Component-Based Approach for Educating Students in Bioinformatics

There is an increasing need for an effective method of teaching bioinformatics. Increased progress and availability of computer-based tools for educating students have led to the implementation of a computer-based system for teaching bioinformatics as described in this paper. Bioinformatics is a recent, hybrid field of study combining elements of biology, statistics, and computer science. This paper describes the implementation of a system called Bioinformatics Foundational Learning Objects Workbench (BioFLOW), that is intended to be used by instructors to educate their students in bioinformatics principles at a variety of grade levels. BioFLOW functions by dividing topics in bioinformatics into modules. These modules cover a wide variety of topics and may be inserted into different courses in bioinformatics-related fields, providing students with a basic understanding of bioinformatics if they choose to pursue it as a field of advanced study. BioFLOW also incorporates a method for assessing the performance of the modules and the students. Assessment includes evaluation of students' learning styles and their performance on quizzes and is designed to evaluate the effectiveness of the modules. Utilized together, these tools can be used to refine the content of the teaching modules and thus provide an effective system for educating students in bioinformatics.      

Bioinformatics and Proteomics: An Engineering Problem Solving-Based Approach

There is a growing need for engineers in the burgeoning fields of bioinformatics and proteomics. The high-throughput nature of both of these related fields has made traditional biological methods, which tend to focus on one or two molecules at a time, obsolete. The consequent deluge of experiment-based information has made engineering and problem-solving skills essential to attack the resulting complex, multiscale problems. Certain technologies, such as robotics automation, microfabrication, control, and signal processing, are particularly amenable to the engineering expertise of electrical and other engineering disciplines. This paper describes methodologies and findings from 6.092/HST.480, two courses taught in 2005, at the Massachusetts Institute of Technology (MIT) that focused on bioinformatics and proteomics with an engineering-based, problem-solving approach. Many questions exist regarding how such interdisciplinary courses should be structured. For example, what should be the prerequisites, and what teaching methods could be successfully used? The course teaching style involved an elaboration, theory-based approach so that students could extend and apply engineering concepts at increasing levels of complexity as the course progressed. In addition, the biological epitomes used were in increasing levels of abstraction. On subsequent evaluations, students had high praise for the teaching, and several pursued further research in this area. Analysis of the student feedback suggested that this course served a previously unfilled need     

Review of "Knowledge Discovery in Bioinformatics: Techniques, Methods, and Applications" (Hu, X. and Pan, Y., Eds.; 2007) [Book Reviews]

This book, part of the Wiley series on Bioinformatics: Computational Techniques and Engineering, includes 16 chapters and 355 pages. It has extensive references on each chapter and an overall index. More than 50 authors contribute to the text, which is largely written as a series of research papers that are combined in book format. There are chapters on protein secondary-structure prediction, protein-protein interaction, protein identification, RNA secondary structure visualization, drug activity comparisons, cancer classification with microarray data, and cancer survival based on gene expression data. Other topics covered include data representation, storage, and access; text mining; and cluster analysis. The book is well written and edited. It is a worthwhile read for any bioinformatician and fulfills its stated objective of presenting cutting-edge research topics.