Research Areas - Biosystems & Computational Biology (BIO)
Modern biology is increasingly reliant on the algorithmic and conceptual tools of computer science and electrical engineering. A major factor is the unprecedented growth in the size and scope of biological data sets, including multi-species genomic data, databases of polymorphic variants, databases of protein structure and RNA structure, gene expression data, biochemical measurements from large-scale gene knockout experiments and biomedical data. Representing, manipulating and integrating such data requires an appreciation of ideas from diverse areas of EECS such as databases, algorithms, artificial intelligence, graphics, signal processing and image processing. Reasoning about the underlying phenomena that give rise to such data require the systems-level thinking that is the underpinning of areas such as control theory, information theory and statistical machine learning. Ideas from circuit design and nanotechnology play key roles in the design of new biological sensors and actuators. Students in EECS who work on biological problems obtain a cross-disciplinary education in EECS and biology, and often play key roles in collaborative research projects involving biology faculty and students.
It is also important to note that the research efforts in biosystems and computational biology in EECS are part of a larger, campus-wide initiative in computational biology. Indeed, many EECS faculty are members of the Center for Computational Biology (CCB), which includes faculty from nine departments and five colleges. For information on the Center and additional course offerings, please visit http://qb3.org/ccb. Note that the CCB does not offer a Ph.D.; rather, students seeking graduate training in computational biology should apply to the department that most closely matches their interests. All courses listed on the CCB website are open to all interested students meeting the prerequisites regardless of departmental affiliations.