Cell population heterogeneity driven by stochastic partition and growth optimality.

Area of Science:

• Cell Biology
• Evolutionary Biology
• Systems Biology

Background:

• Understanding cell population heterogeneity is crucial in biology.
• Cellular differentiation into subtypes often arises from homogeneous single-cell processes.
• The mechanisms driving population bimodality under constant conditions require further elucidation.

Purpose of the Study:

• To investigate how population bimodality can emerge from homogeneous single-cell processes.
• To model the stochastic partitioning of cellular components and its effect on cell population distributions.
• To explain the maintenance of aneuploid states within cell populations.

Main Methods:

• Development of a mathematical model based on stochastic partitioning of a cell component with an optimal copy number.
• Analysis of the influence of partition error variance and growth rate tolerance on distribution outcomes.
• Application of the model to explain aneuploid state maintenance and other cellular components.

Main Results:

• Population bimodality can arise even with homogeneous cell-level processes and constant environments.
• The emergence of unimodal or bimodal distributions is contingent upon the variance of partition errors and growth rate tolerance.
• The model provides a theoretical framework for understanding aneuploid state maintenance.

Conclusions:

• Stochastic partitioning of cellular components is a key mechanism driving cell population heterogeneity.
• The model offers insights into cell differentiation, aneuploidy, and the regulation of organelle/plasmid abundance.
• This work contributes to a deeper understanding of evolutionary and developmental processes at the cellular level.