One of the key processes during embryo development is the differentiation of progenitor cells into fully developed neurons. Recent advances in single cell RNA and ATAC sequencing enable us to study this process in much finer detail than what was previously possible [1]. In particular, this opens possibilities for the construction of computational models that capture this behavior.

The goal of this thesis is to study the currently available methods for the inference of gene regulatory networks and Boolean networks, such as [2] and [3]. The student should then develop a workflow that is able to infer such model directly from single cell measurements. Finally, the model should be validated against existing prior knowledge on the topic of gene regulation and neuron differentiation.

[1] Di Bella, Daniela J., et al. "Molecular logic of cellular diversification in the mouse cerebral cortex." Nature 595.7868 (2021): 554-559.

[2] Kamimoto, Kenji, et al. "Dissecting cell identity via network inference and in silico gene perturbation." Nature 614.7949 (2023): 742-751.

[3] Paulevé, Loïc. "Marker and source-marker reprogramming of Most Permissive Boolean networks and ensembles with BoNesis." Peer Community Journal 3 (2023).