Flux prediction using artificial neural network (ANN) for the upper part of glycolysis

Abstract : The selection of optimal enzyme concentration in multienzyme cascade reactions for the highest product yield in practice is very expensive and time-consuming process. The modelling of biological pathways is a difficult process because of the complexity of the system. The mathematical modelling of the system using an analytical approach depends on the many parameters of enzymes which rely on tedious and expensive experiments. The artificial neural network (ANN) method has been successively applied in different fields of science to perform complex functions. In this study, ANN models were trained to predict the flux for the upper part of glycolysis as inferred by NADH consumption, using four enzyme concentrations i.e., phosphoglucoisomerase, phosphofructokinase, fructose-bisphosphate-aldolase, triose-phosphate-isomerase. Out of three ANN algorithms, the neuralnet package with two activation functions, "logistic" and "tanh" were implemented. The prediction of the flux was very efficient: RMSE and R2 were 0.847, 0.93 and 0.804, 0.94 respectively for logistic and tanh functions using a cross validation procedure. This study showed that a systemic approach such as ANN could be used for accurate prediction of the flux through the metabolic pathway. This could help to save a lot of time and costs, particularly from an industrial perspective. The R-code is available at: https://github.com/DSIMB/ANN-Glycolysis-Flux-Prediction.
Document type :
Journal articles
Complete list of metadatas

Cited literature [75 references]  Display  Hide  Download

https://www.hal.inserm.fr/inserm-02196731
Contributor : Myriam Bodescot <>
Submitted on : Monday, July 29, 2019 - 1:12:40 PM
Last modification on : Monday, September 2, 2019 - 9:43:07 AM

File

journal.pone.0216178.pdf
Publication funded by an institution

Identifiers

Citation

Anamya Nagaraja, Nicolas Fontaine, Mathieu Delsaut, Philippe Charton, Cedric Damour, et al.. Flux prediction using artificial neural network (ANN) for the upper part of glycolysis. PLoS ONE, Public Library of Science, 2019, 14 (5), pp.e0216178. ⟨10.1371/journal.pone.0216178⟩. ⟨inserm-02196731⟩

Share

Metrics

Record views

67

Files downloads

52