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Outline

Outline. Live! demo of functional genomics GSEA and AraPath GeneMANIA Dynamic system Flux balance system PhysioNet ResponseNet. Outline (2). Network clustering Understanding multi - cellular systems  Multi - cellular network Synthetic biology Networks in synthetic biology.

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Outline

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  1. Outline • Live! demo of functional genomics • GSEA and AraPath • GeneMANIA • Dynamic system • Flux balance system • PhysioNet • ResponseNet

  2. Outline (2) • Network clustering • Understanding multi-cellular systems  • Multi-cellular network • Synthetic biology • Networks in synthetic biology

  3. Gene set Enrichment Analysis • Enrichment Analysis • If you wish to assign biological meaning to some group of genes, you will apply a functional enrichment analysis. • Usually the function is represented by a Gene Ontology term. BiNGO is a tool to determine which GO terms are significantly overrepresented in a gene set.

  4. Hypergeometricdistribution where N is the population size m is the number of success states in the population n is the number of draws k is the number of successes • This approach has been criticized for ignoring biological knowledge regarding how genes work together.

  5. Gene Set Enrichment Analysis Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states  (e.g. phenotypes). 

  6. PNAS 2005

  7. Abstract Although genome-wide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common.

  8. GSEA considers experiments with genome-wide expression profiles from samples belonging to two classes, labeled 1 or 2. Genes are ranked based on the correlation between their expression and the class distinction by using any suitable metric. Given an a priori defined set of genes S, the goal of GSEA is to determine whether the members of S are randomly distributed throughout L or primarily found at the top or bottom.

  9. DEMO

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