Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism

Breanna Guppy; Colleen Mitchell; Eric Taylor

doi:10.48550/arxiv.2407.08844

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Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism

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Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism

Breanna Guppy, Colleen Mitchell and Eric Taylor

arXiv.org

Cornell University

07/11/2024

DOI: 10.48550/arxiv.2407.08844

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Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Metabolic fluxes are the rates of life-sustaining chemical reactions within a cell and metabolites are the components. Determining the changes in these fluxes is crucial to understanding diseases with metabolic causes and consequences. Kinetic flux profiling (KFP) is a method for estimating flux that utilizes data from isotope tracing experiments. In these experiments, the isotope-labeled nutrient is metabolized through a pathway and integrated into the downstream metabolite pools. Measurements of proportion labeled for each metabolite in the pathway are taken at multiple time points and used to fit an ordinary differential equations model with fluxes as parameters. We begin by generalizing the process of converting diagrams of metabolic pathways into mathematical models composed of differential equations and algebraic constraints. The scaled differential equations for proportions of unlabeled metabolite contain parameters related to the metabolic fluxes in the pathway. We investigate flux parameter identifiability given data collected only at the steady state of the differential equation. Next, we give criteria for valid parameter estimations in the case of a large separation of timescales with fast-slow analysis. Bayesian parameter estimation on simulated data from KFP experiments containing both irreversible and reversible reactions illustrates the accuracy and reliability of flux estimations. These analyses provide constraints that serve as guidelines for the design of KFP experiments to estimate metabolic fluxes.

Mathematics - Dynamical Systems

Quantitative Biology - Quantitative Methods

Details

Title: Subtitle: Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism
Creators: Breanna Guppy - University of Iowa
Colleen Mitchell - University of Iowa
Eric Taylor - University of Iowa
Resource Type: Preprint
Publication Details: arXiv.org
Publisher: Cornell University; Ithaca, New York
DOI: 10.48550/arxiv.2407.08844
eISSN: 2331-8422
Language: English
Date posted: 07/11/2024
Academic Unit: Mathematics
Record Identifier: 9984658350802771

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