NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

"NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems"

Abstract: Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. In this paper, a metabolic system refers to a subgraph of a metabolic network, where nodes are compounds (substrates and products) and edges are labeled with the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm. [pdf]

Authors: Matthew C. Schmidt, Andrea M. Rocha, Kanchana Padmanabhan, Yekaterina Shpanskaya, Jill Banfield, Kathleen Scott, James R. Mihelcic, and Nagiza F. Samatova

Acknowledgement: This work was supported in part by the U.S. Department of Energy, Office of Science, the Office of Advanced Scientific Computing Research (ASCR) and the Office of Biological and Environmental Research (BER) and the U.S. National Science Foundation (Expeditions in Computing). The work by AR was supported by the Delores Auzenne Fellowship and the Alfred P. Sloan Minority PhD Scholarship Program. Oak Ridge National Laboratory is managed by UT-Battelle for the LLC U.S. D.O.E. under contract no. DEAC05-00OR22725. This work was also supported by NSF Expeditions in Computing.

Citation

Main software

Author: Matthew C. Schmidt
Download link: NIBBS-Search

For more information contact: Dr. Nagiza Samatova - samatova@csc.ncsu.edu

Visualization software

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