Title: Efficient α, β-motif finder for identification of phenotype-related functional modules

Abstract

Background: Microbial communities in their natural environments exhibit phenotypes that can directly cause particular diseases, convert biomass or wastewater to energy, or degrade various environmental contaminants. Understanding how these communities realize specific phenotypic traits (e.g., carbon fixation, hydrogen production) is critical for addressing health, bioremediation, or bioenergy problems. Results: In this paper, we describe a graph-theoretical method for in silico prediction of the cellular subsystems that are related to the expression of a target phenotype. The proposed (a, b)-motif finder approach allows for identification of these phenotype-related subsystems that, in addition to metabolic subsystems, could include their regulators, sensors, transporters, and even uncharacterized proteins. By comparing dozens of genome-scale networks of functionally associated proteins, our method efficiently identifies those statistically significant functional modules that are in at least a networks of phenotype-expressing organisms but appear in no more than b networks of organisms that do not exhibit the target phenotype. It has been shown via various experiments that the enumerated modules are indeed related to phenotype-expression when tested with different target phenotypes like hydrogen production, motility, aerobic respiration, and acid-tolerance. Conclusion: Thus, we have proposed a methodology that can identify potential statistically significant phenotyperelated functional modules. The functional module ismore » modeled as an (a, b)-clique, where a and b are two criteria introduced in this work. We also propose a novel network model, called the two-typed, divided network. The new network model and the criteria make the problem tractable even while very large networks are being compared. The code can be downloaded from http://www.freescience.org/cs/ABClique/« less

Authors:

Schmidt, Matthew C. ^[1]; Rocha, Andrea M. ^[2]; Padmanabhan, Kanchana ^[1]; Chen, Zhengzhang ^[1]; Scott, Kathleen ^[3]; 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 Environmental Engineering
3. Univ. of South Florida, Tampa, FL (United States). Dept. of Integrative Biology

Publication Date:

Sat Jan 01 00:00:00 EST 2011

Research Org.:

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC), Biological and Environmental Research (BER). Biological Systems Science Division

OSTI Identifier:

1626280

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

BMC Bioinformatics

Additional Journal Information:

Journal Volume: 12; Journal Issue: 1; Journal ID: ISSN 1471-2105

Publisher:

BioMed Central

Country of Publication:

United States

Language:

English

Subject:

59 BASIC BIOLOGICAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology