Single-cell behavior and population heterogeneity: solving an inverse problem to compute the intrinsic physiological...

Area of Science:

• Quantitative Biology
• Systems Biology
• Computational Biology

Background:

• Cell population balance models describe microbial population dynamics, accounting for phenotypic heterogeneity.
• Accurate modeling requires knowledge of single-cell reaction/division rates and partition probability density functions (intrinsic physiological state functions).

Purpose of the Study:

• To present a robust computational procedure for accurately estimating intrinsic physiological state (IPS) functions in heterogeneous cell populations.
• To assess the impact of various parameters on the accuracy of IPS function estimation.

Main Methods:

• Solving an inverse problem using phenotypic distributions of overall, dividing, and newborn cell subpopulations.
• Parametric analysis of discretization, non-parametric estimators, phenotypic distribution characteristics, and partitioning probability density functions.
• Assessment of finite sampling and measurement errors on IPS function accuracy.

Main Results:

• A computational procedure was developed to accurately estimate IPS functions for heterogeneous cell populations.
• Parametric analysis identified key factors influencing estimation accuracy, including distribution characteristics and sampling errors.
• The procedure was successfully applied to estimate IPS functions for an E. coli population with a genetic toggle network.

Conclusions:

• The study provides an integrated experimental and computational framework for quantifying single-cell behavior in heterogeneous populations.
• This framework is a powerful tool for advancing the understanding of microbial population dynamics and single-cell physiology.