Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models.

Autor: Wilken SE; Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany. Electronic address: wilkenst@hhu.de., Besançon M; Department for AI in Society, Science, and Technology, Zuse Institute Berlin, Takustraße 7, 14195, Berlin, Germany., Kratochvíl M; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg., Foko Kuate CA; Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany., Trefois C; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg., Gu W; Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, L-4367, Belvaux, Luxembourg., Ebenhöh O; Institute of Quantitative and Theoretical Biology, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany; Cluster of Excellence on Plant Sciences, Heinrich-Heine-Universität Düsseldorf, Universitätsstraße 1, 40225, Düsseldorf, Germany.
Jazyk: angličtina
Zdroj: Metabolic engineering [Metab Eng] 2022 Nov; Vol. 74, pp. 72-82. Date of Electronic Publication: 2022 Sep 21.
DOI: 10.1016/j.ymben.2022.09.002
Abstrakt: Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.
(Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)
Databáze: MEDLINE
Externí odkaz: