Independence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.

TitleIndependence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.
Publication TypeJournal Article
Year of Publication2018
AuthorsXue, Hongqi, Shuang Wu, Yichao Wu, Juan C. Ramirez Idarraga, and Hulin Wu
JournalStat Med
Volume37
Issue17
Pagination2630-2644
Date Published2018 07 30
ISSN1097-0258
KeywordsAlgorithms, Computer Simulation, Gene Regulatory Networks, Humans, Mathematics, Models, Statistical, Nonlinear Dynamics
Abstract

Mechanism-driven low-dimensional ordinary differential equation (ODE) models are often used to model viral dynamics at cellular levels and epidemics of infectious diseases. However, low-dimensional mechanism-based ODE models are limited for modeling infectious diseases at molecular levels such as transcriptomic or proteomic levels, which is critical to understand pathogenesis of diseases. Although linear ODE models have been proposed for gene regulatory networks (GRNs), nonlinear regulations are common in GRNs. The reconstruction of large-scale nonlinear networks from time-course gene expression data remains an unresolved issue. Here, we use high-dimensional nonlinear additive ODEs to model GRNs and propose a 4-step procedure to efficiently perform variable selection for nonlinear ODEs. To tackle the challenge of high dimensionality, we couple the 2-stage smoothing-based estimation method for ODEs and a nonlinear independence screening method to perform variable selection for the nonlinear ODE models. We have shown that our method possesses the sure screening property and it can handle problems with non-polynomial dimensionality. Numerical performance of the proposed method is illustrated with simulated data and a real data example for identifying the dynamic GRN of Saccharomyces cerevisiae.

DOI10.1002/sim.7669
Alternate JournalStat Med
Original PublicationIndependence screening for high dimensional nonlinear additive ODE models with applications to dynamic gene regulatory networks.
PubMed ID29722041
PubMed Central IDPMC6940146
Grant ListR01 AI087135 / AI / NIAID NIH HHS / United States
HHSN266200700008C / AI / NIAID NIH HHS / United States
HHSN272201000055C / AI / NIAID NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
P30 AI078498 / AI / NIAID NIH HHS / United States