Modeling of gene regulatory networks from RNA-Seq data

Systems biology is a research area which aims to understand living systems as a whole, instead of focusing on single biological entities. Gene regulatory network inference is a systems biology approach which predicts interactions between genes with the help of high-throughput data.

As the underlying structure of many networks is not (completely) known, one focus of systems biology is uncovering the complex and dynamic interactions between genes. The research area called ‘network inference (NI)’ aims at the deduction of network structures utilizing high-throughput data with help of reverse engineering techniques. In most cases transcriptome data is used. NI consists of three parts:

  1. the identification of potential regulators,
  2. the prediction of target genes and
  3. the inference of the mode of interaction (e.g. activation or repression).

The advance of Next-Generation-Sequencing of cDNAs derived from RNA samples (RNA-Seq) allows to study transcriptomes with a so far unreachable depth and quality. On the other hand, data pre-processing poses new challenges. Here, the authors describe a work-flow combining RNA-Seq data analysis with NI. In particular, the advance of RNA-Seq allows researchers to perform transcriptome studies of interacting (micro-) organisms using the same technology without having to separate RNA samples (‘dual RNA-Seq’). This allows to predict GRNs of organisms which interact with each other.

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.

Linde J, Schulze S, Henkel SG, Guthke R. (2016) Data- and knowledge-based modeling of gene regulatory networks: an update. EXCLI J 14:346-78. [article]

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