Mathematics and Statistics at EMBL
Section of a logical model of a signaling network in the hepatocellular carcinoma HepG2 cell line. The upper-right inset shows a subset of the data used to validate the model.
Figure adapted from Saez-Rodriguez et al. Mol. Syst. Biol. 2009 under the conditions of the Creative Commons Attribution-Non-Commercial-Share Alike 3.0 Licence.
Click on the figure for a larger version.
Network modeling applied to medical questions
Cells in our body are able to sense the environment and precisely react to external stimulation. The molecular machinery responsible for this information processing, particularly the pathways that transduce information to the cellular nucleus, is altered in human disease and therefore contains potential targets for therapeutics.
Our group develops computational methods and tools to analyse signal transduction networks, and we collaborate closely with experimentalists to tackle jointly specific biological questions about their deregulation, and to predict effective therapeutic targets. We develop mathematical models that integrate high-throughput data with various sources of prior knowledge, with an emphasis on providing both predictive power of new experiments and insight on the functioning. Towards this end, we combine statistical methods with models describing the mechanisms of signal transduction either as logical or physico-chemical systems.
J. Saez-Rodriguez∗, L. G. Alexopoulos∗, M. Zhang, M. K. Morris, D. A. Lauffenburger, P. K. Sorger. Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Research, 71(16): 1-12, 2011.
J. Saez-Rodriguez*, L. G. Alexopoulos*, G. Stolovitzky. Setting the standards in signal transduction research. Science Signaling, 4, pe10, 2011.
J. Saez-Rodriguez∗, L. G. Alexopoulos∗, J. Epperlein R. Samaga, D. A. Lauffenburger, S. Klamt, P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction. Molecular Systems Biology, 5:331, 2009.
[* denotes equal contribution]