BMI Course Information
BMI 520 Modeling Gene Regulatory Networks (3)
Instructor Information |
|
Fall 2009 |
Course not offered |
Spring 2009 |
Course not offered |
Catalog Description
Computational and mathematical modeling used to approximate gene regulatory networks as well as signaling pathways and inference of model parameters.
Prerequisites
BMI 505 and 516 with a C or better and admission to any SCI graduate program
Textbook and Other Materials
An Introduction to Systems Biology: Design Principles of Biological Circuits (Chapman & Hall/CRC Mathematical and Computational Biology Series) by Uri Alon (Author) Chapman & Hall/CRC; 1st Ed (July 7, 2006).
Course Learning Outcomes
Students who complete this course will be able to:
Understand basic cell biology and cell regulatory systems
- Understand high throughput molecular measurement technologies and relevant biological knowledgebase
- Understand other functional biological data relevant to modeling of gene regulatory networks
- Understand modeling of gene regulatory networks and signaling pathways and inference of model parameters from high throughput molecular data
- Demonstrate a thorough understanding of diverse high throughput molecular measurements and relevant biological knowledgebase.
- Demonstrate the ability to formulate regulatory relations among molecular components into quantitative and computational models.
- Demonstrate a thorough understanding of various mathematical and computational models for gene regulatory networks and signaling pathways.
- Demonstrate the ability to apply various machine learning and reverse engineering techniques to learn model parameters from observable high throughput molecular data.
- Discuss the limitations of each modeling and inference technique, and the necessary information for the techniques.
- Demonstrate the ability to improve existing modeling and inference techniques.
Major Topics and Time Covered
Overview of basic cell biology and cell regulatory systems
Overview of high throughput molecular measurements and biological knowledgebase
- Gene expression microarrays, array-based CGH data
- Protein interaction data
- Biomedical literature
Computational models for gene regulatory networks
- Boolean networks and probabilistic Boolean networks
- Graph-based models and Bayesian networks
- Rule-based model (AI approach)
- Differential equation-based models
- Incorporation of biological knowledge into modeling as prior knowledge
Inference algorithms for models
- Parameter estimation
- Reverse engineering
- Bayesian learning