YoungRae Cho

1
Seminar 2009
Identification of Functional Modules and Hub
Proteins in Protein Interaction Networks

• Young-Rae Cho
• Department of Computer Science and Engineering
• State University of New York at Buffalo

2
What is Bioinformatics?

• Bioinformatics
• Interdisciplinary research area to manage and
  analyze biological data

Techniques
Data
Applications
3
What is Bioinformatics?
Computational Techniques
Data Mining Machine Learning
Data Mining
Biomedical Applications
Knowledge
Biological Data
Functional Characterization
Genome Proteome Networks
Functional Characterization Disease
Diagnosis Drug Development
Networks
4
Overview

• Introduction
• Protein Interaction Networks and Their Structural
  Properties
• Preprocess - Network Weighting
• Integration of Gene Ontology using Semantic
  Similarity Measures
• Functional Module Identification
• Weighted Interaction Networks ?
  ? Functional Modules
• Hub Protein Identification
• Weighted Interaction Networks ?
  ? Hub Proteins
• Conclusion

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Biological Network

• Definition
• Directed or undirected graph representation
• Biological molecules as nodes and
• biochemical reactions or biophysical
  interactions as edges
• Examples
• Metabolic networks
• Signal transduction networks
• Gene regulatory networks
• Protein interaction networks
• Importance
• Provide a global view of cellular organizations
  and biological processes
• Applicable to systematic approaches for knowledge
  discovery

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Protein-Protein Interaction (PPI)

• Biological Meaning of PPI
• Proteins interact with each other for stability
  and functionality
• Most cellular functions are performed in a
  protein complex level
• Interaction evidence is interpreted as functional
  coherence / consistency
• Determination of PPIs
• Experimental methods
• Yeast two-hybrid systems, Mass
  spectrometry, Protein microarray
• Computational methods
• Homology search, Gene fusion analysis,
  Phylogenetic profiles
• Problem of PPI data
• Current PPI databases include a large amount of
  false positives / false negatives
• ? Unreliability

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Protein Interaction Network

• Representation of Protein Interaction Networks
• Undirected, un-weighted graph G(V,E),
• a set of nodes V as proteins and a set
  of edges E as interactions
• Problem of Protein Interaction Networks
• Large scale
• Complex connectivity

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Structural Properties

• Small-world Phenomenon ( Watts Strogatz )
• Appearance of networks in the middle of regular
  and random networks
• Higher average clustering coefficient than
  expected by random chance
• Significantly small average shortest path length
• Scale-free Distribution ( Barabasi Albert )
• Network growth by preferential attachment
• Power law degree distribution a few high degree
  nodes, many low degree nodes
• Clustering coefficient distribution independent
  to degree

9
Overview

• Introduction
• Protein Interaction Networks and Their Structural
  Properties
• Preprocess - Network Weighting
• Integration of Gene Ontology using Semantic
  Similarity Measures
• Functional Module Identification
• Weighted Interaction Networks ?
  ? Functional Modules
• Hub Protein Identification
• Weighted Interaction Networks ?
  ? Hub Proteins
• Conclusion

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Network Weighting Schemes

• Motivation
• Unreliable protein interaction networks
• Transforming un-weighted graph to weighted graph
• by assigning the interaction reliability
  (or intensity) into each edge as a weight
• Unsupervised Approaches
• Using network connectivity, e.g., common
  neighbors, alternative paths
• Problem unreliable weights
• Supervised Approaches
• Using other resources verifying interactions,
  e.g., gene sequence, gene expression
• Integrating Gene Ontology data in my works
• the most comprehensive
• well-curated

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Gene Ontology (GO)

• Structure
• Terms (Concepts) well-defined biological
  description
• Relationships is-a / part-of
  (general-to-specific) between terms
• Annotation
• If a protein is annotated on a term, then it is
  also annotated on the terms on the
• 
  
  paths towards root.

? Transitivity
P5
P1
P1, P2, P3
P1, P2, P4
P2, P3
P1, P6
P1, P2, P3, P6
P2, P3
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Semantic Similarity

• Reliability of Interacting Proteins
• Average (or Maximum) semantic similarity of
  pair-wise terms
• including the interacting proteins in
  annotations
• Structure-based Approaches
• Path length or Common parent terms
• Problem all edges should represent the uniform
  specificity
• Information Content-based Approaches
• Information content of a term T is defined as
  log(P(T))
• simxy - log ( Pi(x,y) )
• where Pi(x,y) is the proportion of the
  annotations of the term including x and y
• Normalized simxy

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Overview

• Introduction
• Protein Interaction Networks and Their Structural
  Properties
• Preprocess - Network Weighting
• Integration of Gene Ontology using Semantic
  Similarity Measures
• Functional Module Identification
• Weighted Interaction Networks ?
  ? Functional Modules
• Hub Protein Identification
• Weighted Interaction Networks ?
  ? Hub Proteins
• Conclusion

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Functional Module Identification

• Functional Module
• A set of molecules that participate in the same
  biological processes or functions
• Sub-network with dense intra-connections and
  sparse interconnection
• Functional Module Identification
• ? Graph clustering problem
• Previous Clustering Approaches
• Density-based methods, e.g., maximum clique,
  quasi clique, clique percolation
• Partition-based methods, e.g., restricted
  neighborhood search, Markov clustering
• Hierarchical methods
• Bottom-up approaches, e.g., distance-based,
  common neighbors
• Top-down approaches, e.g., minimum cut,
  betweenness cut

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Functional Influence Model

• Functional Influence
• Influence factors normalized weights, inverse
  of degree
• Measurements
• Single-path-based method
• O( V E )
• All-path-based method NP
• Random-walk-based method
• O( V3 ) iteration O( V4 )

Improvement by an efficient algorithm
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Flow Simulation

• Information Flow Simulation
• Computation of functional influence infs(x) of s
  on x ? V based on random walks
• Input a weighted interaction network and a
  source node s
• Output functional influence pattern of s
• Algorithm
• Initialize infs(s)
• Compute initial flow finit(s ? y) by
• Update infs(y) by
• Compute flow fs(y ? z) by
• Repeat 3 and 4 until fs(y ? z) is less than a
  threshold ?

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Lower-level Algorithm
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Schematic View
0.15
0.28
0.65
0.79
0.45
Pattern Clustering
0.27
1.26
0.83
1.0
1.74
0.41
0.92
0.89
1.38
0.11
0.31
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Time Complexity

• Efficiency
• Traces only connecting nodes to calculate
  functional influence of a source
• Removes trivial flow, being less than ?, as early
  as possible
• Run Time
• Theoretical upper bound is unknown ( not depends
  on the network diameter )
• Test potential factors ( nodes, density,
  average degree ) with synthetic networks

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Accuracy

• Experiment
• Data yeast protein interaction network from DIP
• Pattern clustering pCluster algorithm (Wang et
  al., SIGMOD 2002)
• Evaluation
• Functional categories and annotations from MIPS
• Hyper-geometric p-value
• Result

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Overview

• Introduction
• Protein Interaction Networks and Their Structural
  Properties
• Preprocess - Network Weighting
• Integration of Gene Ontology using Semantic
  Similarity Measures
• Functional Module Identification
• Weighted Interaction Networks ?
  ? Functional Modules
• Hub Protein Identification
• Weighted Interaction Networks ?
  ? Hub Proteins
• Conclusion

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Hub Protein Identification

• Hub Protein
• Centrally located node in the modular structure
  of a protein interaction network
• ( a structural hub )
• Functionally essential protein
• Previous Centrality Measurements
• Closeness centrality
• Betweenness centrality
• Bridging centrality

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Functional Influence Model

• Functional Influence
• Influence factors normalized weights, inverse
  of degree
• Measurements
• Single-path-based method
• O( V E )
• All-path-based method NP
• Random-walk-based method
• O( V3 ) iteration O( V4 )

Improvement by a heuristic algorithm
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Path Strength

• Single-path-based path strength
• All-path-based path strength
• sums up the k-length path strength for all
  possible k
• uses the threshold of maximum k

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Network Conversion

• Network Conversion
• Input a protein interaction network / Output
  a hierarchical tree structure
• Algorithm
• Centrality (weighted closeness) of a node a
• Set of ancestor nodes T(a) of a
• Parent node p(a) of a
• Hub Confidence Measurement
• Set of child nodes D(a) of a
• Set of descendent nodes La of a
• Hub confidence H(a) of a

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Schematic View

• Hub Confidence
• How strongly a node plays a role as a structural
  hub
• Not fully depends on the hierarchical level in
  the tree structure

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Structural Hubs

• Top 10 Structural Hubs in the Yeast Protein
  Interaction Network
• Not related to their degree
• Each one has several different functions

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Lethality

• Biological Essentiality
• Evaluated by comparing with lethal proteins
• Lethality has been determined by protein
  knock-out experiments
• Result

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Conclusion

• Problems
• Complex and unreliable connectivity in protein
  interaction networks
• Contributions
• Reliable network generation by edge weighting
• Hidden knowledge discovery, e.g., patterns or
  taxonomy
• Collaboration with existing computational
  techniques
• Future Works
• Integration with multiple data sources
• Comparative analysis across organisms

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Questions?