Functional annotation of regulatory pathways.
BACKGROUND
Standardized annotations of biomolecules in interaction networks (e.g. Gene Ontology) provide comprehensive understanding of the function of individual molecules. Extending such annotations to pathways is a critical component of functional characterization of cellular signaling at the systems level.


RESULTS
We propose a framework for projecting gene regulatory networks onto the space of functional attributes using multigraph models, with the objective of deriving statistically significant pathway annotations. We first demonstrate that annotations of pairwise interactions do not generalize to indirect relationships between processes. Motivated by this result, we formalize the problem of identifying statistically overrepresented pathways of functional attributes. We establish the hardness of this problem by demonstrating the non-monotonicity of common statistical significance measures. We propose a statistical model that emphasizes the modularity of a pathway, evaluating its significance based on the coupling of its building blocks. We complement the statistical model by an efficient algorithm and software, Narada, for computing significant pathways in large regulatory networks. Comprehensive results from our methods applied to the Escherichia coli transcription network demonstrate that our approach is effective in identifying known, as well as novel biological pathway annotations.


BACKGROUND
Narada is implemented in Java and is available at http://www.cs.purdue.edu/homes/jpandey/narada/.
Pandey
Jayesh
J
Department of Computer Science, Purdue University, USA. jpandey@cs.purdue.edu
Koyutürk
Mehmet
M
Kim
Yohan
Y
Szpankowski
Wojciech
W
Subramaniam
Shankar
S
Grama
Ananth
A
D000465:Algorithms; D003198:Computer Simulation; D004282:Documentation / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D008954:Models, Biological; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*; D012984:Software
D020543:Proteome