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A stochastic model for neural progenitor dynamics in the mouse cerebral cortex
Frédérique Clément1, Jules Olayé2
1Université Paris Saclay, Inria, Centre Inria de Saclay, 91120, Palaiseau, France.
Mathematical Biosciences
|April 1, 2024
Summary
This study presents a stochastic model for mouse cerebral cortex neurogenesis, accurately predicting neuron numbers and spatial distribution. The model aligns with experimental data in control and mutant scenarios.
Area of Science:
- Developmental Neuroscience
- Computational Biology
- Genetics
Background:
- Embryonic neurogenesis in the mouse cerebral cortex involves complex cell dynamics.
- Understanding progenitor cell behavior and neuronal differentiation is crucial for developmental studies.
Purpose of the Study:
- To develop a stochastic model for mouse cerebral cortex neurogenesis.
- To analyze the dynamics of progenitor cells and neurons using compound Poisson processes.
- To predict neuron numbers and their layer distribution.
Main Methods:
- Designed a stochastic model using compound Poisson processes.
- Derived analytical expressions for cell number expectation and variance.
- Performed numerical simulations to illustrate model dynamics.
- Investigated the impact of stochastic transition rates and cell cycle duration.
Main Results:
- The model accurately predicts the number of neurons and intermediate progenitors.
- It accounts for cell type dynamics and spatial distribution into cortical layers.
- Model outputs are consistent with experimental data across embryonic ages and in mutant situations.
Conclusions:
- The stochastic model provides a robust framework for studying embryonic neurogenesis.
- It successfully captures key aspects of cell population dynamics and neuronal layering.
- The model serves as a valuable tool for analyzing experimental data in developmental contexts.
