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Project Group 5: Systems medicine of infectious diseases

Head:
Max von Kleist
Deputy:
Maureen Smith

Tasks

"Systems medicine of infectious diseases" aims to better understand infection and transmission mechanisms, as well as evolutionary dynamics, and to describe them using methods from the field of data science and mathematical modeling. This serves both to create basic knowledge and to better evaluate prevention-, treatment- and surveillance strategies. Our work ranges from the mathematical formulation of the problem, the development or adaptation of algorithms, the evaluation on biomedical data to the implementation and publication of open source software.

Infection events take place at different levels - from molecules to cells, to the organism and the social environment through which a pathogen is spreading. In addition, the mix is constantly changing. For example, the pathogen evolves through evolution, or the social environment in which the pathogen spreads is affected by changes in contact behavior.

The project group aims to capture these complex influences and their interactions using mathematical methods, and ultimately to derive concrete recommendations for action. This includes both "bottom-up" and "top-down" approaches. An example of a bottom-up approach is a model based on knowledge of the dynamics of an infection process to make predictions about the efficiency of a drug in infection prevention. An example of a “top-down” approach is the estimation of infection incidence based on the rate at which the viral genome changes at the population level.

In both approaches, molecular biological "omics" data, as well as clinical and epidemiological parameters are taken into account and linked with each other. The project group "Systems Medicine of Infectious Diseases" therefore acts as a bridge between molecular & epidemiological research, bioinformatics, data science & artificial intelligence. The project group complements existing competencies at the RKI and interlinks them even more closely to ensure that the best possible protection from infections will be achieved.

Methods:

  • Mathematical modeling & simulation.
  • Biomedical data science, statistics, optimization & artificial intelligence.
  • Method development in above mentioned areas.
  • Software development

Figure presents the interaction of evolutionary dynamics of a pathogen in an individual with the  treatment of the infection. Explanation of the figure is given in the next paragraph. Source: RKI Figure presents the interaction of evolutionary dynamics of a pathogen in an individual with the treatment of the infection. Explanation of the figure is given in the next paragraph. Source: RKI

Top left: Phenotypes (=properties) of different pathogen variants depend on the drug treatment. In the example, when treated with treatment 1, certain variants (black dots) can continue to multiply sufficiently to maintain the pathogen population (they are resistant). All other variants disappear over time. When treated with treatment 2, it is other variants that can prevail (red dots). Top right: Due to evolutionary distance, it is not clear if a resistant variant actually emerges before the pathogen population collapses. Bottom left: Population dynamics of the pathogen in the infected individual after the start of a treatment. In this case, a resistant variant emerges by chance and becomes established from ≈ day 120. Bottom right: Skillful choice of therapy keeps the pathogen population small and the resistant variant cannot establish itself, because intermediary variants become eliminated before the resistant variant emerges.

Open positions

Open positions are available via the job advertisements of the Robert Koch Institute.

For internship, bachelor, master or diploma thesis projects, we are always looking for dedicated and highly motivated students. For these and other inquiries, please contact the group leader directly.

Software

The integration of the developed methods into freely available software is an important goal of our group. Software is linked on
http://systems-pharmacology.de/softwares.

Publications

Further down this page, selected publications are listed. A comprehensive list of recent publications is available at
http://www.disease-control.org/publications/

Date: 07.02.2022

Publications

  • Smyth R, Ye L, Gribling AS, Bohn P, Kibe A, Börtlein Ch, Uddav A, Shazeb A, Olguin-Nava M, Smith M, Caliskan N, von Kleist M (2021): Short and long-range interactions in the HIV-1 5’UTR regulate genome dimerization and Pr55 Gag binding. Article. Posted Date: December 10th, 2021.
    Nature Molecular and Structural Biology (accepted, preprint): 2-48. more

  • Smith MR, Trofimova M, Weber A, Duport Y, Kühnert D, von Kleist M (2021): Rapid incidence estimation from SARS-CoV-2 genomes reveals decreased case detection in Europe during summer 2020.
    Nat. Commun. 12 (1): 6009. Epub Oct 14. doi: 10.1038/s41467-021-26267-y. more

  • Zhang L, Wang J, von Kleist M (2021): Numerical approaches for the rapid analysis of prophylactic efficacy against HIV with arbitrary drug-dosing schemes.
    PLoS Comput. Biol. 17 (12): e1009295. Epub Dec 23. doi: 10.1371/journal.pcbi.1009295. more

  • van der Toorn W, Oh DY, Bourquain D, Michel J, Krause E, Nitsche A, von Kleist M; working group on SARS-CoV-2 Diagnostics at RKI (Beermann S, Böttcher S, Dorner BG, Dürrwald R, von Kleist M, Kleymann-Hilmes J, Kröger S, Mielke M, Nitsche A, Oh DY, Seifried J, Voigt S, Wolff T) (2021): An intra-host SARS-CoV-2 dynamics model to assess testing and quarantine strategies for incoming travelers, contact person management and de-isolation.
    Patterns (NY) 2 (6): 100262. Epub Apr 20. doi: 10.1016/j.patter.2021.100262. more

  • Duwal S, Dickinson L, Khoo S, von Kleist M (2019): Mechanistic framework predicts drug-class specific utility of antiretrovirals for HIV prophylaxis.
    PLoS Comput. Biol. 15 (1): e1006740. Epub Jan 30. doi: 10.1371/journal.pcbi.1006740. more

  • Smyth RP, Smith MR, Jousset AC, Despons L, Laumond G, Decoville Th, Cattenoz P, Moog Ch, Jossinet F, Mougel M, Paillart J-Ch, von Kleist M, Marquet R (2018): In cell mutational interference mapping experiment (in cell MIME) identifies the 5′ polyadenylation signal as a dual regulator of HIV-1 genomic RNA production and packaging.
    Nucleic Acids Res. 46 (9, 18 May 2018): e57, doi: 10.1093/nar/gky152. more

  • Yousef KP, Meixenberger K, Smith MR, Somogyi S, Gromöller S, Schmidt D, Gunsenheimer-Bartmeyer B, Hamouda O, Kücherer C, von Kleist M (2016): Inferring HIV-1 transmission dynamics in Germany from recently transmitted viruses
    J. Acquir. Immune Defic. Syndr. 73 (3): 356-363. Epub Jul 5. doi: 10.1097/QAI.0000000000001122. more

  • Smyth RP, Despons L, Huili G, Bernacchi S, Hijnen M, Mak J, Jossinet F, Weixi L, Paillart J-Ch, von Kleist M, Marquet R (2015): Mutational interference mapping experiment (MIME) for studying RNA structure and function.
    Nat Methods Sep; 12 (9): 866-72. doi: 10.1038/nmeth.3490. Epub 2015 Aug 3. more