Inferring a Gene Regulatory Network from Gene Expression Data. An Overview of Best Methods and a Reverse Engineering Approach

Gene Regulatory Networks (GRNs) are widely used to understand processes in cellular organisms. The spread of viruses and the development of new unknown diseases require the employment of algorithmic tools to support research in this direction. Several methods have been developed to infer a GRN from gene expression data observed in the field, each with its own features. In this article, we provide an overview of the most popular methods in this field to highlight their advantages and weaknesses. In addition, a reverse engineering framework is presented in order to facilitate the inference process and provide researchers with an artificial environment capable of replicating gene expression from genes by simulating their behavior in the real world.