Generating Realistic <i>In Silico</i> Gene Networks for Performance Assessment of Reverse Engineering Methods

TLDR

Reverse engineering methods are usually first evaluated on simulated data from in silico networks to assess performance before applying them to real biological networks. This paper introduces a method for generating biologically plausible in silico networks to enable realistic performance assessment of network inference algorithms. Rather than relying on random graph models, the method constructs network structures by extracting modules from known biological interaction networks. Applied to the yeast transcriptional regulatory network, the extracted modules preserve functional and structural properties of the original network, and the method was chosen as the gold standard for the third DREAM challenge.

Abstract

Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper, we present a method for generating biologically plausible in silico networks, which allow realistic performance assessment of network inference algorithms. Instead of using random graph models, which are known to only partly capture the structural properties of biological networks, we generate network structures by extracting modules from known biological interaction networks. Using the yeast transcriptional regulatory network as a test case, we show that extracted modules have a biologically plausible connectivity because they preserve functional and structural properties of the original network. Our method was selected to generate the “gold standard” networks for the gene network reverse engineering challenge of the third DREAM conference (Dialogue on Reverse Engineering Assessment and Methods 2008, Cambridge, MA).