12 research outputs found
NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems
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. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are 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 ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS
List of top ranking pathways and their enrichment score for the phenotype acid-tolerance.
<p>List of top ranking pathways and their enrichment score for the phenotype acid-tolerance.</p
Hydrogen-related enzymes detected by different methods.
<p>: Students' -test; : Mutual Information; : NIBBS-Search .</p
General overview of hydrogen production through the formate pathway.
<p>General overview of hydrogen production through the formate pathway.</p
MULE vs. NIBBS runtime comparison.
<p>Runtimes (y-axis), with trendline, of the MULE algorithm for the various support count thresholds (x-axis) used by the MBS-Enum algorithm. Total runtime required by the NIBBS-Search algorithm drawn as horizontal dotted line.</p
Known aerobic related enzymes that make up the TCA cycle and the glyoxylate bypass that are present (+) or absent (−) in the data set identified by the NIBBS algorithm and T-Test approach.
<p>Known aerobic related enzymes that make up the TCA cycle and the glyoxylate bypass that are present (+) or absent (−) in the data set identified by the NIBBS algorithm and T-Test approach.</p
Comparison between NIBBS, T-Test and Mututal Information [<b>4</b>].
<p>Comparison between NIBBS, T-Test and Mututal Information <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002490#pcbi.1002490-Slonim1" target="_blank">[<b>4</b>]</a>.</p
Schematic of key metabolic pathways for hydrogen production in <i>Clostridium acetobutylicum</i>.
<p>Arrows with larger width indicate a series of reactions. Arrows with narrow width indicate individual reactions. Enzymes: 1, glycolytic enzymes; 2, pyruvate ferredoxin oxidoreductase (E.C. 1.2.7.1); 3, hydrogenase (E.C.1.12.7.2); 4, phosphotransacetylase (E.C. 2.3.1.8); 5, acetate kinase (E.C. 2.7.2.1); 6, acetyl-CoA acetyltransferase (thiolase) (E.C. 2.3.1.9); 7, -hydroxybutyryl-CoA dehydrogenase (E.C. 1.1.1.157); 8, crotonase (E.C. 4.2.1.55); 9, butyryl-CoA dehydrogenase (E.C. 1.3.99.2); 10, phosphotransbutyrylase (E.C.2.3.1.19); 11, butyrate kinase (E.C. 2.7.2.7). Abbreviations: Ferredoxin (Fd); Coenzyme A (CoASH).</p
TCA and rTCA metabolic pathways.
<p>TCA cycle: gray arrows; rTCA: black arrows; EC numbers: white boxes; Pathway specific EC numbers: TCA-specific (gray), rTCA-specific (black).</p