Publications

Highlight publications

Computational Methods

N.Kusch, A. Schuppert (2020), Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms, https://doi.org/10.1371/journal.pone.0238961

Schätzle, L.K., Esfahani, A. H., Schuppert A., Methodological Challenges in Translational Drug Response Modeling in Cancer, PLoS Computational Biology, (2020) https://doi.org/10.1371/journal.pcbi.1007803

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 Biologyvolume 81, pages769–798(2020)

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, https://doi.org/10.1093/bioinformatics/btz145

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

H. Fröhlich, R. Balling, N. Beerenwinkel, O. Kohlbacher, S. Kumar, Th. Lengauer, 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. Ziemek and B. Zupan (2018) From hype to reality: data science enabling personalized medicine, BMC Medicine, 2018 16:150

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

Krishnan J., Porta Mana P., Helias M., Diesmann M. and Di Napoli E., (2018), Perfect detection of spikes in the Linear Sub-threshold Dynamics of Point Neurons, Front. Neuroinform. 11:75.

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:https://doi.org/10.1515/mcma-2018-0018

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

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

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., 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

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

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.

Systems Medicine Applications

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).

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

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

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

Kuepfer L, Schuppert A., Systems Medicine in Pharmaceutical Research and Development. Methods Mol Biol, Volume 1386 (2016), p. 87-104,

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

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

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, doi.org/10.1371/journal.pcbi.1005890

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)

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

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,  https://doi.org/10.1002/sctm.18-0180

Computing for Health Care

Winter, A. et al., (2018) Smart Medical Information Technology for Healthcare (SMITH), Methods Inf Med 2018; 57(S 01): e92-e105

Schuppert, A., Theisen, S., Fränkel, P., Weber-Carstens, S., Karagiannidis, C. (2021) Bundesweites Belastungsmodell für Intensivstationen durch COVID-19, Medizinische Klinik – Intensiv- und Notfallmedizin, https://link.springer.com/article/10.1007/s00063-021-00791-7#auth-A_-Schuppert

Schuppert, A.,  Polotzek, K., Schmitt, J. , Busse, R., Karschau, J. , Karagiannidis, C.,  (2021) Different spreading dynamics throughout Germany during the second wave of the COVID-19 pandemic: link to public health interventions https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3804989 (accepted for publication by The Lancet - Regional Health Europe)

Stem Cell Systems Biology

Stumpf PS, Arai F, MacArthur, BD.
Modeling Stem Cell Fates using Non-Markov Processes
Cell Stem Cell. 2021; 28 (2): 187-190
https://doi.org/10.1016/j.stem.2021.01.009

Stumpf PS, Du X, Imanishi H, Kunisaki Y, Semba Y, Noble T, Smith RC, Rose-Zerili M, West JJ, Oreffo RO, Niranjan M, Akashi K, Arai F, MacArthur, BD.
Transfer learning efficiently maps bone marrow cell types from mouse to human using single-cell RNA sequencing
Communications Biology 2020;  3: 736
https://doi.org/10.1038/s42003-020-01463-6

Arai F*, Stumpf PS*, Ikushima YM*, Hosokawa K, Roch A, Lutolf MP, Suda T, MacArthur BD.
Machine Learning of Hematopoietic Stem Cell Divisions from Paired Daughter Cell Expression Profiles Reveals Effects of Aging on Self-Renewal
Cell Systems. 2020; 11 (6): 640-652.e5
https://doi.org/10.1016/j.cels.2020.11.004

Stumpf PS, Arai F, MacArthur, BD.
Heterogeneity and ‘memory’ in stem cell populations
Physical Biology. 2020; 16 (6): 065013
https://doi.org/10.1088/1478-3975/abba85

Stumpf PS, Schätzle LK, Schuppert A.
Transfer-lernen in der Biomedizin
Biospektrum 6/2020;  26: 682–684.
https://doi.org/10.1007/s12268-020-1459-2

Fidanza A, Stumpf PS, Ramachandran P, Tamagno S, Babtie A, Lopez-Yrigoyen M, Taylor AH, Easterbrook J, Henderson B, Axton R, Henderson NC, Medvinsky A, Ottersbach K, Romanò N, Forrester LM.
Single cell analyses and machine learning define hematopoietic progenitor and HSC-like cells derived from human PSCs. Blood. 2020; 136 (25): 2893–2904.
https://doi.org/10.1182/blood.2020006229