Computational Functional Genomics, Spring 2005

Abstract:: Study and discussion of computational approaches and algorithms for contemporary problems in functional genomics. Topics include DNA chip design, experimental data normalization, expression data representation standards, proteomics, gene clustering, self-organizing maps, Boolean networks, statistical graph models, Bayesian network models, continuous dynamic models, statistical metrics for model validation, model elaboration, experiment planning, and the computational complexity of functional genomics problems.

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Expasy Proteomics Tools

Abstract:: This module describes the many proteomics tools available from the ExPASy website. Tools are introduced for protein identification and characterization from amino acid composition, fingerprint mass spectroscopy and other mass spectroscopy techniques. A

Abstract:: Introduction to computational biology including the fundamentals of protein and nucleic acid sequence analysis, phylogenetic analysis, motif finding, hidden Markov models, and 3D structure prediction and modeling. An overview of emerging fields including expression profiling, quantitative image analysis and the modeling of cellular signal transduction networks are also included. Subject designed for advanced undergraduates and graduate students with strong backgrounds in either molecular biology or computer science but not necessarily both. Two self-study tracks are offered, introducing either basic statistical methods and programming (to biologists) or the fundamentals of molecular biology (to computer scientists). Lectures combine both perspectives to illustrate how computation is having a significant impact on modern biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.