Annual Scientific Report 2015

Julio-Saez Rodriguez
PhD University of Magdeburg, 2007. Postdoctoral
work at Harvard Medical School and M.I.T.
At EMBL-EBI since 2010.
Joint appointment at Genome Biology Unit
(EMBL-HD).
with information on signalling pathways. Using these
novel methods we will address questions such as: What
are the origins of the profound differences in signal
transduction between healthy and diseased cells and, in
the context of cancer, between normal and transformed
cells? What are the differences in signal transduction
among cancer types? Can we use these differences to
predict disease progression? Do these differences reveal
valuable targets for drug development? Can we study the
side effects of drugs using these models?
Selected publications
Cancer Cell Line Encyclopedia Consortium, Genomics
of Drug Sensitivity in Cancer Consortium, et al. (2015)
Pharmacogenomic agreement between two cancer cell
line data sets. Nature 528:84-87
population responses to toxic compounds assessed
through a collaborative competition. Nature Biotechnol.
33:933
Henriques D, Rocha M, Saez-Rodriguez J, Banga JR
(2015) Reverse engineering of logic-based differential
equation models using a mixed-integer dynamic
optimisation approach. Bioinformatics 31:2999-3007
Terfve CD, Wilkes EH, Casado P, et al. (2015). Large
scale models of signal propagation in human cells
derived from discovery phosphoproteomic data, Nature
Commun. 6:8033
Eduati F, Mangravite FM, Wang T, et al. (2015)
Opportunities and limitations in the prediction of
A method developed by the Saez-Rodriguez group takes the noisy data from MS experiment, which is a large
network with many interconnected cascades of kinase activities, filters the noise, and integrates the data. This
is done entirely in the context of what is known about kinases and their substrates, so that it shows activities are
connected. This gives a new perspective on how a given drug is impacting the system under study.
2015 EMBL-EBI Annual Scientific Report 142