Course Category：Advanced Course
Aims & Requirements:
The objective of this course is the introduction of molecular networks and the computational approaches used for network analysis. We will concentrate on the following topics: 1) Introduction to molecular networks and databases; 2) Estimating the reliability of observed protein interactions; 3) Protein function prediction based on networks; 4) Protein domain interactions, 5) Identification of active network modules, 6) Pathway inference, and 7) Power law distribution and network motifs. The students will learn the basics of biological networks and the tools needed for network analysis. The materials will be from the up-to-date literature on molecular network analysis and the students will be able to begin their own research on molecular network after the course.
The prerequisite for the course will be basic probability, statistics, and combinatorics at the junior graduate level. Some knowledge on biology will be helpful, but not necessary.
Topic 1. Introduction to basic molecular biology, molecular networks, and data sources (3 hours).
Topic 2. Estimating reliability of protein interaction networks, prediction of protein interactions, Characteristics of molecular networks (power-law distribution, degree distribution vs lethality, network motifs) (3 hours)
Topic 3. Integrated approaches for protein function prediction (3 hours)
Topic 4. Gene module identification (3 hours)
Topic 5. Integrated approaches for the identification of active subnetworks (3 hours)
Topic 6. Disease genes and networks. Integrated approaches for pathway inference (3 hours)
Topic 7. Integrated approaches for protein domain interaction prediction (3 hours)
Topic 8. Statistical significance of dense networks (2 hours)
Topic 9. Future research topics and perspectives (1 hour)
Due to the nature of the topics, no text books are currently available. Instead we will use papers from the literature as References:.