ECS 234: Computational Functional Genomics

ECS 234
Computational Functional Genomics
Effective Term
2016 Spring Quarter
Learning Activities
Lecture - 3.0 hours
Discussion - 1.0 hours
Bioinformatics methods for analysis and inference of functional relationships among genes using large-scale genomic data, including methods for integration of gene expression, promoter sequence, TF-DNA binding and other data, and approaches in modeling of biological networks.
ECS 124; Graduate standing in Computer Science or Life Sciences.
Enrollment Restrictions
Pass One and Pass Two open to Graduate Students in Computer Science only.

Summary of Course Content
I. Biology, Biotechnologies, Experiments
A. DNA, transcription, translation
B. Large-scale technologies: DNA sequencing, Gene Expression Arrays, Protein- DNA, and Protein-Protein Interactions
C. Experiments and Data

II. Systems Modeling
A. Modeling, Simulation, Inference
B. Levels of modeling of genomic systems
C. Large-Scale Data Modeling

III. Bioinformatics and Data Mining of Large-Scale Data
A. Gene Expression analysis (statistics, classification, clustering)
B. Sequence analysis (promoter region analysis)
C. TF-DNA and Protein-Protein interactions analysis (topological properties and comparison)

IV. Gene Network Inference
A. Graph Models
B. Boolean Networks
C. Bayesian Networks
D. Linear Additive Models

V. Combining Heterogeneous Data Sources
A. Sequence + gene expression
B. Gene Expression + protein-protein interactions
C. Methods for general data integration

Illustrative Reading
Selected technical papers and class notes will be used.

Potential Course Overlap
There is no significant overlap with other courses.

Course Category