Exploring biological interaction networks with tailored weighted quasi-bicliques.

Wen-Chieh Chang,Oliver Eulenstein,Sudheer Vakati,Roland Krause

doi:10.1186/1471-2105-13-s10-s16

Abstract

BackgroundBiological networks provide fundamental insights into the functional characterization of genes and their products, the characterization of DNA-protein interactions, the identification of regulatory mechanisms, and other biological tasks. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges.ResultsWe introduce novel weighted quasi-biclique problems to identify functional modules in biological networks when represented by bipartite graphs. In difference to previous quasi-biclique problems, we include biological interaction levels by using edge-weighted quasi-bicliques. While we prove that our problems are NP-hard, we also describe IP formulations to compute exact solutions for moderately sized networks.ConclusionsWe verify the effectiveness of our IP solutions using both simulation and empirical data. The simulation shows high quasi-biclique recall rates, and the empirical data corroborate the abilities of our weighted quasi-bicliques in extracting features and recovering missing interactions from biological networks.

Highlights

Cellular processes such as transcription, replication, metabolic catalyses, or the transport of substances are carried out by molecules that are associated in functional modules, and are often realized as physical interaction within protein complexes
Unweighted quasi-biclique approaches have been used in the past to identify modularity in protein interaction networks when presented as bipartite graphs that are spanned between different features of proteins, e.g. binding sites and domain content function [3,5]
A bipartite graph, denoted by (U + V, E), is a graph whose vertex set can be partitioned into the sets U and V such that its edge set E consists only of edges {u, v} where u Î U and v Î V (U and V are independent sets)

Summary

Introduction

Cellular processes such as transcription, replication, metabolic catalyses, or the transport of substances are carried out by molecules that are associated in functional modules, and are often realized as physical interaction within protein complexes. Capturing the modularities of molecular networks accurately will gain insights into cellular processes and gene function Before such modularities can be reliably inferred, challenging computational problems have to be overcome. While these approaches aim to solve NP-hard problems using heuristics, they were able to identify some highly interactive protein complexes [6,7]. Due to the experimental and biological complexity, their computational exploitation faces many algorithmic challenges

Methods

Results

Conclusion