Nonlinear memory in cell-division dynamics across species.

Area of Science:

• Quantitative Biology
• Cell Biology
• Dynamical Systems

Background:

• Cell-size homeostasis is crucial for cell function and relies on regulated growth and division.
• Advanced imaging (e.g., mother machines) enables tracking cell-size dynamics across generations.
• Current understanding of cell-size control mechanisms within a dynamical-systems framework is limited.

Purpose of the Study:

• To develop and apply a novel framework for inferring stochastic-differential-equation models of cell growth and division.
• To investigate the role of nonlinear memory effects in cell division timing.
• To quantitatively evaluate prevailing cell homeostasis models (sizer, adder, timer).

Main Methods:

• Inference of stochastic-differential-equation models with Poisson noise from experimental time-series data.
• Parameterization of Poisson intensity incorporating cell size and ancestral history to capture nonlinear memory.
• Application of the framework to cell-size trajectories of *Escherichia coli*, *Bacillus subtilis*, *Schizosaccharomyces pombe*, and *Dictyostelium discoideum*.

Main Results:

• The developed framework successfully infers quantitative models of stochastic cell growth and division.
• Analysis revealed that cell division timing in multiple organisms often involves significant nonlinear memory components.
• The popular linear-memory models (sizer, adder, timer) were found to be insufficient in many cases.

Conclusions:

• Existing linear-memory paradigms for cell homeostasis may need reevaluation and generalization.
• Nonlinear memory plays a substantial role in regulating cell division across diverse species.
• The inference framework is broadly applicable to modeling stochastic jump processes in various scientific fields.