An omnidirectional visualization model of personalized gene regulatory networks

Abstract

Gene regulatory networks (GRNs) have been widely used as a fundamental tool to reveal the genomic mechanisms that underlie the individual’s response to environmental and developmental cues. Standard approaches infer GRNs as holistic graphs of gene co-expression, but such graphs cannot quantify how gene–gene interactions vary among individuals and how they alter structurally across spatiotemporal gradients. Here, we develop a general framework for inferring informative, dynamic, omnidirectional, and personalized networks (idopNetworks) from routine transcriptional experiments. This framework is constructed by a system of quasi-dynamic ordinary differential equations (qdODEs) derived from the combination of ecological and evolutionary theories. We reconstruct idopNetworks using genomic data from a surgical experiment and illustrate how network structure is associated with surgical response to infrainguinal vein bypass grafting and the outcome of grafting. idopNetworks may shed light on genotype–phenotype relationships and provide valuable information for personalized medicine.

Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk

“…We argue that cancer biomarkers may not only be interpreted as single genes, but also include the pattern of how each gene interacts with every other gene to form a complex but organized network. Chen et al’s [ 18 ] idopNetworks are among the most advanced networks to omnidirectionally reveal the topological differences of gene interactions across spatiotemporal gradients. To reconstruct such idopNetworks for neuroblastoma risk, we coin the concept of expression index (EI), defined as the total expression amount of all genes on a patient or sample, and plot the expression of individual genes against the ordered EI across samples ( Figure 1 ).…”

“…These approaches can also only reconstruct context-agnostic networks, failing to reveal the change in network structure in response to environmental and developmental signals. We implement and modify Chen et al's [18] networking Figure 7. Voronoi treemaps that visualize fine-grained idopNetworks among genes from module 20 (A) and 29 (B) for high-risk and low-risk patients.…”

“…, L). Unlike classic ODEs with respect to time, ODEs in Equation 1are specified by the EI derivative, which are thus called quasi-dynamic ODEs (qdODEs) [18].…”

“…Increasing evidence shows that a complex disease is controlled by a full set of genome-wide genes [37], indicating the necessity of reconstructing an omnigenic network. Although it is highly challenging to reconstruct large networks, Chen et al [18] has integrated developmental modularity and Dunbar's law, which enables them to reconstruct G(E i ) from high-or even ultrahigh-dimensional data of genes. Taken together, we will reconstruct informative, dynamic, omnidirectional, and personalized networks (idopNetworks).…”

“…More recently, Chen et al [ 18 ] developed an interdisciplinary framework for reconstructing informative, dynamic, omnidirectional, and personalized networks (idopNetworks) using steady-state gene expression data from regular genomic experiments. The idopNetwork model can maximize the utility efficiency of genomic data to reconstruct biologically meaningful, context-specific, and large-scale but sparse networks.…”