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-aldolas

SUBMITTER: Ajjolli Nagaraja A 

PROVIDER: S-EPMC6505829 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature