https://hdl.handle.net/20.500.11811/615 2026-05-27T21:23:53Z https://hdl.handle.net/20.500.11811/14145 A stochastic modelling framework for cancer patient trajectories Wieland, Vincent; Hasenauer, Jan Cancer is a major burden of disease around the globe and one of the leading causes of premature death. The key to improve patient outcomes in modern clinical cancer research is to gain insights into dynamics underlying cancer evolution in order to facilitate the search for effective therapies. However, most cancer data analysis tools are designed for controlled trials and cannot leverage routine clinical data, which are available in far greater quantities. In addition, many cancer models focus on single disease processes in isolation, disregarding interaction. This work proposes a unified stochastic modelling framework for cancer progression that combines (stochastic) processes for tumour growth, metastatic seeding, and patient survival to provide a comprehensive understanding of cancer progression. In addition, our models aim to use non-equidistantly sampled data collected in clinical routine to analyse the whole patient trajectory over the course of the disease. The model formulation features closedform expressions of the likelihood functions for parameter inference from clinical data. The efficacy of our model approach is demonstrated through a simulation study involving four exemplary models, which utilise both analytic and numerical likelihoods. The results of the simulation studies demonstrate the accuracy and computational efficiency of the analytic likelihood formulations. We found that estimation can retrieve the correct model parameters and reveal the underlying data dynamics, and that this modelling framework is flexible in choosing the precise parameterisation. This work can serve as a foundation for the development of combined stochastic models for guiding personalized therapies in oncology. 2025-05-22T00:00:00Z https://hdl.handle.net/20.500.11811/14144 Assessment of uncertainty quantification in universal differential equations Schmid, Nina; Fernandes del Pozo, David; Waegeman, Willem; Hasenauer, Jan Scientific machine learning is a new class of approaches that integrate physical knowledge and mechanistic models with data-driven techniques to uncover the governing equations of complex processes. Among the available approaches, universal differential equations (UDEs) combine prior knowledge in the form of mechanistic formulations with universal function approximators, such as neural networks. Integral to the efficacy of UDEs is the joint estimation of parameters for both the mechanistic formulations and the universal function approximators using empirical data. However, the robustness and applicability of these resultant models hinge upon the rigorous quantification of uncertainties associated with their parameters and predictive capabilities. In this work, we provide a formalization of uncertainty quantification (UQ) for UDEs and investigate key frequentist and Bayesian methods. By analyzing three synthetic examples of varying complexity, we evaluate the validity and efficiency of ensembles, variational inference and Markov-chain Monte Carlo sampling as epistemic UQ methods for UDEs. 2025-04-02T00:00:00Z https://hdl.handle.net/20.500.11811/14143 Next-generation discovery: empowering organoid research with machine learning, artificial intelligence, and mathematical modeling Pushpa Ramesan, Sneha; Boovadira Poonacha, Jasmitha; Pathirana, Dilan; Merkt, Simon; Maass, Christian; Hasenauer, Jan; Reckzeh, Elena S. Organoids have rapidly matured into powerful model systems. The field is pushing organoids toward architectural sophistication and functional fidelity, with longitudinal experiments producing ever-larger and more complex datasets. As a result, computational methods have become indispensable for experimental design, data analysis, and predictive modeling, as well as for obtaining mechanistic insights. In this review, we survey recent progress at the interface of organoid research and computational approaches, discuss key challenges on both fronts, and outline future directions to maximize impact in biomedical research through convergent, synergistic efforts. 2026-03-13T00:00:00Z https://hdl.handle.net/20.500.11811/14142 Parameter estimation and model selection for the quantitative analysis of oncolytic virus therapy in zebrafish Liu, Yuhong; Pathirana, Dilan; Hasenauer, Jan Oncolytic virus therapy (OVT) is emerging as a potent alternative to conventional cancer treatments by employing engineered viruses that selectively infect and lyse tumor cells while sparing normal tissues. Although mathematical models have been developed to elucidate the dynamics of OVT and inform personalized therapies, they are often specific to certain organisms. Mathematical models tailored to more recently developed animal models of OVT, such as zebrafish, are not yet available. Here, we introduce the first mathematical model of OVT trained on zebrafish data from published studies to bridge the gap. We explore a variety of mathematical model structures and perform parameter estimation and model selection. The selected model Effectively captures the observed tumor dynamics, i.e. delayed tumor shrinkage, and provides valuable insights into the underlying mechanisms of OVT in zebrafish. Our work establishes the groundwork for advancing experimental studies in zebrafish, contributing to the design of more Effective cancer treatment strategies in the future. 2025-08-13T00:00:00Z