Statistical Genetics and Bioinformatics

About the Program

Mission:
The idea of the Program is to integrate mathematical, statistical and computer methods to analyze biological, biochemical and biophysical data. The Graduate Program in Statistical Genetics and Bioinformatics will respond to the urgent need for highly-trained specialists in this interdisciplinary field, recently expressed by Francis Collins, head of the National Human Genome Research Institute who talked about "the paucity of trained individuals who are experts in both computational methods and biology" (Science, March 31, 2000). Also "Biology in the 21st century is rapidly becoming an information science." (Science, March 10, 2000) and "Genomic technologies and computational advances are leading to an information revolution in biology and medicine." (Science, March 17, 2000).

Several examples of research that will be conducted by successful graduates of the program are listed below:

• Developing methodologies for gene mapping (locating genes of hereditary diseases), analysis of evolutionary trees of genes and proteins, and evaluation of multiple gene expression in cancer cells (future diagnostic tool for cancer). This includes pursuing basic research in these areas as well as their applications in finding molecular causes of cancer and other diseases, design of proteins and developing statistical tools for analysis of massive data sets created by new experimental techniques.
• Analysis of gene-expression, which will allow identification of the functional role of genes and sequence annotation will allow classification of families of proteins based on alignment and related computational techniques. The levels of expression of various genes in different tissues at different stages in development or the cell cycle will provide great insight into many biological processes.
• Developing tools to analyze and correlate genomic data, rather then the generation of the sequence itself. Algorithmic advances are essential for comprehending the genomic information being accrued and for developing models of biological information.
• Utilizing numerical methods to integrate information at one level (gene, protein, pathways, cell, tissue, etc.) to predict functional consequences at another level. One example would be the use of genomic information to predict co-regulated protein expression and pathway redundancies (in metabolic pathways or signal transduction pathways for example). This area is sometimes called "genetic circuits".
• Utilization of knowledge of protein dynamics to predict protein-protein interactions (such as occur during receptor-ligand binding in cell adhesion or membrane ion channel activation).
• Developing algorithms for pharmaceutical Computer-Aided Design (CAD) tools. The primary goals are to shorten the duration of the drug design process and reduce its cost by generating better leads. The resulting computational problems are of formidable combinatorial complexity, making computer-assisted molecular biology one of the most important challenges facing applied computer science today.

Core Curriculum

Faculty

Graduate Studies at Rice University