  Statistical inference of genetic regulatory networks is essential for
understanding temporal interactions of regulatory elements inside the cells.
For inferences of large networks, identification of network structure is
typical 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 using
the techniques from functional data analysis (FDA) by regarding the observed
time course expression data as continuous-time curves. For networks with a
large number of genes, we take advantage of the sparsity of the networks by
penalizing the linear coefficients with a L_1 norm. The ability of the
algorithm to infer network structure is demonstrated using the cell-cycle time
course data for Saccharomyces cerevisiae.
