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

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 themore » 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 g 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/« less

Authors:

Hendrix, Willam ^[1]; Rocha, Andrea M. ^[2]; Padmanabhan, Kanchana ^[1]; Choudhary, Alok ^[3]; Scott, Kathleen ^[4]; Mihelcic, James R. ^[2]; Samatova, Nagiza F. ^[1]

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+ Show Author Affiliations

1. North Carolina State Univ., Raleigh, NC (United States). Dept. of Computer Science; Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Computer Science and Mathematics Division
2. Univ. of South Florida, Tampa, FL (United States). Dept. of Civil and Environment Engineering
3. Northwestern Univ., Evanston, IL (United States). Dept. of Electrical Engineering and Computer Science
4. Univ. of South Florida, Tampa, FL (United States). Dept. of Integrative Biology

Publication Date:

Mon Jan 24 00:00:00 EST 2011

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States); Northwestern Univ., Evanston, IL (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); National Science Foundation (NSF)

OSTI Identifier:

1626645

Grant/Contract Number:  

AC05-00OR22725; FC02-07ER25808; FG02-08ER25848; SC0001283; SC0005309; SC0005340; OCI-0724599; CNS-0830927; CCF-0621443; CCF-0833131; CCF-0938000; CCF-1029166; CCF-1043085

Resource Type:

Accepted Manuscript

Journal Name:

BMC Systems Biology

Additional Journal Information:

Journal Volume: 5; Journal Issue: 1; Journal ID: ISSN 1752-0509

Publisher:

BioMed Central

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; 62 RADIOLOGY AND NUCLEAR MEDICINE; 97 MATHEMATICS AND COMPUTING; Mathematical & computational biology; Hydrogen Production; Dense Subgraph; Pyruvate Formate Lyase; Bitmap Index; Target Phenotype