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Mathematical models of organoid cultures

Research output: Contribution to journalReview article

Original languageEnglish
Article number873
Number of pages10
JournalFrontiers in Genetics
Volume10
DOIs
DateAccepted/In press - 20 Aug 2019
DatePublished (current) - 19 Sep 2019

Abstract

Organoids are engineered three-dimensional tissue cultures derived from stem cells and capable of self-renewal and self-organization into a variety of progenitors and differentiated cell types. An organoid resembles the cellular structure of an organ and retains some of its functionality, while still being amenable to in vitro experimental study. Compared with two-dimensional cultures, the three-dimensional structure of organoids provides a more realistic environment and structural organization of in vivo organs. Similarly, organoids are better suited to reproduce signalling pathway dynamics in vitro, due to a more realistic physiological environment. As such, organoids are a valuable tool to explore the dynamics of organogenesis, and offer routes to personalized preclinical trials of cancer progression, invasion, and drug response.
Complementary to experiments, mathematical and computational models are valuable instruments in the description of spatiotemporal dynamics of organoids. Simulations of mathematical models allow the study of multiscale dynamics of organoids, at both the intra- and inter-cellular levels. Mathematical models also enable to understand the underlying mechanisms responsible for phenotypic variation and the response to external stimulation in a cost- and time-effective manner.
Many recent studies have developed laboratory protocols to grow organoids resembling different organs such as intestine, brain, liver, pancreas, and mammary glands. However, the development of mathematical models specific to organoids remains comparatively underdeveloped. Here, we review the mathematical and computational approaches proposed so far to describe and predict organoid dynamics, reporting the simulation frameworks used and the models’ strengths and limitations.

    Research areas

  • organoids, mathematical modelling, agent-based models, 3D tissue, differential equations, Computational modelling

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    Rights statement: This is the final published version of the article (version of record). It first appeared online via Frontiers at https://doi.org/10.3389/fgene.2019.00873 . Please refer to any applicable terms of use of the publisher.

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    Licence: CC BY

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