Prediction of microbial growth via the hyperconic neural network approach

Abstract

The design and control of biological reactors depends upon the correct calibration and selection of kinetic parameters from the exponential phase in the microbial growth. The kinetic microbial parameters that are reported in the literature of biochemical engineering are obtained based on specific growth conditions, which limits their potential application in a wide variety of dynamic models. In this paper, we propose an adaptive machine-learning approach to predict microbial growth, and provide information on the effect that variations of pH and concentration of the culture media have on the growth rate. We study biological reactions and obtain a set of experimental exploratory data using the Pseudomonas aeruginosa (i.e., P. aeruginosa). The versatile and robust metabolism of P. aeruginosa is responsible for its ability to grow in different environment conditions, even at low nutrient and oxygen levels, in a sample range of temperatures (4∘-42∘C) and polluted sites. Our first contribution is to propose a technique to gather experimental data via measurements of optical density, microbial growth, from the Multiskan™ FC Microplate Photometer, Thermo Scientific. Our second contribution consists of building a surrogate model by successfully adapting the Hyperconic Multilayer Perceptron (HC-MLP), a novel computational and mathematical approach, to predict microbial growth across all the set of conditions of the experimental design. HC-MLP is a state-of-the-art method that is used to define complex non-linear decision boundaries, in the parameters’ space, using a mix of ideas from conformal geometry and neural networks, and focusing on quadratic hyper-surfaces through multiple hidden layers. Consequently, we generate precise surrogates, which can even predict microbial growth in values not evaluated during the experimental stage. Finally, our statistical testing and comparisons validate that the proposed experimental, mathematical and computational framework is robust and capable of predicting the dynamic growth of bacteria P. aeruginosa using two main operation conditions: pH and concentration culture media at pH 7.0. In the future, we plan to apply our proposed methodology to other bacterial strains and advance HC-MLP for forecasting dynamics of other multiple microbial measurements under a wide variety of conditions.