Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling

By Hulin Wu, Tao Lu, Hongqi Xue, Hua Liang. 2014

92 citations recorded by the source corpus.

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.

Subfields: Molecular Biology

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Citations in the corpus

Network Reconstruction From High-Dimensional Ordinary Differential Equations (2016, methodology): “A few authors have recently considered modeling large-scale dynamical systems from biology using ODEs (Henderson and Michailidis, 2014; Wu et al., 2014), under the assumption that the right-hand side of (1) is additive, (11) Henderson and Michailidis (2014) and Wu et al. (2014) approximate the…”
Learning Large Scale Ordinary Differential Equation Systems (2017, background): “Penalised versions of the inverse collocation methods – as alternatives to minimising (4) – have also been proposed by e.g., Lu et al. (2011) and Wu et al. (2014) to promote sparse solutions.”