Catalyzing Inquiry at the Interface of Computing and Biology

342 CATALYZING INQUIRY

somewhat like an M.B.A. with a science requirement. Some programs, such as Stanford’s, are adminis-
tered by the medical school, leading to a focus on medical informatics as well as bioinformatics. This
would include topics such as clinical trials and image analysis, which would not show up in a more
traditional genomics-focused bioinformatics degree.
The Research Training Program of the Keck Center for Computational and Structural Biology is
intended to develop one of two different kinds of expertise. Emerging from this program, a trainee
would be a computational expert well versed in computer science and quantitative methods who
would also be knowledgeable in at least one application area of biological significance, or an expert in
some biological area (e.g., molecular biology) who would also be aware of the most advanced concepts
in computing. Students entering from computational backgrounds take at least three courses in biol-
ogy-biochemistry-biophysics areas, while students entering from biological backgrounds at least three
courses in computational areas. In addition, all students take an introductory course in computational
science. Dissertation research is supervised by a committee with faculty members as required by
the student’s home department, but with representation from the computational biology faculty
at other Keck Center institutions as well. Research can be undertaken in areas including the visualiza-
tion of biological complexes, the development of DNA and protein sequence analysis, and advanced
simulations.

Box 10.4

Competence and Expertise in Computer Science for Biology Students

The BIO2010 report recommended that all biology students receive instruction in computer science, distin-

guishing among three levels of competency. From lowest to highest, these include the following:

• Fluency. Based on the NRC report Being Fluent with Information Technology, fluency refers to the ability
  of biology students to use information technology today and to adapt to changes in IT in the future. For
  example, they need a basic understanding of how computers work and of programming, and a higher degree
  of fluency in using networks and databases. Students should also be exposed to laboratory experiences using
  MEDLINE, GenBank, and other biological databases, as well as physiological and ecological simulations. For
  example, students could be asked to use computer searches to track down all known information about a
  given gene and the protein it encodes, including both structure and function. This would involve exploring the
  internal structure of the gene (exons, introns, promoter, transcription factor binding sites); the regulatory
  control of the gene; sequence homologues of the gene and the protein; the structure and function of the
  protein; gene interaction networks and metabolic pathways involving the protein; and interactions of the
  protein with other proteins and with small molecules.


• Capability in program design for computational biology and genomics applications. Students at this level
  acquire the minimal skills required to be effective computer users within a computationally oriented biology
  research team. For example, they would learn structured software development and selected principles of
  computer science, with applications in computational biology and allied disciplines, and would use examples
  and tutorials drawn from problems in computational biology.


• Capability in developing software tools for use by the biology community. At this sophisticated level,
  students need a grounding in discrete mathematics, data structures, and algorithms, as well as database man-
  agement systems, information systems, software engineering, computer graphics, or computer simulation
  techniques. Students at this level would be able to design and specify database and information systems for
  use by the entire community. Of special interest will be tools that require background in graph theory, com-
  binatorics, and computational geometry as applications in high-throughput genomics research and rational
  drug design become increasingly important.

SOURCE: Adapted from National Research Council, BIO2010: Transforming Undergraduate Education for Future Research Biologists, The

National Academies Press, Washington, DC, 2003.