Symbolic Compartmental Models

Cells continuously produce and degrade multiple small and large molecules, essential for maintaining homeostasis. The study of this dynamics has gained momentum since the development of pulse-chase  methods, utilising 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 analysed molecules or their immediate labeling, which does not always hold. We have developed a rigorous analytical framework that interprets the readouts of dynamic labeling experiments as the distribution of metabolic ages, the time that molecules have spent within a cell, and which accounts for a variety of complicating factors in real-life experiments, including delayed label input, metabolic system growth, or complex degradation patterns of molecules.

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Symbolic Compartmental Model is a publicly available python package developed by Elad Noor in collaboration with our group to aid in the experimental quantification of dynamic parameters of metabolism using the age-based framework and is based on a general compartmental model approach. The package allows for constructing, simulating, fitting, quality-assessing, and reconfiguring compartmental models of metabolic systems and enables one to exact a variety of dynamic parameters of steady-state metabolism including, among others, metabolic ages, half-lives, residence times, and decay rates. The package is based on the symbolic calculations package sympy but can also perform numerical calculations.