Computational systems biology: understanding mammalian cell fates using genome-scale network models (2011-2013)

In this Proposal we address one of the most-challenging problems facing modern bioscience: understanding the mammalian cell as a dynamic biomolecular system and diseases such as cancer as abnormal states of this system. Using genome, transcriptome and epigenome sequence data from more than 700 human pancreatic tumours and matched normal controls, we will construct a rigorous cell-state model that predicts signalling and transcriptomic phenotypes; identify the molecules, pathways and networks responsible for these phenotypes; and map the transcriptional landscape of normal and abnormal cell development. Our results and software will change research practice in mammalian systems biology.