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Julio Saez-Rodriguez

Current position: Group leader, European Molecular Biology Laboratory

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PhD Max-Planck-Institute for the Dynamics of Complex Technical Systems & University of Magdeburg (Germany), 2007

‘Licenciatura’ (approx. M. S.), Chemical Engineering, University of Oviedo (Spain), 2001


I am interested in applying engineering methods to understand signal transfer and decision processing in mammalian cells in health and disease. In particular, I am developing computational approaches to use high-throughput proteomics data to understand signaling networks. As a first challenge, we found that it is not trivial to link in an efficient manner high-throughput data to mathematical models: the data has to be stored in a structure manner with additional information (metadata), processed (normalized, etc.), visualized and then exported for analysis. To facilitate these steps, we have developed an open-source MATLAB toolbox called DataRail.

With the data conveniently processed, sophisticated insight can be obtained with detailed, mechanistic models but, due to the large number of unknown parameter values, modeling very large networks is an arduous task. Therefore, we are using a simplified description based on Boolean logic that encapsulates the topology and causality of the network without dealing with kinetic parameters. Using data from different cell-types we are able to determine cell-specific models, which can help to identify targets for drug discovery that influence selectively cancerous cells. These methods are embedded in CellNetOptimizer (CNO), a MATLAB toolbox that works in concert with DataRail. We are applying this method to different data sets. As a proof of principle, application of this method to primary and transformed liver cells allowed us to uncover significant differences in the rewiring of their signaling networks.

Previous work
During my PhD, I worked on different topics related to the mathematical modeling and analysis of signal transduction pathways in mammalian cells. I worked on an engineering-inspired modular approach to decompose and analyze signaling networks, as well as model discrimination, parameter estimation, and model reduction issues. Furthermore, I was involved in the development and application of qualitative and structural approaches to signaling networks, based on a logical formalism. As case study I has used mainly the signaling processes in T-cells, as well as EGF signaling.


L. G. Alexopoulos∗, J. Saez-Rodriguez∗, B. Cosgrove, D. A. Lauffenburger, P. K. Sorger. Networks inferred from biochemical data reveal profound differences in TLR and inflammatory signaling between normal and transformed hepatocytes. Mol. Cell. Proteomics, in press. [∗ These authors contributed equally to this work.]

M. K. Morris, J. Saez-Rodriguez, P. K. Sorger, D. A. Lauffenburger. Logic-based models for the analysis of cell signaling networks. Biochemistry, 49(15):3216-24, 2010.

R. J. Prill, D. Marbach, J. Saez-Rodriguez, P. K. Sorger, L. G. Alexopoulos, X. Xue, N. D. Clarke, G. Altan-Bonnet, G. Stolovitzky. Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges. PLoS One, 5(2): 9202, 2010.

J. Saez-Rodriguez∗ , L. 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. [∗ These authors contributed equally to this work.] * Featured in Research highlights Nature Biotechnology 28: 45, 2010.

A. Mitsos, I. Melas, P. Siminelaki, A. Chairakaki, J. Saez-Rodriguez, L. G. Alexopoulos. Identifying Drug Ef fects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data. PLoS Comp. Biology, 5(12): e1000591, 2009.

D. Wittmann, J. Krumsiek, J. Saez-Rodriguez, D. A. Lauf fenburger, S. Klamt, F. Theis. Transforming Boolean Models to Continuous Models: Methodology and Application to T – Cell Receptor Signaling. BMC Systems Biology, 3:98, 2009.

R. Samaga, J. Saez-Rodriguez, L. Alexopoulos, P. K. Sorger, S. Klamt. The logic of EGR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comp. Biol., 5(8): e1000438, 2009.

B. Aldridge, J. Saez-Rodriguez, J. Muhlich, P. K. Sorger, D. A. Lauffenburger. Fuzzy logic analysis of kinase pathway crosstalk in TNF/EGF/Insulin-induced signaling. PLoS Comp. Biol. 5(4): e1000340, 2009.

J. Saez-Rodriguez*, A. Goldsipe*, J. Muhlich, L.G. Alexopoulos, B. Millard, D.A. Lauffenburger, and P.K. Sorger. Flexible Informatics for Linking Experimental Data to Mathematical Models via DataRail. Bioinformatics, 15;24(6):840-7 2008. [*both authors contributed equally to this work]. Download DataRail doi:10.1093/bioinformatics/btn018

X. Wang, L. Simeoni, J. Lindquist, J. Saez-Rodriguez, A. Ambach, E. D. Gilles, S. Kliche, B. Schraven. Dynamics of proximal signaling events after TCR/CD8-mediated induction of proliferation or apoptosis in mature CD8+ T-cells. J. Immunology, 180:6703-6712, 2008. abstract

J. Saez-Rodriguez, A. Hammerle-Fickinger, O. Dalal, S. Klamt, E. D. Gilles and C. Conradi. On the multistability of signal transduction motifs. IET Syst. Biol. 2(2):80, 2008. doi:10.1049/iet-syb:20070012

J. Saez-Rodriguez, L. Simeoni, J. Lindquist, R. Hemenway, U. Bommhardt, B. Arndt, U.-U. Haus, R. Weismantel, E. D. Gilles, S. Klamt, and B. Schraven. A Logical Model Provides Insights into T Cell Receptor Signaling. PloS Comp. Biol., 3, 8:e163, 2007. doi:10.1371/journal.pcbi.0030163

A. Kremling and J. Saez-Rodriguez. Systems biology – an engineering perspective. J. Biotechnology, 129, 2:329-351, 2007. doi:10.1186/j.jbiotec.2007.02.009

J. Saez-Rodriguez*, S. Mirschel*, R. Hemenway, S. Klamt, E. D. Gilles, and M. Ginkel. Visual set-up of logical models of signaling and regulatory networks with ProMoT. BMC Bioinformatics, 7:506, 2006. [*both authors contributed equally to this work]. doi:10.1186/1471-2105-7-506

S. Klamt, J. Saez-Rodriguez, and E. D. Gilles. Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Systems Biology, 1:2, 2007. doi:10.1186/1752-0509-1-2

S. Klamt*, J. Saez-Rodriguez*, J. Lindquist, L. Simeoni, and E. D. Gilles. A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinformatics, 7:56, 2006 [*both authors contributed equally to this work]. doi:10.1186/1471-2105-7-56

H. Conzelmann, J. Saez-Rodriguez, T. Sauter, B.N. Kholodenko, and E. D. Gilles. A domain-oriented approach to the reduction of combinatorial complexity in signal transduction networks. BMC Bioinformatics, 7:34, 2006. doi:10.1186/1471-2105-7-34

C. Conradi*, J. Saez-Rodriguez*, E.D. Gilles, and J. Raisch. Using chemical reaction network theory to discard a kinetic mechanism hypothesis. IEE Proc. Systems Biology, 152(4):243-248, 2005 [*both authors contributed equally to this work]. doi:10.1049/ip-syb:20050045

J. Saez-Rodriguez, A. Kremling, and E. D. Gilles. Dissecting the puzzle of life: Modularization of signal transduction networks. Comput. Chem. Eng., 29(3):619-629, 2005. doi:10.1016/j.compchemeng.2004.08.035

H. Conzelmann, J. Saez-Rodriguez, T. Sauter, E. Bullinger, F. Allgöwer, and E. D. Gilles. Reduction of mathematical models of signal transduction networks: Simulation-based approach applied to EGF receptor signaling. Systems Biology, 1(1):159-169, 2004. doi:10.1049/sb:20045011

J. Saez-Rodriguez, A. Kremling, H. Conzelmann, K. Bettenbrock, and E. D. Gilles. Modular analysis of signal transduction networks. IEEE Contr. Syst. Mag., 24(4):35-52, 2004. doi:10.1109/MCS.2004.1316652


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