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Dr Julio Saez-Rodriguez

Research Interests

A primary characteristic of many immune diseases is a deregulation in how they process and react to extracellular information. The goal of our group is to acquire a functional understanding of signalling networks, their deregulation in disease, and how molecular therapeutics interact with them. Our research is hypothesis-driven, and based on close collaborations with experimental groups. 

A key emphasis of our research is to build models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine the prior knowledge of the underlying biochemical processes with dedicated, functional, signalling data (typically phosphoproteomics upon stimulation with growth factors and cytokines).

In parallel, we study drugs’ modes of action by analysing genomic and phenotypic data collected in large-scale drug screenings. We then strive to combine this information with our prior knowledge of the underlying pathways to ultimately build integrated mechanistic models. Our premise is that these will have enhanced ability to discern the mode of action of existing therapies and provide avenues for the development of new drugs.

Key Publications

Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning? Drug Discov Today. 2013 Apr;18(7-8):350-7.

Prill RJ, Saez-Rodriguez J, Alexopoulos LG, Sorger PK, Stolovitzky G. Crowdsourcing network inference: the DREAM predictive signaling network challenge. Sci Signal. 2011 Aug 30;4(189):mr7. 

Saez-Rodriguez J, Alexopoulos LG, Stolovitzky G. Setting the standards for signal transduction research. Sci Signal. 2011 Feb 15; 4(160)

Saez-Rodriguez J, Alexopoulos LG, Epperlein J, Samaga R, Lauffenburger DA, Klamt S, Sorger PK. Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol. 2009;5:331. 

Saez-Rodriguez J, Simeoni L, Lindquist JA, Hemenway R, Bommhardt U, Arndt B, Haus UU, Weismantel R, Gilles ED, Klamt S, Schraven B. A logical model provides insights into T cell receptor signaling. PLoS Comput Biol. 2007 Aug;3(8)  

Saez-Rodriguez J, Goldsipe A, Muhlich J, Alexopoulos LG, Millard B, Lauffenburger DA, Sorger PK. Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics. 2008 Mar 15;24(6):840-7.

An illustration of how we use our logic modelling method CellNOpt to better understand deregulation of signal transduction in disease. Left: simple pathway model; right: experimental data and match between model simulations and data.