Inference of Genetic Networks from Time Course Expression Data Using Functional Regression with Lasso Penalty

Abstract

Statistical inference of genetic regulatory networks is essential for understanding temporal interactions of regulatory elements inside the cells. In this work, we propose to infer the parameters of 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.