A network representation can be a powerful representation for many biological and biomedical problems. Network biology is an emerging area that encompasses theory and applications of networks to study complex systems such as living organisms. This course surveys the current literature on computational, graph-theoretic approaches that use network algorithms for biological modeling, analysis, interpretation, and discovery.
The material covered in this class will come from published literature, review articles, and selected book chapters. Students will participate in discussions of papers and gain hands-on experience in network biology by implementing class projects. This class should be of interest to students from multiple disciplines including computer science, engineering, math, statistics, microbiology, biochemistry, and genetics.
The goal of this course is to provide students an introduction to different computational problems that arise in the biological networks, key algorithms to solve these problems, and in-depth case studies showing practical applications of these concepts. The course will provide the necessary relevant background in machine learning, graph theory, and molecular biology needed to grasp the concepts introduced in the class.
Some experience with programming, computer science algorithms, and probability. Students can email the instructors about questions and concerns about their background.
Office hours: Tues 2:30-3:30 pm, WID 3168
Office hours: Thurs 4:00-5:00 pm, WID 3268
Meeting time and location
Time: Tues/Thurs 1:00-2:15 pm
Location: ENGR HALL 2321