Evolution as a Basis for Bioinformatics Education

John R. Jungck, John Greenler, Sam Donovan

Biology Department, Beloit College, 700 College Street, Beloit, WI 53511


Poster presented at the 40th American Society for Cell Biology Annual Meeting, December 9-13, 2000, San Francisco.

Published in Supplement to Molecular Biology of the Cell Volume 11, December 2000 p. 26a.  


With the extraordinary number of opportunities available to scientists with expertise in bioinformatics, numerous institutions are developing curricula for both undergraduate and graduate students. In response to this challenge, the BioQUEST Curriculum Consortium (BioQUEST = Quality Undergraduate Educational Tools and Simulations in Biology), a fourteen year old national curricular reform initiative, in collaboration with EOT-PACI (Education, Outreach and Training - Partnership for Advanced Computing Infrastructure), is developing problem solving approaches to bioinformatics that stress the foundational importance of evolutionary biology. Ming-Ying Leung and J. Aaron Cassill (NSF DUE EMD Award - #9981104) have defined bioinformatics as the "study [that] integrates mathematical and computational techniques with biological knowledge to extract, organize, and interpret information from a wealth of genetic sequence data obtained from various genome projects." This foregrounding of mathematics and computer science in bioinformatics education has meant either that students major in these two disciplines with a minimal exposure to biology or that students in biology take almost all of their cognate coursework in these two areas. Unfortunately, both types of extant programs have to date ignored any deep education in evolutionary biology. BioQUEST has a long history of trying to help undergraduates learn long term strategies of research by working on open-ended problems with powerful professional tools with a consistent learner-centered pedagogical philosophy: problem posing, problem solving, and persuading peers. In this case, we (http://www.bioquest.org/bioinformatics) have combined the use of a powerful bioinformatics package, Biology Workbench, (http://workbench.sdsc.edu) developed at the supercomputer centers at the University of Illinois and the University of California San Diego, with typologies of evolutionary problem solving that we have developed to differentiate between spatial, temporal, and genealogical hypotheses or between evolutionary, genetic, and developmental biological levels of analysis.

Poster Elements

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One cell, three genomes

Phylogenetic Profiling

Paralogous relationships

Orthologous realationships

Xenologous relationships

Chronological Analysis

Geneological Analysis

Spatial Analysis

Bioinformatics education project

Project Support

BioQUEST Curriculum Consortium