Computer Science

ECS 234 Computational Functional Genomics

ECS 234 COMPUTATIONAL FUNCTIONAL GENOMICS (4) II

Lecture: 3 hours

Discussion: 1 hour

Prerequisite: Course 124, graduate standing in Computer Science of Life Sciences

Grading: Letter; project (60%), presentation (30%), class participation (10%)

Catalog Description:
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.

Goals:

  1. Introduction to computational functional genomics
  2. Summarizing the state-of-the art in gene regulation modeling
  3. Elucidating the potential of large-scale biological data, and their integration, for functional inferences
  4. Emphasizing interdisciplinary research

Expanded Course Description:

  1. Biology, Biotechnologies, Experiments
    1. DNA, transcription, translation
    2. Large-scale technologies: DNA sequencing, Gene Expression Arrays, Protein-DNA, and Protein-Protein Interactions
    3. Experiments and Data
  2. Systems Modeling
    1. Modeling, Simulation, Inference
    2. Levels of modeling of genomic systems
    3. Large-Scale Data Modeling
  3. Bioinformatics and Data Mining of Large-Scale Data
    1. Gene Expression analysis (statistics, classification, clustering)
    2. Sequence analysis (promoter region analysis)
    3. TF-DNA and Protein-Protein interactions analysis (topological properties and comparison)
  4. Gene Network Inference
    1. Graph Models
    2. Boolean Networks
    3. Bayesian Networks
    4. Linear Additive Models
  5. Combining Heterogeneous Data Sources
    1. Sequence + gene expression
    2. Gene Expression + protein-protein interactions
    3. Methods for general data integration

Textbook:
None

References:
Selected technical papers and class notes will be used

Project:
Students will use methods taught in class to follow the process of gene regulation inference from available data. Both theoretical and applied projects will be suggested. The projects will be done in groups consisting of a fair mix of life science and computer science students.

Presentations:
Students will present technical papers or software used for gene regulation inference.

Computer Usage:
Possible use for projects.

Instructor: V. Filkov

Prepared by: V. Filkov (February 2006)

Overlap Statement:
There is no significant overlap with other courses.

01/07

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