NEXT-FBA: a hybrid stoichiometric/data-driven approach to improve intracellular flux predictions
File(s)
Published version
Author(s)
Morrissey, James
Barberi, Gianmarco
Strain, Benjamin
Facco, Pierantonio
Kontoravdi, Cleo
Type
Journal Article
Abstract
Genome-scale metabolic models (GEMs) have been widely utilized to understand cellular metabolism. The application of GEMs has been advanced by computational methods that enable the prediction and analysis of intracellular metabolic states. However, the accuracy and biological relevance of these predictions often suffer from the many degrees of freedom and scarcity of available data to constrain the models adequately. Here, we introduce NEXT-FBA (Neural-net EXtracellular Trained Flux Balance Analysis), a novel computational methodology that addresses these limitations by utilizing exometabolomic data to derive biologically relevant constraints for intracellular fluxes in GEMs. We achieve this by training artificial neural networks (ANNs) with exometabolomic data from Chinese hamster ovary (CHO) cells and correlating it with 13C-labeled intracellular fluxomic data. By capturing the underlying relationships between exometabolomics and cell metabolism, NEXT-FBA predicts upper and lower bounds for intracellular reaction fluxes to constrain GEMs. We demonstrate the efficacy of NEXT-FBA across several validation experiments, where it outperforms existing methods in predicting intracellular flux distributions that align closely with experimental observations. Furthermore, a case study demonstrates how NEXT-FBA can guide bioprocess optimization by identifying key metabolic shifts and refining flux predictions to yield actionable process and metabolic engineering targets. Overall, NEXT-FBA aims to improve the accuracy and biological relevance of intracellular flux predictions in metabolic modelling for bioprocess optimization, with minimal input data requirements for pre-trained models.
Date Issued
2025-09-01
Date Acceptance
2025-03-18
Citation
Metabolic Engineering, 2025, 91, pp.130-144
ISSN
1096-7176
Publisher
Elsevier BV
Start Page
130
End Page
144
Journal / Book Title
Metabolic Engineering
Volume
91
Copyright Statement
© 2025 The Authors. Published by Elsevier Inc. on behalf of International Metabolic Engineering Society. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
10.1016/j.ymben.2025.03.010
Subjects
Metabolic Engineering Morrissey
J.
Barberi
G.
Strain
B.
Facco
P.
Kontoravdi
C.
NEXT-FBA: A hybrid stoichiometric/data-driven approach to improve intracellular flux predictions
Metabolic Engineering
Publication Status
Published
Date Publish Online
2025-03-19