Fundamentals and Applications of Artificial Neural Network Modelling of Continuous Bifidobacteria Monoculture at a Low Flow Rate

Sergey Dudarov, Elena Guseva, Yury Lemetyuynen, Ilya Maklyaev, Boris Karetkin, Svetlana Evdokimova, Pavel Papaev, Natalia Menshutina and Victor Panfilov
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Sergey Dudarov: Department of Information Computer Technology Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Elena Guseva: Department of Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Yury Lemetyuynen: Department of Information Computer Technology Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Ilya Maklyaev: Department of Information Computer Technology Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Boris Karetkin: Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Svetlana Evdokimova: Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Pavel Papaev: Department of Information Computer Technology Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Natalia Menshutina: Department of Chemical and Pharmaceutical Engineering, Mendeleev University of Chemical Technology, 125047 Moscow, Russia
Victor Panfilov: Biotechnology Department, Mendeleev University of Chemical Technology, 125047 Moscow, Russia

Data, 2022, vol. 7, issue 5, 1-19

Abstract: The application of artificial neural networks (ANNs) to mathematical modelling in microbiology and biotechnology has been a promising and convenient tool for over 30 years because ANNs make it possible to predict complex multiparametric dependencies. This article is devoted to the investigation and justification of ANN choice for modelling the growth of a probiotic strain of Bifidobacterium adolescentis in a continuous monoculture, at low flow rates, under different oligofructose (OF) concentrations, as a preliminary study for a predictive model of the behaviour of intestinal microbiota. We considered the possibility and effectiveness of various classes of ANN. Taking into account the specifics of the experimental data, we proposed two-layer perceptrons as a mathematical modelling tool trained on the basis of the error backpropagation algorithm. We proposed and tested the mechanisms for training, testing and tuning the perceptron on the basis of both the standard ratio between the training and test sample volumes and under the condition of limited training data, due to the high cost, duration and the complexity of the experiments. We developed and tested the specific ANN models (class, structure, training settings, weight coefficients) with new data. The validity of the model was confirmed using RMSE, which was from 4.24 to 980% for different concentrations. The results showed the high efficiency of ANNs in general and bilayer perceptrons in particular in solving modelling tasks in microbiology and biotechnology, making it possible to recommend this tool for further wider applications.

Keywords: artificial neural network (ANN); modelling; two-layer perceptron; neuron; error backpropagation algorithm; Bifidobacterium adolescentis; continuous monoculture (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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