Age-based approach to characterize the dynamics of cellular processes

Abstract Cells continuously produce and degrade multiple small and large molecules, essential for maintaining homeostasis. The study of these dynamics has gained momentum since the development of pulse-chase and wash-in/out methods, utilizing fluorescent or isotopic labeling of cellular components to assess properties such as turnover rates or half-lives. However, standard analyses of these experiments often depend on simplifications such as the homogeneity of analyzed molecules or their immediate labeling, which do not always hold. Here, we present a rigorous analytical framework that interprets the readouts of dynamic labeling experiments as the distribution of metabolic ages, defined as the time that molecules have spent within a cell, and show that metabolic ages can be quantified by dynamic labeling with minimal assumptions. Using 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 or complex degradation patterns. To aid in the experimental quantification of dynamic parameters, we introduce a compartmental model framework including 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. Significance Statement To be functional, cells must balance the production and degradation of biological molecules. This is often studied by labeling newly-made molecules with isotopic or fluorescent labels. However, determining parameters of these processes, such as degradation rates, is not easy and is challenged by non-instantaneous labeling and complex degradation patterns. We describe a generic framework for interpreting the results of dynamic labeling experiments based on the concept of metabolic age, defined as the time since a molecule entered the metabolic system. By analyzing the effects of heat stress on protein stability in yeast, we illustrate how this framework and its implementation in a custom opensource package enable us to standardize the determination of various dynamic parameters of metabolism.