Structure Identification for Gene Regulatory Networks

Abstract

Cellular networks usually consist of numerous chemical species, such as DNA, RNA, proteins and small molecules, etc. Different biological tasks are generally performed by complex interactions among these species. These interactions can rarely be directly measured, and it is widely believed that causal relationship identification is essential in understanding biological behaviors of a cellular network. Challenging issues here include not only the large number of interactions to be estimated, but also many restrictions on probing signals. In this talk, we will discuss how to incorporate power law and robust state estimations into cellular network structure identification, with the purpose to increase accuracy of causal regulation estimations. The developed methods have been tested on an artificially constructed linear system, a Mitogen-Activated Protein Kinase pathway model, some DREAM initiative in silico data and some in vivo data. Compared with the widely adopted methods, computation results show that parametric estimation accuracy can be significantly increased and false positive errors can be greatly reduced.