Protein network analysis Network motifs Network clusters / modules Co-clustering networks &...
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Transcript of Protein network analysis Network motifs Network clusters / modules Co-clustering networks &...
![Page 1: Protein network analysis Network motifs Network clusters / modules Co-clustering networks & expression Network comparison (species, conditions) Integration.](https://reader035.fdocuments.in/reader035/viewer/2022062620/551aa263550346856e8b45ac/html5/thumbnails/1.jpg)
Protein network analysis
• Network motifs• Network clusters / modules• Co-clustering networks & expression• Network comparison
(species, conditions)• Integration of genetic & physical nets• Network visualization
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Network motifs
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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
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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?
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All 3-node directed subgraphs
What is the frequency of each in the network?
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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
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Schematic view of network motif detection
Networks are randomized preserving node degree
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Concentration of feedforward motif:
Mean+/-SD of 400 subnetworks
(Num. appearances of motif divided byall 3 node connected subgraphs)
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Transcriptional network results
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Neural networks
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Food webs
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World Wide Web
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Electronic circuits
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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
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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/d2
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Integration ofnetworks and expression
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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 profileProtein statesPatient genotype (SNP state)Enzyme activityRNAi phenotype
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A scoring system for expression “activity”
A B C D
0312
2303
2011
1221
4
3
2
1
14
1221
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Scoring over multiple perturbations/conditionsPert
urb
ati
on
s /c
on
dit
ion
s
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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
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Simulated Annealing Algorithm
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Network regions whose genes change on/off or off/on after knocking out different genes
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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)
SensitiveNot sensitiveNot tested
MMS sensitivity in ~25% of strains
Screening against a network of protein interactions…
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Begley et al., Mol Cancer Res, (2002)
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Networks responding to DNA damage as revealed byhigh-throughput phenotypic assays
Begley et al., Mol Cancer Res, (2002)
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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
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Project onto a large network of human-human and human-HIV protein interactions
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Network modules associated with infection
Konig et al. Cell 2008
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Network-based classification
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NETWORK-BASED CLASSIFICATION
Disease aggression(Time from Sample Collection SCto Treatment TX)
Chuang et al. MSB 2007Lee et al. PLoS Comp Bio 2008
Ravasi et al. Cell 2010
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The Mammalian Cell Fate Map:Can we classify tissue type using expression, networks, etc?
Gilbert Developmental Biology 4th Edition
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Interaction coherence within a tissue class
BA
BA
BA
Endoderm
Mesoderm
Ectoderm (incl. CNS)
r = 0.9
r = 0.0
r = 0.2
Taylor et al. Nature Biotech 2009
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Protein interactions, not levels, dictate tissue specification
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Functional Enrichment
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Gene Set Enrichment Analysis - GSEA -
::: Introduction.
MITBroad 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.)
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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…
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Dataset distribution Num
ber o
f 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
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ClassA ClassB
ttest cut-off
FDR<0.05
FDR<0.05
...testing genes independently...
Biological meaning?
Gene Set Enrichment Analysis - GSEA -
::: Introduction.
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Correlation w
ith CLA
SS
-
+
ClassA ClassB
Gene Set 1
ttest cut-off
Gene Set 2
Gene Set 3
Gene set 3enriched in Class B
Gene set 2enriched in Class A
Gene Set Enrichment Analysis - GSEA -
::: Introduction.
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Subramaniam, PNAS 2005
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NES
pval
FDR
Gene Set Enrichment Analysis - GSEA -
::: Introduction.
The Enrichment Score :::
Benjamini-Hochberg
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Network Alignment
Species 1 vs. species 2
Physical vs. genetic
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Kelley et al. PNAS 2003Ideker & 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 2004Scott et al. RECOMB
2005Sharan & Ideker Nat. Biotech. 2006
Suthram et al. Nature 2005
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Conserved Plasmodium / Saccharomyces protein complexes
Plasmodium-specificprotein complexes
Suthram et al. Nature 2005La Count et al. Nature 2005
Plasmodium: a network apart?
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Human vs. Mouse TF-TF Networks in Brain
Tim Ravasi, RIKEN Consortium et al. Cell 2010
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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 tomulti-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…
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Integration of genetic and physical interactions
160 between-pathway models
101 within-pathway models
Num interactions:1,102 genetic933 physical
Kelley and Ideker Nature Biotechnology (2005)
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Systematic identification of “parallel pathway” relationships in yeast
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Unified Whole Cell Model of Genetic and
Physical interactions
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A dynamic DNA damage module map
Bandyopadhyay et al. Science (2010)