Computational systems medicine and disease control
Dr. Max von Kleist
Our group develops methods for computational systems medicine and computational systems biology and applies it to questions arising in infectious disease research and developmental processes.
We welcome students with a bioinformatics and/or a mathematical biology/data science background who are excited about developing computational methods in one of our three main research areas.
We are interested in modelling the efficacy of pharmaceutical and non-pharmaceutical interventions in preventing viral infection.
Quantifying the clinical efficacy of preventive therapies (vaccine, prophylaxis, non-pharmaceutic interventions) 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). Novel compounds have to be compared against the approved vaccine or prophylaxis, worsening the statistical challenge. 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 pharmacology 1,2. This work is done in collaboration with clinical- and epidemiological partners at RKI, Johns Hopkins, Harvard, the Imperial College, as well as the Gates Foundation & WHO.
We are interested in deciphering kinetic mechanisms that drive brain wiring during development.
What are the mechanisms that determine that particular types of neurons make synaptic connections? Recent work indicates that there is no `key-and-lock mechanism’ that decides which particular types of neurons make synaptic connections. Rather, synapses can in principle be made between any types of neurons, but their colocalization in time and space alters their propensity to do so. To study the underlying mechanisms, we are developing stochastic modelling and model-inference for time-lapse super-resolution microscopy data derived from developing brains generated by the Hiesinger Lab (FU Berlin)3,4
RNA Genotype-Phenotype Mapping:
We are interested in high-throughput methods for inferring the functional contribution of nucleotides in non-coding RNA.
It is now established, that non-coding RNA (ncRNA) regulates virtually every cellular process. In viruses, due to their compact genomes, ncRNA is thought to be play a major role in their replication cycle. We previously developed methods to evaluate mutational interference mapping experiments (MIME)5,6. Using these methods, the functional contribution of every nucleotide in an ncRNA can be studied from a single experiment. These methods are now extended with chemical probing and single molecule sequencing on nanopores by our collaboration partner in the Smyth Lab (HIRI Würzburg) to create rich data sets containing structural and functional information.7 To utilize these data, we are developing computational methods that allow deconvolving ncRNA structural ensembles and to associate structures with functions of interest.8
1An intra-host SARS-CoV-2 dynamics model to assess testing and quarantine strategies for incoming travelers, contact person management and de-isolation. W van der Toorn, ..., Max von Kleist, Patterns (Cell press) 2, 100262, 2021 https://doi.org/10.1016/j.patter.2021.100262
2Hybrid 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
3 Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes. L. Zhang, J. Wang, M. von Kleist, biRXiV, 2021 (preprint)
4Serial synapse formation through filopodial competition for synaptic seeding factors. M.N. Ozel, A. Kulkarni, A. Hasan, J. Brummer, M. Moldenhauer, I.-M. Daumann, H. Wolfenberg, V. Dercksen, F.R. Kiral, M. Weiser, S. Prohaska, M. von Kleist*, P.R. Hiesinger*, Developmental Cell 50, 447-61, 2019
5Kinetic restriction of synaptic partner choice through filopodial autophagy. F. R. Kiral, G. A. Linneweber, S. V. Georgiev, B. A. Hassan, M. von Kleist and P. R. Hiesinger, Nature Communications 11, 1325, 2020
6Mutational 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
7In 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
8 Short and long-range interactions in the HIV-1 5'UTR regulate genome dimerization and Pr55Gag binding, L. Ye, ..., M. von Kleist, R.P. Smyth, submitted 2021
For more information visit the website of the Systems Pharmacology group.