Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling

• 成果类型: Article
• 署名作者: Wu, Hulin; Lu, Tao; Xue, Hongqi; Liang, Hua
• 署名单位: University of Rochester; State University of New York (SUNY) System; University at Albany, SUNY; George Washington University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.859617
• 发表日期: 2014
• 页码: 700-716
• 关键词: time-varying coefficients; parameter-estimation; variable selection; local asymptotics; Bayesian networks; regression; inference; INFORMATION; activation; reduction

摘要

The gene regulation network (GRN) is a high-dimensional complex system, which can be represented by various mathematical or statistical models. The ordinary differential equation (ODE) model is one of the popular dynamic GRN models. High-dimensional linear ODE models have been proposed to identify GRNs, but with a limitation of the linear regulation effect assumption. In this article, we propose a sparse additive ODE (SA-ODE) model, coupled with ODE estimation methods and adaptive group least absolute shrinkage and selection operator (LASSO) techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects. The asymptotic properties of the proposed method are established and simulation studies are performed to validate the proposed approach. An application example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method.