About this Research Topic
The interface of computational modeling and 3D in vitro models, particularly organoid technology, represents a significant advancement in the field of biological research. Organoids are self-organized, three-dimensional cell-based in vitro models that replicate the key functional, structural, and biological complexities of organs. Despite their simplified nature compared to actual organs, organoids exhibit a high level of complexity that necessitates advanced tools for comprehensive understanding.
Recent studies have demonstrated the potential of computational modeling to elucidate the intricate processes involved in organoid initiation, development, and functionality. However, there remains a gap in integrating these computational tools with experimental research to achieve a more quantitative and predictive understanding of organoid dynamics and physiology. This research topic seeks to address this gap by fostering collaboration between experimental researchers and computational modelers.
This research topic aims to connect experimental researchers and computational modelers to acquire a more quantitative description and prediction of organoid dynamics and physiology. The goal of leveraging various numerical and mathematical approaches is to advance the understanding of organoid technology, from their morphogenesis and development to their functionality. Specific questions to be addressed include how different modeling techniques can simulate organoid behavior and how these models can be validated against experimental data.
To gather further insights into the integration of computational modeling with organoid technology, we welcome articles addressing, but not limited to, the following themes:
- Multiphysics and multiscale modeling of physiological organoids
- Modeling of organoids corresponding to pathologies and their response to treatment
- Novel numerical approaches to simulate organoids
- Image-based algorithms to quantify organoids
- AI-based algorithms to quantify and predict organoid behavior
- Validation of mathematical models in comparison with experiments
Recent studies have demonstrated the potential of computational modeling to elucidate the intricate processes involved in organoid initiation, development, and functionality. However, there remains a gap in integrating these computational tools with experimental research to achieve a more quantitative and predictive understanding of organoid dynamics and physiology. This research topic seeks to address this gap by fostering collaboration between experimental researchers and computational modelers.
This research topic aims to connect experimental researchers and computational modelers to acquire a more quantitative description and prediction of organoid dynamics and physiology. The goal of leveraging various numerical and mathematical approaches is to advance the understanding of organoid technology, from their morphogenesis and development to their functionality. Specific questions to be addressed include how different modeling techniques can simulate organoid behavior and how these models can be validated against experimental data.
To gather further insights into the integration of computational modeling with organoid technology, we welcome articles addressing, but not limited to, the following themes:
- Multiphysics and multiscale modeling of physiological organoids
- Modeling of organoids corresponding to pathologies and their response to treatment
- Novel numerical approaches to simulate organoids
- Image-based algorithms to quantify organoids
- AI-based algorithms to quantify and predict organoid behavior
- Validation of mathematical models in comparison with experiments
Keywords: agent-based models, multicellular systems, computational models, organoids, mathematical modeling, deep-learning algorithms, image-based analysis
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
