Inferring Signaling and Gene Regulatory Network from Genetic and Genomic Information

Abstract

Biological systems respond to environmental changes and genetic variations. One of the essential tasks of systems biology is to untangle the signaling and gene regulatory networks that respond to environmental changes or genetic variations. However, unwiring the complex gene regulatory program is extremely challenging due to the large number of variables involved in these regulatory programs. The traditional single gene centered strategy turns out to be both insufficient and inefficient for studying signaling and gene regulatory networks. With the emergence of various high throughput technologies, such as DNA microarray, ChIP-chip, etc., it becomes possible to interrogate the biological systems at genome scale efficiently and cost effectively. As these high throughput data are accumulating rapidly, there exists a clear demand for methods that effectively integrate these data to elucidate the complex behaviors of biological systems. In this chapter, we discuss several recently developed computational models that integrate diverse types of high throughput data, particularly, the genetic and genomic data, as examples for the systems approaches that untangle signaling and gene regulatory networks.