Network Analysis in Systems Biology

An introduction to network analysis and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research.

About The Course

The course Network Analysis in Systems Biology provides an introduction to network analysis and statistical methods used in contemporary Systems Biology, Bioinformatics and Systems Pharmacology research. Students will learn how to construct, analyze and visualize different types of molecular networks, including gene regulatory networks connecting transcription factors to their target genes, protein-protein interaction networks, cell signaling pathways and networks, drug-target and drug-drug similarity networks and other functional association networks. Methods to process raw data from genome-wide RNA (microarrays and RNA-seq) and proteomics (IP-MS and phosphoproteomics) profiling will be presented. Processed data will be clustered, and gene-set enrichment analyses methods will be covered. The course will also discuss topics in network systems pharmacology including processing and integrating databases of drug-target interactions, drug structure, drug/adverse-events, and drug induced gene expression signatures.  

Half of the course will be devoted to describing major data resources and how these can be processed into networks, bi-partite graphs and gene set libraries for data reuse and integration. Special consideration will be made for the concept of research focus biases and experimental biases. The other half of the course will be devoted to analyses, including unsupervised clustering, data visualization techniques, network topological properties, and gene-set enrichment analyses. The course will be useful for students who encounter large datasets in their own research, typically genome-wide. The course will teach the students how to use existing software tools such as those developed by the Ma’ayan Laboratory at Mount Sinai, but also other freely available tools. In addition the course requires the students to write short scripts in Python and participate in crowdsourcing micro-  and mega-task projects. The ultimate aim of the course is to enable students to utilize the data resources and methods they learn here for analyzing their own data for their own projects.

Frequently Asked Questions

  • Will I get a Statement of Accomplishment after completing this class?
  • Yes. Students who successfully complete the course will receive a Statement of Accomplishment signed by the Course Director.

  • What are the pre-requisites for the class?
  • The course is designed to accommodate students from diverse backgrounds. Specifically, background in molecular biology, statistics, and computer programming is most helpful, but such background is not assumed or required.

  • How difficult is the class?

    The class can be easy if the student is only concerned with playing a relatively passive role. However, students are encouraged to engage in the course and take initiative and exercise their creativity. This may require more time and effort but would be more fun and rewarding. 

  • Recommended Background

    Basic courses in statistics and molecular biology are useful but not required. Ability to write short scripts in languages such as Python would be useful but not necessary.