A discrete-time, multi-type generational inheritance branching process model of cell proliferation

Area of Science:

• Cell biology
• Mathematical modeling
• Population dynamics

Background:

• Mammalian cell populations can exhibit subpopulations with varying cell lifetimes, even when originating from a single cell.
• The inheritance of cell lifetimes has been a focus of experimental and mathematical research.
• Previous studies by Axelrod et al. provided data on cell lifetimes over multiple generations using primary and secondary colonies.

Purpose of the Study:

• To mathematically model experimental data on cell lifetimes and proliferation inheritance.
• To investigate multi-type Galton-Watson branching process models for cell population dynamics.
• To explain the observed variance and correlations in cell counts between primary and secondary colonies.

Main Methods:

• Derivation of covariance for cell counts in primary and secondary colonies using multi-type Galton-Watson branching process models.
• Mathematical modeling to fit experimental data from Axelrod et al.
• Simulations to explore the relationship between variance and correlation coefficients.

Main Results:

• Successful modeling of experimental data using two subpopulations with distinct proliferation rates.
• Demonstrated that daughter cell proliferation rates are primarily determined by maternal rates.
• Simulations revealed a trade-off between high variance and correlation coefficients in cell counts.
• Experimental data points were found at the boundary of the attainable simulation region.

Conclusions:

• The inheritance of cell proliferation rates plays a crucial role in mammalian cell population dynamics.
• Multi-type Galton-Watson branching process models provide a robust framework for analyzing cell population heterogeneity.
• Experimental findings align with theoretical predictions at the limits of parameter space, suggesting specific inheritance mechanisms.