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
2 Department of Pediatrics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
3Greehey Children’s Cancer Research Institute, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA
4 Department of Epidemiology and Biostatistics, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA


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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. 
In general, the ability of inferring regulatory networks highly depends on network architecture and model, data availability, and inference methods. The importance of an effectiveness network model lies in its ability to combat large data noise and provide biologically interpretable results. The main challenge of uncovering robust and biologically meaning context-specific and time varying networks is to develop an effective network model and a robust inference approach from limited and noisy high throughput data. We hypothesize that a pathway-centric instead of the popular gene (protein) centric network architecture and an enrichment-based inference framework will result in more robust and insightful networks that provide better understanding of the temporal and context specific functions of regulatory networks.


A novel approach PathRNet was proposed for reconstructing the context specific dynamic regulatory networks by integrating microarray gene expression profiles, NCI-NC [1]signaling pathway information, Transfac database [2], and Molecule Signature Database [3] 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.

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