Knowledge-fused differential dependency network models for detecting significant rewiring in biological networks

TL;DR

This paper introduces a novel method for detecting significant changes in biological networks across different conditions by integrating prior knowledge into a differential dependency network model, validated on synthetic and real datasets.

Contribution

It formulates a convex optimization approach that combines data and prior knowledge to accurately identify network rewiring in biological systems.

Findings

Successfully applied to yeast and breast cancer data

Accurately distinguishes significant rewiring from noise

Validated with synthetic datasets

Abstract

Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets. We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.