Reverse Engineering Gene Regulatory Networks by Integrating Multi-Source Biological Data

During last two decades, enormous amount of biological data generated by highthroughput analytical methods in biology produces vast patterns of gene activity, highlighting the need for systematic tools to identify the architecture and dynamics of the underlying GRN (He et al. 2009). Here, the system identification problem falls naturally into the category of reverse engineering; a complex genetic network underlies a massive set of expression data, and the task is to infer the connectivity of the genetic circuit (Tegner et al. 2003). However, reverse engineering of a global GRN remains challenging because of several limitations including the following: