Data- and knowledge-based modeling of gene regulatory networks: an update

Abstract

Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data. In this review, we present current and updated network inference methods focusing on novel techniques for data acquisition, network inference assessment, network inference for interacting species and the integration of prior knowledge. After the advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) we discuss in detail its application to network inference. Furthermore, we present progress for large-scale or even full-genomic network inference as well as for small-scale condensed network inference and review advances in the evaluation of network inference methods by crowdsourcing. Finally, we reflect the current availability of data and prior knowledge sources and give an outlook for the inference of gene regulatory networks that reflect interacting species, in particular pathogen-host interactions.

Keywords: RNA-Seq; gene regulatory networks; modeling; network inference; prior knowledge; reverse engineering.

Table 1. GRN inference methods

Table 2. DREAM Challenges. DREAM#: Running number of challenge; Short Title and reference if any; Data given for the challenge

Figure 1. Workflow of GRN inference. Systems Biology Cycle of wet lab (experiment) and dry lab work: Experiments lead to RNA-Seq data, which need to be preprocessed and features have to be selected (more detailed steps are shown in grey boxes). A GRN is inferred for selected features. Predicted interactions are validated leading to more knowledge and new hypotheses. Both analysis of experimental data (data preprocessing and feature selection) and modeling (network inference) is supported by prior knowledge.

Figure 2. Regulated gene expression and model network representation. External stimuli (ligands binding to receptors on the cell’s surface) may trigger an alteration in gene expression. Via signal transduction, the most important regulators, the transcription factors, are influenced. They regulate the transcription of DNA to mRNA, which subsequently is translated to proteins. Those regulated biological processes can be transformed to a network model (inference), whose main nodes represent genes or their products (typically on the level of regulated transcription).

Figure 3. Medium-scale network. 824 interactions inferred using the modified regression method LARS for 503 genes of the ‘gold standard’ of the human pathogenic fungus Candida albicans (Linde et al., 2011, and Altwasser et al., 2012). The red-coloured hubs represent the genes MAL2, SIR2, SNF1 and STE11.