Computational bioengineering generally describes the science of computational approaches to biological problems below the cellular level. Although evolving, Computational Bioengineering has matured to comprise kernel sub-disciplines. These include biological sequence analysis, the structure and function of proteins and nucleic acids, genetic networks and gene expression, molecular evolution, and hypothesis generation from large-scale data sources.
Through modeling and analyzing systems, Computational Bioengineering provides a rationalization of hypothesis formation, thereby reducing the problem space confronted by experimental approaches in traditional biology. A computational progenitor to this is rational drug design, routinely employed by pharmaceutical companies. In the genome enabled era, random approaches to genetic engineering are increasingly complemented by rational approaches where Computational Bioengineering plays a pivotal role.
Central methodologies brought to bear on these problems are derived from probability and statistics, signal processing, algorithms and their analysis, linguistics, graph theory, linear algebra, differential equations and optimization theory, database theory, and data mining. The Computational Bioengineering core at KU provides the student with formal course work in methodologies and applications with an emphasis on research.