Linking Computer Science and Science Students

Modern bioinformatics research gathers vast amounts of data. While data management strategies are integral to computer science they are often not included as part of a graduate curriculum in chemistry, biology or bioengineering. In order to forge partnerships between computer science and science departments, Professor Walt and Computer Science Professor Carla Brodley teamed up to offer a new course in the Fall of 2006: Interdisciplinary Research: Computer Science Problems in Biology, Chemistry, and Bioengineering. In the Fall of 2006, the course was a one semester seminar. Each week, one science student would present an overview of his or her research and data management challenge. The presentations were designed to include background information in biology, chemistry and bioengineering for the computer science students, and were followed by a group discussion of possible solutions. Computer Science students who were interested in the project would team up with the scientist presenter and arrange future meetings, including visiting the laboratories and getting hands on experience with the data collection processes. After projects were selected, each week several of the teams presented their approaches to solving the data processing needs of the scientific problems. Participating science students felt their research was greatly facilitated as a result of their partnership with a computer science graduate student. In one case, a valuable computer program was generated that allowed data to be more easily extracted from experiments two bacterial strains using a fiber optic sensor. Another participant commented that the computer program reduced his workload by designing assays in seconds which previously had taken him months to complete. Computer science students enjoyed the opportunity to learn more about scientific research and to apply their knowledge to specific research problems. The course will be offered again in the fall of 2008, but as a year long course rather than just a semester long course.

Projects included: Analyzing fluorescence images of single cells to determine the nature of their chemical communication pathways; developing methods for connecting muscular movements with nerve cell responses in caterpillars to understand how neural signals lead to coordinated movement; processing complex sensor array responses to vapors to determine if algorithms could be developed that enable recognition of unknown vapors; developing gene analysis software to select sequences containing the most information for inclusion in a DNA array; analyzing images of stem cells to identify key cellular components automatically without human intervention; developing an algorithm for following single molecules moving on a surface using atomic force microscope images. The Science students learned a lot about data mining and machine learning and how these methods could be applied to research problems. Computer Science students learned a lot of science. Most important was that all students learned to work collaboratively and realized that it takes a lot of hard work and effort to learn one another’s problems and the specialized language of each discipline.

Students gained an appreciation of the value of working on interesting interdisciplinary problems that generate large data sets and solving complex problems that require sophisticated Computer Science solutions. Students learned how to explain their approaches to non-specialists in a way that conveyed all the necessary information in a way that was understandable.

For many of the projects, the course was only the beginning. Numerous collaborative integrated research projects between Computer Science and Chemistry, Biology, and Bioengineering students were stimulated by the class. A list of resulting projects and papers can be found on the Results page