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Protein network analysis Network motifs Network clusters / modules Co-clustering networks & expression Network comparison (species, conditions) Integration.

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Presentation on theme: "Protein network analysis Network motifs Network clusters / modules Co-clustering networks & expression Network comparison (species, conditions) Integration."— Presentation transcript:

1 Protein network analysis Network motifs Network clusters / modules Co-clustering networks & expression Network comparison (species, conditions) Integration of genetic & physical nets Network visualization

2 Network motifs

3 Network Motifs (Milo, Alon et al.) Motifs are “patterns of interconnections occurring in complex networks.” That is, connected subgraphs of a particular isomorphic topology The approach queries the network for small motifs (e.g., of < 5 nodes) that occur much more frequently than would be expected in random networks Significant motifs have been found in a variety of biological networks and, for instance, correspond to feed-forward and feed-back loops that are well known in circuit design and other engineering fields. Pioneered by Uri Alon and colleagues

4 Motif searches in 3 different contexts How many motifs (connected subgraph topologies) exist involving three nodes? If the graph is undirected? If the graph is directed?

5 All 3-node directed subgraphs What is the frequency of each in the network?

6 Outline of the Approach Search network to identify all possible n-node connected subgraphs (here n=3 or 4) Get # occurrences of each subgraph type The significance for each type is determined using permutation testing, in which the above process is repeated for many randomized networks (preserving node degrees– why?) Use random distributions to compute a p-value for each subgraph type. The “network motifs” are subgraphs with p < 0.001

7 Schematic view of network motif detection Networks are randomized preserving node degree

8 Concentration of feedforward motif: Mean+/-SD of 400 subnetworks (Num. appearances of motif divided by all 3 node connected subgraphs)

9 Transcriptional network results

10 Neural networks

11 Food webs

12 World Wide Web

13 Electronic circuits

14 Interesting questions Which networks have motifs in common? Which networks have completely distinct motifs versus the others? Does this tell us anything about the design constraints on each network? E.g., the feedforward loop may function to activate output only if the input signal is persistent (i.e., reject noisy or transient signals) and to allow rapid deactivation when the input turns off E.g., food webs evolve to allow flow of energy from top to bottom (?!**!???), whereas transcriptional networks evolve to process information

15 Identifying modules in the network Rives/Galitski PNAS paper 2003 Define distance between each pair of proteins in the interaction network E.g., d = shortest path length To compute shortest path length, use Dijkstra’s algorithm Cluster w/ pairwise node similarity = 1/d 2

16 Integration of networks and expression

17 Querying biological networks for “Active Modules” Ideker et al. Bioinformatics (2002) Interaction Database Dump, aka “Hairball” Active Modules Color network nodes (genes/proteins) with: Patient expression profile Protein states Patient genotype (SNP state) Enzyme activity RNAi phenotype

18 A scoring system for expression “activity” ABCD

19 Scoring over multiple perturbations/conditions Perturbations /conditions

20 Searching for “active” pathways in a large network Score subnetworks according to their overall amount of activity Finding the highest scoring subnetworks is NP hard, so we use heuristic search algs. to identify a collection of high-scoring subnetworks (local optima) Simulated annealing and/or greedy search starting from an initial subnetwork “seed” During the search we must also worry about issues such as local topology and whether a subnetwork’s score is higher than would be expected at random

21 Simulated Annealing Algorithm

22 Network regions whose genes change on/off or off/on after knocking out different genes

23 Initial Application to Toxicity: Networks responding to DNA damage in yeast Tom Begley and Leona Samson; MIT Dept. of Bioengineering Systematic phenotyping of gene knockout strains in yeast Evaluation of growth of each strain in the presence of MMS (and other DNA damaging agents) Sensitive Not sensitive Not tested MMS sensitivity in ~25% of strains Screening against a network of protein interactions…

24 Begley et al., Mol Cancer Res, (2002)

25 Networks responding to DNA damage as revealed by high-throughput phenotypic assays Begley et al., Mol Cancer Res, (2002)

26 Host-pathogen interactions regulating early stage HIV-1 infection Genome-wide RNAi screens for genes required for infection utilizing a single cycle HIV-1 reporter virus engineered to encode luciferase and bearing the Vesicular Stomatitis Virus Glycoprotein (VSV-G) on its surface to facilitate efficient infection… Sumit Chanda

27 Project onto a large network of human-human and human-HIV protein interactions

28 Network modules associated with infection Konig et al. Cell 2008

29 Network-based classification

30 NETWORK-BASED CLASSIFICATION Disease aggression (Time from Sample Collection SC to Treatment TX) Chuang et al. MSB 2007 Lee et al. PLoS Comp Bio 2008 Ravasi et al. Cell 2010

31 The Mammalian Cell Fate Map: Can we classify tissue type using expression, networks, etc? Gilbert Developmental Biology 4 th Edition

32 Interaction coherence within a tissue class B B A A B B A A B B A A Endoderm Mesoderm Ectoderm (incl. CNS) r = 0.9 r = 0.0 r = 0.2 Taylor et al. Nature Biotech 2009

33 Protein interactions, not levels, dictate tissue specification

34 Functional Enrichment

35 Gene Set Enrichment Analysis - GSEA - ::: Introduction. MIT Broad Institute v 2.0 available since Jan 2007 Version 2.0 includes Biocarta, Broad Institute, GeneMAPP, KEGG annotations and more... Platforms: Affymetrix, Agilent, CodeLink, custom... GSEA (Subramanian et al. PNAS. 2005.)

36 GSEA applies Kolmogorov-Smirnof test to find assymmetrical distributions for defined blocks of genes in datasets whole distribution. Gene Set Enrichment Analysis - GSEA - ::: Introduction. Is this particular Gene Set enriched in my experiment? Genes selected by researcher, Biocarta pathways, GeneMAPP sets, genes sharing cytoband, genes targeted by common miRNAs …up to you…

37 Dataset distribution Number of genes Gene Expression Level Gene Set Enrichment Analysis - GSEA - ::: Introduction. ::: K-S test The Kolmogorov–Smirnov test is used to determine whether two underlying one-dimensional probability distributions differ, or whether an underlying probability distribution differs from a hypothesized distribution, in either case based on finite samples. The one-sample KS test compares the empirical distribution function with the cumulative distribution functionspecified by the null hypothesis. The main applications are testing goodness of fit with the normal and uniform distributions. The two-sample KS test is one of the most useful and general nonparametric methods for comparing two samples, as it is sensitive to differences in both location and shape of the empirical cumulative distribution functions of the two samples. Gene set 1 distribution Gene set 2 distribution

38 ClassA ClassB ttest cut-off FDR<0.05...testing genes independently... Biological meaning? Gene Set Enrichment Analysis - GSEA - ::: Introduction.

39 Correlation with CLASS - + ClassA ClassB Gene Set 1 ttest cut-off Gene Set 2 Gene Set 3 Gene set 3 enriched in Class B Gene set 2 enriched in Class A Gene Set Enrichment Analysis - GSEA - ::: Introduction.


41 Subramaniam, PNAS 2005

42 NES pval FDR Gene Set Enrichment Analysis - GSEA - ::: Introduction. The Enrichment Score ::: Benjamini-Hochberg

43 Network Alignment Species 1 vs. species 2 Physical vs. genetic

44 Kelley et al. PNAS 2003 Ideker & Sharan Gen Res 2008 Cross-comparison of networks: (1) Conserved regions in the presence vs. absence of stimulus (2) Conserved regions across different species Sharan et al. RECOMB 2004 Scott et al. RECOMB 2005Sharan & Ideker Nat. Biotech. 2006 Suthram et al. Nature 2005

45 Conserved Plasmodium / Saccharomyces protein complexes Plasmodium-specific protein complexes Suthram et al. Nature 2005 La Count et al. Nature 2005 Plasmodium: a network apart?

46 Human vs. Mouse TF-TF Networks in Brain Tim Ravasi, RIKEN Consortium et al. Cell 2010

47 Finding physical pathways to explain genetic interactions Adapted from Tong et al., Science 2001 Genetic Interactions: Classical method used to map pathways in model species Highly analogous to multi-genic interaction in human disease and combination therapy Thousands are being uncovered through systematic studies Thus as with other types, the number of known genetic interactions is exponentially increasing…

48 Integration of genetic and physical interactions 160 between- pathway models 101 within- pathway models Num interactions: 1,102 genetic 933 physical Kelley and Ideker Nature Biotechnology (2005)

49 Systematic identification of “parallel pathway” relationships in yeast

50 Unified Whole Cell Model of Genetic and Physical interactions

51 A dynamic DNA damage module map Bandyopadhyay et al. Science (2010)

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