Most complex systems have a natural “network” representation with nodes representing the components of the system and edges representing interactions among the components. Network biology is an emerging area that encompasses theory and applications of networks to study complex systems such as living organisms. This special topics course surveys the current literature on computational 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. This class should be of interest to students from multiple disciplines including computer science, engineering, statistics, microbiology, biochemistry and genetics.
The goal of this course is to provide students an introduction to different computational problems that arise in the analysis of biological networks, key algorithms to solve these problems, and in-depth case studies showing practical applications of these algorithms. 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. Students will gain experience in applying some of the well-known algorithms for network analysis on real biological data.
None required. Students can email the instructor about questions and concerns about their background.
Meeting time and location:
Time: Tues/Thurs 1:00-2:15 pm
Location Computer Sciences 1263