This project introduces the concepts of a new multidisciplinary field (bioinformatics) within the existing computing curriculum by incorporating research-oriented, self-contained learning modules. Prospects for majors in the field of bioinformatics are expanding, including opportunities for graduate study and employment. This project fulfills this demand by introducing the notion of self-contained modules that encapsulate not only the core biological principles necessary to fully appreciate the computational aspect of the subject, but they also provide theoretical and practical exposure to the related computational biology algorithms and research areas. Graduates who know how to identify and work on solving leading research problems gain the significant benefit of being able to apply bioinformatics to a broad range of problems. In addition to being immersed in a burgeoning research sector, graduates of this program, who have free access to the newest knowledge and skills in the field as it evolves, may enjoy a competitive advantage in the workforce. Specifically, this project involves developing five lecture series. Each lecture series is comprised of 2-3 lectures and 1-2 laboratory modules devised for integration within the existing courses. The modules are designed with the objective of serving as academic examples of some of the computer science concepts covered in courses such as databases, analysis of algorithms, networking, data structures, etc. Thus, the main goal is to have a CS major cognizant of the computational challenges in bioinformatics. These modules all have some common characteristics. They are self contained, designed to be delivered with 3 to 4 hours of student contact, present the bioinformatics research concepts within a computer science context, incorporate a hands-on laboratory component, and integrate cognitive assessment with the objective of helping students appreciate the depth of their understanding of new concepts taught. The modules include: BINF 01 - The Biological Database Lectures Module (Biological Databases, GenBank Schema, Biological Database Federation, Biological Databases and Federation Architecture); BINF 02 - The Information Retrieval Lectures Module (Sequence Similarity Algorithms in Bioinformatics, Bio-Database Indexing Strategies, Genome Database Search Algorithms, Searching Genomic and Protein Databases with BLAST); BINF 03 - The Intelligent Models for Mining Biological Data Lectures Module (Computational Models of Biological Sequence, Hidden Markov Models for Biological Patterns, Machine Learning Paradigms applied to Bioinformatics, Applying Hidden Markov Models for Sequence Analysis, Information Theoretical Measure of Surprise); BINF 04 - The Adaptive Middleware Lectures Module (The Bioinformatics Open Source Project, CORBA and BioCORBA, Java Servlets and Distributed Annotation Systems, Internet Sequence Analysis with BioJava and BioPerl, Dazzle Servlet and DAS); and, BINF 05 - The Evolutionary Tree Computation Lectures Module (Computational Complexity of Constructing Evolutionary Trees, Cluster Based Methods for Evolutionary Tree, Hamming Distance and Parsimonious Trees, PHYLIP - The Phylogenetics Analysis Program).
