Analytical cell size distribution: lineage-population bias and parameter inference

Area of Science:

• Microbiology
• Cell Biology
• Biophysics

Background:

• Cell size control mechanisms are crucial for bacterial physiology.
• Existing models often assume free-growing populations, neglecting lineage-specific dynamics.
• Mother machine experiments offer insights into single-cell behavior over generations.

Purpose of the Study:

• To derive analytical cell size distributions for size-controlled cells in single-lineage experiments.
• To compare these distributions with those from batch cultures.
• To investigate the impact of growth rates, division partitioning, and noise on cell size distributions.

Main Methods:

• Derivation of analytical steady-state cell size distributions.
• Modeling of exponential and power-law single-cell growth.
• Analysis of deterministic and stochastic volume partitioning.
• Comparison with experimental data from Escherichia coli mother machine.

Main Results:

• Analytical solutions for lineage cell size distributions under various growth and division conditions.
• Demonstration that cells are smaller in populations than in lineages for exponential growth and symmetric division.
• Identification that lineage-population bias depends only on single-cell growth rate when noise is introduced.
• Validation of models with Escherichia coli mother machine data.

Conclusions:

• Cell size distributions in single lineages are fundamentally different from batch cultures.
• The derived models accurately predict cell cycle parameters from experimental data.
• Noise in cell division or growth can abolish the lineage-population bias, impacting population heterogeneity.