Biology subjects

Meyer-Hermann, M.

Publications and source records attributed to Meyer-Hermann, M..

3 recordsLinked to original sources

bioRxiv2018

Estimation of the cancer risk induced by rejuvenation therapy with young blood and treatment recommendations

In recent years the transfer of blood from young to old individuals was shown to bear the potential of rejuvenation of stem cell activity. While this process might increase life expectancy by prolonging functionality of organs, higher cell replication rates bear also the risk of cancer. The extent of this risk is not known.\n\nWhile it is difficult to evaluate this cancer risk in experiments, this is possible with a mathematical model for tissue homeostasis by stem cell replication and associated cancer risk. The model suggests that young blood treatments can induce a substantial delay of organ failure with only minor increase in cancer risk. The benefit of rejuvenation therapy as well as the impact on cancer risk depend on the biological age at the time of treatment and on the overall cell turnover rate of the organs. Different organs have to be considered separately in the planning of the systemic treatment. In particular, the model predicts that the treatment schedules successfully applied in mice are not directly transferable to humans and guidelines for successful protocols are proposed. The model presented here may be used as a guidance for the development of treatment protocols.\n\nAdditional informationThere is NO competing interests.

systems biology↗

bioRxiv2018

MoonFit, a minimal interface for fitting ODE dynamical models, bridging simulation by experimentalists and customization by C++ programmers

The modelling of biological systems often consists into differential equation models that need to be fitted to experimental data. During this complex process, the practical experience of the biologist and the theoretical abstraction of the modeller require back-and-forth refinements of the model, design of new experiments and inclusion of more data-points into the fitting procedure. Available optimization interfaces rarely simultaneously allow customizations by the programmer and the capacity for the biologist to perform simulations or optimizations with a simple interface.\n\nHere, we provide the C++ code of a graphical user interface based on a user defined minimal C++ ODE model class. The graphical interface allows to perform simulations and optimizations without any knowledge in programming. The code was designed minimal and modular to be easily modified, with maximal freedom to link customized optimization libraries, solver or hand-made scripts. Moonfit is powerful enough to fit and compare models with high dimensionality, multiple datasets, to automatize optimizations, and to perform iterative fittings using data interpolation. We believe this will ease the interaction between modellers and experimental partners.\n\nAvailability: Moonfit is freely available via c++ source code and accompanying scripts from gitlab.com/Moonfit/MoonLight.

systems biology↗

bioRxiv2017

Quantitative characterization of CTLA4 trafficking and turnover using a combined in vitro and in silico approach

CTLA4 is an essential negative regulator of T cell immune responses and is a key checkpoint regulating autoimmunity and anti-tumour immunity. Genetic mutations resulting in a quantitative defect in CTLA4 are associated with the development of an immune dysregulation syndrome. Endocytosis of CTLA4 is rapid and continuous with subsequent degradation or recycling. CTLA4 has two natural ligands, the surface transmembrane proteins CD80 and CD86 that are shared with the T cell co-stimulatory receptor CD28. Upon ligation with CD80/CD86, CTLA4 can remove these ligands from the opposing cells by transendocytosis. The efficiency of ligand removal is thought to be highly dependent on the processes involved in CTLA4 trafficking. With a combined in vitro-in silico study, we quantify the rates of CTLA4 internalization, recycling and degradation. We incorporate experimental data from cell lines and primary human T cells. Our model provides a framework for exploring the impact of altered affinity of natural ligands or therapeutic anti-CTLA4 antibodies and for predicting the effect of clinically relevant CTLA4 pathway mutations. The presented methodology for extracting trafficking rates can be transferred to the study of other transmembrane proteins.

immunology↗