Tao Lu, Ph.D., Assistant Professor, Department of Epidemiology and Biostatistics, State University of New York, Albany

"High-Dimensional Nonparametric ODE Models for Dynamic Gene Regulation Networks"

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 talk, I present a nonparametric ODE models, coupled with the two-stage smoothing-based ODE estimation methods and adaptive group LASSO techniques, to model dynamic GRNs that could flexibly deal with nonlinear regulation effects.  The method has sound theoretical properties and some benefits in computational efficiency and estimation accuracy.  An example for identifying the nonlinear dynamic GRN of T-cell activation is used to illustrate the application of this model.

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