Program Description
Computational Life Sciences is currently one of the most important and exciting areas in all of science and technology, which is positioned at the intersection of modern biology, quantitative modeling and high performance computing. Computational Life Sciences is helping to provide a fundamental understanding of complex biological systems and offers the potential to significantly impact a wide variety of technologies, including drug discovery, novel therapies for human, animal and plant diseases, metabolic engineering and efficient production of traditional and high-value foodstuffs.
Computational Life Sciences can be divided into three major categories:
| Bioinformatics: | The research, development or application of computational tools and approaches for expanding the use of biological, medical, behavioral or health data, including those to acquire, store, organize, archive, analyze, or visualize such data. |
| Computational Biology: | The development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavioral, and social systems. |
| Systems Biology: | The development of quantitative, mechanistic based models of the whole cell, collections of cells or large pieces of the cellular machinery, where the objective is an integrated picture that compliments the reductionist viewpoint of molecular biology. |
In 2001 Purdue University established an interdisciplinary graduate specialization program in Computational Life Sciences (CLS) at the MS level. Starting in 2006 fall CLS specialization will be available at MS & Ph.D levels for students in departments that participate in the CLS program. The program provides students with the opportunity to study a specific science or engineering discipline along with gaining skills in CLS. The aim of the program is to produce students who have learned about computational tools and techniques in the life sciences. These skills, in turn, will help prepare them for discovery and implementation of algorithms that facilitate the understanding of biological processes.