Generation of Realistic Gene Regulatory Networks by Enriching for Feed-Forward Loops

Abstract

The regulatory relationships between genes and proteins in a cell form a gene regulatory network (GRN) that controls the cellular response to changes in the environment. A number of inference methods to reverse engineer the original GRN from large-scale expression data have recently been developed. However, the absence of ground-truth GRNs when evaluating the performance makes realistic simulations of GRNs necessary. One aspect of this is that local network motif analysis of real GRNs indicates that the feed-forward loop (FFL) is significantly enriched. To simulate this properly, we developed a novel motif-based preferential attachment algorithm, FFLatt, which outperformed the popular GeneNetWeaver network generation tool in reproducing the FFL motif occurrence observed in literature-based biological GRNs. It also preserves important topological properties such as scale-free topology, sparsity, and average in/out-degree per node. We conclude that FFLatt is well-suited as a network generation module for a benchmarking framework with the aim to provide fair and robust performance evaluation of GRN inference methods.

Ensemble Learning Based Gene Regulatory Network Inference

“…Interestingly, the inferred GRNs have two major characteristics that have been observed in small-world networks: low average shortest path length (here two nodes are separated on average by less than three edges), and relatively high clustering coefficient (between 0.1 and 0.4). This so-called small-world network topology is a well-known characteristic of biological networks in general and also of GRNs [36]. Moreover, the inferred GRNs, and particularly those generated by the Rank-full and the Z-score-full integration schemes, exhibit a disassortative topology, i.e., a negative degree-degree assortativity Pearson correlation.…”