Course Description

This course introduces students to computational methods and tools used in modern biological research. Topics include sequence analysis, genome assembly, phylogenetics, and molecular modeling.

Learning Objectives

• Understand the basic principles of computational biology
• Develop programming skills for biological data analysis
• Apply machine learning techniques to biological problems
• Design and implement algorithms for sequence analysis

Prerequisites

• CS 2100 (Data Structures)
• BIO 2010 (Introduction to Molecular Biology)

Textbooks

• Durbin, R., Eddy, S., Krogh, A., & Mitchison, G. (1998). Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge University Press.
• Pevsner, J. (2015). Bioinformatics and functional genomics (3rd ed.). Wiley-Blackwell.

Grading

• Assignments: 40%
• Midterm Exam: 20%
• Final Project: 30%
• Participation: 10%

Weekly Schedule

Week 1: Introduction to Computational Biology

• History and development of computational biology
• Biological data types and databases

Week 2: Sequence Alignment

• Pairwise sequence alignment
• BLAST and its applications

Week 3: Multiple Sequence Alignment

• Progressive alignment methods
• Profile hidden Markov models

[…continues for 15 weeks…]