Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling

Hulin Wu, Tao Lu, Hongqi Xue and Hua Liang

Journal of the American Statistical Association, 2014, vol. 109, issue 506, 700-716

Abstract: 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.

Date: 2014
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Citations: View citations in EconPapers (6)

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DOI: 10.1080/01621459.2013.859617

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