Computational Approaches for Modeling Signal Transduction Networks

Abstract

Complex disease like cancer is highly heterogeneous. The molecular mechanisms underlying the disease often involve in dysfunction of signal transduction networks (STNs). Understanding the characteristics and spatiotemporal dynamics of the STNs is critical for our knowledge in complex disease. Over the past decade, massive amounts of biological and biomedical data have been generated at an unprecedented pace, calling for advanced computational methods and tools for effectively mining such data in order to uncover complex dynamic processes of STNs. In this article, we introduce the concept of STNs, the structural motifs in STNs, and the databases and analysis tools for STNs, and then discuss the application of computational modeling of STNs to complex disease studies.