Age-based approach to characterize the dynamics of cellular processes

Area of Science:

• Cellular and Molecular Biology
• Biophysics
• Systems Biology

Background:

• Cellular homeostasis relies on continuous molecular production and degradation.
• Pulse-chase experiments using labeling assess molecular turnover but often rely on simplifying assumptions.
• Existing methods may not accurately reflect complex cellular dynamics like delayed labeling or cell growth.

Purpose of the Study:

• To reframe steady-state dynamic labeling experiments using the concept of metabolic age.
• To develop a method for quantifying molecular dynamics with minimal assumptions.
• To provide a practical framework for analyzing protein kinetics in yeast.

Main Methods:

• Interpreting labeling experiment readouts as a distribution of metabolic ages.
• Developing a compartmental model framework.
• Creating an open-source software package for data analysis.

Main Results:

• Demonstrated connection between labeling dynamics and parameters like half-lives, decay rates, and residence times.
• Showed how delayed input, cell growth, and complex degradation affect interpretations.
• Quantified dynamic parameters and protein kinetic pool structure in budding yeast.

Conclusions:

• Metabolic age provides a robust framework for analyzing molecular dynamics in cells.
• The developed model and software enable accurate quantification of kinetic parameters.
• This approach enhances understanding of cellular protein turnover across different growth conditions.