Next Article in Journal
Human Synovia Contains Trefoil Factor Family (TFF) Peptides 1–3 Although Synovial Membrane Only Produces TFF3: Implications in Osteoarthritis and Rheumatoid Arthritis
Previous Article in Journal
Combined Treatment with Low-Dose Ionizing Radiation and Ketamine Induces Adverse Changes in CA1 Neuronal Structure in Male Murine Hippocampi
Previous Article in Special Issue
Substrate-Related Factors Affecting Cellulosome-Induced Hydrolysis for Lignocellulose Valorization
Open AccessArticle

Construction of a Robust Cofactor Self-Sufficient Bienzyme Biocatalytic System for Dye Decolorization and its Mathematical Modeling

MNR Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai 200136, China
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(23), 6104; https://doi.org/10.3390/ijms20236104
Received: 16 November 2019 / Revised: 2 December 2019 / Accepted: 2 December 2019 / Published: 3 December 2019
A triphenylmethane reductase derived from Citrobacter sp. KCTC 18061P was coupled with a glucose 1-dehydrogenase from Bacillus sp. ZJ to construct a cofactor self-sufficient bienzyme biocatalytic system for dye decolorization. Fed-batch experiments showed that the system is robust to maintain its activity after 15 cycles without the addition of any expensive exogenous NADH. Subsequently, three different machine learning approaches, including multiple linear regression (MLR), random forest (RF), and artificial neural network (ANN), were employed to explore the response of decolorization efficiency to the variables of the bienzyme system. Statistical parameters of these models suggested that a three-layered ANN model with six hidden neurons was capable of predicting the dye decolorization efficiency with the best accuracy, compared with the models constructed by MLR and RF. Weights analysis of the ANN model showed that the ratio between two enzymes appeared to be the most influential factor, with a relative importance of 54.99% during the decolorization process. The modeling results confirmed that the neural networks could effectively reproduce experimental data and predict the behavior of the decolorization process, especially for complex systems containing multienzymes. View Full-Text
Keywords: glucose 1-dehydrogenase; triphenylmethane reductase; cofactor regeneration; dye decolorization; multiple linear regression; random forest; artificial neural network; modeling glucose 1-dehydrogenase; triphenylmethane reductase; cofactor regeneration; dye decolorization; multiple linear regression; random forest; artificial neural network; modeling
Show Figures

Figure 1

MDPI and ACS Style

Ding, H.; Luo, W.; Yu, Y.; Chen, B. Construction of a Robust Cofactor Self-Sufficient Bienzyme Biocatalytic System for Dye Decolorization and its Mathematical Modeling. Int. J. Mol. Sci. 2019, 20, 6104.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop