Our research is hypothesis-driven and tailored towards producing mathematical models that integrate diverse data sources. Because of this, we collaborate closely with experimental groups. A key emphasis of our work is to build models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine the existing knowledge of the underlying biochemical processes with functional data.
A major focus of our group is the development of logic models of signaling networks that are trained with data derived from mass spectrometry and antibody-based technologies, as well as single-cell approaches.
In parallel, we analyse 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.
While our research is driven by applications, we develop open-source computational tools that we share freely with the scientific community.
Finally, we also support the development of crowdsourcing, in particular collaboratives competitions for systems biology, through the DREAM challenges.
Luis Tobalina Segura
The following people spent over 6 months with us:
|Emanuel Gonçalves||2012-2017||PhD Student|
|Fatemeh Ghavidel||2015-2016||Postdoc (w O Stegle & A Brazma)|
|Pisanu Buphamalai||2015-2016||Trainee (w. M Brehme)|
|Johannes Stephan||2014-2015||Postdoc (w O Stegle)|
|Vitor Costa||2013-2013||Master Thesis|
|Michael Schubert||2012-2016||PhD Student|
|Michael Menden||2011-2016||PhD student|
|Thomas Cokelaer||2011-2015||Staff Scientist|
|Martijn van Iersel||2011-2012||Postdoc|
|Camille Terfve||2010-2014||PhD Student|
|David Henriques||2010-2011||Master Thesis|
Turei et al. Nature Methods, 2016 [Cover Nature Methods]
Sciacovelli et al. Nature, 2016
Saez-Rodriguez et al. Nature Rev Genet, 2016 [Cover Nature Rev. Genet.]
Iorio et al. Cell, 2016
Hill et al. Nature Methods, 2016 [Cover Nature Methods]
CCLE & GDSC Nature, 2016
Terfve et al. Nature Commun, 2015
Eduati et al. Nature Biotechnol, 2015
Gonçalves E et al., Cell Syst, 2017
Gönen M et al., Cell Syst, 2017
Penas DR et al., PLoS One, 2017
Roumeliotis TI et al., Cell Rep, 2017
Traynard P et al., CPT Pharmacometrics Syst Pharmacol, 2017
Antoranz A et al., Drug Discov Today, 2017
Toolbox for creating logic models of signaling networks and training them against data.More about CellNOpt
CellNetOptimizer (CellNOpt) is a toolbox for creating logic-based models of signal transduction networks, and training them against high-throughput biochemical data, and is freely available both for R and matlab.
|github.com/cellnopt/cellnopt||CellNopt.org||Terfve et al., BMC Sys Bio, 2012|
Collection of literature curated signaling pathways and the Python module pypath.More about OmniPath
Python package to access Bioinformatics Web Services.More about BioServices
Interface between R and Cytoscape.More about Cyrface
Cyrface establishes an interface between R and Cytoscape by using different Java-R libraries, e.g. Rserve, RCaller. Cyrface can be used as a Cytos cape plug-in, e.g. to run R commands within Cytoscape, or used as a library to allow your plug-in to connect to R.
|github.com/saezlab/cyrface||saezlab.github.io/cyrface||Gonçalves et al., F1000Research, 2013|
Cytoscape plug-in for SBGN maps.More about CySBGN
CySBGN is a Cytoscape plug-in that extends the use of Cytoscape visualization and analysis features to SBGN maps. CySBGN adds support to Cytoscape to import, export, visualize, validate and analyse SBGN maps.
|github.com/saezlab/cysbgn||saezlab.github.io/cysbgn||Gonçalves et al, BMC Bioinformatics, 2013|
Code used in the scoring of DREAM challenges.More about DREAMTools
R/Cytoscape pipeline to compare drug and disease gene expression profiles.More about DrugVsDisease
DrugVsDisease (DvD) provides a pipeline, available through R or Cytoscape, for the comparison of drug and disease gene expression profiles from public microarray repositories.
|github.com/saezlab/DrugVsDisease||saezlab.github.io/DrugVsDisease||Pacini et al., Bioinformatics 2013|
Python library dedicated to the study pharmacogenomic relationships.More about GDSCTools
GDSCTools is an open-source Python library dedicated to the study pharmacogenomic relationships in the context of the GDSC (Genomics of Drug Sensitivity in Cancer) project. The main developer is Thomas Cokelaer (Institut Pasteur), and it is a joint effort with the groups of Mathew Garnett (Sanger Institute) and Julio Saez-Rodriguez.GDSCTools is hosted by GitHub and documented here.
Python package to infer kinase activities from phosphoproteomics datasets.More about KinAct
Global optimization toolbox including metaheuristic and Bayesian methods.More about MEIGO
MEIGO is a global optimization toolbox that includes a number of metaheuristic methods as well as a Bayesian inference method for parameter estimation. It is developed jointly with the group of Julio Banga.MEIGO is described in (Egea et al, BMC Bioinformatics, 2014), and hosted here.
|gingproc.iim.csic.es/meigo.html||Egea et al, BMC Bioinformatics, 2014|
R package for the randomisation of bipartite graphs.More about Birewire
Birewire is an R package implementing high-performing routines for the randomisation of bipartite graphs preserving their node degrees (i.e. Network Rewiring), through the Switching Algorithm (SA) . BiRewire analytically estimates the number of Switching Steps to be performed in order for the similarity between the original network and its rewired version to reach a plateau (i.e. to achieve the maximal level of randomness) according to the lower bound.
|github.com/saezlab/BiRewire||saezlab.github.io/BiRewire||Iorio et al, BMC Bioinformatics|
An R package for interactive network guided connectivity mapping.More about iNRG-cMap
iNRG_cMap (iterative NetwoRk Guided connectivity Mapping) a strategy that refines these unbiased approaches by making use of prior knowledge about the analysed compounds to compute refined transcriptional signatures of drug response. Making use of transcriptional data from the Connectivity Map and building on the MANTRA method, iNRG_cMap is able to disentangle spurious effects due to non-relevant secondary drug effects, thus enhancing the predictive power of the resulting refined signatures.
|github.com/saezlab/iNRG_cMAP||saezlab.github.io/iNRG_cMAP||Iorio et al Plos ONE 2015|
R package to identify pathway-level enrichments of genetic alterations.More about SLAPenrich
An R package to infer pathway activity from gene expression dataMore about PROGENy
The wordcloud is based on the number of joint publications with our collaborators
We are part of the Joint Research Center for Computational Biomedicine. We are thankful to our present and past collaborators, including (in alphabetic order):
|2017-2022||EU IMI -TransQST: Translational quantitative systems toxicology to improve the understanding of the safety of medicines|
|2016-2019||ERACOSYSMED (BMBF) SYS4MS: Personalizing health care in Multiple Sclerosis using sytems medicine tools|
|2016-2019||EU H2020-PHC-02-2015: PrECISE: Personalized engine for Cancer integrative study and evaluation|
|2016-2020||BMBF LiSym: Liver systems medicine|
|2015-2019||EU H2020-MSCA-ITN-2014: SyMBioSys: Systematic Models for Biological Systems Engineering|
|2014-2018||Open Targets: Target identification and validation using pathway activities derived from functional genomics data|
|2014-2018||Open Targets: Novel Target Identification Through Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cancer Cell Lines|
While we do not have any specific opening at the moment, we are generally looking for talented PhD students and Postdocs interested in working at the interface of computational science, biomedicine and pharmacology. You are expected to hold a degree in statistics, mathematics, physics, engineering or computer science, or a degree in biological science with substantial experience in computational and statistical work.
We also have opportunities for student assistants (HiWi), master thesis, and internships, for a period of typically at least six months. We have often funding available for these positions.
If you are interested, please contact us at . Please include your CV, academic transcripts and the names of several references. In your e-mail please explain in which specific project, or recent publication you are interested and why. Please also explain how you think you could fit in our group. Include the words ’saezlab' in the email subject. Non-specific applications without this expression of interest or sent to a different address will not be considered.