Modelling conversion of adult skin cells to neurons
Abstract: Scientists are now able to directly convert one somatic cell type into another using a procedure known as direct lineage reprogramming or transdifferentiation. In this procedure, transcription factors which are important for initiating a rewriting of the gene expressions are introduced in the cell. One specific type of reprogramming involves generating dopamine producing neurons from human adult fibroblast skin cells. The transdifferentiation procedure in human cells has proven challenging. So far, conversion schemes are not able to generate satisfactory levels of mature neurons. However, experimental efforts are made to overcome this. Succeeding in generating a high yield conversion scheme would open up new pathways for medical treatments and disease modeling of diseases such as Parkinson's disease. In this thesis, we study a model built in silico for a gene circuit proven to be important in the transdifferentiation from human adult fibroblast cells to neurons. Using experimental time series of gene expression obtained from a recently found high-yield neural conversion scheme, the model is capable to capture the experimental data dynamics. The system exhibits at least two attractors: one representing a neuronal state, and the other a non-neuronal state. A stochastic simulation was conducted for identifying strategies leading to high-yield neural conversion. The aim of the model presented here is to improve our understanding of the underlying dynamics, which may lead to a high yield neural conversion scheme applicable in vitro and in vivo.
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