Institute for Computational Biomedicine

AG Univ. Prof. Andreas Schuppert

Schuppert Group is focusing on hybrid modelling and next generation computational technologies, such as quantum computing, for a broad range of applications in HealthCare and Digital Patients.


    • SMITH 
      The use case ASIC: Algortihmic Surveillance in Intensive Care is one of the use cases of the BMBF-funded consortium SMITH. ASIC develops digital solutions to improve intensive care for patients suffering from ARDS, acute respiratory distress syndrome.
    • SimLab Digital Patientis part of the NHR4CES consortium.
      The Simlab will foster High Performance Computing for Medical applications. We strive to set up a HPC platform for the development and application of digital patient models for research and clinics.
    • The JPND project NeuroNode is an international consortium of research groups in UK, Canada, Sweden and Germany.
      Its vision is to understand physiological processes in brain with relevance to rare diseases. Our part is dedicated to learning the underlying mechanisms from data and their systems biology modelling
    • HDS-LEE - Helmholtz graduate school for Data Science – Life, Earth and Energy intends to develop new mathematical methods and computational technologies for a broad range of applications. Within HDS-LEE we develop hybrid technologies to learn the structure of poorly understood biological mechanisms, e.g. drug response, from data.
    • ASIC: Algorithmic Surveillance of ICU patients

      The demand for intensive care medicine will strongly increase over the next years facing unmet medical needs, such as early diagnosis of the acute respiratory distress syndrome (ARDS). Within the ASIC project we are developing a system for continuous analysis of data obtained from the hospital patient data management system (PDMS) in order to enable model-based ‘algorithmic surveillance’ of the state of critically ill patients. We are developing and integrating a hybrid modeling system which consists of two components: the Virtual Patient (VP) and the Diagnostic Expert Advisor (DEA). The VP is a model-based system which relies on the physiological models of respiratory and cardiac system and allows personalized modeling of a patient physiology. In contrast to the VP the DEA is a data-driven component which utilizes ML tools and will support the VP model in stratification of patients and parameters estimation for individual patients. These two components together build up a hybrid modeling system which will enable individual prognosis for a particular patient.

    • Hybrid Modelling 
      Integration of prior knowledge into machine learning technologies in order to reduce the data demand for training, to foster the reliability of the models and to enable extrapolation, plays a crucial role for digitalization of medicine. Based on our prior work on structured hybrid models integrating process structures and machine learning in chemical engineering, we focus our research on the special mathematical challenges arising from the integration of medical data structures as well as the structures of medical knowledge into machine learning. Our research projects are dedicated to clinical applications characterized by heterogeneous, multi-scale structures in close cooperation with clinical and non-clinical research partners.
    • Prognosis
      In the BMBF-funded project prognosis we will develop new tools to predict the dynamics of pandemic and epidemic scenarios and its impact on the health care system in Germany. Together with our partners, primarily at TU Dresden and U Leipzig, we will combine physics-based and machine learning components to enable reliable predictions on short and medium time scales supporting the management of complex scenarios for the health care system in critical epidemic and pandemic situations.

    Andreas Schuppert

    Andreas Schuppert is Professor for Computational Biomedicine and founding director of the Joint Research Center for Computational Biomedicine at RWTH Aachen University. He studied Physics and got a PhD in mathematics from University Stuttgart, followed by research and development positions in chemical-pharmaceutical industry. In 2007 he became Adjunct Professor at RWTH Aachen, where he became founding director of the Joint Research Center for Computational Biomedicine in 2013, a private-public partnership between RWTH Aachen University Hospital Aachen and Bayer AG. Since 2017 he is head of the Institute for Computational Biomedicine. His focus is on research and development of hybrid modelling technologies with focus on applications in intensive care, oncology, pain research. During the Covid19 pandemics he developed the DIVI prognosis tool and focused on pattern recognition in pandemic dynamics.

    Hülya Ulu-Esser
    Administrative Assistant
    Tel.: 0241 80-85890
    Fax: 0241 80-3385890

    Joana Elena Meyer

    As part of the Helmholtz School for Data Science in Life, Earth and Energy (HDS-LEE) I am investigating hybrid models integrating adaptive control AI and knowledge-based components for predictive simulation in medicine. The goal is to demonstrate the algorithm on Covid19 pandemic data or patient data from intensive care patients and assess the impact of new control measures on the outcome."

    Jorge Guzman Maldonado

    Jorge Guzman Maldonado is a PhD student in Schuppert’s group and part of the HDS-LEE graduate school (Helmholtz School for Data Science in Life, Earth and Energy). He is working on the application of hybrid models, a combination of mechanistic modelling and machine learning techniques, to the reconstruction of signalling networks from drug response data. His background is in physics and mathematical engineering (UACH, Mexico) and he has a master in mathematical modelling (L’Aquila, Italy and Hamburg, Germany) with focus on complex systems. His research interests lie in the field of systems biology, particularly network modelling and inverse problems.

    Kilian Merkelbach

    Hospitals collect and store data on their patients with the intent to facilitate better treatment. These data include the doses and types of medication administered, the medical procedures applied, laboratory results and other patient-related information. If properly utilized, this information could be used to find hidden patterns and enable precision medicine: What kind of medication would help this patient best? What intervention can be used for patients especially at risk, and who are they? Which research questions should be addressed in new clinical studies? This technology has the potential of improving care for patients and lowering operating costs for hospitals at the same time. However, utilizing this information is a challenging problem. The data is very high-dimensional (there are many attributes), it is temporally complex (there are some regions with no or little data and some regions with dense data) and is sparse (the same data was not collected for each patient). In my work, I address these challenges using deep-learning-based dimensionality reduction and clustering techniques."

    Konstantin Sharafutdinov

    Konstantin Sharafutdinov studied applied mathematics and physics at the Moscow Institute of Physics and Technology (Moscow, Russia) and physics at the Cologne University (Cologne, Germany). He joined the JRC in 2018 to work in the Algorithmic surveillance of ICU patients with acute respiratory distress syndrome project and to pursue a PhD. Konstantin's doctoral research focuses on the application of hybrid modeling approaches to predict critical conditions arising during an ICU stay of a patient. He combines mechanistic knowledge encoded in pulmonary and respiratory models with modern AI and ML techniques to achieve an improved prediction of critical states and classification of patient subpopulations in heterogeneous ICU databases. His research interests also include real world data mining, personalized medicine, digitalization in medicine and smart support systems.

    Lisa-Katrin Schaetzle

    After finishing my PhD in the group of Andreas Schuppert in November 2020, I am continuing my research as a postdoctoral fellow. My thesis focused on the systematic modeling of drug efficacy in cancer patients, including the development of the R-package FORESEE. My current work expands to other projects of the group as well as organizational aspects of the Center for Computational Life Sciences."

    Moein Einollahzadeh Samadi

    Moein Samadi is a doctoral candidate in Computational Engineering Science at RWTH Aachen University and an associated Ph.D. student at the Helmholtz School for Data Science in Life, Earth, and Energy (HDS-LEE). Moein's doctoral research focuses on Design of experiments for the applications of mechanistic/data-driven hybrid models in life sciences. He develops methods and pipelines for incorporating clinical and biological mechanistic knowledge into conventional machine learning methods with the goal of improving the explainability, predictivity, and reliability of AI in medicine.

    Richard Polzin

    As a PhD student in the Institute for Computational Biomedicine I'm currently working on the prediction of lung failure in intensive care units. After obtaining the bachelor's degree at FH Aachen, as well as a master's degree in Maastricht I've joined the team of Prof. Schuppert in 2018.
    While studying in Maastricht my focus was on machine learning and natural language processing. The project I'm currently working on aims to improve patient care, especially with respect to acute repspiratory distress syndrom, in the ICU. Thus regular measurements for various parameters are utilized and techniques applied focus on time-series modelling and prediction."

    Younes Mueller

    Younes Müller studied Computer Science at the RWTH Aachen. After graduating he became a research assistant at the JRC, where he worked as student researcher before. There he helped to develop and publish HybridML, a software platform for generating and training hybrid models, using TensorFlow and Casadi. Using HybridML he created a hybrid machine learning model to investigate the relationship between socio-economic covariates and COVID-19 spreading in German federal states. He works in the PROGNOSIS project, a joint project of 5 German Universities, aiming to predict COVID-19 spreading and ICU usage in Germany. He is in charge of the CMDA software, a small, lightweight tool to preprocess time series and generate features for machine learning, developed by Jorge Guzman.
    ScienceDirect: HybridML: Open source platform for hybrid modeling - ScienceDirect



    Jayesh Sudhir Bhat
    Dr. Marc Brehme
    Maximilian Eck
    Pejman Farhadi Ghalati
    Dr. Ali Hadizadeh Esfahani
    Dr. Jeyashree Krishnan
    Dr. Nina Kusch
    Dr. Michael Lenz
    Dr. Maryam Montazeri
    Dr. Satya Swarup Samal

    A.  Original papers

    • A.Schuppert (1989): Error estimates and stability of ultrasound tomography reconstructions. In: Model Optimization in Exploration Geophysics, vol. 3 (A.Vogel, ed.). Vieweg, Braunschweig.
    • A.Schuppert (1993): A model for the creep of oriented high-modulus fibers. Macromol. Chem.Theory Simul. 2, 643-651.
    • N.Nefedov, K.Schneider, A.Schuppert (1994): Jumping behavior in singularly perturbed systems modelling bimolecular reactions. Weierstrass Institute for Analysis and Stochastics (WIAS) preprint No. 137, ISSN 0946-8633.
    • S.Miesbach, A.Schuppert (1995): Neuronale Netze zur Analyse und Optimierung chemischer Produktionsprozesse. Der Maschinenmarkt 101, 40-43.
    • A.Schuppert (1995): Mathematik in der Modellierung am Beispiel der mechanischen Eigenschaften von polymeren Hochleistungsfasern. In: Mathematik in der Praxis - Fallstudien aus Industrie, Wirtschaft, Naturwissenschaften und Medizin (A.Bachem, M.Jünger, R.Schrader, eds.). Springer, Heidelberg. 137-150.
    • A.Schuppert (1995): Modelling the influence of monomer properties on hydrogen bond density in oriented aramid copolymers. J.Chem.Soc.Faraday Trans. 91, 2629-2631.
    • N.Nefedov, K.Schneider, A.Schuppert (1996): Jumping behavior of the reaction rate of fast bimolecular reactions. Z.Angew.Math.Mech. 76, 69-72.
    • M.Efendiev, B.Fiedler, A.Schuppert (1996): Upscaling of some exothermic reactions from the chemical industry. Preprint 15/96 of the Special Programme "Dynamics, analysis, efficient simulation and ergodic theory" of the German Federal Research Foundation (DFG). Z.Angew.Math.Mech. 76, 153-156.
    • B.Fiedler, A.Schuppert, M.Efendiev (1996): Thermally stable upscaling of a model exothermic reactor. Preprint 38/96 of the Special Programme "Dynamics, analysis, efficient simulation and ergodic theory" of the DFG.
    • A.Schuppert (1996): Modelling reaction kinetics - an interdisciplinary challenge for mathematics. In: Progress in Industrial Mathematics (H.Neunzert, ed.). Wiley & Teubner, Stuttgart. 159-165.
    • A.Schuppert (1996): New approaches to data-oriented reaction modelling. Proceedings of the 3rd Workshop on Modelling of Chemical Reaction Systems (IWR, Heidelberg).
    • M.Efendiev, A.Schuppert, M.Wolfrum (1997): Upscaling of chemical reactors: Comparison of mathematical and experimental results. In: Proceedings on Analysis, Numerics, and Applications. Addison Wesley Longman.
    • M.Efendiev, M.Wolfrum, A.Schuppert (1997): Isothermal upscaling of chemical reactors: Two examples. Nonlin.Anal.Theory Meth.Appl. 30, 3455-3461.
    • B.Fiedler, A.Schuppert, M.Efendiev (1997): Thermisch stabiles Upscaling einer exothermen Modellreaktion. In: Mathematik: Schlüsseltechnologie für die Zukunft (K.H.Hoffmann, W.Jaeger, T.Lohmann, H.Schunck, eds.), Springer, Heidelberg, 83-90.
    • M.Bruns, J.Baurmeister, T.Schaefer, and A.Schuppert (1997): Mit Simulation Reserven mobilisieren. Nachr.Chem.Tech.Lab. 45, 483-485.
    • G.Mogk, A.Schuppert (1998): Dynamische Modellierung und Analyse chemischer Reaktionssysteme. Technical Report Federal Research Ministry (BMBF) 03 D0022C
    • A.Schuppert (1998): Prozessmodellierung mit Hybridmodellen. Chem.Ing.Tech. 70, 1090-1091.
    • A.Schuppert (1999): Extrapolability of structured hybrid models: A key to the optimization of complex processes. In: Proceedings of the International Conference on Differential Equations (Equadiff) (B.Fiedler, K.Groeger, J.Sprekels, eds.) World Scientific Publishing, 1135-1151.
    • M.A.Efendiev, A.Schuppert (1999). Stability analysis of chemical reactors. In: Scientific Computing in Chemical Engineering II (F,Keil, ed.) Springer, 225-230.
    • R.Perne, A.Schuppert (2000): Advanced modelling and simulation technologies - The key challenges for process optimization. In: Proceedings of AspenWorld 2000, Orlando/FL (on CD).
    • M.A.Efendiev, G.Habermehl, A.Schuppert (2000): On the stability of complex chemical reactors: an example, In: VW-Stiftung Proceedings (H.Schulze, ed.), Cottbus, 1-12.
    • R.Perne, A.Schuppert (2002): Process Software in the Chemical Industry – the Challenge of Complexity. Comp.Aid.Chem.Eng. 10, 23-30.
    • G.Mogk, Th.Mrziglod, A.Schuppert (2002): Application of Hybrid Models in the Chemical Industry. Comp.Aid.Chem.Eng. 10, 931-936.
    • B.Fiedler, M.Efendiev, L.Lerman, J.Rademacher, A.Schuppert (2003): Stability analysis of reactors from the chemical industry. In: Mathematics – Key Technology for the Future, Springer Berlin (W.Jäger, ed.), 175-183.
    • S.Rommel, A.Schuppert (2004): Data Mining for Bioprocess Optimization. Eng.Life Sci. 4, 266-270.
    • A.Ohrenberg, C.v.Törne, A.Schuppert, B.Knab (2005): Application of Data Mining and Evolutionary Optimization in catalyst discovery and high-throughput experimentation – techniques, strategies, software. QSAR & Comb.Sci. 24, 29-37.
    • A.Schuppert, R.Perne (2005): Data Mining mit Prozessdaten. Automatisierungstechnik, 53, 342-349.
    • G.Schopfer, O.Kahrs, W.Marquardt, M.Warncke, T.Mrziglod, A.Schuppert (2005): Semi-empirical process modelling with integration of commercial modelling tools, In: Proceedings of ESCAPE-15, Elsevier (L.Puigjaner, A.Espuña, eds.), 595-600.
    • B.Fiedler, A.Schuppert (2008): Local identification of scalar hybrid models with tree structure, IMA Journal of Applied Mathematics, 73, 449-476.
    • S.Schneckener, L.Görlitz, L., H.Ellinger-Ziegelbauer, H.-J. Ahr, A Schuppert (2010): An elastic network theory to identify characteristic stress response genes, Comp.Biol.Chem. 34, 193-202.
    • R.Ummanni, F.Mundt, H.Pospisil, S.Venz, C.Scharf, C.Barett, M.Fälth, J.Köllermann, R.Walther, T.Schlomm, G.Sauter, C.Bokemeyer, H.Sültmann, A.Schuppert, T.H.Brümmendorf, S.Balabanov (2011): Identification of clinically relevant protein targets in prostate cancer with 2D-DIGE coupled mass spectrometry and systems biology network platform. PLoS One. Feb 11;6(2):e16833.
    • A.Schuppert (2011): Efficient reengineering of meso-scale topologies for functional networks in biomedical applications. J.Math.Ind. 1:6 doi:10.1186/2190-5983-1-6.
    • S.Schneckener, N.Arden, A.Schuppert, (2011): Quantifying stability in gene list ranking across microarray derived clinical biomarkers, BMC Med.Genom. 4:73 doi:10.1186/1755-8794-4-73.
    • F.J.Müller, A.Schuppert, (2011): Few inputs can reprogram biological networks, Nature 478, doi:10.1038/nature10543.
    • B.M.Schuldt, F.J.Müller, A.Schuppert, (2012): What can networks do for you? In: New frontiers of network analysis in Systems Biology. (A.Ma'ayan, B.D.MacArthur, eds.) Springer, 173-194.
    • A.Schuppert. (2012): Mathematics in Health Care (invited Feature). Newsletter of the European Mathematical Society, 83, 29-36.
    • B.D.MacArthur, A.Sevilla, M.Lenz, F.J.Müller, B.Schuldt, A.Schuppert, S.J.Ridden, M.Fidalgo, J.Wang, I.R.Lemischka (2012): nanog-dependent feedback loops maintain alternate states of stem cell pluripotency, Nature Cell Biology, (doi:10.1038/ncb2603).
    • Schuldt BM, Guhr A, Lenz M, Kobold S, MacArthur BD, et al. (2013): Power-Laws and the Use of Pluripotent Stem Cell Lines. PLoS ONE 8(1): e52068. doi:10.1371/journal.pone.0052068.
    • M. Krauss, R. Burghaus, J. Lippert, M. Niemi, P. Neuvonen, A. Schuppert, St. Willmann, L. Kuepfer, L. Görlitz (2013):Using Bayesian-PBPK modeling for assessment of inter-individual variability and subgroup stratification, In Silico Pharmacology, 1:6 doi:10.1186/2193-9616-1-6.
    • Schaller S, Willmann S, Lippert J, Schaupp L, Pieber T, Schuppert A, Eissing T. (2013): A Generic Integrated Physiologically based Whole-body Model of the Glucose-Insulin-Glucagon Regulatory System. CPT: Pharmacometrics & Systems Pharmacology  2, e65; doi:10.1038/psp.2013.40.
    • Lenz M., Schuldt B.-M., MüllerF.-J., Schuppert, A.(2013): PhysioSpace: Relating gene expression experiments from heterogeneous sources using shared physiological processes PLoS ONE 01/2013; 8(10):e77627.
    • Balabanov S, Wilhelm T, Venz S, Keller G, Scharf C, Pospisil H, Braig M, Barett C, Bokemeyer C, Walther R, Brümmendorf TH, Schuppert A, Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors PLoS One [2013, 8(1):e53668] PMID:23326482 PMCID:PMC3541187.
    • Braig M., Pällmann N., Preukschas M., Steinemann D., Hofmann W., Gompf A., Streichert T., Braunschweig T.,  Copland M., Rudolph K L., Bokemeyer C., Koschmieder S., Schuppert A., Balabanov S, Brümmendorf T H  (2014): A 'telomere-associated secretory phenotype' (TASP) cooperates with BCR-ABL to drive malignant proliferation of leukemic cells. Leukemia, doi: 10.1038/leu.2014.95.
    • Wolkenhauer O., Auffray Ch., Brass O., Clairambault J., Deutsch A., Drasdo D., Gervasio F., Preziosi L., Maini Ph., Marciniak-Czochra A., Kossow Ch., Kuepfer L., Rateitschak K., Ramis-Conde I., Ribba B., Schuppert A., Smallwood R., Stamatakos G., Winter F., Byrne H. (2014): Enabling multiscale modeling in systems medicine, Genome Medicine, 6:21.
    • Lenz M, Goetzke R, Schenk A, Schubert C, Veeck J, Hemeda H, Koschmieder S, Zenke M, Schuppert A, Wagner W. (2015): Epigenetic biomarker to support classification into pluripotent and non-pluripotent cells. Sci Rep. Volume 5, p. 8973.
    • Wagener R, Lenz M, Schuldt B, Lenz I, Schuppert A, Siebert R, Müller FJ. (2015): Investigation of potential traces of pluripotency in germinal-center-derived B-cell lymphomas driven by MYC. Blood Cancer J   Volume 5 (2015) p. e317.
    • Schaller S, Lippert J, Schaupp L, Pieber T, Schuppert A, Eissing T (2015): Robust PBPK/PD based Model Predictive Control of Blood Glucose. IEEE Trans Biomed Eng. 2015 Nov 2. [Epub ahead of print].
    • Krauss M, Tappe K, Schuppert A, Kuepfer L, Goerlitz L (2015): Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations, PLoS ONE 10(10):e0139423.
    • Kuepfer L, Schuppert A., Systems Medicine in Pharmaceutical Research and Development. Methods Mol Biol, Volume 1386 (2016), p. 87-104.
    • Lenz M, Müller FJ, Zenke M, Schuppert A, Principal components analysis and the reported low intrinsic dimensionality of gene expression microarray data. Sci Rep [2016, 6:25696] PMID:27254731 PMCID:PMC4890592.
    • Brehme M, Koschmieder S, Montazeri M, Copland M, Oehler VG, Radich JP, Brümmendorf TH, Schuppert A, Combined Population Dynamics and Entropy Modelling Supports Patient Stratification in Chronic Myeloid Leukemia. Sci Rep [2016, 6:24057] PMID:27254731 PMCID:PMC4890592.
    • Krauss M, Hofmann U, Schafmayer C, Igel S, Schlender J, Mueller C, Brosch M, Schoenfels W, Erhart W, Schuppert A, Block M, Schaeffeler E, Boehmer G, Goerlitz L, Hoecker J, Lippert J, Kerb R, Hampe J, Kuepfer L, Schwab M (2017) Translational learning from clinical studies predicts drug pharmacokinetics across patient populations. npj Systems Biology and Applications 3(1): p. 11.
    • Stumpf P. S., Smith R.C.G., Lenz M., Schuppert A., Müller F.-J., Babtie A., Chan Th. E., Stumpf M.P.H., Please C. P., Howison S. D., Arai F. , MacArthur B.D., (2017) Stem Cell Differentiation as a Non-Markov Stochastic Process, Cell Systems , Volume 5 , Issue 3 , 268 - 282.e7.
    • Arne Schenk, Ahmed Ghallab, Ute Hofmann, Reham Hassan, Michael Schwarz, Andreas Schuppert, Lars Ole Schwen, Albert Braeuning, Donato Teutonico, Jan G. Hengstler & Lars Kuepfer (2017) Physiologically-based modelling in mice suggests an aggravated loss of clearance capacity after toxic liver damage, Scientific Reports 7, doi:10.1038/s41598-017-04574-z.
    • Ehsani A., Niedenfuehr S., Eissing Th., Behnken S., Schuppert A. (2017) How to Use Mechanistic Metabolic Modeling to Ensure High Quality Glycoprotein Production, Computer Aided Chemical Engineering, Vol. 40, 2839-2844.
    • Reiss L K, Schuppert A, Uhlig, St.  (2018) Inflammatory processes during acute respiratory distress syndrome: a complex system, Current Opinion in Critical Care: February 2018 - Volume 24 - Issue 1 - p 1–9.
    • Esfahani A., Sverchkova A., Saez-Rodriguez J., Schuppert A., Brehme M. (2018) A systematic atlas of chaperome deregulation topologies across the human cancer landscape, PLoS Comp. Biology,
    • Apweiler Rolf, Beissbarth Tim, Berthold Michael R, Blüthgen Nils, Burmeister Yvonne, Dammann Olaf, Deutsch Andreas, Feuerhake Friedrich, Franke Andre, Hasenauer Jan, Hoffmann Steve, Höfer Thomas, Jansen Peter LM, Kaderali Lars, Klingmüller Ursula,  Koch Ina, Kohlbacher Oliver, Kuepfer Lars, Lammert Frank Maier Dieter, Pfeifer Nico Radde Nicole, Rehm Markus, Roeder Ingo, Saez-Rodriguez Julio, Sax Ulrich, Schmeck Bernd, Schuppert Andreas, Seilheimer Bernd, Theis Fabian, Vera Julio, Wolkenhauer, Olaf (2018) Whither Systems Medicine? Experimental & Molecular Medicine vol. 50, page e453 (2018) doi:10.1038/emm.2017.290.
    • Schuppert A., Mrziglod Th., (2018) Hybrid Model Identification and Discrimination with Practical Examples from the Chemical Industry, in: Hybrid Modelling in Process Industries, Jarka Glassey & Moritz v. Stosch eds., CRC Press, p63-88.
    • Turnhoff L., Kusch N., Schuppert A. (2018) “Big Data and Dynamics” – The mathematical toolkit towards personalized medicine, in: Patterns of Dynamics, Gurevich P., Hell J., Sandstede B., Scheel A., Springer Proceedings in Mathematics & Statistics, p. 338-370.
    • Winter, A. et al., (2018) Smart Medical Information Technology for Healthcare (SMITH), Methods Inf Med 2018; 57(S 01): e92-e105.
    • H. Fröhlich, R. Balling, N. Beerenwinkel,, O. Kohlbacher, S. Kumar, Th. Lengauerm M.H. Maathuis, Y. Moreau, S.A. Murphy, T.M. Przytycka, M. Rebhan, H. Röst, A. Schuppert, M. Schwab, R. Spang, D. Stekhoven, J. Sun, A. Weber, D. Ziemenk and B. Zupan (2018) From hype to reality: data science enabling personalized medicine, BMC Medicine, 2018, 16:150.
    • Ch. Müller, F. Weysser, Th. Mrziglod, A. Schuppert (2018) Markov-Chain Monte-Carlo Methods and non-identifiabilities, Monte Carlo Methods and Applications, Vol. 24, 3, DOI:
    • Boulier, F., Fages, F., Radolescu, O., Samal, S., Schuppert, A., Seiler W., Sturm Th., Walcher S., Weber, A. (2019) The SYMBIONT project: symbolic methods for biological networks, ACM Communications in Computer Algebra, Volume 52 Issue 3, September 2018 Pages 67-70.
    • Turnhoff L, Hadizadeh Esfahani A, Montazeri M, Kusch N, Schuppert A. (2019) FORESEE: a tool for the systematic comparison of translational drug response modeling pipelines, Bioinformatics, Volume 35, Issue 19, 1 October 2019, Pages 3846–3848,
    • Farhadi-Galati, P., Samal, S.S., Bhat J.S., Deisz R., Marx G., Schuppert A. (2019) Critical Transitions in Intensive Care Units: A Sepsis Use Case, Scientific Reports volume 9, Article number: 12888 (2019).
    • Stalmann, U., Ticcioni, F., Li, R., Wong ,A., Cowley, G., Root, D.E., Heckl, D., Snoeren, I., Martinez, S., Brümmendorf, T.B., Schuppert, A., Costa, I., Ebert, B., Schneider, R., (2019) Deconstructing the Clonal Advantage and Clonal Stability of 5q- Candidate Genes in Del(5q) MDS on a Single Cell Level Blood (2019) 134 (Supplement_1): 559.,
    • Kusch N., Turnhoff, L., Schuppert A., (2019) Modeling from Molecule to Disease and Personalized Medicine, in: Handbook of Biomarkers and precision Medicine, C. Carini, M. Fidock, A. van Gool eds., CRC Press, pp 245-250.
    • Kuepfer, L. Schuppert A. (2019) Systems Biology Approaches to Identify new Biomarkers, in: Handbook of Biomarkers and precision Medicine, C. Carini, M. Fidock, A. van Gool eds., CRC Press, pp 143-148.
    • Baumeister, J., Chatain, N., Hubrich, A., Maie’ T., Costa I.G., Denecke, B., Han L., Küstermann C., Sontag S., Sere’ K., Strathmann K., Zenke M., Schuppert A., Brümmendorf, T. H., Kranc, K. R., Koschmieder S., Gezer, D., (2020) Hypoxia-inducible factor 1 (HIF-1) is a new therapeutic target in JAK2V617F-positive myeloproliferative neoplasms, Leukemia, 14.11.2019,
    • Ehsani, A., Kappatou, Ch.D., Mhamdi A., Mitsos A., Schuppert A. Niedenfuehr, S., (2019) Towards Model-Based Optimization for Quality by Design in Biotherapeutics Production, Computer Aided Chemical Engineering, Volume 46, 2019, Pages 25-30.
    • Schätzle, L.K., Esfahani, A. H., Schuppert A., Methodological Challenges in Translational Drug Response Modeling in Cancer, PLoS Computational Biology, (2020)
    • Krishnan, J., Torabi, R., Di Napoli, E., Schuppert A., (2020) A Modified Ising Model of Barabási-Albert Network with Gene-type Spin, Journal of Mathematical Biology volume 81, pages769–798(2020).
    • Chrysoula Dimitra Kappatou, Alireza Ehsani, Sebastian Niedenführ, Adel Mhamdi, Andreas Schuppert, Alexander Mitsos, Quality-targeting dynamic optimization of monoclonal antibody production, Computers & Chemical Engineering, Volume 142, 2020, 107004, ISSN 0098-1354,
    • Müller, Ch., Diedam, H. Mrziglod, Th., Schuppert A., (2020) A neural network assisted Metropolis adjusted Langevin algorithm, Monte Carlo Methods and Applications, Volume 26: Issue 2,
    • N.Kusch, A. Schuppert (2020), Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms, PLoS One,
    • Malvin Jefri, Scott Bell, Huashan Peng, Nuwan Hettige, Gilles Maussion, Vincent Soubannier, Hanrong Wu, Heika `Silveira, Jean-Francoix Theroux, Luc Moquin, Xin Zhang, Zahia Aouabed, Jeyashree Krishnan, Liam A. O’Leary, Lilit Antonyan, Ying Zhang, Vincent McCarty, Naquib Mechawar, Alain Gratton, Andreas Schuppert, Thomas M. Durcan, Edward A. Fon, Carl Ernst (2020) Stimulation of L‐type calcium channels increases tyrosine hydroxylase and dopamine in ventral midbrain cells induced from somatic cells, Stem Cells Translational Medicine,
    • Schuppert, A., Theisen, S., Fränkel, P., Weber-Carstens, S., Karagiannidis, C. (2021) Bundesweites Belastungsmodell für Intensivstationen durch COVID-19, Medizinische Klinik – Intensiv- und Notfallmedizin,,
    • Schuppert, A., Polotzek, K., Schmitt,J., Busse,R., Karschau, J., Karagiannidis, Ch. (2021) Different spreading dynamics throughout Germany during the second wave of the COVID-19 pandemic: a time series study based on national surveillance data, The Lancet Regional Health Europe, 26. June 2021, DOI:
    • Peine, A., Hallawa, A., Bickenbach, J., Dartmann G., Begic Fazlic, L., Schmeink, A., Ascheid, G., Thiemermann, C., Schuppert, A., Kindle, R., Celi, L., Marx, G., Martin L. Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care. npj Digit. Med. 4, 32 (2021).
    • Günster, Ch., Busse, R., Spoden, M., Rombey, T., Schillinger, G., Hoffmann, W., Weber-Carstens, S., Schuppert, A., Karagiannidis, Ch. (2021) 6-month mortality and readmissions of hospitalized COVID-19 patients: A nationwide cohort study on 8679 patients in Germany, PLoS One,
    • Schuppert, A., Weber-Carstens, S. & Karagiannidis, C. Intensivbettenbedarf für COVID‑19 im Herbst/Winter 2021. Med Klin Intensivmed Notfmed (2021).
    • Ali Hadizadeh Esfahani, Janina Maß, Asis Hallab, Bernhard M Schuldt, David Nevarez, Björn Usadel, Mark-Christoph Ott, Benjamin Buer, Andreas Schuppert, Plant PhysioSpace: a robust tool to compare stress response across plant species, Plant Physiology, 2021;, kiab325,
    • Baumeister, J., Maié, T., Chatain, N., Gan, L., Weinbergerova, B., de Toledo M., Eschweiler, J., Maurer A., Mayer J., Kubesova B., Racil Z., Schuppert, A., Costa I., Koschmieder S., Brümmendorf T.H., Gezer D.Early and late stage MPN patients show distinct gene expression profiles in CD34+ cells. Ann Hematol (2021).
    • Scott Bell, Vincent McCarty, Huashan Peng, Malvin Jefri, Nuwan Hettige, Lilit Antonyan, Liam Crapper, Liam A. O'Leary, Xin Zhang, Ying Zhang, Hanrong Wu, Diane Sutcliffe, Ilaria Kolobova, Thad A. Rosenberger, Luc Moquin, Alain Gratton, Jelena Popic, Ilse Gantois, Patrick S. Stumpf, Andreas A. Schuppert, Naguib Mechawar, Nahum Sonenberg, Michel L. Tremblay, Hyder A. Jinnah, Carl Ernst, Lesch-Nyhan disease causes impaired energy metabolism and reduced developmental potential in midbrain dopaminergic cells, Stem Cell Reports, Volume 16, Issue 7, 2021, Pages 1749-1762, ISSN 2213-6711,
    • Oliver Maassen, MSc; Sebastian Fritsch, MD; Julia Palm, MSc; Saskia Deffge, MSc; Julian Kunze, MD; Gernot Marx, MD, Prof Dr, FRCA; Morris Riedel, Prof Dr; Andreas Schuppert, Prof Dr; Johannes Bickenbach, MD, Prof Dr (2021) Future Medical Artificial Intelligence Application Requirements and Expectations of Physicians in German University Hospitals: Web-Based Survey , Journal of Medical Internet Research.
    • Schuppert, Andreas and Polotzek, Katja and Karschau, Jens and Karagiannidis, Christian, (2021) Effectiveness of extended shutdown measures during the ´Bundesnotbremse´introduced in the third SARS-CoV-2 wave in Germany,Infection,
    • Claudia Merger, Timo Reinartz, Stefan Wessel, Carsten Honerkamp, Andreas Schuppert, Moritz Helias, Global hierarchy vs. local structure: spurious self-feedback in scale-free networks, Phys. Rev. Research 3.033272.
    • Sebastian Fritsch, Konstantin Sharafutdinov,  Gernot Marx, Andreas Schuppert, Johannes Bickenbach (2021) Biometric covariates and outcome in COVID-19 patients: Are we looking close enough?, BMC Infect Dis. 2021 Nov 4;21(1):1136. doi: 10.1186/s12879-021-06823-z.
    • Kilian Merkelbach, Artur M. Schweidtmann, Younes Müller, Patrick Schwoebel, Adel Mhamdi, Alexander Mitsos, Andreas Schuppert, Thomas Mrziglod, Sebastian Schneckener, HybridML: Open source platform for hybrid modeling, Computers & Chemical Engineering, Volume 160, 2022, 107736,ISSN 0098-1354,
    • Fritsch S, Sharafutdinov K, Schuppert A, Bickenbach J. Nutzung von künstlicher Intelligenz zur Bekämpfung der COVID-19-Pandemie [Usage of Artificial Intelligence in the Combat against the COVID-19 Pandemic]. Anasthesiol Intensivmed Notfallmed Schmerzther. 2022 Mar;57(3):185-197. German. doi: 10.1055/a-1423-8039. Epub 2022 Mar 23. PMID: 35320841.
    • Hettige NC, Peng H, Wu H, Zhang X, Yerko V, Zhang Y, Jefri M, Soubannier V, Maussion G, Alsuwaidi S, Ni A, Rocha C, Krishnan J, McCarty V, Antonyan L, Schuppert A, Turecki G, Fon EA, Durcan TM, Ernst C. FOXG1 dose tunes cell proliferation dynamics in human forebrain progenitor cells. Stem Cell Reports. 2022 Mar 8;17(3):475-488. doi: 10.1016/j.stemcr.2022.01.010. Epub 2022 Feb 10. PMID: 35148845; PMCID: PMC9040178.
    • Schuppert A, Karagiannidis C. Dynamische Entwicklung der COVID-19-Intensivbelegung im Herbst/Winter 2021/22 in Abhängigkeit von den 7-Tage-Inzidenzen [Dynamic simulation of COVID-19 intensive care bed occupancy in fall/winter 2021/22 as a function of 7-day incidences]. Med Klin Intensivmed Notfmed. 2022 Apr;117(3):206-208. German. doi: 10.1007/s00063-022-00904-w. Epub 2022 Feb 9. PMID: 35138413; PMCID: PMC8983622.
    • Stalmann USA, Ticconi F, Snoeren IAM, Li R, Gleitz HFE, Cowley GS, McConkey ME, Wong AB, Schmitz S, Fuchs SNR, Sood S, Leimkühler NB, Martinez-Høyer S, Banjanin B, Root D, Brümmendorf TH, Pearce JE, Schuppert A, Bindels EMJ, Essers MA, Heckl D, Stiehl T, Costa IG, Ebert BL, Schneider RK. Genetic barcoding systematically compares genes in del(5q) MDS and reveals a central role for CSNK1A1 in clonal expansion. Blood Adv. 2022 Mar 22;6(6):1780-1796. doi: 10.1182/bloodadvances.2021006061. PMID: 35016204; PMCID: PMC8941465.

    B.  Patents

    • A.Schuppert (1980): Verfahren und Vorrichtung zur zweidimensionalen stroboskopischen Abtastung der Lineargeschwindigkeit eines Körpers. German Patent no. P2833262.9.
    • B.Lohmann, A.Schuppert, J.Kämpfer, M.Warncke (2001) Method for determining a complex correlation pattern from process and plant data (EP 1313030).
    • A.Schuppert (2001): Method for the identification of pharmacophores (EP 1451750).
    • A.Schuppert, A.Ohrenberg (2003): Method and computer for experimental design (PCT/EP03/03424 (WO)).
    • A.Schuppert, R.Burghaus, C.v.Törne, S.Schwers, U.Strop, H.Kallabis (2007): Method for identifying predictive biomarkers from patient data, WO/2007/07/9875.
    • A.Schuppert, H.Ellinger-Ziegelbauer, H.J.Ahr (2008): Method for determining the behavior of a biological system after a reversible disturbance, WO/2008/006469.

    AG Stumpf

    The stem cell systems biology group aims to understand how stem cells contribute to health and disease. This work includes analyses of (multi-) omics data, for instance obtained from single-cell sequencing but also clinical data. We use computer models and machine learning to make sense of these data.


      • Bio2integrate
        Integration of data from wearable devices (smart watch) and clinical data in order to improve the pain management for patients suffering from small fiber neuropathy. To read more about this work see
      • MPN/MDS(UK Aachen – UK Düsseldorf)
        In cooperation with clinicians from the University Hospitals Aachen and Düsseldorf, this project aims to better understand cancer subtypes and disease progression through pattern recognition of Myeloproliferative and Myelodysplastic neoplasms.
      • TREAT-SGS
        Schinzel-Giedion Syndrome (SGS) is a rare disease that affects young children. One debilitating symptom of SGS are frequent seizures that are difficult to manage using conventional therapies. In this consortium project we conduct a preclinical study to repurpose a drug to treat the seizures associated with Schinzel-Giedion-Syndrome. The project is funded by European Joint Programme on Rare Diseases via the Deutsche Forschungsgemeinschaft (DFG).
      • Stem cell - niche interactions
        The aim of this project is to better understand the influence of stem-cell-niche interactions on self-renewal of blood stem cells in normal hematopoiesis. We are also interested in how these interactions change as the organism ages.
        Project partners:
        Fumio Arai & Hisa Yao, Kyushu University, Fukuoka, Japan.
        To read more about this work, see Arai et al. Cell Systems. 2020; 11 (6): 640-652.e5
      • Theory of stem cell differentiation
        This project aims establish theoretical foundations of stem cell differentiation and self-renewal. Modern single-cell methods can read the molecular status of individual cells in exquisite detail. However, the full complexity of the molecular composition of cells remains inaccessible. We argue that mathematical models that take these unknown configurations into account enable a better understanding of stem cell differentiation dynamics in the absence of detailed knowledge of the full molecular status of the cell.
        Project partners:
        Ben MacArthur, University of Southampton, UK
        Fumio Arai, Kyushu University, Fukuoka, Japan.
        To read more about this work see:
        Stumpf, Arai, MacArthur, Cell Stem Cell (2021).
        Stumpf et al. Cell Systems (2017).

      Patrick S. Stumpf

      Group leader with an interest in stem cell systems biology.
      Google Scholar:


      Marc-Daniel Hagel

      Marc-Daniel Hagel began studying biology at the RWTH Aachen focusing mainly on molecular medicine, but he, later on, switched his focus toward computational biology and machine learning. As a PhD candidate, he is now working on mutation-driven developmental perturbations of gene expression in the SGS Project. The goal of this project is to find the underlying cause for the increased neuronal excitability which results in painful epileptic seizures in children suffering from this developmental disease. Therefore Marc-Daniel Hagel's focus lies on investigating differences in gene expression, genetic networks, and changes in brain development to find the driving forces as well as suitable therapeutic access points.

      Alexandra Valeanu

      Alexandra is a M.Sc. student in Biomedical Engineering at RWTH Aachen University. Her research focuses on single-cell data analysis of early brain development to study stem cell differentiation.

      Jonas Kupschus

      Jonas is a Molecular Biomedicine master student at Heinrich-Heine-University Düsseldorf. He works on his master thesis here at the institute within the Bio2Treat project. His work focuses on applying machine learning techniques to time-series data of chronic-pain patients. His research interests include neurodegenerative diseases and the human microbiome.



      Joana Meyer (Ph.D. student in AG Schuppert)
      Johannes Lohmeyer (M.Sc. student at RWTH Aachen University)

      For an up-to-date list of publications please see Google Scholar