Methodology for the calibration and analysis of stochastic models for heterogeneous intracellular processes with applications in cancer development

PN 2-9

Project description

This project aims to develop methodology for the calibration of stochastic modeling approaches for intracellular processes to single cell and bulk data, with applications to cancer development. While deterministic modeling approaches for cellular processes are a standard approach in systems biology, and many tools are available for parameter estimation and analysis beyond, the integration of data into stochastic modeling approaches is still challenging. Studying sources of heterogeneity in cell populations, which are key to understand emerging heterogeneity on higher scales, is highly facilitated by a rapid development of (time resolved) single cell measurement techniques. In our previous project PN 2-1 B, we have worked on various stochastic modeling approaches for biochemical reaction networks, and we will extend these and develop methodology towards an upscaling to larger systems. We will focus on data integration, model evaluation and detection of model discrepancies, which ultimately allows to study heterogeneity in signaling pathways and deregulations in cancer cells, a prerequisite to understand patient-specific responses to treatment strategies.

Project information

Project title Methodology for the calibration and analysis of stochastic models for heterogeneous intracellular processes with applications in cancer development
Project leaders Nicole Radde (Markus Morrison, Anneli Guthke)
Project staff Vincent Wagner, doctoral researcher
Project duration February 2023 – December 2025
Project number PN 2-9
  • Preceding project 2-1 B
    Data-integrated modeling to provide novel solutions for individualizing cancer therapy and predicting treatment success

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. 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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