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

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)....

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Bibliographic Details
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
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Summary: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.
ISSN:0168-1656
1873-4863
DOI:10.1016/j.jbiotec.2004.07.003