In this talk, I will present a multiscale model of neuroblastoma organoids, which are child tumors of the sympathetic nervous system derived from the primitive neural crest cells. At the molecular level, we implemented a simple gene regulatory network (GRN) in the form of a toggle switch between two genes, one driving the stemness and the other characterizing a differentiated phenotype. This GRN has been encoded as a biologically realistic mechanistic model driven by transcriptional burst for gene expression. Cellular decision-making (proliferation, death, differentiation) is driven by protein concentrations produced by the GRN. To check the realism of the 3D structures obtained, we compared the simulation results, both visually and using appropriate statistics, with immunohistochemistry (IHC) images, using quantitative measurements of spatial distributions.
