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Statistical inference of genetic regulatory networks is essential forunderstanding temporal interactions of regulatory elements inside the cells.For inferences of large networks, identification of network structure istypical achieved under the assumption of sparsity of the networks.  When the number of time points in the expression experiment is not too small,we propose to infer the parameters in the ordinary differential equations usingthe techniques from functional data analysis (FDA) by regarding the observedtime course expression data as continuous-time curves. For networks with alarge number of genes, we take advantage of the sparsity of the networks bypenalizing the linear coefficients with a L_1 norm. The ability of thealgorithm to infer network structure is demonstrated using the cell-cycle timecourse data for Saccharomyces cerevisiae.

Inference of genetic networks from time course expression data using functional regression with lasso penalty