Journal article
Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism
Bulletin of mathematical biology, Vol.87(1), 7
11/27/2024
DOI: 10.1007/s11538-024-01386-x
PMCID: PMC11602815
PMID: 39601930
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.
Details
- Title: Subtitle
- Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism
- Creators
- Breanna Guppy - University of IowaColleen Mitchell - University of IowaEric B. Taylor - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Bulletin of mathematical biology, Vol.87(1), 7
- DOI
- 10.1007/s11538-024-01386-x
- PMID
- 39601930
- PMCID
- PMC11602815
- NLM abbreviation
- Bull Math Biol
- ISSN
- 0092-8240
- eISSN
- 1522-9602
- Publisher
- Springer US
- Grant note
- Division of Diabetes, Endocrinology, and Metabolic Diseases
We would like to thank Mitchell Riley and Evelyn Smith for helping review and improve the manuscript. We appreciate the insightful comments from Joe Sauder. We would also like to thank the reviewers for their constructive feedback which helped refine the manuscript.
- Language
- English
- Date published
- 11/27/2024
- Academic Unit
- Molecular Physiology and Biophysics; Fraternal Order of Eagles Diabetes Research Center; Mathematics
- Record Identifier
- 9984752657002771
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