TY - JOUR
T1 - Metabolic Fluxes Using Deep Learning Based on Enzyme Variations
T2 - Application to Glycolysis in Entamoeba histolytica
AU - Oulia, Freddy
AU - Charton, Philippe
AU - Lo-Thong-Viramoutou, Ophélie
AU - Acevedo-Rocha, Carlos G.
AU - Liu, Wei
AU - Huynh, Du
AU - Damour, Cédric
AU - Wang, Jingbo
AU - Cadet, Frederic
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Deep learning
KW - Deep neural network
KW - Flux prediction
KW - Glycolysis
KW - Metabolic pathway
KW - Pathway modeling
U2 - 10.3390/ijms252413390
DO - 10.3390/ijms252413390
M3 - Journal article
C2 - 39769154
AN - SCOPUS:85213207104
SN - 1661-6596
VL - 25
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
IS - 24
M1 - 13390
ER -