European Conference on Mathematical and Theoretical Biology (ECMTB 2026)

Large-Scale Mechanistic Modeling for Patient-Specific Drug Recommendation and Organoid Testing in Colorectal Cancer

14 Jul 2026, 18:30

2h

University of Graz

Poster Mathematical Oncology Poster Presentations

Speaker

Moritz Richter (Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany, Bonn Center for Mathematical Life Sciences, University of Bonn, Bonn, Germany)

Description

Colorectal cancer (CRC) remains a major cause of cancer morbidity and mortality, increasing mostly in adults younger than 50. Precision therapy and drug repurposing approaches may improve outcomes for patients who are ineligible for or do not respond to standard targeted therapy. We describe a computational framework that constructs individualized mechanistic “digital twin” models from patient transcriptional profiles. Proposed in silico drug combinations are then evaluated in patient-derived organoids (PDOs).

To this end, biopsy tissue from CRC patients is used to derive PDO lines and to generate bulk RNA-seq from both original tumor tissue and the established PDOs. Large-scale mechanistic models \cite{frohlich_efficient_2018} of intracellular signaling important for proliferation and survival form the basis of these in silico models, augmented with semi-mechanistic machine-learning components to represent treatment effects not captured by the mechanistic scaffold. Models are trained on transcriptional and cell-viability data from cell lines in CCLE and GDSC. In a second step, these models are individualized by selectively (de-)activating aberrant gene/protein species and parametrizing components using gene expression data. Model construction, parameter estimation and simulation are performed using AMICI, pyPESTO and PEtab \cite{frohlich_amici:_2021}, \cite{schalte_pypesto:_2023}, \cite{schmiester_petabinteroperable_2021}.

Bibliography

@article{frohlich_efficient_2018,

title = {Efficient {Parameter} {Estimation} {Enables} the {Prediction} of {Drug} {Response} {Using} a {Mechanistic} {Pan}-{Cancer} {Pathway} {Model}},

volume = {7},

issn = {2405-4712},

url = {https://www.sciencedirect.com/science/article/pii/S2405471218304381},

doi = {10.1016/j.cels.2018.10.013},

abstract = {Mechanistic models are essential to deepen the understanding of complex diseases at the molecular level. Nowadays, high-throughput molecular and phenotypic characterizations are possible, but the integration of such data with prior knowledge on signaling pathways is limited by the availability of scalable computational methods. Here, we present a computational framework for the parameterization of large-scale mechanistic models and its application to the prediction of drug response of cancer cell lines from exome and transcriptome sequencing data. This framework is over 104 times faster than state-of-the-art methods, which enables modeling at previously infeasible scales. By applying the framework to a model describing major cancer-associated pathways ({\textgreater}1,200 species and {\textgreater}2,600 reactions), we could predict the effect of drug combinations from single drug data. This is the first integration of high-throughput datasets using large-scale mechanistic models. We anticipate this to be the starting point for development of more comprehensive models allowing a deeper mechanistic insight.},

number = {6},

urldate = {2026-03-15},

journal = {Cell Systems},

author = {Fröhlich, Fabian and Kessler, Thomas and Weindl, Daniel and Shadrin, Alexey and Schmiester, Leonard and Hache, Hendrik and Muradyan, Artur and Schütte, Moritz and Lim, Ji-Hyun and Heinig, Matthias and Theis, Fabian J. and Lehrach, Hans and Wierling, Christoph and Lange, Bodo and Hasenauer, Jan},

month = dec,

year = {2018},

keywords = {systems biology, mechanistic modeling, cancer signaling, sequencing data, drug response, drug synergy, biomarker, parameter estimation},

pages = {567--579.e6},

}

@article{schalte_pypesto:_2023,

title = {{pyPESTO}: a modular and scalable tool for parameter estimation for dynamic models},

volume = {39},

copyright = {https://creativecommons.org/licenses/by/4.0/},

issn = {1367-4803, 1367-4811},

shorttitle = {{pyPESTO}},

url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btad711/7443974},

doi = {10.1093/bioinformatics/btad711},

abstract = {Abstract



          Summary

          Mechanistic models are important tools to describe and understand biological processes. However, they typically rely on unknown parameters, the estimation of which can be challenging for large and complex systems. pyPESTO is a modular framework for systematic parameter estimation, with scalable algorithms for optimization and uncertainty quantification. While tailored to ordinary differential equation problems, pyPESTO is broadly applicable to black-box parameter estimation problems. Besides own implementations, it provides a unified interface to various popular simulation and inference methods.





          Availability and implementation

          pyPESTO is implemented in Python, open-source under a 3-Clause BSD license. Code and documentation are available on GitHub (https://github.com/icb-dcm/pypesto).},

language = {en},

number = {11},

urldate = {2026-03-15},

journal = {Bioinformatics},

author = {Schälte, Yannik and Fröhlich, Fabian and Jost, Paul J and Vanhoefer, Jakob and Pathirana, Dilan and Stapor, Paul and Lakrisenko, Polina and Wang, Dantong and Raimúndez, Elba and Merkt, Simon and Schmiester, Leonard and Städter, Philipp and Grein, Stephan and Dudkin, Erika and Doresic, Domagoj and Weindl, Daniel and Hasenauer, Jan},

editor = {Mathelier, Anthony},

month = nov,

year = {2023},

pages = {btad711},

}

@article{frohlich_amici:_2021,

title = {{AMICI}: high-performance sensitivity analysis for large ordinary differential equation models},

volume = {37},

copyright = {https://creativecommons.org/licenses/by/4.0/},

issn = {1367-4803, 1367-4811},

shorttitle = {{AMICI}},

url = {https://academic.oup.com/bioinformatics/article/37/20/3676/6209017},

doi = {10.1093/bioinformatics/btab227},

abstract = {Abstract



          Summary

          Ordinary differential equation models facilitate the understanding of cellular signal transduction and other biological processes. However, for large and comprehensive models, the computational cost of simulating or calibrating can be limiting. AMICI is a modular toolbox implemented in C++/Python/MATLAB that provides efficient simulation and sensitivity analysis routines tailored for scalable, gradient-based parameter estimation and uncertainty quantification.





          Availabilityand implementation

          AMICI is published under the permissive BSD-3-Clause license with source code publicly available on https://github.com/AMICI-dev/AMICI. Citeable releases are archived on Zenodo.





          Supplementary information

          Supplementary data are available at Bioinformatics online.},

language = {en},

number = {20},

urldate = {2026-03-15},

journal = {Bioinformatics},

author = {Fröhlich, Fabian and Weindl, Daniel and Schälte, Yannik and Pathirana, Dilan and Paszkowski, Łukasz and Lines, Glenn Terje and Stapor, Paul and Hasenauer, Jan},

editor = {Mathelier, Anthony},

month = oct,

year = {2021},

pages = {3676--3677},

}

@article{schmiester_petabinteroperable_2021,

title = {{PEtab}—{Interoperable} specification of parameter estimation problems in systems biology},

volume = {17},

issn = {1553-7358},

url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1008646},

doi = {10.1371/journal.pcbi.1008646},

abstract = {Reproducibility and reusability of the results of data-based modeling studies are essential. Yet, there has been—so far—no broadly supported format for the specification of parameter estimation problems in systems biology. Here, we introduce PEtab, a format which facilitates the specification of parameter estimation problems using Systems Biology Markup Language (SBML) models and a set of tab-separated value files describing the observation model and experimental data as well as parameters to be estimated. We already implemented PEtab support into eight well-established model simulation and parameter estimation toolboxes with hundreds of users in total. We provide a Python library for validation and modification of a PEtab problem and currently 20 example parameter estimation problems based on recent studies.},

language = {en},

number = {1},

urldate = {2026-03-15},

journal = {PLOS Computational Biology},

author = {Schmiester, Leonard and Schälte, Yannik and Bergmann, Frank T. and Camba, Tacio and Dudkin, Erika and Egert, Janine and Fröhlich, Fabian and Fuhrmann, Lara and Hauber, Adrian L. and Kemmer, Svenja and Lakrisenko, Polina and Loos, Carolin and Merkt, Simon and Müller, Wolfgang and Pathirana, Dilan and Raimúndez, Elba and Refisch, Lukas and Rosenblatt, Marcus and Stapor, Paul L. and Städter, Philipp and Wang, Dantong and Wieland, Franz-Georg and Banga, Julio R. and Timmer, Jens and Villaverde, Alejandro F. and Sahle, Sven and Kreutz, Clemens and Hasenauer, Jan and Weindl, Daniel},

month = jan,

year = {2021},

keywords = {Systems biology, Algorithms, Simulation and modeling, Language, Optimization, Software tools, Libraries, System stability},

pages = {e1008646},

}