ANALYSIS OF GENETIC NETWORKS USING ATTRIBUTED GRAPH MATCHING

1
ANALYSIS OF GENETIC NETWORKS USING ATTRIBUTED
GRAPH MATCHING
2
BACKGROUND

• Completion of sequencing projects
• Need for functional discovery
• Emerging area of study Large scale genomic
  analysis
• Similarity of living systems

3
GENETIC NETWORKS

• Modelling genetic networks
• Interaction of genes and proteins
• Relationship between topology and function

4
MOTIVATION

• Common biological processes
• Comparison of networks
• Discovering missing interactions
• Discovering missing genes

5
GRAPH MATCHING
G1
Search-based Algorithm Pruning Techniques
G2
6
ROADMAP

• Scale-Free Networks
• Modelling Genetic Networks
• Graph Matching
• Algorithm
• Results

7
SCALE-FREE NETWORKS
8
COMPLEX NETWORKS

• Small-world model
• WWW
• Human acquaintances network
• Citation networks
• Biological networks

9
SMALL-WORLD

• Features
• Characteristic path length
• Clustering coefficient
• Sparseness

10
SMALL-WORLD

• Somewhere in between regular random graphs

11
SMALL-WORLD

• Highly clustered
• Short diameter

12
SCALE-FREE NETWORKS

• Complex networks biological, social, www, power
  grid, citation etc.
• Power low connectivity
• P(k) k -a
• Hubs - authorities

13
SCALE-FREE NETWORKS

• Application for testing scale free behavior
• Yeast
• Helicobacter Pylori
• Mycoplasma Pnuemonia
• Mycoplasma Genitelium
• Linear log-log graph
• Slope a

14
SCALE-FREE NETWORKS

• Slope is calculated by least mean square method

15
TOPOLOGY FUNCTIONALITY

• Small diameter
• ease of dissemination of information
• ease of restoring after disturbance
• Cliquishness
• Alternate paths are found
• Heterogeneity
• Random removal does not effect the network
• Hubs are vulnerable to attack

16
BIOLOGICAL ASPECTS

• Multifunctionality
• Grouped into functional units
• Stability
• Reason Most of the interactions are between hubs
  and authorities

17
MODELLING GENETIC NETWORKS
18
TYPES OF GENETIC NETWORKS

• Categorized by data sources
• Metabolic pathways
• Gene expression arrays
• Protein interactions
• Gene interactions

19
INTERACTION MAPS

• High level perspective
• Nodes Genes or proteins
• Edges Presence of an interaction
• Data sources
• Two-hybrid analysis
• Fusion analysis
• Chromosomal proximity
• Phylogenetic analysis

20
GRAPH MATCHING
21
PROBLEM DEFINITION

• Attributed Relational Graph (ARG)
• G V, E, X.
• V v1, v2, , vn Nodes
• E e1, e2, , em Edges
• X x1, x2,,xn Attributes

22
INEXACT SUBGRAPH MATCHING

• Allow for
• Mismatching attribute values
• Missing nodes
• Missing links
• Also called error-correcting subgraph isomorphism
• NP-Complete

23
SEARCH TECHNIQUES

• Cost function
• Pruning (Structure Constraints)
• Backtracking

24
ATTRIBUTED GRAPH MATCHING TOOL
25
ATTRIBUTE MATCHING

• Amino Acid Sequence Content Composition
• array of 20, percentage of each aa
• Amino acid grouped into classes array of 6
• Amino acid triples grouped into classes array of
  216
• MKVLNKNEL

6 x 6 x 6
26
ATTRIBUTE MATCHING
Difference in amino acid composition values of
gene pairs for M. Genitalium and M. Pneumoniae.
Score
observations
27
STRUCTURAL CONSTRAINTS

• Effect of scale-free behaviour
• Connectivity information Highly heterogeneous,
  thus start with most connected and work around it
• Pruning strategy comparibility is determined by
  power low

28
STRUCTURAL CONSTRAINTS

• Neigborhood connectivity
• Choose the neighbor at the next stage
• Backtracking
• Component by component
• Go back to the neighbor with the most
  connectivity within the component

29
TEST CASE

• Mycoplasma Genitalium
• smallest genome (470 ORFs)
• Mycoplasma Pnuemoniae
• Very similar, superset (688 ORFs)

30
TEST CASE...

• Mycoplasma Genitalium
• 232 nodes
• 211 links
• Mycoplasma Pnuemoniae
• 267 nodes
• 257 links
• Inputs

• MGE links
• MPN links

• MGE synonyms
• MPN synonyms

• MGE amino acid sequence
• MPN amino acid sequence

31
RESULTS
MGE
MPN
32
DISCOVERY OF MISSING DATA

• Missing link
• Link between in MPN632 and MPN637 is missing in
  our data but exists in literature

33
DISCOVERY OF MISSING DATA

• Missing node with known COG
• MPN236--- MPN237---MPN238---MPN678
• MG098 ----MG099-----MG100----MG459
• MG459 is ortholog of MPN678

34
DISCOVERY OF MISSING DATA

• Missing node without known ortholog

35
CONCLUSION

• Large-scale genomics
• Interaction data captures system structure and
  dynamics
• Graph matching exploits the scale-free
  characteristics
• Novel interactions and genes can be identified

36
ACKNOWLEDGEMENT

• YASEMIN TÜRKELI