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

Erik K Zhivkoplias,Thomas Hillerton,Oleg Vavulov,Erik L L Sonnhammer

doi:10.3389/fgene.2022.815692

Erik K Zhivkoplias, Thomas Hillerton + Show 2 more

Open Access

https://doi.org/10.3389/fgene.2022.815692

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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.

Highlights

Understanding large-scale biological relationships between genes and the proteins they encode remains a great challenge in systems biology
How the depletion of these motifs contributes to the function of the gene circuitry, and how it relates to the evolution of gene regulatory networks, remains to be answered
The novelty of the presented algorithm is that it generates networks with boosted feedforward loop (FFL) motifs, which are known to be important for network dynamics

Summary

Introduction

Understanding large-scale biological relationships between genes and the proteins they encode remains a great challenge in systems biology. The wide availability of system-level expression datasets has given rise to a variety of reverse engineering methods that aim to reconstruct the hidden regulatory gene–gene and gene–protein relationships. Such relationships form a gene regulatory network (GRN) that regulates developmental processes in organisms and controls adaptation to changes in the environment (Davidson, 2010). A variety of GRN inference methods are commonly used (Margolin et al, 2006; Faith et al, 2007; Friedman et al, 2010; Huynh-Thu et al, 2010; Zavlanos et al, 2011) to tackle this problem It was the focus of four separate Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenges, with DREAM5 being the most recent one (Marbach et al, 2012)

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