Computational and Systems Biology
The Computational and Systems Biology specialty curriculum is designed to help students learn how to use mathematical and computational techniques to understand biological and chemical processes. Any student pursuing a Ph.D. with a background and interest in this field can add a Computational and Systems Biology specialty to his or her Ph.D.
Research in Computational and Systems Biology can be categorized into three general areas: bioinformatics, theoretical biology, and biostatistics. Applications of these computational concepts, methods, and algorithms to different questions results in a variety of research topics, including:
- Biophysics and Structural Biology – Protein structure and function prediction; analysis of biological sequences and 3-D structures; macromolecular interactions and biological networks; molecular evolution
- Chemical Biology – Computer programs to analyze small organic molecules; design of materials and drugs; chemical dynamics
- Genetics and Genomics – DNA polymorphism analysis; microRNA target prediction and validation; statistical genetics
- Quantitative Microscopy – Computational image analysis, statistical data analysis, and mathematical modeling to extract molecular and cellular organization and dynamics from microscopy data.
- Systems Biology – Computational reconstruction and analysis of biological networks; modeling of complex, nonlinear systems; principles underlying spatial-temporal organization of molecular networks; DNA and protein microarray data analysis
Participation in a weekly series of alternating student work-in-progress/journal club presentations and seminars by invited faculty contributes to each student’s success.
A new curriculum is currently in development.
Computational and Systems Biology students have access to UT Southwestern’s internal high-performance computing facilities, housed by the Biochemistry Department and the McDermott Center for Human Growth and Development, and to supercomputers from the Texas Advanced Computing Center (TACC) at the University of Texas at Austin. TACC resources are among the largest in the country, and its most powerful supercomputer, Ranger, peaks in performance at around 500 trillion floating point operations per second (teraflops). In addition to computation and data storage, TACC provides courses in advanced computing such as parallel and grid computing.
Message from the Chairs
Computational approaches have become increasingly important in biomedical research. Since computation is usually cheap and fast, starting a project in silico could be highly beneficial for experimental design. Learning how to pose a question that can be addressed by computation, set up computational experiments, and interpret the results are key to success.
The main feature of our approach to students is flexibility. Since our students come from varying backgrounds, ranging from computer science to experimental biology, everyone receives individualized attention depending on their needs and is free to select the courses they are most interested in to shape their knowledge.
In addition to specialized and personalized coursework, our students meet at the Work In Progess and Journal Club sessions. We discuss their projects and the most stimulating papers in the field, and our students learn from each other. The final goal is to achieve confidence and independence in designing and carrying out computational experiments that are both instructive and efficiently address specific biological problems.