Systems biology has helped to better understand the complexity of gene-to-gene interactions at a genome-wide level through the practical application of network theory. Over the last decade, different types of gene networks have been built to assist in gene function prediction and the modeling of transcriptional regulation in plants. While gene co-expression networks (GCNs) focus on general gene-to-gene relationships, gene regulatory networks (GRNs) depict the hierarchical connections among transcription factors and target genes. Here, we provide two strategies to produce gene networks based on high-throughput transcriptomic data—aggregated GCNs (aggGCNs) and inferred GRNs. A custom in-house pipeline is presented to build aggGCNs, while GRNs are inferred through the implementation of the GENIE3 algorithm. All of the presented code is publicly available, both in this chapter and associated Git repositories, and even though the workflows have been developed for particular plant species such as grapevine and tomato, they can be adapted to any other plant species or eukaryotes.