Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Desoyer, C; Loibner, D; Brislinger, D; Baumgartner, C.
Computational frameworks for modelling cancer across scales
CLIN TRANSL DISCOV. 2025; 5(6): e70093
Doi: 10.1002/ctd2.70093
Web of Science
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- Co-authors Med Uni Graz
- Brislinger Dagmar
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- Abstract:
- Purpose: Cancer progression is a non-linear, multiscale process driven by the interaction of molecular, cellular and tissue systems, which ultimately leads to tumour growth, invasion and metastasis. Understanding and predicting these dynamics is essential for improving diagnostics and personalising therapy. Mathematical and computational modelling has become central to this effort, enabling in silico simulations of progression and treatment response.Highlights Provides an integrative overview of computational frameworks for modelling cancer across biological scales. Illustrates the characteristics, advantages, and limitations of ODE/PDE, agent-based, hybrid multiscale, and integrative translational models. Highlights how these paradigms collectively bridge mechanistic understanding with clinical translation. Presents a conceptual roadmap linking modelling methodologies to precision oncology applications.
- Find related publications in this database (Keywords)
- agent-based models
- cancer
- computational oncology
- digital twins
- hybrid models
- machine learning
- mathematical modelling
- mechanistic learning
- multiscale modelling
- precision medicine