Sparse additive ordinary differential equations for dynamic gene regulatory network modeling

Hulin Wu, Tao Lu, Hongqi Xue, Hua Liang

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


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 dentifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the usefulness of the proposed method.

Original languageEnglish
Pages (from-to)700-716
Number of pages17
JournalJournal of the American Statistical Association
Issue number506
StatePublished - 2014


  • Adaptive group LASSO
  • Dynamic systems
  • High-dimensional data
  • Nonparametric additive models
  • Time course microarray data
  • Variable selection


Dive into the research topics of 'Sparse additive ordinary differential equations for dynamic gene regulatory network modeling'. Together they form a unique fingerprint.

Cite this