Single-cell RNA-seq of frequent and rare populations in mouse bone marrow
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE122465 | GEO | 2019/08/08
REPOSITORIES: GEO
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Project description:Schittler2010 - Cell fate of progenitor cells, osteoblasts or chondrocytes
Mathematical model describing the mechanism of differentiation of mesenchymal stem cells to bone (osteoblasts) or cartilage (chondrocytes) cells.
This model is described in the article:
Cell differentiation modeled via a coupled two-switch regulatory network.
Schittler D, Hasenauer J, Allgöwer F, Waldherr S.
Chaos 2010 Dec; 20(4): 045121
Abstract:
Mesenchymal stem cells can give rise to bone and other tissue cells, but their differentiation still escapes full control. In this paper we address this issue by mathematical modeling. We present a model for a genetic switch determining the cell fate of progenitor cells which can differentiate into osteoblasts (bone cells) or chondrocytes (cartilage cells). The model consists of two switch mechanisms and reproduces the experimentally observed three stable equilibrium states: a progenitor, an osteogenic, and a chondrogenic state. Conventionally, the loss of an intermediate (progenitor) state and the entailed attraction to one of two opposite (differentiated) states is modeled as a result of changing parameters. In our model in contrast, we achieve this by distributing the differentiation process to two functional switch parts acting in concert: one triggering differentiation and the other determining cell fate. Via stability and bifurcation analysis, we investigate the effects of biochemical stimuli associated with different system inputs. We employ our model to generate differentiation scenarios on the single cell as well as on the cell population level. The single cell scenarios allow to reconstruct the switching upon extrinsic signals, whereas the cell population scenarios provide a framework to identify the impact of intrinsic properties and the limiting factors for successful differentiation.
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2024-09-02 | BIOMD0000000493 | BioModels
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