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

SUBMITTER: Zhivkoplias EK 

PROVIDER: S-EPMC8872634 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature