Data-integrated modeling to provide novel solutions for individualizing cancer therapy and predicting treatment success

PN 2-1 B

Project description

In PN 2-1, we develop novel data-integrated solutions to address the gap between the molecular understanding of network-coded death decisions in cancer cells and their higher scale consequences on patient outcome. The overall goal is the development of novel systems medicine tools that can be used to predict treatment success and to optimize treatment strategies for heterogeneous cancer types. PN 2-1B will focus on systems-theoretical methods in order to facilitate the modeling process regarding workflows for model calibration to diverse experimental data on different scales, model evaluation and experiment design. Therefore, we will build on our expertise in statistical learning approaches for parameter estimation, allowing for a consistent uncertainty quantification. Various data on different scales ranging from single cell molecular data up to the tissue level and patient specific information will be used jointly with PN 2-1A to ultimately test if clinical patient outcome can be predicted.

Project information

Project title Data-integrated modeling to provide novel solutions for individualizing cancer therapy and predicting treatment success
Project leaders Nicole Radde (Markus Morrison)
Project duration December 2019 – May 2023
Project number PN 2-1 B
  • Follow-up project PN 2-9
    Methodology for the calibration and analysis of stochastic models for heterogeneous intracellular processes with applications in cancer development

Publications PN 2-1 B and PN 2-9

  1. 2023

    1. V. Wagner and N. Radde, “The impossible challenge of estimating non-existent moments of the Chemical Master Equation,” Bioinformatics, vol. 39, no. Supplement_1, Art. no. Supplement_1, Jun. 2023, doi: 10.1093/bioinformatics/btad205.
    2. V. Wagner, R. Strässer, F. Allgöwer, and N. E. Radde, “A provably convergent control closure scheme for the Method of Moments of the Chemical Master Equation,” Journal of Chemical Theory and Computation, vol. 19, no. 24, Art. no. 24, Dec. 2023, doi: https://doi.org/10.1021/acs.jctc.3c00548.

  2. 2022

    1. V. Wagner, S. Höpfl, V. Klingel, M. C. Pop, and N. E. Radde, “An inverse transformation algorithm to infer parameter distributions from population snapshot data,” IFAC-PapersOnLine, vol. 55, no. 23, Art. no. 23, 2022, doi: https://doi.org/10.1016/j.ifacol.2023.01.020.
    2. V. Wagner, B. Castellaz, M. Oesting, and N. Radde, “Quasi-Entropy Closure: A Fast and Reliable Approach to Close the Moment Equations of the Chemical Master Equation,” Bioinformatics, vol. 38, no. 18, Art. no. 18, 2022, doi: 10.1093/bioinformatics/btac501.

  3. 2020

    1. S. Adam et al., “DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation,” Nature Communications, vol. 11, no. 1, Art. no. 1, 2020, doi: 10.1038/s41467-020-17531-8.
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