1. WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS
Biological Network Analysis and Representational Implications
MAR 2008
Richard H. Scheuermann, Ph.D.
Chief, Division of Biomedical Informatics
U.T. Southwestern Medical Center

2. Motivation The cell functions as a system of integrated components
There is increasing evidence that the cell system is composed of modules
A “module” in a biological system is a discrete unit whose function is separable from those of other modules
Modules defined based on functional criteria reflect the critical level of biological organization (Hartwell, et al.)
A modular system can reuse existing, well-tested modules
The notion of regulation requires the assembly of individual components into modular networks
Functional modules can then be assembled together into cellular networks
Thus, identifying functional modules and their relationship from biological networks is important to the understanding of the organization, evolution and interaction of the cellular systems they represent
The discipline of Systems Biology is based on this concept
Example module - RNA polymerase and associated components of the basic transcriptional machinery and transcription
The reuse of existing modules takes advantage of the building materials of evolution and yet also allow for the flexibility of responding to changing external conditions

3. MoNet
MoNet

4. Definition of network modules1 2 3

5. Edge betweeness
Girvan-Newman proposed an algorithm to find social communities within human population networks
Utilized the concept of edge betweenness as a unit of measure
defined as the number of shortest paths between all pairs of vertices that run through it
edges between modules tend to have higher values
Provides a quantitative criterion to distinguish edges inside modules from the edges between modules
Betweenness = 20

6. A new definition of network modules
Definition of module degree:
Given a graph G, let U be a subgraph of G (U G). The number of edges within U is defined as the indegree of U, ind(U). The number of edges that connect U to remaining part of G (G−U) is defined as the outdegree of U, outd(U).
Definition of module:
A subgraph U G is a module if ind(U) outd(U).
A subgraph is a complex module if it can be separated into at least two modules by removing edges inside it. Otherwise, it is a simple module.
Adjacent relationship between modules:
Given two subgraphs U, V G, U and V are adjacent if UV= and there are edges in G connecting vertices in U and V.

7. Interaction Networks
Large component of the S. cerevisiae protein interaction network
DIP database
2440 proteins & 6241 interactions
Large component of the Homo sapiens protein interaction network
BIOGRID database
6656 proteins & interactions

8. dMoNet Modules 99 dMoNet simple modules 3 to 201 nodes in size
Include 1700 nodes out of the original 2440 nodes and 3459 of the 6241 edges
156 dMoNet simple modules
3 to 1048 nodes in size
Include 3169 nodes out of the original 6656 nodes and 6949 of the edges

9. Validation of modules
Annotated each protein with the Gene OntologyTM (GO) terms from the Saccharomyces Genome Database (SGD) (Cherry et al. 1998; Balakrishna et al)
Quantified the co-occurrence of GO terms using the hypergeometric distribution analysis
The results show that each module has statistically significant co-occurrence of functional GO categories

10. S. cerevisiae dMoNet Module Evaluation
Hub Node
GO Annotation
Annotated in Genome
Module Size
Annotated in Module
Probability
Modularity
005
NOP4
ribosome biogenesis and assembly
163 76 58
1.66E-60
1.54
003
LSM8
RNA splicing, via transesterification reactions with bulged adenosine as nucleophile 84 93 50
6.49E-55
2.22
001
SRP1
nucleocytoplasmic transport 99
201 62
2.81E-44
1.47
009
SEC22,BOS1,SED5
Golgi vesicle transport
102 44 36
1.44E-43
1.62
008
PWP2
rRNA processing
115 45 35
2.64E-39
1.04
018
ATP1
ATP biosynthetic process 16 21
2.83E-37
23.50
002
PRE1
proteolysis
103
111
9.31E-35
1.65
033
VPH1
hydrogen ion homeostasis 17 14
2.32E-34
1.67
013
CEF1
RNA splicing 96 30 25
3.63E-34
1.46
039
CFT2
cyclin catabolic process 12 11
3.22E-28
8.50
Top 10 yeast network modules with lowest co-clustering p-values. The p-value threshold corresponding to a 5% chance of committing a Type I error based on the Bonferroni correction given a data set of size 2440 is 2.05E-05. Of the 99 modules, 84 have biological process co-clustering p-values below this threshold.

14. Proposed Module Naming Convention

15. Topologies and Measures

16. Ontology for Biomedical Investigation (OBI) Data Transformation Branch

17. Summary of terms Network analysis methods
Network components - network (graph), node (protein), edge (interaction), module (subgraph)
Component properties (qualities) - connectivity, degree, betweeness, density, modularity, edge weight
Topologies - star, ring, mesh, linear, combinations

18. Acknowledgements Network Analysis Support (NIAID)
Feng Luo (Clemson)
Roger Chang (UCSD)
Maya El Dayeh (SMU)
Yuhang Wang (SMU)
Preetam Ghosh (UTSW)
OBI Data Tranformation
Helen Parkinson (EBI)
Melanie Courtot (BCCRC)
Ryan Brinkman (BCCRC)
Elisabetta Manduchi (UPenn)
James Malone (EBI)
Monnie McGee (SMU
Support (NIAID) 1N 1N