La Jolla, CA

Patient-Specific Multi-Scale Modeling of Heart Disease

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