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Open Access Research article

DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules

Willam Hendrix12, Andrea M Rocha3, Kanchana Padmanabhan12, Alok Choudhary4, Kathleen Scott5, James R Mihelcic3 and Nagiza F Samatova12*

Author Affiliations

1 Department of Computer Science, North Carolina State University, Raleigh, 27695, USA

2 Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, 37831, USA

3 Department of Civil and Environmental Engineering, University of South Florida, Tampa, 33620, USA

4 Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, 60208, USA

5 Department of Integrative Biology, University of South Florida, Tampa, 33620, USA

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BMC Systems Biology 2011, 5:172  doi:10.1186/1752-0509-5-172

Published: 24 October 2011

Abstract

Background

Identifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, cellular subsystem refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in.

Results

In this paper we introduce a fast and theoretically guranteed method called DENSE (Dense and ENriched Subgraph Enumeration) that can take in as input a biologist's prior knowledge as a set of query proteins and identify all the dense functional modules in a biological network that contain some part of the query vertices. The density (in terms of the number of network egdes) and the enrichment (the number of query proteins in the resulting functional module) can be manipulated via two parameters γ and μ, respectively.

Conclusion

This algorithm has been applied to the protein functional association network of Clostridium acetobutylicum ATCC 824, a hydrogen producing, acid-tolerant organism. The algorithm was able to verify relationships known to exist in literature and also some previously unknown relationships including those with regulatory and signaling functions. Additionally, we were also able to hypothesize that some uncharacterized proteins are likely associated with the target phenotype. The DENSE code can be downloaded from http://www.freescience.org/cs/DENSE/ webcite