Age-based approach to characterize the dynamics of cellular processes

Abstract

Cells continuously produce and degrade molecules, essential for maintaining homeostasis. The study of these dynamics has gained momentum since the development of pulse–chase methods, utilizing fluorescent or isotopic labeling to assess properties such as turnover rates or half-lives. However, standard analyses of these experiments often depend on assumptions such as the homogeneity of analyzed molecules or their immediate labeling, which do not always hold. Here, we show that the readouts of steady-state dynamic labeling experiments can be interpreted as the distribution of metabolic ages, defined as the time since each molecule entered the metabolic system, and that metabolic ages can be quantified with minimal assumptions. Using this age-based interpretation, we demonstrate how the experimentally observed labeling dynamics is connected to a variety of dynamic parameters including half-lives, decay rates, and residence times and how these interpretations are affected by the conditions of delayed input, cell growth, or complex degradation patterns. To aid in the experimental quantification of dynamic parameters, we introduce a compartmental model framework as well as an open-source software package. We illustrate the framework’s practical utility by quantifying dynamic parameters and determining the kinetic pool structure of budding yeast proteins at optimal and suboptimal growth temperatures.