Metabolic Fluxes Using Deep Learning Based on Enzyme Variations: Application to Glycolysis in Entamoeba histolytica

Freddy Oulia, Philippe Charton, Ophélie Lo-Thong-Viramoutou, Carlos G. Acevedo-Rocha, Wei Liu, Du Huynh, Cédric Damour, Jingbo Wang, Frederic Cadet*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Metabolic pathway modeling, essential for understanding organism metabolism, is pivotal in predicting genetic mutation effects, drug design, and biofuel development. Enhancing these modeling techniques is crucial for achieving greater prediction accuracy and reliability. However, the limited experimental data or the complexity of the pathway makes it challenging for researchers to predict phenotypes. Deep learning (DL) is known to perform better than other Machine Learning (ML) approaches if the right conditions are met (i.e., a large database and good choice of parameters). Here, we use a knowledge-based model to massively generate synthetic data and extend a small initial dataset of experimental values. The main objective is to assess if DL can perform at least as well as other ML approaches in flux prediction, using 68,950 instances. Two processing methods are used to generate DL models: cross-validation and repeated holdout evaluation. DL models predict the metabolic fluxes with high precision and slightly outperform the best-known ML approach (the Cubist model) with a lower RMSE (≤0.01) in both cases. They also outperform the PLS model (RMSE ≥ 30). This study is the first to use DL to predict the overall flux of a metabolic pathway only from variations of enzyme concentrations.

Original languageEnglish
Article number13390
JournalInternational Journal of Molecular Sciences
Volume25
Issue number24
ISSN1661-6596
DOIs
Publication statusPublished - 2024

Keywords

  • Artificial intelligence
  • Deep learning
  • Deep neural network
  • Flux prediction
  • Glycolysis
  • Metabolic pathway
  • Pathway modeling

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