A single source k-shortest paths algorithm to infer regulatory pathways in a gene network.
BACKGROUND
Inferring the underlying regulatory pathways within a gene interaction network is a fundamental problem in Systems Biology to help understand the complex interactions and the regulation and flow of information within a system-of-interest. Given a weighted gene network and a gene in this network, the goal of an inference algorithm is to identify the potential regulatory pathways passing through this gene.


RESULTS
In a departure from previous approaches that largely rely on the random walk model, we propose a novel single-source k-shortest paths based algorithm to address this inference problem. An important element of our approach is to explicitly account for and enhance the diversity of paths discovered by our algorithm. The intuition here is that diversity in paths can help enrich different functions and thereby better position one to understand the underlying system-of-interest. Results on the yeast gene network demonstrate the utility of the proposed approach over extant state-of-the-art inference algorithms. Beyond utility, our algorithm achieves a significant speedup over these baselines.


BACKGROUND
All data and codes are freely available upon request.
Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA.
Yu-Keng
YK
Shih
Srinivasan
S
Parthasarathy
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D053263:Gene Regulatory Networks; D008962:Models, Theoretical; D012441:Saccharomyces cerevisiae / Q000235:genetics; D049490:Systems Biology / Q000379:methods*