1

WORKSHOP ON ONTOLOGIES OF CELLULAR NETWORKS

27 - 28 MAR 2008

Richard H. Scheuermann, Ph.D.Chief, Division of Biomedical Informatics

U.T. Southwestern Medical Center

Biological Network Analysis and Representational Implications

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

3

MoNet

4

Definition of network modules

1 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 UV= and there are

edges in G connecting vertices in U and V.

7

•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 & 19022 interactions

Interaction Networks

8

•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 19022 edges

dMoNet Modules

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

Module # 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 16 2.83E-37 23.50

002 PRE1 proteolysis 103 111 44 9.31E-35 1.65

033 VPH1 hydrogen ion homeostasis 17 14 14 2.32E-34 1.67

013 CEF1 RNA splicing 96 30 25 3.63E-34 1.46

039 CFT2 cyclin catabolic process 12 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.

S. cerevisiae dMoNet Module Evaluation

11

12

13

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– 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)– 1N0140041– 1N0140076