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:
(Num. appearances of motif divided by
all 3 node connected subgraphs)
Mean+/-SD of 400 subnetworks
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 of
networks and expression
Querying biological networks for “Active Modules”
Color network nodes (genes/proteins) with:
Patient expression profile
Protein states
Patient genotype (SNP state)
Enzyme activity
RNAi phenotype
Active Modules
Ideker et al. Bioinformatics (2002)
Interaction Database
Dump, aka “Hairball”
A scoring system for expression “activity”
A
1 
1

2
1

3
3

4 
2
B
C
2
 2
1
0
0
3
1
3
D
1

 2

2

0
Perturbations
/conditions
Scoring over multiple perturbations/conditions
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)
Sensitive
Not sensitive
Not 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 by
high-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 SC
to Treatment TX)
Chuang et al. MSB 2007
Lee 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
r = 0.9
A
B
Endoderm
r = 0.0
A
B
Mesoderm
r = 0.2
A
Ectoderm (incl. CNS)
B
F = A-B
Taylor et al. Nature Biotech 2009
Protein interactions, not levels, dictate tissue specification
Functional Enrichment
::: Introduction.
Gene Set Enrichment Analysis - GSEA -
GSEA
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...
(Subramanian et al. PNAS. 2005.)
::: Introduction.
Gene Set Enrichment Analysis - GSEA -
GSEA applies Kolmogorov-Smirnof test to find assymmetrical distributions for defined
blocks of genes in datasets whole distribution.
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…
::: Introduction.
Gene Set Enrichment Analysis - GSEA -
::: 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 hypo
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
in both location and shape of the empirical cumulative distribution functions of the two samples.
Dataset distribution
Number of genes
Gene set 1 distribution
Gene set 2 distribution
Gene Expression Level
::: Introduction.
Gene Set Enrichment Analysis - GSEA -
ClassA
ClassB
FDR<0.05
...testing genes independently...
ttest cut-off
FDR<0.05
Biological meaning?
::: Introduction.
Gene Set Enrichment Analysis - GSEA -
Correlation with CLASS
ttest cut-off
ClassA ClassB
Gene
Set 1
Gene
Set 2
Gene
Set 3
Gene set 3
enriched in Class B
Gene set 2
enriched in Class A
+
Subramaniam, PNAS 2005
::: Introduction.
Gene Set Enrichment Analysis - GSEA -
The Enrichment Score
NES
pval
FDR
Benjamini-Hochberg
Network Alignment
Species 1 vs. species 2
Physical vs. genetic
Cross-comparison of networks:
(1) Conserved regions in the presence vs. absence of stimulus
(2) Conserved regions across different species
Kelley et al. PNAS 2003
Ideker & Sharan Gen Res 2008
Suthram et al. Nature 2005
Sharan & Ideker Nat. Biotech. 2006
Sharan et al. RECOMB 2004
Scott et al. RECOMB 2005
Plasmodium: a network apart?
Plasmodium-specific
protein complexes
Conserved Plasmodium / Saccharomyces
protein complexes
Suthram et al. Nature 2005
La Count et al. Nature 2005
Human vs. Mouse TF-TF Networks in Brain
Tim Ravasi, RIKEN Consortium et al. Cell 2010
Finding physical pathways to explain genetic interactions
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…
Adapted from Tong et al., Science 2001
Integration of genetic and physical interactions
160 betweenpathway models
101 withinpathway models
Num interactions:
1,102 genetic
933 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)