Applying Differential Neural Networks to Characterize Microbial Interactions in an Ex Vivo Gastrointestinal Gut Simulator

Modeling Microbial Community Networks: Methods and Tools for Studying Microbial Interactions

Microbial interactions function as a fundamental unit in complex ecosystems. By characterizing the type of interaction (positive, negative, neutral) occurring in these dynamic systems, one can begin to unravel the role played by the microbial species. Towards this, various methods have been developed to decipher the function of the microbial communities. The current review focuses on the various qualitative and quantitative methods that currently exist to study microbial interactions. Qualitative methods such as co-culturing experiments are visualized using microscopy-based techniques and are combined with data obtained from multi-omics technologies (metagenomics, metabolomics, metatranscriptomics). Quantitative methods include the construction of networks and network inference, computational models, and development of synthetic microbial consortia. These methods provide a valuable clue on various roles played by interacting partners, as well as possible solutions to overcome pathogenic microbes that can cause life-threatening infections in susceptible hosts. Studying the microbial interactions will further our understanding of complex less-studied ecosystems and enable design of effective frameworks for treatment of infectious diseases.

Anticandidal and Antibiofilm Effect of Synbiotics including Probiotics and Inulin-Type Fructans

Background: There is great interest in the search for new alternatives to antimicrobial drugs, and the use of synbiotics is a promising approach to this problem. This study evaluated the growth inhibition and antibiofilm activity of the short-chain fatty acids produced by Lacticaseibacillus rhamnosus and Pediococcus acidilactici in combination with inulin-type fructans against Candida albicans. Methods: The growth inhibition of Candida was evaluated using microdilution analysis in 96-well microtiter plates; different concentrations of cell-free supernatants of Lacticaseibacillus rhamnosus and Pediococcus acidilactici were exposed to Candida albicans. The antibiofilm assessment was carried out using the crystal violet staining assay. The short-chain fatty acids were analyzed by gas chromatography. Results: The clinically isolated Candida albicans interacted with supernatants from Lacticaseibacillus rhamnosus and Pediococcus acidilactici and showed significant growth inhibition and antibiofilm formation versus the controls. Lactate and acetic acid were elevated in the supernatants. The results suggest that the supernatants obtained from the synbiotic combinations of Lacticaseibacillus rhamnosus and Pediococcus acidilactici with inulin-type fructans can inhibit the growth and biofilm formation against a clinically isolated Candida albicans strain. Conclusions: These results suggest that synbiotic formulations could be a promising alternative to antifungal drugs in candidiasis therapy.

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

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.

Mathematical Modeling of a Domestic Wastewater Treatment System Combining a Septic Tank, an Up Flow Anaerobic Filter, and a Constructed Wetland

Systems combining anaerobic bioreactors with constructed wetlands (CW) have proven to be adequate and efficient for wastewater treatment. Detailed knowledge of removal dynamics of contaminants can ensure positive results for engineering and design. Mathematical modeling is a useful approach to studying the dynamics of contaminant removal in wastewater. In this study, water quality monitoring was performed in a system composed of a septic tank (ST), an up flow anaerobic filter (UAF), and a horizontal flow constructed wetland (HFCW). Biological oxygen demand (BOD5), chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), NH3, organic nitrogen (ON), total suspended solids (TSS), NO2−, and NO3− were measured biweekly during a 3-month period. First-order kinetics, multiple linear regression, and mass balance models were applied for data adjustment. First-order models were useful to predict the outlet concentration of pollutants (R2 > 0.87). Relevant multiple linear regression models were found, which could be applied to facilitate the system’s monitoring and provide valuable information to control and improve biological and physical processes necessary for wastewater treatment. Finally, the values of important parameters (μmax, Ks,  and Yx/s) in mass-balance models were determined with the aid of a differential neural network (DNN) and an optimization algorithm. The estimated parameters indicated the high robustness of the treatment system since performance stability was found despite variations in wastewater composition.