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COMP SCI 775

COMPUTATIONAL NETWORK BIOLOGY

3 credits.

Introduces networks as a powerful representation in many real-world domains including biology and biomedicine. Encompasses theory and applications of networks, also referred to as graphs, to study complex systems such as living organisms. Surveys the current literature on computational, graph-theoretic approaches that use network algorithms for biological modeling, analysis, interpretation, and discovery. Enables hands-on experience in network biology by implementing computational projects.

Prerequisites & course information

Requisites: Graduate/professional standing Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Recognize problems in molecular biology that are appropriate for network modeling Audience: Graduate 2. Identify appropriate network algorithms for different tasks Audience: Graduate 3. Evaluate the strengths and weaknesses of different network algorithms designed for a specific biological problem Audience: Graduate 4. Gain practical experience in applying a select set of network algorithms on real data and evaluate its outputs Audience: Graduate 5. Understand the algorithmic and statistical concepts of different network-based approaches Audience: Graduate Learning Outcomes: 1. Recognize problems in molecular biology that are appropriate for network modeling Audience: Graduate 2. Identify appropriate network algorithms for different tasks Audience: Graduate 3. Evaluate the strengths and weaknesses of different network algorithms designed for a specific biological problem Audience: Graduate 4. Gain practical experience in applying a select set of network algorithms on real data and evaluate its outputs Audience: Graduate 5. Understand the algorithmic and statistical concepts of different network-based approaches Audience: Graduate

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