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Abstract Details

Activity Number: 114
Type: Topic Contributed
Date/Time: Monday, July 30, 2012 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #305927
Title: High-Dimensional Nonlinear ODEs for Dynamic Gene Regulatory Network
Author(s): Shuang Wu*+ and Yichao Wu and Hongqi Xue and Hulin Wu
Companies: University of Rochester and North Carolina State University and University of Rochester and University of Rochester
Address: 601 Elmwood Avenue, Rochester, NY, 14642,
Keywords: Differential equations ; Independence screening ; Nonnegative garrote ; Time course microarray data ; Two-stage smoothing-based method ; Yeast cell cycles

The gene regulatory network (GRN) is a complex control system and identification of a high-dimensional network from large-scale gene expression data remains to be a challenging task. It is well known that gene regulatory processes are characterized by nonlinear dynamics. However, most previous works have assumed linear effects in GRNs, due to mathematical and computational difficulties in nonlinear systems. In this work, we propose to model the GRN using a set of nonlinear ordinary differential equations (ODEs), which offers a description of the gene network as a continuous time dynamical system. To attain the skeletal structure of the network, we combine the idea of independence screening and the penalized least square approach to perform variable selection and parameter estimation in nonlinear models. We studied the asymptotic properties of the proposed procedure in a high-dimensional setting and also its numerical performance through Monte Carlo simulations. The proposed method is illustrated through an application example for identifying the dynamic GRN from yeast cell cycle progression data.

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