Computational systems medicine and disease control
Prof. Dr. Max von Kleist
Research area: Mathematics for Data Science
Website:von Kleist Lab
RESEARCH BACKGROUND:
Our group develops methods for computational systems medicine and computational systems biology and applies it to questions arising in fundamental science and infectious disease research. We welcome students with a solid background in mathematical biology (data science, modelling and simulation) and theoretical foundations in bioinformatics who are excited about the research in these areas. Specifically, we are looking for a highly qualified student to work on:
POSSIBLE PROJECTS:
- Learning RNA structural ensembles from direct (nanopore) RNA sequencing of chemically probed molecules:
RNA is a functionally diverse molecule that is a central player in the regulation of many biological processes where they execute a vast array of cellular functions. As such, RNA is increasingly recognized as a therapeutic target, therapeutic drug, and as a tool for synthetic biology. RNA function is based on its capacity to fold into intricate 3d structures formed from regions of single stranded and double stranded RNA. Axiomatic to RNA’s functional plasticity is structural heterogeneity. Rather than folding into a single structural conformation, RNA molecules can often adopt several different folds, each with a different function.
Experimentally, structural information can be derived by ‘chemical probing’1 of RNA molecules. The chemical probes selectively modify RNA-residues that are unpaired (‘free’ Watson-Crick edge). Following a ‘probing experiment’, chemical modifications can be detected by next generation sequencing, mapped onto the sequence and used for predicting RNA structure(s). Within the domain of RNA structure-function inference, we intend to further our existing technical expertise1,2,3. In particular, we intend to develop computational (ML) methods to resolve RNA structural ensembles of the HIV-1 transcriptome in cells from high-throughput data provided by our experimental partners at the HIRI Würzburg that combine chemical probing1 with long-read nanopore direct RNA sequencing (dRNA-seq)5. A particular challenge will be the differentiation between sequencing- and base-calling errors during direct RNA sequencing6, as well as multiplex dRNA sequencing to reduce inter-experimental noise as well as costs7,8. The project will involve aspects of bioinformatics, data science & statistics, as well as supervised and unsupervised machine learning. In particular, training of recurrent neural networks for nanopore base- and modification calling, as well as unsupervised methods for cluster detection (= structural conformations) in base-called read-ensembles will be the key to this project.
- Immunoepidemiology and HIV-1 chemoprevention
We are interested in modelling the efficacy of pharmaceutical interventions in preventing viral infection (HIV-1), as well as in modelling the interplay between immunity at the population level and the dynamics of viral pathogens (SARS-CoV-2, mpox).
Immuno-epidemiology: We hypothesize that the evolution, as well as the severity of many respiratory viruses is largely driven by population immunity. To study this phenomenon, we combine (molecular) surveillance data9 and infection history10 to quantify, for each emerging strain, how many individuals are susceptible to it at each point in time11. Similarly, for sexually transmitted viruses like mpox, we estimate whether agent-based temporal contact networks can sustain spreading under waning immunity at local vs. global scales12. Lastly, we are interested in reconstructing case-severity over time (and infer mechanisms that drive it).
Chemoprevention: Quantifying the clinical efficacy of preventive therapies (vaccine, prophylaxis) poses an immense statistical (and monetary) challenge: As an example, a single COVID vaccine had to be tested on 40,000 individuals to obtain ~100 evaluable data points (= infections) distributed over two intervention arms (placebo, e.g. 95 infections vs. vaccine, 5 infections). To allow investigating dose-effect relationships and to derive a mechanistic understanding of the mode-of-action of preventive measures, we develop integrative stochastic modelling and simulation techniques in the field of systems medicine and systems pharmacology13-15. We use these models16,17 in collaboration with clinical- and epidemiological partners at RKI, Johns Hopkins, Harvard to consult decision makers and to develop guidelines for HIV prevention with the WHO.
DESIRABLE SKILLS AND QUALIFICATIONS:
These projects require a good understanding of mathematical modelling and stochastic simulation techniques13-15.
REFERENCES:
1General Principles for the Detection of Modified Nucleotides in RNA by Specific Reagents. M. Helm, …, Y. Motorin. Advanced Biology, 5, 2100866
2Mutational Interference Mapping Experiment (MIME) for studying the relationship between RNA structure and function. R.P. Smyth*, L. Despons, G. Huili, S. Bernacchi, M. Hijnen, J. Mak, F. Jossinet, L. Weixi, J-C. Paillart, M. von Kleist*, R. Marquet*, Nature Methods, 12, 866 , 2015
3In cell Mutational Interference Mapping Experiment (in cell MIME) identifies 5’ PolyA as a dual regulator of HIV-1 genomic RNA production and packaging, R.P. Smyth*$, M.R. Smith$, A-C. Jousset, L. Despons, G. Laumond, T. Decoville, P. Cattenoz, C. Moog, F. Jossinet, M. Mougel, J.-C. Paillart, M. von Kleist*, R. Marquet*, Nucleic Acids Research, 46, e57, 2018
4Short and long-range interactions in the HIV-1 5'UTR regulate genome dimerization and Pr55Gag binding, L. Ye, ..., M. von Kleist, R.P. Smyth, Nature Struct & Mol Biol, 29, 306 2022
5Computational methods for RNA modification detection from nanopore direct RNA sequencing data. M. Furlan, …, T. Leonardi. RNA Biology, 18, 31, 2021
6Sequencing accuracy and systematic errors of nanopore direct RNA sequencing. W. Liu-Wei, W. Van der Toorn, P. Bohn, M. Hölzer, RP. Smyth, M. von Kleist. BMC Genomics 25:528 (2024)
7Demultiplexing and barcode-specific adaptive sampling for nanopore direct RNA sequencing W. van der Toorn* , P. Bohn*, W. Liu-Wei , M. Olguin-Nava, RP Smyth+, M. von Kleist + , Nat Commun 16:3742 (2025).
8WarpDemuX-tRNA: barcode multiplexing for nanopore tRNA sequencing. W. van der Toorn, IS Naarmann-de Vries, W.Liu-Wei, C. Dieterich, M. von Kleist Nucleic Acids Research 53: gkaf873 (2025)
9 Integrated Molecular Surveillance for SARS-CoV-2 (IMS-SC2) Laboratory Network. Advancing Precision Vaccinology by Molecular and Genomic Surveillance of Severe Acute Respiratory Syndrome Coronavirus 2 in Germany, 2021 Oh DY, Hölzer M, …, Kröger S*, von Kleist M*, Wolff T*. Clin Infect Dis. 2022 Aug 15;75(Suppl 1):S110-S120
10Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020. Smith, M.R., Trofimova, M., …, von Kleist M., Nat Commun 12, 6009 (2021)
11SARS-CoV-2 Evolution on a Dynamic Immune Landscape. Raharinirina N. A. *, Gubela N.*, Börnigen D.*, Smith M. R.*, Oh D.-Y., Budt M., Mache C., Schillings C., Fuchs S., Dürrwald R., Wolff T., Hölzer M., Paraskevopoulou S., von Kleist M. Nature, 639: 196 (2025)
12 Behavior change and infection induced immunity led to the decline of the 2022 Mpox outbreak in Berlin. Gubela, N., Kim, H.-Y. Lunchenko, N., Stern, D., Michel, J., Nitsche, A., Schmidt, A.J., Marcus, U. and von Kleist M.Communications Medicine, 2026; 6: 81.
13Hybrid stochastic framework predicts efficacy of prophylaxis against HIV: An example with different dolutegravir regimen, S. Duwal, L. Dickinson, S. Khoo and M. von Kleist, PLoS Computational Biology, 14, e1006155, 2018
14Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes. L. Zhang, J. Wang, M. von Kleist, PLoS Comput Biol 17(12): e1009295
15Model-based predictions of protective HIV PrEP adherence levels in cisgender women. L. Zhang*, S. Iannuzzi*, A. Chaturvedula, E. Urungu, J. Haberer, CH Hendrix, M. Von Kleist, Nature Medicine, 2023; 29: 2753.
16 Pharmacological markers of HIV prevention for oral pre-exposure prophylaxis in MSM. S. Iannuzzi, M. Müller, Y. Yu, L. Zhang, CW Hendrix, RR Bies, M. von Kleist*. Nature Commun , 2026; 17: 4213.
17Modelling the impact of initiation delay, duration and prior PrEP on the efficacy of post-exposure prophylaxis containing a tenofovir/emtricitabine backbone. L. Zhang, S. Collins, J Fox, M. Von Kleist J Int AIDS Soc., 2025; 28: e26454
ABOUT MAX VON KLEIST:
Since 2022: W3 Professor of “Mathematics of Data Sciences” at Freie Universität Berlin
Since 2021: Head of project group P5, Robert Koch-Institute Berlin
2020 – 2021: Head of bioinformatics research unit (MF1) Robert Koch-Institute Berlin
2014 – 2019: Junior research group leader (BMBF funded), Freie Universität Berlin
2011 – 2014: Junior research group leader, DFG research center MATHEON
2010 – 2011: Post-doctoral research assistant, Dep. of Mathematics & Computer Science (AG Schütte), Freie Universität Berlin, Germany
2006 – 2009: Doctoral research in mathematics, Prof. Wilhelm Huisinga, DFG-research center MATHEON & Hamilton Institute/National University of Ireland, Maynooth, Ireland
2004 – 2005: Visiting Scientist, AstraZeneca R&D, Sweden
2002 – 2003: Research internship in virology, University of Zürich, Switzerland
2000 – 2005: Undergraduate studies in bioinformatics, Freie Universität Berlin, Germany
FURTHER QUALIFICATIONS:
Teaching and supervision certificate at the Freie Universität Berlin (SUPPORT)
Führungskräfte Training programme at Robert Koch-Institute Berlin (FÜRKI)
