Linking lineage and population observables in biological branching processes

Area of Science:

• Cellular dynamics
• Population biology
• Statistical physics

Background:

• Fluctuation theorems connect microscopic and macroscopic properties in physical systems.
• Understanding cell growth dynamics is crucial for biology and medicine.
• Previous inequalities on cell generation times were limited to specific models.

Purpose of the Study:

• Derive fluctuation theorems for cell populations using a novel dynamics model.
• Establish inequalities comparing population doubling time and mean generation time.
• Extend the applicability of these inequalities to broader classes of cell growth models.

Main Methods:

• Developed a population dynamics model inspired by ensembles of growing cells.
• Derived fluctuation theorems linking lineage-level and population-level observables.
• Analyzed size-controlled and age-controlled cell growth models.

Main Results:

• Established a set of fluctuation theorems for growing cell populations.
• Demonstrated that specific inequalities hold for size-controlled models, not just age-controlled ones.
• Showed the broad applicability of these theoretical findings.

Conclusions:

• The derived inequalities are robust and applicable to various cell growth control mechanisms.
• These findings refine the interpretation of single-cell experiments measuring bacterial growth.
• Provides a theoretical framework for analyzing population dynamics from single-cell data.