Identifying the dynamic gene regulatory network during latent HIV-1 reactivation using high-dimensional ordinary differential equations Online publication date: Sat, 24-Mar-2018
by Jaejoon Song; Michelle Carey; Hongjian Zhu; Hongyu Miao; Juan Camilo Ramírez; Hulin Wu
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 11, No. 1/2, 2018
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.
Online publication date: Sat, 24-Mar-2018
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