Network Analysis in Systems Biology Avi Ma’ayan, PhD

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

Workload: 6-8 hours/week 

Author:

Avi Ma’ayan, PhDAssociate ProfessorDepartment of Pharmacology and Systems TherapeuticsIcahn School of Medicine at Mount Sina

Sessions:

October 2013 (7 weeks long)
Future sessions

About the Course

The course Network Analysis in Systems Biology provides an introduction to network analysis and statistical methods used in contemporary Systems Biology 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 using databases of drug-target interactions, drug structure, drug/adverse-events, and drug induced gene expression signatures.  

Half of the course will contain theoretical discussions of advanced topics in the field but with the assumption that the course participants are from diverse backgrounds. The course will also have practical tutorials for analyzing various high content experimental datasets going from the raw data to finished high quality figures for publication. Standard statistical methods for high content data analysis will be covered. The course is appropriate for beginning graduate students and advanced undergraduates. Lectures provide background knowledge in understanding the properties of large datasets collected from mammalian cells. In the course we will teach how these datasets can be analyzed to extract new knowledge about the system. Such analyses include clustering, data visualization techniques, network construction, 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 software tools. In addition the course requires the students to write short scripts in Python and participate in crowdsourcing microtask  projects. The ultimate aim of the course is to enable students to utilize the methods they learn here for analyzing their own data for their own projects.

Course Syllabus

Topics covered include:

• Properties of Complex Systems: Biological vs. Technological Networks
• Introduction to Molecular and Cell Biology for Engineers
• Algorithms to Generate Network Topologies: Rich-Get-Richer, Small-World, Duplication-Divergence and Null Random Models
• Network Motifs and Network Evolution
• Types of Networks in Systems Biology
• Drawing Networks: Ball-and-Stick Diagrams and other Network Visualizations
• Gene Expression Data Analysis: Microarrays, RNA-seq and ChIP-seq
• Hierarchical Clustering Plots
• Principle Component Analysis and Multi-Dimensional Scaling
• Gene Set Enrichment Analysis and Fuzzy Set Enrichment Analysis
• Integrating Multiple Types of Large Datasets
• From Gene Expression to Pathways: Expression2Kinases and Genes2Networks
• Functional Association Networks: Sets2Networks
• Network Pharmacology: Drug-Drug Similarity and Drug-Target Networks
• Crowdsourcing: Microtasks and Megatasks
• Developing Web Apps for Solving Systems Biology Research Problems

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.  

Suggested Readings

Review articles and selected original research articles will be discussed in the lectures and can enhance understanding, but these are not required to complete the course. All materials will be from open access journals or will be provided as links to e-reprints, so there will be no cost to the student.

Course Format

The class will consist of lecture videos, which are between 8 and 15 minutes in length.  Each lecture will include a quiz and a homework assignment.

For evaluation, students will be mainly graded through their participation in the assignments and quiz completion.

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