CSCE555 BioinformaticsCSCE555 BioinformaticsLecture 18 Network Biology:

Comparison of Networks Across Species

Meeting: MW 4:00PM-5:15PM SWGN2A21

Instructor: Dr. Jianjun Hu

Course page: http://www.scigen.org/csce555

University of South CarolinaDepartment of Computer Science and Engineering

2008 www.cse.sc.edu.

In the beginning there was In the beginning there was DNA…DNA…

Liolios K, Tavernarakis N, Hugenholtz P, Kyrpides, NC. The Genomes On Line Database (GOLD) v.2: a monitor of genome projects worldwide. NAR 34, D332-334

……then came protein then came protein interactionsinteractions

Arabidopsis

PPI network

E. Coli

PPI network

Yeast PPI network

Comparative Genomics to Comparative Genomics to Comparative InteractomicsComparative InteractomicsEvolutionary conservation implies

functional relevance◦ Sequence conservation implies functional

conservation◦ Network conservation implies functional

conservation too!What new insights might we gain from

network comparisons? (Why should we care?)

Network comparisons allow Network comparisons allow us to:us to:Identify conserved functional

modulesQuery for a module, ala BLASTPredict functions of a modulePredict protein functionsValidate protein interactionsPredict protein interactions

Only possible with network comparisons

Possible with existing techniques, but improved with network comparisons

What is a Protein Interaction What is a Protein Interaction Network?Network?

Proteins are nodes

Interactions are edges

Edges may have weights

Yeast PPI network

H. Jeong et al. Lethality and centrality in protein networks. Nature 411, 41 (2001)

The Network Alignment The Network Alignment ProblemProblemGiven k different protein

interaction networks belonging to different species, we wish to find conserved sub-networks within these networks

Conserved in terms of protein sequence similarity (node similarity) and interaction similarity (network topology similarity)

Example Network Example Network AlignmentAlignment

Sharan and Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, pp. 427-433, 2006

General Framework For General Framework For Network Alignment AlgorithmsNetwork Alignment Algorithms

Sharan and Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, pp. 427-433, 2006

Network construction

Scoring function

Alignment algorithm

Building Co-expression NetworksBuilding Co-expression Networks

1

.81

1

-.6

-.7Gene A

Gene B

Gene C

Ge

ne

B

Ge

ne

A

Ge

ne

C

Pearson Correlation

=.8

-.7 -.6

Genes

Arrays

Microarray data

Expression

Balaji S. Srinivasan

Two AlgorithmsTwo Algorithms

NetworkBLAST (covered today)Sharan et al. Conserved patterns of protein interaction in multiple species. PNAS, 102(6):1974-1979, 2005.

Græmlin Flannick et al. Græmlin: General and robust alignment of multiple large interaction networks. Genome Res 16: 1169-1181, 2006.

Overview ofOverview of

Sharan et al. Conserved patterns of protein interaction in multiple species. PNAS, 102(6):1974-1979, 2005.

Estimation of Interaction Estimation of Interaction ProbabilitiesProbabilities In the preprocessing step, edges in

the network are given a reliability score using a logistic regression model based on three features:

1. Number of times an interaction was observed

2. Pearson correlation coefficient between expression profiles

3. Proteins’ small world clustering coefficient

Network Alignment Network Alignment GraphsGraphs Construct a Network Alignment Graph to

represent the alignment Nodes contain groups of sequence similar

proteins from the k organisms Edges represent conserved interactions. An edge between two nodes is present if:

1. One pair of proteins directly interacts, the rest are distance at most 2 away

2. All protein pairs are of distance exactly 23. At least max(2, k – 1) protein pairs directly

interact Tries to account for interaction deletions

Example Network Alignment Example Network Alignment GraphGraph

Nodes

a

b

c

a’

b’

c’

a’’

b’’

c’’

ab

c

a’

b’

c’

a’’

b’’

c’’

Network alignment graph

Individual species’ PPI network

Species X Species Y Species Z

Scoring FunctionScoring FunctionSharan et al. devise a scoring

scheme based on a likelihood model for the fit of a single sub-network to the given structure

High scoring subgraphs correspond to structured sub-networks (cliques or pathways)

Only network topology is scored, node similarity is not

Log Likelihood Ratio ModelLog Likelihood Ratio ModelMeasures the likelihood that a

subgraph occurs if it is a conserved network vs. that if it were a randomly constructed network

Randomly constructed network preserves degree distribution for nodes

logPr(Subgraph occurs | Conserved Network)

Pr(Subgraph occurs | Random Network)

Log Likelihood Ratio ModelLog Likelihood Ratio Model(i) in a real subnetwork, each interaction should be present independently with high probability, and (ii) in a random subnetwork, the probability of an interaction between any two proteins depends on their total number of connections in the network.

Likelihood Ratio Scoring of a Protein Likelihood Ratio Scoring of a Protein Complex in a Single SpeciesComplex in a Single Species

U : a subset of vertices (proteins) in the PPI graphOU : collection of all observations on vertex pairs in UOuv : interaction between proteins u, v observedMs : conserved network modelMn: random network (null) modelTuv : proteins u, v interactFuv : proteins u, v do not interactβ : probability that proteins u, v interact in conserved modelpuv : probability that edge u, v exists in a random model

Probability of complex being observed in a conserved network model

Probability of subgraph being observed in a random network model

Likelihood Ratio Scoring of a Likelihood Ratio Scoring of a Protein Complex in a Single SpeciesProtein Complex in a Single SpeciesHence, log likelihood for a

complex occurring in a single species is given by

For multiple complexes across different species, it is the sum of the log likelihoods

L(A, B, C) = L(A) + L(B) + L(C)

Example of Complex Example of Complex ScoringScoring

Nodes

a

b

c

a’

b’

c’

a’’

b’’

c’’

ab

c

a’

b’

c’

a’’

b’’

c’’

Conserved complex A in the Network alignment graph

Individual species’ PPI network

L(A) = L(X1) + L(Y1) + L (Z1)

Complex X1 in Species X

Complex Y1 in Species Y

Complex Z1 in Species Z

Alignment algorithmAlignment algorithmProblem of identifying conserved

sub-networks reduces to finding high scoring subgraphs

NP-complete problemHeuristic solution:

◦Greedy extension of high scoring seeds

◦(Does this sound familiar? BLAST?)◦Common to both papers discussed

Alignment algorithmAlignment algorithm

1. Find seeds for each node v in the alignment graph

a. Find high scoring paths of 4 nodes by exhaustive search

b. Greedily add 3 other nodes one by one, that maximally increase the score of the seed

Alignment algorithmAlignment algorithm

2. Iteratively add or remove nodes to increase the overall score of the node

Original seeds are preserved Limit size of discovered subgraphs

to 15 nodes Record up to 4 highest scoring

subgraphs discovered around each node

Alignment algorithmAlignment algorithm

3. Filter subgraphs with a high degree of overlap

Iteratively find high scoring subgraph and remove all highly overlapping ones remaining

ResultsResults

Conserved network regions within yeast (orange ovals), fly (green rectangles) and worm (blue hexagons) PPI networks.

ResultsResultsPrediction of protein function

• ‘Guilt by association’

• If a conserved cluster or path is significantly enriched in a functional annotation

Prediction of protein interactions

Predictions based on 2 strategies:

• Evidence that proteins with similar sequences interact

• Co-occurrence of proteins in the same conserved cluster or path

• Experimental verification of Yeast interactions using Y2H yielded 40-62% success rate

Overview ofOverview ofFast, scalable, network alignment

◦Scales linearly in number of networks compared

◦NetworkBLAST scales exponentiallySupports efficient querying of

modulesSpeed-sensitivity control via user

defined parameter◦Not supported in NetworkBLAST

Input to the AlgorithmInput to the AlgorithmWeighted protein interaction

graphs◦Weights represent probability that

proteins interact◦Constructed via network integration

algorithmA phylogenetic tree relating the

species in the desired alignment◦Used for progressive alignment

Key Ideas of GraiminKey Ideas of GraiminGenerating An Initial Alignment

From The SeedGreedy Seed Extension PhaseProgressive alignment technique

using the phylogenetic tree

ResultsResults

Functional module identification using network alignment

ResultsResults

Multiple alignment of 10 networks showing possible cell division module

Functional annotation using network alignment

The Future of Network The Future of Network ComparisonComparison

Græmlin

Græmlin?

Sharan and Ideker. Modeling cellular machinery through biological network comparison. Nature Biotechnology 24, pp. 427-433, 2006

SummarySummaryThe problem: Network

comparison/comparative interactomes

NetworkBlast algorithmBrief introduction foThe analogy between sequence

comparison and network comparison

Reference & Reference & AcknowledgementsAcknowledgementsChuan Sheng FooSharan et al. Conserved patterns of protein

interaction in multiple species. PNAS. February 8, 2005 | vol. 102 | no. 6 | 1974-1979