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Bioinformatics Advance Access published online on November 8, 2006
Bioinformatics, doi:10.1093/bioinformatics/btl562
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© The Author (2006). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org
Received May 12, 2006
Revised October 30, 2006
Accepted November 2, 2006

Article

Modular organization of protein interaction networks

Feng Luo 1 *, Yunfeng Yang 2, Chin-Fu Chen 3, Roger Chang 4, Jizhong Zhou 5, and Richard H. Scheuermann 4

1 Department of Computer Science, 100 McAdams Hall, Clemson University, Clemson, SC 29634-0974, USA; Department of Genetics and Biochemistry, 100 Jordan Hall, Clemson, SC 29634, USA
2 Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
3 Department of Genetics and Biochemistry, 100 Jordan Hall, Clemson, SC 29634, USA
4 Department of Pathology, U.T. Southwestern Medical Center, 5323 Harry Hines Blvd. Dallas, TX 75390-9072, USA
5 Insitute for Environmental Genomics and Department of Botany and Microbiology, University of Oklahoma, Norman, OK 73019, USA

* To whom correspondence should be addressed.
Feng Luo, E-mail: luofeng{at}cs.clemson.edu


  Abstract

Motivation: Accumulating evidence suggests that biological systems are composed of interacting, separable, functional modules. Identifying these modules is essential to understand the organization of biological systems.

Result: In this paper, we present a framework to identify modules within biological networks. In this approach, the concept of degree is extended from the single vertex to the sub-graph, and a formal definition of module in a network is used. A new agglomerative algorithm was developed to identify modules from the network by combining the new module definition with the relative edge order generated by the Girvan-Newman algorithm. A JAVA program, MoNet, was developed to implement the algorithm. Applying MoNet to the yeast core protein interaction network from the database of interacting proteins (DIP) identified 86 simple modules with sizes larger than 3 proteins. The modules obtained are significantly enriched in proteins with related biological process GeneOntgology terms. A comparison between the MoNet modules and modules defined by Radicchi et al. (2004) indicates that MoNet modules show stronger co-clustering of related genes and are more robust to ties in betweenness values. Further, the MoNet output retains the adjacent relationships between modules and allows the construction of an interaction web of modules providing insight regarding the relationships between different functional modules. Thus, MoNet provides an objective approach to understand the organization and interactions of biological processes in cellular systems.

Availability: MoNet is available upon request from the authors.

Associate Editor: Satoru Miyano
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