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In Vitro Brain Organoids and Computational Models to Study Cell Death in Brain Diseases.
- Source :
-
Methods in molecular biology (Clifton, N.J.) [Methods Mol Biol] 2022; Vol. 2515, pp. 281-296. - Publication Year :
- 2022
-
Abstract
- Understanding the mechanisms underlying the formation and progression of brain diseases is challenging due to the vast variety of involved genetic/epigenetic factors and the complexity of the environment of the brain. Current preclinical monolayer culture systems fail to faithfully recapitulate the in vivo complexities of the brain. Organoids are three-dimensional (3D) culture systems that mimic much of the complexities of the brain including cell-cell and cell-matrix interactions. Complemented with a theoretical framework to model the dynamic interactions between different components of the brain, organoids can be used as a potential tool for studying disease progression, transport of therapeutic agents in tissues, drug screening, and toxicity analysis. In this chapter, we first report on the fabrication and use of a novel self-filling microwell arrays (SFMWs) platform that is self-filling and enables the formation of organoids with uniform size distributions. Next, we will introduce a mathematical framework that predicts the organoid growth, cell death, and the therapeutic responses of the organoids to different therapeutic agents. Through systematic investigations, the computational model can identify shortcomings of in vitro assays and reduce the time and effort required to improve preclinical tumor models' design. Lastly, the mathematical model provides new testable hypotheses and encourages mathematically driven experiments.<br /> (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)
- Subjects :
- Brain
Cell Death
Computer Simulation
Humans
Brain Diseases metabolism
Organoids
Subjects
Details
- Language :
- English
- ISSN :
- 1940-6029
- Volume :
- 2515
- Database :
- MEDLINE
- Journal :
- Methods in molecular biology (Clifton, N.J.)
- Publication Type :
- Academic Journal
- Accession number :
- 35776358
- Full Text :
- https://doi.org/10.1007/978-1-0716-2409-8_17