Abstract

Biological systems are composed of highly dynamic and interconnected molecular networks that drive biological decision processes. The goal of systems biology is to describe, quantify and predict the information flow and functional behaviour of living systems in a formal language and with an accuracy that parallels our characterisation of other physical systems such as Jumbo-jets. Decades of targeted molecular and biological studies have led to numerous pathway models of developmental and disease related processes. However, so far no global models have been derived from pathways, capable of predicting cellular trajectories in time, space or disease. The development of high- throughput methodologies has further enhanced our ability to obtain quantitative genomic, proteomic and phenotypic readouts for many genes/proteins simultaneously. Here, I will discuss how it is now possible to derive network models through computational integration of systematic, large-scale, high- dimensional quantitative data sets. I will review our latest advances in methods for exploring phosphorylation networks. In particular I will discuss how the combination of quantitative mass- spectrometry, systems-genetics and computational algorithms (NetworKIN [1] and NetPhorest [4]) made it possible for us to derive systems-level models of JNK and EphR signalling networks [2,3]. Finally, I shall discuss work we have done in comparative phospho-proteomics and network evolution [5,6].

1. Linding et al., Cell 2007
2. Bakal et al., Science 2008
3. Jørgensen et al., Science 2009
4. Miller et al., Science Signaling 2008
5. Tan et al., Science Signaling 2009
6. Tan et al., Science 2009