Dynamic gene regulatory network (GRN) construction from high-throughput time course data.

One of the fundamental goals in computational systems biology is to model gene expression levels and to use such models to predict the behaviour of the cell under various external/internal conditions. In any given cell, thousands of genes are expressed and work together to ensure the cell's function, fitness, and survival. Thanks to advances in technology and collaborative efforts in the scientific community, there is now an abundance of gene expression data collected over time (see: http://genestudy.org/).

Identifying gene regulatory network (GRN) models from these big data sets can shed light on the genetic mechanisms that occur when the cellular processes are impaired, for instance, when they are subject to an infection or disease.  A network is simply a collection of connected objects. We refer to the objects as nodes and the connections between the nodes as edges. GRNs consider each gene as a node and the direct or indirect interactions between genes as edges. Edges indicate whether the dependence between any pair of genes is inductive, with an increase in the amount of one gene leading to an increase in the other, or inhibitory, with an increase in one leading to a decrease in the other.

Why are GRNs significant?

  • Identifying these networks from genomic big data can shed light on the genetic mechanisms that occur when the cellular processes are impaired, for instance when they are subject to an infection or disease.
  • A GRN can be used to derive novel biological hypothesis about the regulation of genes, which can then be investigated in-depth in wet-lab experiments.
  • GRN can be used as biomarkers, e.g., for diagnostic, predictive or prognostic purposes. Network-based biomarkers consider the interaction structure between individual genes explicitly. In contrast, biomarkers based on individual genes which neglect the interaction structure completely. 


Deng, N. Ramirez, J.C., Carey, M., Miao, H., Arias, C.A., Rice, A.P., Wu H. (2019), Investigation of temporal and spatial heterogeneities of the immune responses to Bordetella pertussis infection in the lung and spleen of mice via analysis and modelling of dynamic microarray gene expression data, Infectious Disease Modelling, 4: 215-226

Carey M., Ramírez C. J., Wu S., Wu H. (2018) A big data pipeline: Identifying dynamic gene regulatory networks from time-course Gene Expression Omnibus data with applications to influenza infection Statistical methods in medical research 27 (7), 1930-1955

Song, J., Carey, M., Zhu, H., Miao, H., Ramfrez, J.C., and Wu H. (2018), Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations, International Journal of Computational Biology and Drug Design (IJCBDD), 11(1/2), 135-153.

Carey M., Wu S., Gan G., Wu H. (2016) Correlation-based iterative clustering methods for time course data: The identification of temporal gene response modules for influenza infection in humans Infectious Disease Modelling 1 (1), 28-39

The GRN Pipeline

The pipeline constructs dynamic gene regulatory networks. It consists of a series of cutting-edge statistical analysis and inference techniques to identify dynamic GRNs from high-dimensional time-course gene expression data. The pipeline is designed to

  1. Pre-process the probe level data, i.e., background adjustment, normalization, and summarization.
  2. Detect the genes with expression levels that change significantly with respect to time, which we call dynamic response genes (DRGs).
  3. Cluster the DRGs into groups of co-expressed genes, which exhibit similar expression levels over time, referred to as temporal “gene response modules” (GRMs).
  4. Obtain the functional annotation (gene ontology terms, associated pathways and a functional gene-enrichment analysis) of the GRMs.
  5. Construct the high-dimensional gene regulatory networks (GRNs) that determines the interactions between the GRMs using differential equation models.
  6. Perform a network feature analysis on the established gene regulatory networks.
  7. Output the results in the form of a manuscript.

See website for more details: http://genestudy.org/help