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Network Analysis in Systems Biology

Part of the Systems Biology Specialization »

An introduction to data integration 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 Big Data analysis in systems biology including statistical methods used to identify differentially expressed genes, performing various types of enrichment analyses, and applying clustering algorithms. You will also learn how to construct, analyze and visualize functional association networks that can be created from many resources, 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 mRNA expression (microarrays and RNA-seq) will be presented. Processed data will be clustered, and gene-set enrichment analyses methods will be covered. The course is mostly about practical tutorials for analyzing various high content experimental datasets, but it also contains theoretical discussions about the mathematics behind the methods and tools. The course is mostly appropriate for beginning graduate students and advanced undergraduates majoring in fields such as biology but also math, physics, chemistry, computer science, biomedical and electrical engineering. The course will be useful for researchers who encounter large datasets in their own research, typically genome-wide. The course will teach 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, you will have the opportunity to participate in crowdsourcing micro- and mega-task projects. The ultimate aim of the course is to enable you to utilize the methods you learn here for analyzing your own data for your own projects, as well as think about the problems we face in the field of computational systems biology.

Course Syllabus

Module 1 - Introductions and Course Overview: 
These first two lectures introduce the Ma'ayan Lab members and then a broad overview about the course is provided by Professor Ma'ayan.

Module 2 - Data Processing and Identifying Differentially Expressed Genes: 
This set of lectures first discuss data normalization methods, and then several lectures are devoted to explaining the problem of identifying differentially expressed genes with the focus on understanding the inner workings of a new method developed by the Ma'ayan Lab called the Characteristic Direction. 

Module 3 - Gene List Enrichment Analyses: 
In this module the emphasis is on tools developed by the Ma'ayan Lab to analyze gene sets. Several tools will be discussed including: EnrichrGEO2EnrichrExpression2Kinases and DrugPairSeeker. In addition, one lecture will be devoted to a method we call enrichment vector clustering we developed, and two lectures will describe the popular GSEA method.

Module 4 - Deep Sequencing Data Analysis: 
A set of lectures will cover the basic steps and popular pipelines to analyze RNA-seq and ChIP-seq data going from the raw data to gene lists to figures. These lectures also cover UNIX/Linux commands and some programming elements of R, a popular freely available statistical software.

Module 5 - PCA, Hierarchical Clustering, Self-Organizing Maps, and Network-based Clustering: 
This module is devoted to various method of clustering: principle components analysis, self-organizing maps, network-based clustering and hierarchical clustering. The theory behind these methods of analysis are covered in detail, and this is followed by some practical demonstration of the methods for applications using R and MATLAB.

Module 6 - Resources for Data Integration: 
Next are lectures about the various types of networks that are typically constructed and analyzed in systems biology and systems pharmacology. These lectures start with the idea of functional association networks (FANs). Following this lecture are several lectures that discuss how to construct FANs from various resources and how to use these networks for analyzing gene lists as well as to construct a puzzle that can be used to connect genomic data with phenotypic data.

Module 7 - Crowdsourcing: 
The final set of lectures presents the idea of crowsourcing. In Coursera we have the opportunity to work together on projects that are difficult to complete alone (microtasks) or compete by thinking and implementing algorithms for solving hard problems (megatasks). You will have the opportunity to participate in three crowdsourcing projects: one microtask and two megatasks. These are projects we designed specifically for this course.

Recommended Background

Basic courses in statistics and molecular biology are useful but not required. Familiarity with environments such as R and MATLAB can 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.


  • 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.