Title: Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations

Authors: Jaejoon Song; Michelle Carey; Hongjian Zhu; Hongyu Miao; Juan Camilo Ramírez; Hulin Wu

Addresses: Department of Biostatistics, The University of Texas MD, Anderson Cancer Center, 1400 Pressler Street, Houston, TX, 77030, USA ' Department of Mathematics and Statistics, McGill University, 805 Sherbrooke Street West, Montreal, H3A 0B9, Canada ' Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, 77030, USA ' Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, 77030, USA ' Faculty of Computer Engineering, Universidad Antonio Nariño, Cl. 58a Bis 3794, Bogotá, Cundinamarca, Colombia ' Department of Biostatistics, The University of Texas School of Public Health, 1200 Pressler Street, Houston, TX, USA, 77030, USA

Abstract: Reactivation of latently infected cells has emerged as an important strategy for eradication of HIV. However, genetic mechanisms of regulation after reactivation remain unclear. We describe a five-step pipeline to study the dynamics of the gene regulatory network following a viral reactivation using high-dimensional ordinary differential equations. Our pipeline implements a combination of five different methods, by detecting temporally differentially expressed genes (step 1), clustering genes with similar temporal expression patterns into a small number of response modules (step 2), performing a functional enrichment analysis within each gene response module (step 3), identifying a network structure based on the gene response modules using ordinary differential equations (ODE) and a high-dimensional variable selection technique (step 4), and obtaining a gene regulatory model based on refined parameter estimates using nonlinear least squares (step 5). We applied our pipeline to a time course gene expression data of latently infected T-cells following a latency-reversion.

Keywords: HIV; human immunodeficiency virus; gene regulatory network; ODEs; ordinary differential equations.

DOI: 10.1504/IJCBDD.2018.090844

International Journal of Computational Biology and Drug Design, 2018 Vol.11 No.1/2, pp.135 - 153

Received: 12 May 2017
Accepted: 09 Oct 2017
Published online: 28 Mar 2018 *

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