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Motivation: Gene regulatory interactions are of fundamental importance tovarious biological functions and processes. However, only a few previouscomputational studies have claimed success in revealing genome-wide regulatorylandscapes from temporal gene expression data, especially for complexeukaryotes like human. Moreover, recent work suggests that these methods stillsuffer from the curse of dimensionality if network size increases to 100 orhigher.  Result: We present a novel scalable algorithm for identifying genome-wideregulatory network structures. The highlight of our method is that its superiorperformance does not degenerate even for a network size on the order of $10^4$,and is thus readily applicable to large-scale complex networks. Such abreakthrough is achieved by considering both prior biological knowledge andmultiple topological properties (i.e., sparsity and hub gene structure) ofcomplex networks in the regularized formulation. We also illustrate theapplication of our algorithm in practice using the time-course expression datafrom an influenza infection study in respiratory epithelial cells.  Availability and Implementation: The algorithm described in this article isimplemented in MATLAB$^\circledR$. The source code is freely available fromhttps://github.com/Hongyu-Miao/DMI.git.  Contact: jliu@cs.rochester.edu; hongyu.miao@uth.tmc.edu  Supplementary information: Supplementary data are available online.

A Scalable Algorithm for Structure Identification of Complex Gene Regulatory Network from Temporal Expression Data