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

## TL;DR

This paper reviews computational models of organoids to understand their growth and function, highlighting how simulations support experimental research.

## Contribution

A systematic review of in silico organoid models at single-cell or subcellular resolution, identifying trends and insights from 32 studies.

## Key findings

- Computational models help explain organoid behaviors like airway rotation and kidney nephron formation.
- Combined in silico and in vitro approaches enhance understanding of organoid development.
- Future models may benefit from machine learning and detailed stem cell regulatory insights.

## Abstract

Organoids are self-organizing multicellular structures generated in vitro that recapitulate the micro-architecture and function of an organ. They are commonly derived from stem cells but can also emerge from pieces of proliferative tissues. Organoid technology has opened novel ways to model development and disease, but it is not without challenges. Computational models of organoids have been established to elucidate organoid growth and facilitate the optimization of organoid cultures. This article is a systematic review of in silico organoid models constructed at single-cell or subcellular resolution. PubMed, Scopus, and Web of Science were searched for original papers published in peer-reviewed journals before 26 September 2025, yielding 439 records after deduplication. Two independent reviewers screened their titles and abstracts, retrieved 84 papers for full-text scrutiny, and identified 32 papers that met the inclusion criteria. They were grouped by organoid type: 12 intestinal, 1 airway, 2 pancreas, 3 neural, 1 kidney, 1 inner cell mass, 9 tumor, and 3 generic. The analysis of these works revealed that computer simulations guided experimental work. Parsimonious computational models provided insights into diverse organoid behaviors, such as the rotation of airway organoids, size oscillations of pancreatic organoids, epithelial patterning of neural tube organoids, or nephron segment formation in kidney organoids. Generally, a deep understanding was achieved through combined in silico and in vitro investigations (e.g., optic cup morphogenesis). Recent research trends suggest that next-generation computational models of organoids may emerge from a more detailed understanding of the complex regulatory circuits that govern stem cell fate, and machine-learning-based, high-throughput imaging of organoids.