protein network analysis network motifs network clusters / modules co-clustering networks &...

Protein network analysis

• Network motifs• Network clusters / modules• Co-clustering networks & expression• Network comparison

(species, conditions)• Integration of genetic & physical nets• Network visualization

Network motifs

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

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?

All 3-node directed subgraphs

What is the frequency of each in the network?

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

Schematic view of network motif detection

Networks are randomized preserving node degree

Concentration of feedforward motif:

Mean+/-SD of 400 subnetworks

(Num. appearances of motif divided byall 3 node connected subgraphs)

Transcriptional network results

Neural networks

Food webs

World Wide Web

Electronic circuits

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

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

Integration ofnetworks and expression

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

A scoring system for expression “activity”

A B C D

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Scoring over multiple perturbations/conditionsPert

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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

Simulated Annealing Algorithm

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

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…

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

Networks responding to DNA damage as revealed byhigh-throughput phenotypic assays

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

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

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

Network modules associated with infection

Konig et al. Cell 2008

Network-based classification

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

The Mammalian Cell Fate Map:Can we classify tissue type using expression, networks, etc?

Gilbert Developmental Biology 4th Edition

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

Protein interactions, not levels, dictate tissue specification

Functional Enrichment

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.)

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…

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

ClassA ClassB

ttest cut-off

FDR<0.05

FDR<0.05

...testing genes independently...

Biological meaning?

Gene Set Enrichment Analysis - GSEA -

::: Introduction.

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.

Subramaniam, PNAS 2005

NES

pval

FDR

Gene Set Enrichment Analysis - GSEA -

::: Introduction.

The Enrichment Score :::

Benjamini-Hochberg

Network Alignment

Species 1 vs. species 2

Physical vs. genetic

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

Conserved Plasmodium / Saccharomyces protein complexes

Plasmodium-specificprotein complexes

Suthram et al. Nature 2005La Count et al. Nature 2005

Plasmodium: a network apart?

Human vs. Mouse TF-TF Networks in Brain

Tim Ravasi, RIKEN Consortium et al. Cell 2010

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…

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)

Systematic identification of “parallel pathway” relationships in yeast

Unified Whole Cell Model of Genetic and

Physical interactions

A dynamic DNA damage module map

Bandyopadhyay et al. Science (2010)