Quantifying Isotopologue Reaction Networks (QIRN): A modelling tool for predicting stable isotope fractionations in complex networks

Abstract

Natural-abundance stable isotope compositions are powerful tools for understanding complex processes across myriad scientific disciplines. However, quantitative interpretation of these signals often requires equally complex models. Previous stable isotope models have treated isotopic compositions as intrinsic properties of molecules or atoms (e.g. δ13C, 13R, etc.). This has proven to be a computationally efficient but inflexible approach. Here, we present a new isotope modelling software tool that combines computational strategies used in metabolic modeling with an understanding of natural isotope fractionations from the geosciences, called Quantifying Isotopologue Reaction Networks (QIRN, “churn”). QIRN treats isotopic properties as distributions of discrete isotopologues, i.e. molecules with different numbers and distributions of isotopic substitutions. This approach is remarkably generalizable and computationally tractable, enabling models of reaction networks with unprecedented complexity. QIRN parameterizes reactions as rate law equations with distinct isotopologues as the reactants and products. Isotope effects are implemented as small changes to the relevant isotopologues’ rate constants. Running this model forward in time gives the numerical solution for steady state isotopologue abundances. Different subsets of the isotopologue population can then be sampled to quantify numerous isotopic proprieties simultaneously (i.e. compound-specific, site-specific, and multiply-substituted isotope compositions). Furthermore, QIRN can model any physical, chemical or biological process as reversible or irreversible. As such, it incorporates both kinetic and equilibrium isotope effects. It can be readily applied to any isotope system (i.e. C, N, O, etc.), though at present can only track two isotopes of one element at a time. Given its generalizability, QIRN has a diverse range of applications. To demonstrate the flexibility and efficiency of QIRN, we reconstructed previous (intrinsic-property) models of sulfate reduction, abiotic amino acid synthesis, lipid biosynthesis, and photosynthesis. In these examples, QIRN consistently reproduced outputs from prior models and predicted isotopic anomalies that have been measured in nature. With its new approach to isotope modelling, QIRN will expand the potential complexity of modelled reaction networks, help predict isotopic signals that can direct experimental efforts, and provide a more efficient means of modeling emerging isotopic properties such as ‘clumped isotopes’.