Cell-Based Computational Models of Organoids: A Systematic Review.

Area of Science:

• Biotechnology
• Computational Biology
• Developmental Biology

Background:

• Organoids are in vitro self-organizing multicellular structures mimicking organ architecture and function.
• Organoid technology enables novel modeling of development and disease, but faces challenges.
• Computational models are crucial for understanding organoid growth and optimizing cultures.

Purpose of the Study:

• To systematically review in silico organoid models at single-cell or subcellular resolution.
• To analyze the insights gained from computational organoid modeling.
• To identify trends in next-generation computational organoid models.

Main Methods:

• Systematic literature search of PubMed, Scopus, and Web of Science.
• Screening of 439 records, with 32 papers meeting inclusion criteria.
• Categorization of identified models by organoid type (intestinal, airway, pancreas, neural, kidney, tumor, etc.).

Main Results:

• Computer simulations effectively guided experimental organoid research.
• Parsimonious models elucidated diverse organoid behaviors (e.g., airway rotation, pancreatic oscillations, neural patterning, kidney nephrogenesis).
• Combined in silico and in vitro investigations achieved deep understanding of organoid morphogenesis.

Conclusions:

• Computational organoid models are valuable tools for experimental guidance and biological insight.
• Future models will likely integrate detailed stem cell regulatory circuits and machine learning with high-throughput imaging.
• In silico approaches significantly advance the field of organoid research.