PathRNet: robust inference of the context specific structure and temporal dynamics of gene regulatory network during KSHV infection of HUVEC from microarray data and existing knowledge
Jia Meng1, Yidong Chen3,4, Shou-Jiang Gao2,3, Yufei Huang1,3
1Department of ECE, University of Texas at San Antonio, San Antonio, TX, USA
Response of cells to changing endogenous or exogenous conditions is governed by intricate molecular interactions, or regulatory network. To invoke the appropriate response, regulatory network is 1) context-specific, i.e, its constituents and topology depends on the phenotypic and experimental context including experimental conditions, tissue types, cell conditions such as damage or stress, macroenvironments of cell, etc. and 2) time varying, i.e., network elements and their regulatory roles change actively over time to properly guide the endogenous cell states such as different stages in a cell cycle. It is of crucial importance to develop systems biology approach to reconstruct regulatory networks for the specific biological processes and diseases to reveal detailed underlying temporal molecular regulations of different aspects including transcription factors binding, post-transcription regulations of microRNA and RNA binding proteins, and post-translational modification such as histone methylation. An ideal computational systems approach should answer two questions: 1) what is the regulatory network for a given exogenous context? 2) How does the context specific regulatory network vary over time to invoke relevant endogenous cell states? Revealing context-specific regulatory network and its temporal dynamics are essential for understanding the molecular mechanisms and functional kinetics underlying diverse phenotypes.
A novel approach PathRNet was proposed for reconstructing the context specific dynamic regulatory networks by integrating microarray gene expression profiles, NCI-NC signaling pathway information, Transfac database , and Molecule Signature Database  together. The nodes of the TFs and pathways and edges represent the regulation between pathways and TFs. The reconstructed networks reveal the dynamics of the network structure as well as the regulatory impact. These dynamic networks paint a system level temporal landscape of the genetic regulatory circuitry.
DATA AND CODE