A multiscale mathematical model of cell dynamics during neurogenesis in the mouse cerebral cortex

• Published in: BMC bioinformatics Vol. 20; no. 1; pp. 470 - 24
• Main Authors: Postel, Marie, Karam, Alice, Pézeron, Guillaume, Schneider-Maunoury, Sylvie, Clément, Frédérique
• Format: Journal Article
• Language: English
• Published: London BioMed Central 14.09.2019; BMC
• Subjects: Abnormalities; Cell cycle; Cell dynamics; Cells (biology); Cellular Biology; Cerebral cortex; Computer simulation; Development Biology; Development of the cerebral cortex; Developmental stages; Division; Dynamics; Embryos; Evolution; In vivo methods and tests; Life Sciences; Mathematical models; Mouse mutant for Ftm/Rpgrip1l; Multiscale mathematical modeling; Neural progenitors; Neural stem cells; Neurogenesis; Neurons; Progenitor cells; S phase; Temporal lobe; Numerical simulations; Development of the cerebral cortex; Cell cycle indexes; Mouse mutant for Ftm/Rpgrip1l; Time varying transfer rates; Neural progenitors; Neurogenesis; Cell dynamics; Multiscale mathematical modeling
• DOI: 10.1186/s12859-019-3018-8
• ISSN: 1471-2105

Background Neurogenesis in the murine cerebral cortex involves the coordinated divisions of two main types of progenitor cells, whose numbers, division modes and cell cycle durations set up the final neuronal output. To understand the respective roles of these factors in the neurogenesis process, we combine experimental in vivo studies with mathematical modeling and numerical simulations of the dynamics of neural progenitor cells. A special focus is put on the population of intermediate progenitors (IPs), a transit amplifying progenitor type critically involved in the size of the final neuron pool. Results A multiscale formalism describing IP dynamics allows one to track the progression of cells along the subsequent phases of the cell cycle, as well as the temporal evolution of the different cell numbers. Our model takes into account the dividing apical progenitors (AP) engaged into neurogenesis, both neurogenic and proliferative IPs, and the newborn neurons. The transfer rates from one population to another are subject to the mode of division (proliferative, or neurogenic) and may be time-varying. The model outputs are successfully fitted to experimental cell numbers from mouse embryos at different stages of cortical development, taking into account IPs and neurons, in order to adjust the numerical parameters. We provide additional information on cell kinetics, such as the mitotic and S phase indexes, and neurogenic fraction. Conclusions Applying the model to a mouse mutant for Ftm/Rpgrip1l, a gene involved in human ciliopathies with severe brain abnormalities, reveals a shortening of the neurogenic period associated with an increased influx of newborn IPs from apical progenitors at mid-neurogenesis. Our model can be used to study other mouse mutants with cortical neurogenesis defects and can be adapted to study the importance of progenitor dynamics in cortical evolution and human diseases.