Saez-Rodriguez Group

Systems Biomedicine

Our goal is to acquire a functional understanding of the deregulation of signalling networks in disease and to apply this knowledge to develop novel therapeutics. We focus on cancer, auto-immune and fibrotic disease.

Research

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 is to build models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine existing biochemical knowledge with functional data. A major focus of our group is the development of logic models of signaling networks. We train these models with data generated with mass spectrometry and antibody-based technologies. We are also very interested in the use of single-cell data.
In parallel, we analyse genomic and phenotypic data collected in large-scale drug screenings. We combine this information with our prior knowledge of the underlying pathways. Thereby, we aim to improve our ability to dissect the mode of action of therapies and provide avenues for developing new ones. While our research is driven by applications, we develop open-source computational tools. We share freely with the scientific community.
Finally, we support scientific crowdsourcing, specifically collaboratives competitions, through the DREAM challenges.
 
Click below for a list of our main ongoing projects.

Group Leader

Julio Saez-Rodriguez

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Secretaries

Erika Schulz

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Postdocs

Melanie Rinas

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Denes Turei

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Attila Gabor

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Jovan Tanevski

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Rosa Hernansaiz Ballesteros

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Javier Perales-Patón

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Alberto Valdeolivas Urbelz

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PhD Students

Aurélien Dugourd

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Enio Gjerga

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Nicolàs Palacio

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Christian Holland

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Hyojin Kim

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Ricardo O. Ramirez-Flores

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Olga Ivanova

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Associated members

Jan Lanzer

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Rebecca Terrall Levinson

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The following people spent over 6 months with us:

Name Duration Position
Charlie Pieterman 2018-2018 intern
Anika Liu 2018-2018 Master Thesis
Panuwat Trairatphisan 2017-2019 Postdoc
Bence Szalai 2017-2018 Postdoc
Ferenc Tajti 2017-2018 Intern
Francesco Ceccarelli 2017-2018 intern
Vigneshwari Subramanian 2017-2018 Postdoc
Mahmoud Ibrahim 2017 -2017 Postdoc
Luis Tobalina Segura 2016-2019 Postdoc
Mi Yang 2015-2019 PhD Student / Postdoc
Angeliki Kalamara 2015-2019 PhD Student
Jakob Wirbel 2015-2017 Intern & Master Thesis
Fatemeh Ghavidel 2015-2016 Postdoc (w O Stegle & A Brazma)
Pisanu Buphamalai 2015-2016 Trainee (w. M Brehme)
Ricardo Ramirez 2015-2016 Trainee
Luz Garcia-Alonso 2014-2018 Postdoc
Johannes Stephan 2014-2015 Postdoc (w O Stegle)
Claudia Hernandez 2014-2015 Trainee
Martí Bernardo-Faura 2013-2015 Postdoc
Ioannis Melas 2013-2014 Postdoc
Vitor Costa 2013-2013 Master Thesis
Luca Cerone 2013-2013 Postdoc
Emanuel Gonçalves 2012-2017 PhD Student
Michael Schubert 2012-2016 PhD Student
Michael Menden 2011-2016 PhD student
Thomas Cokelaer 2011-2015 Staff Scientist
Martijn van Iersel 2011-2012 Postdoc
Federica Eduati 2011 - 2017 Postdoc
Francesco Iorio 2010-2017 Postdoc / Senior Bioinformatician
Aidan MacNamara 2010-2014 Postdoc
Camille Terfve 2010-2014 PhD Student
David Henriques 2010-2011 Master Thesis

Publications

Latest Publications

Preprints

Tools

CellNOpt

Toolbox for creating logic models of signaling networks and training them against data.

More about CellNOpt

OmniPath

Collection of literature curated signaling pathways and the Python module pypath.

More about OmniPath

PROGENy

An R package to infer pathway activity from gene expression data

More about PROGENy

DoRothEA

Framework to estimate single sample TF activities from gene expression data based on a manually curated human regulon

More about DoRothEA

BioServices

Python package to access Bioinformatics Web Services.

More about BioServices

PHONEMeS

A tool to build logic models from discovery mass-spectrometry based Phosphoproteomic data.

More about PHONEMeS

Cyrface

Interface between R and Cytoscape.

More about Cyrface

CySBGN

Cytoscape plug-in for SBGN maps.

More about CySBGN

DREAMTools

Code used in the scoring of DREAM challenges.

More about DREAMTools

DrugVsDisease

R/Cytoscape pipeline to compare drug and disease gene expression profiles.

More about DrugVsDisease

GDSCTools

Python library dedicated to the study pharmacogenomic relationships.

More about GDSCTools

KinAct

Python package to infer kinase activities from phosphoproteomics datasets.

More about KinAct

MEIGO

Global optimization toolbox including metaheuristic and Bayesian methods.

More about MEIGO

Birewire

R package for the randomisation of bipartite graphs.

More about Birewire

iNRG-cMap

An R package for interactive network guided connectivity mapping.

More about iNRG-cMap

SLAPenrich

R package to identify pathway-level enrichments of genetic alterations.

More about SLAPenrich

CARNIVAL

An R for identifying relevant signalling pathways upon compounds’ perturbation from gene expression data

More about CARNIVAL

lipyd

lipyd is a Python module for lipidomics LC MS/MS data analysis.

More about lipyd

The wordcloud is based on the number of joint publications with our collaborators

 

We are thankful to our present and past collaborators, including (in alphabetic order):

  • Ruedi Aebersold, ETH Zurich
  • Leonidas G. Alexopoulos, National Technical University of Athens
  • Bernd Bodenmiller, University of Zurich
  • Julio Banga, (Bio)Process Engineering Group, CSIC
  • Pedro Beltrao, EMBL-EBI
  • CoLoMoTo Consortium, in particular Claudine Chaouiya, Denis Thieffry, Tomas Helikar, and Laurence Calzone
  • Thorsten Cramer, RWTH Aachen
  • Pedro R. Cutillas, Barts
  • Christian Frezza, MRC – Cambridge
  • DREAM Challenges, in particular Gustavo Stolovitzky
  • Matthias Gaida, Heidelberg University Hospital
  • Mathew Garnett and Ultan McDermott, Sanger Institute
  • Anne-Claude Gavin, EMBL Heidelberg
  • Steffen Klamt, Max Planck Institute
  • Tamás Korcsmáros, Earlham Institute and Institute of Food Research
  • Rafael Kramann, RWTH Aachen
  • Douglas Lauffenburger, Massachusetts Institute of Technology
  • Christoph Merten, EMBL
  • Alexander Mitsos, RTWH Aachen, Germany
  • Rainer Pepperkok, EMBL
  • Miguel Angel Pujana, IDIBELL
  • Miguel Rocha, University of Minho
  • Eduard Sabido, CRG
  • Anne Siegel, INRIA/IRISA
  • Peter K. Sorger, Harvard Medical School
  • Oliver Stegle, EMBL-EBI
  • Pablo Villoslada, IDIBAPS – Hospital Clinic of Barcelona
Duration Name
2019-2022

individualizedPaediatricCure: Cloud-based virtual-patient models for precision paediatric oncology

2019-2022

LaMarcK: Longitudinal Multiomics Characterization of disease using prior Knowledge

2019-2021

Validating microfluidics-based personalized cancer therapy in mouse models

2019-2021

Informatics for Life

2017-2021

TransQST: Translational quantitative systems toxicology to improve the understanding of the safety of medicines

2017-2018

Analysis of mass spectrometry proteomics and drug treatment

2016-2020

LiSym: Liver systems medicine

2016-2019

SYS4MS: Personalizing health care in Multiple Sclerosis using sytems medicine tools

2016-2018

PrECISE: Personalized engine for Cancer integrative study and evaluation

2016-2018

CPTAC DREAM Challenge

2015-2019

Joint Research Center for Computational Biomedicine

2015-2018

SyMBioSys : Systematic Models for Biological Systems Engineering Training Network

2014-2016

Novel Target Identification Through Genome-Scale CRISPR-Cas9 Knockout Screening in Human Cancer Cell Lines

2014-2016

Target identification and validation using pathway activities derived from functional genomics data

2013-2014

CombiMS: a novel drug discovery method based on systems biology: combination therapy and biomarkers for Multiple Sclerosis

2012-2015

BioPreDyn: New Bioinformatics Methods and Tools for Data-Driven Predictive Dynamic Modelling in Biotechnological Applications

2012-2014

Understanding drug mode of action via statistical integration of functional genomic studies and literature-derived signalling networks

2011-2012

Analysis of mass spectrometry phosphoproteomics data in the context of insulin signal processing and functional alterations thereof

Jobs

We do not have any specific opening at the moment. However, we always welcome applications of potential postdoctoral or PhD students interested in using computational tools to analyze large biomedical data sets to understand and target human disease. 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.

Candidates should email their CV (including names of several references) and a letter of interest to jobs.saez {at} bioquant.uni-heidelberg.de. The letter of interest has to be tailored to our group and explain how you would fit here. In your e-mail please explain in which specific project, or recent publication you are interested and why.  Non-specific applications without this tailored expression of interest or sent to a different address will not be considered.