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. Towards this goal, we integrate big (‘Omics’) data with mechanistic molecular knowledge into statistical and machine learning methods, and we share our tools as free open-source packages.

We are at the Institute for Computational Biomedicine at the Medical Faculty of Heidelberg University and Heidelberg University Hospital. We are also part of the Molecular Medicine Partnership Unit (MMPU) between the Medical Faculty of the University of Heidelberg and the European Molecular Biology Laboratory (EMBL) and of ELLIS Heidelberg.


Our research is application-driven and tailored towards producing computational models that integrate diverse data sources to better understand and treat diseases. Because of this, we collaborate closely with experimental groups. A major emphasis is to build context-specific models that are both mechanistic (to provide understanding) and predictive (to generate novel hypotheses). To build these models, we combine existing biochemical knowledge with different types of large scale data. We believe that this biological knowledge can be instrumental to move from pure correlation to causation in large data sets, and thereby identify the molecular processes that underlie specific phenomena.
We develop and apply methods to extract mechanistic features from diverse omics data,  recently also for single-cell data. We then combine these multi-omics data sets into causal networks. Finally, we build dynamic models of specific subsystems using logic formalisms that we can analyze and simulate to predict the effect of new perturbations.
We apply these strategies in the context of many disease conditions. Particular areas of interest for us are cancer, in particular  large-scale drug screenings,  fibrosis (in particular in kidney, heart, and liver), and co-morbidities in heart failure
While our research is driven by applications, we develop open-source computational tools that 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



Erika Schulz


Project manager

Marzia Sidri



Hanna Schumacher



Denes Turei


Attila Gabor


Aurélien Dugourd


Jovan Tanevski


Javier Perales-Patón


Katharina Zirngibl


Arezou Rahimi


Martín Garrido Rodríguez-Córdoba


Sebastian Lobentanzer


Ahmet Sureyya Rifaioglu


Pablo Rodríguez Mier


Ece Kartal


PhD Students

Ricardo O. Ramirez-Flores


Olga Ivanova


Daniel Dimitrov


Pau Badia i Mompel


Sophia Müller-Dott


Robin Fallegger


Associated members

Jan Lanzer


Rebecca Terrall Levinson


Nadine Tüchler


Bence Szalai


Fabian Fröhlich


The following people spent over 6 months with us:

Name Duration Position
Bartosz Bartmanski 2021-2021 Postdoc
Eleanor Fewings 2020-2021 Bioinformatician
Rosa Hernansaiz Ballesteros 2019-2021 PostDoc
Igor Bulanov 2019-2020 intern
Minoo Ashtiani 2019-2020 intern
Alice Driessen 2019-2020 intern
Ana Victoria Ponce-Bobadilla 2019-2020 Postdoc
Alberto Valdeolivas Urbelz 2019-2020 Postdoc
Charlie Pieterman 2018-2018 intern
Anika Liu 2018-2018 Master Thesis
Hyojin Kim 2017-2020 Phd student
Nicolas Palacio-Escat 2017-2020 PhD Student
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
Christian Holland 2017 -2021 PhD Student
Mahmoud Ibrahim 2017 -2017 Postdoc
Enio Gjerga 2016-2020 Phd student
Luis Tobalina Segura 2016-2019 Postdoc
Melanie Rinas 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



Python package to access Bioinformatics Web Services.

More about BioServices


R package for the randomisation of bipartite graphs.

More about Birewire


Interface between R and Cytoscape.

More about Cyrface


Cytoscape plug-in for SBGN maps.

More about CySBGN


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


Python library dedicated to the study pharmacogenomic relationships.

More about GDSCTools


An R package for interactive network guided connectivity mapping.

More about iNRG-cMap


Python package to infer kinase activities from phosphoproteomics datasets.

More about KinAct


Python module for lipidomics LC MS/MS data analysis

More about lipyd


Global optimization toolbox including metaheuristic and Bayesian methods.

More about MEIGO


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

More about SLAPenrich


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
  • Christian Frezza, MRC – Cambridge
  • DREAM Challenges, in particular Gustavo Stolovitzky
  • Mathew Garnett and Ultan McDermott, Sanger Institute
  • 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 Rocha, University of Minho
  • Anne Siegel, INRIA/IRISA
  • Peter K. Sorger, Harvard Medical School
  • Oliver Stegle, EMBL-EBI
  • Pablo Villoslada, IDIBAPS – Hospital Clinic of Barcelona
  • Martin Zeier and Susanne Delecluse, Kidney Center Heidelberg
Duration Name Agency

DECIDER: Improved clinical decisions via integrating multiple data levels to overcome chemotherapy resistance in high-grade serous ovarian cancer

European Union

LiSyM-Krebs: Mechanism-based Multiscale Model to Dissect the Tipping Point from Liver Cirrhosis to Hepatocellular Carcinoma

German Ministry of Education and Research (BMBF)

DeepSC2: Deep learning for single-cell genomics in cancer

German Ministry of Education and Research (BMBF)

Changes in the microbiome in PsA patients undergoing biologics therapy

Centers for Personalized Medicine Baden-Württemberg

Analysis of essentiality in cancer using CRISPR-Cas9 screenings


MSCare-A Systems Medicine Approach to Stratification of  Cancer Recurrence

German Ministry of Education and Research (BMBF)

Analysis of multi-omics and spatial transcriptomics data


BioDATEN : Bioinformatics DATa Environment

Ministry of Science, Research and Art, Baden Wuerttemberg

Single cell resolution of human chronic kidney disease for precision medicine in nephrology

DFG German Research Council
2020 - 2024

StrategyCKD: System omics to unravel the gut-kidney axis in Chronic Kidney Disease

European Uunion H220-MSCA-ITN
2020 - 2022

HPC Center of Excellence in Personalised Medicine – PerMedCoE

European Union (H2020-EU.

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

European Union H2020 Health

LaMarcK: Longitudinal Multiomics Characterization of disease using prior Knowledge

German Ministry of Education and Research (BMBF)

Validating microfluidics-based personalized cancer therapy in mouse models

DFG German Research Council

Informatics for Life

Klaus-Tschira Stiftung

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

EU IMI Innovative Medicines Initiative

Analysis of mass spectrometry proteomics and drug treatment


LiSym: Liver systems medicine


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


PrECISE: Personalized engine for Cancer integrative study and evaluation

EU H2020 Health


National Cancer Institute (USA)

Joint Research Center for Computational Biomedicine

Bayer AG

SyMBioSys : Systematic Models for Biological Systems Engineering Training Network

EU H2020 Health MSCA-ITN

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

Center for Therapeutic Target Validation

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

Center for Therapeutic Target Validation

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

EU H2020 Health

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

EU H2020 Health

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

Medical Research Council (UK)

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



For all positions, candidates should email their CV and a letter of interest to jobs.saez {at}, including names of (ideally 3) references.  The letter of interest has to be tailored to our group, mentioning projects or articles of our group that you find interesting, and explaining how you would fit in our group. Please also provide a pointer to a code repository if possible.  Non-specific applications without this tailored expression of interest or sent to a different address will not be considered.

We are continous looking for postdoctoral fellows, PhD students, and staff scientists to better understand and treat diseases including cancer and heart disease by analyzing multi-omics data sets, including single-cell and spatially resolved data. The positions are is in the context of various national and  international collaborations. Candidates interested in using bioinformatics, machine learning, and mathematical modeling to analyze big data to advance personalized medicine are encouraged to apply. You are expected to hold a degree in statistics, mathematics, physics, engineering, computer science, or a degree in biological science with substantial experience in computational and statistical work.
Current specific openings: (1) PostDoc fellowship (2) Staff Scientist – and also contact us if you are interested in another type of position.

– We have opportunities for student assistants (HiWi), master/bachelor theses, and internships. In general, these are for a period of six months or longer, although shorter internships of 3-4 months are possible, in particular for local students. Besides the general information above, please include information on the lectures you have attended in your bachelor and (if already there) master courses.