Dataset Information

Computational and biological inference of gene regulatory networks of the LINE-1 retrotransposon.

ABSTRACT: Computational approaches were used to define structural and functional determinants of a putative genetic regulatory network of murine LINE-1 (long interspersed nuclear element-1), an active mammalian retrotransposon that uses RNA intermediates to populate new sites throughout the genome. Polymerase (RNA) II polypeptide E AI845735 and mouse DNA homologous to Drosophila per fragment M12039 were identified as primary attractors. siRNA knockdown of the aryl hydrocarbon receptor NM_013464 modulated gene expression within the network, including LINE-1, Sgpl1, Sdcbp, and Mgst1. Genes within the network did not exhibit physical proximity and instead were dispersed throughout the genome. The potential impact of individual members of the network on the global dynamical behavior of LINE-1 was examined from a theoretical and empirical framework.

SUBMITTER: Ramos KS

PROVIDER: S-EPMC2065750 | biostudies-literature | 2007 Aug

REPOSITORIES: biostudies-literature

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Computational and biological inference of gene regulatory networks of the LINE-1 retrotransposon.

Ramos Kenneth S KS He Qiang Q Kalbfleisch Ted T Montoya-Durango Diego E DE Teneng Ivo I Stribinskis Vilius V Brun Marcel M

Genomics 20070522 2

Computational approaches were used to define structural and functional determinants of a putative genetic regulatory network of murine LINE-1 (long interspersed nuclear element-1), an active mammalian retrotransposon that uses RNA intermediates to populate new sites throughout the genome. Polymerase (RNA) II polypeptide E AI845735 and mouse DNA homologous to Drosophila per fragment M12039 were identified as primary attractors. siRNA knockdown of the aryl hydrocarbon receptor NM_013464 modulated ...[more]

PMID: 17521869

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Project description:Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the field of systems biology. Currently, there are two main approaches in GRN analysis using time-course observation data, namely an ordinary differential equation (ODE)-based approach and a statistical model-based approach. The ODE-based approach can generate complex dynamics of GRNs according to biologically validated nonlinear models. However, it cannot be applied to ten or more genes to simultaneously estimate system dynamics and regulatory relationships due to the computational difficulties. The statistical model-based approach uses highly abstract models to simply describe biological systems and to infer relationships among several hundreds of genes from the data. However, the high abstraction generates false regulations that are not permitted biologically. Thus, when dealing with several tens of genes of which the relationships are partially known, a method that can infer regulatory relationships based on a model with low abstraction and that can emulate the dynamics of ODE-based models while incorporating prior knowledge is urgently required. To accomplish this, we propose a method for inference of GRNs using a state space representation of a vector auto-regressive (VAR) model with L1 regularization. This method can estimate the dynamic behavior of genes based on linear time-series modeling constructed from an ODE-based model and can infer the regulatory structure among several tens of genes maximizing prediction ability for the observational data. Furthermore, the method is capable of incorporating various types of existing biological knowledge, e.g., drug kinetics and literature-recorded pathways. The effectiveness of the proposed method is shown through a comparison of simulation studies with several previous methods. For an application example, we evaluated mRNA expression profiles over time upon corticosteroid stimulation in rats, thus incorporating corticosteroid kinetics/dynamics, literature-recorded pathways and transcription factor (TF) information.

Dataset Information

Computational and biological inference of gene regulatory networks of the LINE-1 retrotransposon.

Publications

Computational and biological inference of gene regulatory networks of the LINE-1 retrotransposon.

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