Parameter Estimation and Identifiability in Kinetic Flux Profiling Models of Metabolism.

Autor: Guppy B; Mathematics, University of Iowa, 2 West Washington Street, Iowa City, IA, 52242, USA.; Applied Mathematical and Computational Sciences, University of Iowa, 2 West Washington Street, Iowa City, IA, 52242, USA., Mitchell C; Mathematics, University of Iowa, 2 West Washington Street, Iowa City, IA, 52242, USA. colleen-mitchell@uiowa.edu.; Applied Mathematical and Computational Sciences, University of Iowa, 2 West Washington Street, Iowa City, IA, 52242, USA. colleen-mitchell@uiowa.edu., Taylor EB; Molecular Physiology and Biophysics, University of Iowa, 51 Newton Road, Iowa City, IA, 52242, USA.; Fraternal Order of Eagles Diabetes Research Center (FOEDRC), University of Iowa, 169 Newton Road, Iowa City, IA, 52246, USA.; Pappajohn Biomedical Institute, University of Iowa, 169 Newton Road, Iowa City, IA, 52246, USA.; University of Iowa Carver College of Medicine, University of Iowa, 451 Newton Road, Iowa City, IA, 52246, USA.
Jazyk: angličtina
Zdroj: Bulletin of mathematical biology [Bull Math Biol] 2024 Nov 27; Vol. 87 (1), pp. 7. Date of Electronic Publication: 2024 Nov 27.
DOI: 10.1007/s11538-024-01386-x
Abstrakt: 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.
Competing Interests: Declarations: The authors have no relevant financial or non-financial interests to disclose. The authors declare that the data supporting the findings of this study are available within the paper or its supplementary information files. This work was supported by Grants NIH RO1 DK104998 (EBT), NIH RO1 DK138664 (EBT), University of Iowa Distinguished Scholar Award (EBT).
(© 2024. The Author(s).)
Databáze: MEDLINE
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