Modelling of glucoamylase thermal inactivation in the presence of starch by artificial neural network

• Published in: Journal of biotechnology 2004-10, Vol.114 (1), p.177-185
• Main Authors: Bryjak, Jolanta, Ciesielski, Krzysztof, Zbiciński, Ireneusz
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Artificial neural network; Aspergillus niger; Aspergillus niger - enzymology; Biological and medical sciences; Biotechnology; Combinatorial Chemistry Techniques; Computer Simulation; Enzyme Activation - radiation effects; enzyme inactivation; Fundamental and applied biological sciences. Psychology; glucan 1,4-alpha-glucosidase; Glucan 1,4-alpha-Glucosidase - chemistry; Glucoamylase; heat inactivation; Hot Temperature; Kinetics; mathematical models; Modelling; Models, Chemical; Neural Networks (Computer); Protein Binding; Protein Denaturation; Stability modulation; Starch - chemistry; Substrate Specificity; Temperature; Thermal inactivation
• ISSN: 0168-1656; 1873-4863
• DOI: 10.1016/j.jbiotec.2004.07.003

Thermal inactivation is suspected to be a limiting factor for use of glucoamylase in starch saccharification at elevated temperatures. Thus, inactivation of the enzyme has been studied in the presence of reagents (enzyme, substrate and product in wide range of concentrations, and moderate stirring). The influence of substrate and glucose as stability modulators showed the complexity of the studied system. Hence, one might expect multilateral correlations that could depreciate some efforts for phenomenological modelling. These obstacles forced to apply artificial neural network (ANN) modelling to map the enzyme activity decays. For this purpose, a dynamic network with four hidden neurons was selected. The database containing 42 data vectors was used for neural model training and verification process. The standard error of calculations and correlation coefficient (0.997–0.999) for dynamic simulations has proved correctness of the developed ANN.