An omnidirectional visualization model of personalized gene regulatory networks

Chixiang Chen, Libo Jiang, Guifang Fu, Ming Wang, Yaqun Wang, Biyi Shen, Zhenqiu Liu, Zuoheng Wang, Wei Hou, Scott A. Berceli, Rongling Wu

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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.

Original languageEnglish (US)
Number of pages1
JournalNPJ systems biology and applications
StatePublished - Jan 1 2019

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • General Biochemistry, Genetics and Molecular Biology
  • Drug Discovery
  • Computer Science Applications
  • Applied Mathematics


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