Network Analysis in Systems Biology

开始时间: 01/05/2015 持续时间: 7 weeks

所在平台: Coursera

课程类别: 生物与生命科学

大学或机构: Icahn School of Medicine at Mount Sinai(西奈山伊坎医学院)

授课老师: Avi Ma’ayan



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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, metabolic networks, drug-target and drug-drug similarity networks and 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.  

We take a case-based approach to teach contemporary statistical and network analysis methods used to analyze data within Systems Biology and Systems Pharmacology research. 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 visualization, and gene-set enrichment analyses. The course will be useful for students who encounter large datasets in their own research, typically genome-wide and would like to learn different methods to understand such data. 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. Students can bring their own data to the course and utilize the methods they learn to analyze their own data for their own projects.


Topics covered include:

  • Types of Biological Networks
  • Principles of Graph Theory and Set Theory for Network Analysis
  • Clustering Algorithms
  • Principal Component Analysis and Singlular Value Decomposition
  • Multivariable, Logistic, and Partial Least Squares Regression
  • Gene Set Enrichment Analysis
  • Gene Expression Data Analysis: Microarrays and RNA-seq
  • Genomic Analysis: CNVs, ChIP-seq and DNA Methylation
  • Analysis of Proteomics and Phosphoproteomics Datasets
  • Integrating Multiple Types of Large Datasets
  • Machine Learning Techniques in Systems Biology
  • Lists2Networks and Enrichr: Gene Set Libraries and Enrichment Analysis
  • Building Networks: Network Expansion and Utilizing Prior Knowledge for Hypothesis Generation
  • Visualization of Networks
  • Analysis of Network Topology I
  • Analysis of Network Topology II
  • From Gene Expression Signatures to Cell Signaling: ChEA, Genes2Networks, KEA and Expression2Kinases
  • Network Pharmacology: Drug-Drug Similarity and Drug-Target Networks
  • Methods to Analyze Network Dynamics using Boolean Networks


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





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