Modeling of Genetic Regulatory Networks in the Differentiation of Neural Crest Stem Cells to Sensory Neurons by Means of Boolean Networks

Biological processes such as development can now be molecularly analyzed through genetic regulatory networks (GRN). In the present study we have generated a GRN comprising the process by which neural crest stem cells develops to two types of sensory neurons (Propioceptors and Nocioceptors). We have also been able to find recurrent patterns of regulation (motifs) that act coordinately to control such process. The results indicate that these motifs occur in similar stages found in the development of erythrocytes from hematopoietic stem cells. The GRN developed is made up of transduction signals, general and specific transcription factors and genes that confer the specific phenotype. The GRN is also suitable for modeling with an appropriate algorithm. Regarding its complexity we then used Classic Boolean Networks (CBN) for this purpose. The model showed key components, as well as the dynamic of the network through changes in initial conditions. The results show that these systems reach attractors of length two and have sense molecularly and morphologically. Finally, the motifs were reflected in the model, suggesting insights for further studies.