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

Mueller, Elliott P. and Wu, Fenfang and Sessions, Alex L. (2022) Quantifying Isotopologue Reaction Networks (QIRN): A modelling tool for predicting stable isotope fractionations in complex networks. Chemical Geology, 610 . Art. No. 121098. ISSN 0009-2541. doi:10.1016/j.chemgeo.2022.121098. https://resolver.caltech.edu/CaltechAUTHORS:20221010-454096500.14

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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'.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.chemgeo.2022.121098DOIArticle
ORCID:
AuthorORCID
Mueller, Elliott P.0000-0002-6837-0409
Wu, Fenfang0000-0003-1134-280X
Sessions, Alex L.0000-0001-6120-2763
Additional Information:The authors gratefully acknowledge Elise Wilkes (Caltech) for helpful discussions while writing this manuscript. We thank Shuhei Ono and one anonymous reviewer for helpful feedback on the manuscript We also also thank Lilly Birk for her graphic design work on the QIRN GUI. This work was made possible by an NSF Graduate Research Fellowship DGE-1745301 (to E.P.M.) and the NASA Astrobiology Institute Grant 80NSSC18M0094 (to A.L.S.).
Group:Division of Geological and Planetary Sciences
Funders:
Funding AgencyGrant Number
NSF Graduate Research FellowshipDGE-1745301
NASA80NSSC18M0094
DOI:10.1016/j.chemgeo.2022.121098
Record Number:CaltechAUTHORS:20221010-454096500.14
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20221010-454096500.14
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:117299
Collection:CaltechAUTHORS
Deposited By: Research Services Depository
Deposited On:13 Oct 2022 21:55
Last Modified:31 Oct 2022 21:24

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