Elina Nürnberg ¹ Mario Vitacolonna ¹ Roman Bruch ² Markus Reischl ² Rüdiger Rudolf ¹ Simeon Sauer ^3*

• ¹ Institute of Molecular and Cell Biology, Mannheim University of Applied Sciences, Mannheim, Germany
• ² Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany
• ³ Faculty of Biotechnology, Mannheim University of Applied Sciences, Mannheim, Germany

3D cell culture models replicate tissue complexity and aim to study cellular interactions and responses in a more physiologically relevant environment compared to traditional 2D cultures. However, the spherical structure of these models makes it difficult to extract meaningful data, necessitating advanced techniques for proper analysis. In silico simulations enhance research by predicting cellular behaviors and therapeutic responses, providing a powerful tool to complement experimental approaches. Despite their potential, these simulations often require advanced computational skills and significant resources, which creates a barrier for many researchers. To address these challenges, we developed an accessible pipeline using open-source software to facilitate virtual tissue simulations. Our approach employs the Cellular Potts Model, a versatile framework for simulating cellular behaviors in tissues. The simulations are constructed from real world 3D image stacks of cancer spheroids, ensuring that the virtual models are rooted in experimental data. By introducing a new metric for parameter optimization, we enable the creation of realistic simulations without requiring extensive computational expertise. This pipeline benefits researchers wanting to incorporate computational biology into their methods, even if they do not possess extensive expertise in this area. By reducing the technical barriers associated with advanced computational modeling, our pipeline enables more researchers to utilize these powerful tools. Our approach aims to foster a broader use of in silico methods in disease research, contributing to a deeper understanding of disease biology and the refinement of therapeutic interventions.