Dr. Laubenbacher received his Ph.D. in mathematics from Northwestern University in 1985. He has been a Professor at the Virginia Bioinformatics Institute and a Professor in the Department of Mathematics at Virginia Tech since 2001. He is also an Adjunct Professor in the Department of Cancer Biology at Wake Forest University in Winston-Salem (NC) and Affiliate Faculty in the Virginia Tech Wake Forest University School of Biomedical Engineering and Sciences. Prior to these appointments Dr. Laubenbacher was Professor of Mathematics at New Mexico State University. He has served as Visiting Faculty at Los Alamos National Laboratories, was a member of the Mathematical Science Research Institute at Berkeley in 1998, and was a Visiting Associate Professor at Cornell University in 1990 and 1993. Current interests in Dr. Laubenbacher's research group include the development of mathematical algorithms and their application to problems in systems biology, in particular the modeling and simulation of molecular networks. An application area of particular interest is cancer systems biology, especially the role of iron metabolism in breast cancer. For more information see his research group's website http://admg.vbi.vt.edu/
1. Cancer systems biology: Our understanding of cancer has been aided by a network centric view. The fundamental relevance of systems biology to the understanding and treatment of cancer is the insight that genes and proteins do not act in isolation, but rather as nodes in complex interactive networks that include multiple feedback mechanisms and redundancies. The design of effective drugs to battle cancer will depend on the understanding of these networks and of the specific network alterations present in an individual tumor. And an understanding of characteristic changes in metabolic networks can lead to new prognostic and diagnostic methods. The complexity of these dynamic networks makes it difficult or impossible to study them without the aid of computer models based on mathematical analysis. This talk will discuss systems biology and mathematical models as an approach to cancer biology by way of two case studies.
2. Algebraic models in systems biology: The long-term goal of molecular systems biology is to understand how the physiology of organisms arises through the dynamic interaction of the molecular constituents of life. Understanding the molecular networks formed in this way is an essential step toward solving many central problems related to human health, sustainable energy, a sustainable food supply, and a healthy environment. Mathematical and statistical models of the networks involved are an essential enabling technology for reaching this goal. This talk will provide some examples of the role mathematics plays in systems biology and will discuss some recent applications of algebraic geometry to this field. No background in mathematical biology is required, and the talk will be accessible to undergraduates and students and faculty from the life sciences.