Bioinformatics 527 (4 Credit Lecture & Lab)
Introduction to Bioinformatics and Computational Biology

 
Course Master:  Brian D. Athey, Ph.D. (bleu@umich.edu)

Class Schedule: Tuesdays and Thursdays 1:00 PM – 2:30 PM

Location:  Room 2062 Palmer Commons (PC)

BI 527 Laboratory Jeffrey R. de Wet, Ph.D. (jrdewet@umich.edu) Laboratory Master and GSI.

Laboratory Schedule: Wednesday or Friday Section 1:00 PM – 2:30 PM

Location: Room 2036 Palmer Commons

Prerequisites: Upper level or graduate level Statistics or concurrent enrollment in Statistics; Calculus I & II; Biochemistry, Molecular Biology, or Cellular Biology; or permission of instructor.

Note: Bioinformatics 527 is required of all students in the Graduate Program in Bioinformatics

This course introduces the essential concepts, computational algorithms, databases and data sources and high-throughput experimental methodologies that are the basis of modern bioinformatics and computational biology. BIOINF 527 consists of two lectures and one laboratory session per week. There will be a homework assignment each week, and there will be two take-home exams. The laboratories will utilize on-line and local computer tools and resources to demonstrate bioinformatics data analysis methods and algorithms. The laboratory will culminate in the presentation of group projects that will be developed over the term.

The class is organized into four topic modules:

Module I: Basics of Bioinformatics

• Introduction to Bioinformatics and Architecture of the Human Genome
• High-Throughput Methods: Chromatin Structure; Gene Expression
• Data, Information and Knowledge Management Concepts and Bioinformatics, Software and Data Policy
• Data resources in Molecular Biology and Information Retrieval:  NCBI, EBI, etc.
• Computational Genomics
• Sequence Alignment, BLAST, and Database Search Statistics

Module II: Computational Phylogeny

• Molecular Evolution and Population Genetics I & II
• Phylogenetics
• 2D and 3D Protein Structure; Protein Structure Databases and Homology Models
• Patterns, Profiles and HMMs; Multiple Sequence Alignment

Module III: Systems Biology

• Introduction to Systems Biology
• Applications of Systems Biology to Medicine
• Systems Biology: Integrating Genomic and Proteomic Technology
• Overview of Key Systems Biology Research Directions
• Proteome Informatics and Statistical Methods, Pathways and Molecular Interactions
• Bayesian Approaches to Systems Biology

Module IV: Modeling

• Multi-scale Modeling and Introduction to Computational Biology
• System modeling I: basics techniques, deterministic & stochastic
• Systems modeling: application to circadian rhythms
• Modeling in viral and microbial systems and Epidemiology
• Modeling in Immunological Systems
• Towards Systems Medicine: The relationship between Bioinformatics and Clinical Informatics

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