Systems Biology
Developing a software program that incorporates novel mathematical methods that can capture and thus more accurately represent cellular, tissue, and ultimately physiological states, and optimization techniques to facilitate rational design of therapeutics to prevent, minimize or reverse the decline of cellular function. An important challenge in the development of new drugs and therapies for complex (multi-factorial) diseases is to obtain an improved and comprehensive representation of the process. A systems biology approach can reconstruct the associations and interactions that lead to a system's behavior by integrating the information derived from these various levels. Identifying these interactions can lead to medical advances, such as earlier markers of disease progression and novel therapeutics to prevent the manifestation of the disease. Obtaining support for this research will provide the necessary preliminary results that we can use to leverage new funding from the agencies mentioned below.
Research Team: Christina Chan (Chemical Engineering), Sarat Dass (Statistics & Probability), Robert Roth (Pharmacology), and Brian Haab (Van Andel Institute).
Integrating genomic methods, analyses and modeling to identify disease resistance genes and pathways in poultry. To understand biological phenomena in their full complexity, multi-disciplinary efforts that synergize individual strengths in experimental biology, database management, statistics, and modeling are necessary for efficient drug design and development. For example, Marek's disease, a viral-induced T cell lymphoma, is a serious agricultural problem and a biomedical model for both cancer and vaccines. Genomic tools such as the chicken genome sequence (announced March 1, 2004 by the NIH), 460,000+ ESTs (the 6th most abundant organism), and Affymetrix arrays (scheduled for fall 2004) are being applied to several areas including Marek's disease resistance. The main hindrance in solving this complex biological problem (and most others) is the inability to efficiently integrate the vast amount of information measured at the DNA (genetic variation), RNA (gene expression variation), and protein (virus-host protein interaction) levels. The goal of this project is to develop methods and computational tools for integrating these data types.
Research Team: Marianne Huebner (Statistics & Probability), Hans Cheng (USDA Avian Disease and Oncology Laboratory), Vince Melfi (Statistics & Probability), Guilherme Rosa (Animal Science)