Progress and Prospects of Bioelectrochemical Systems: Electron Transfer and Its Applications in the Microbial Metabolism

Zheng, Tao; Li, Jin; Ji, Yaliang; Zhang, Wenming; Fang, Yan; Xin, Fengxue; Dong, Weiliang; Wei, Ping; Ma, Jiangfeng; Jiang, Min

doi:10.3389/fbioe.2020.00010

Cited by 111 publications

(43 citation statements)

References 105 publications

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“…Bioelectrochemical systems have become of great interest to the electrochemistry community for exploring avenues of renewable energy generation, biosensing systems, and bioelectrosynthesis [1][2][3]. Specifically, microbial electrochemical systems are fit for long-term environmental deployment suitable for wastewater management and monitoring applications [4][5][6]. Among the many types of microbial electrochemical systems, the technology used most often for biosensing is the microbial fuel cell since the substrate of the bacterial cells is commonly the driver of the current output.…”

Section: Introductionmentioning

confidence: 99%

The Use of Electroactive Halophilic Bacteria for Improvements and Advancements in Environmental High Saline Biosensing

Gaffney

Simoska

2021

Biosensors

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Halophilic bacteria are remarkable organisms that have evolved strategies to survive in high saline concentrations. These bacteria offer many advances for microbial-based biotechnologies and are commonly used for industrial processes such as compatible solute synthesis, biofuel production, and other microbial processes that occur in high saline environments. Using halophilic bacteria in electrochemical systems offers enhanced stability and applications in extreme environments where common electroactive microorganisms would not survive. Incorporating halophilic bacteria into microbial fuel cells has become of particular interest for renewable energy generation and self-powered biosensing since many wastewaters can contain fluctuating and high saline concentrations. In this perspective, we highlight the evolutionary mechanisms of halophilic microorganisms, review their application in microbial electrochemical sensing, and offer future perspectives and directions in using halophilic electroactive microorganisms for high saline biosensing.

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Section: Introductionmentioning

confidence: 99%

The Use of Electroactive Halophilic Bacteria for Improvements and Advancements in Environmental High Saline Biosensing

Gaffney

Simoska

2021

Biosensors

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“…A dispersion of nanocrystalline platinum anchored CNTs in water was used in a two-chambers MFC to accelerate the mediated electron transfer [ 150 ]. In a report by Sharma et al the electron transfer efficiency of the E. coli -based bioelectrochemical system was enhanced by electrode modification with the CNTs [ 151 ].…”

Section: Biosensing Applications Of Mess For Microbial Detectionmentioning

confidence: 99%

Microbial Electrochemical Systems: Principles, Construction and Biosensing Applications

Hassan

Febbraio

Andreescu

2021

Sensors

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Microbial electrochemical systems are a fast emerging technology that use microorganisms to harvest the chemical energy from bioorganic materials to produce electrical power. Due to their flexibility and the wide variety of materials that can be used as a source, these devices show promise for applications in many fields including energy, environment and sensing. Microbial electrochemical systems rely on the integration of microbial cells, bioelectrochemistry, material science and electrochemical technologies to achieve effective conversion of the chemical energy stored in organic materials into electrical power. Therefore, the interaction between microorganisms and electrodes and their operation at physiological important potentials are critical for their development. This article provides an overview of the principles and applications of microbial electrochemical systems, their development status and potential for implementation in the biosensing field. It also provides a discussion of the recent developments in the selection of electrode materials to improve electron transfer using nanomaterials along with challenges for achieving practical implementation, and examples of applications in the biosensing field.

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“…Random forest (RF) (Breiman, 2001 ; Song et al, 2017 ; Cheng and Hu, 2018 ; Cheng, 2019 ; Ru et al, 2019 ; Xu et al, 2019 ; Lv et al, 2020b ) is a kind of bagging algorithm containing many decision trees, which has been widely used in computer science, bioinformatics, and so on. Each tree in the forest is generated by different samples and features.…”

Section: Classifier Selectionmentioning

confidence: 99%

BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR–Drug Interaction Based on Wordbook Learning From Sequences

Qiu

Yuan

et al. 2021

Front. Cell Dev. Biol.

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Background: As a class of membrane protein receptors, G protein-coupled receptors (GPCRs) are very important for cells to complete normal life function and have been proven to be a major drug target for widespread clinical application. Hence, it is of great significance to find GPCR targets that interact with drugs in the process of drug development. However, identifying the interaction of the GPCR–drug pairs by experimental methods is very expensive and time-consuming on a large scale. As more and more database about GPCR–drug pairs are opened, it is viable to develop machine learning models to accurately predict whether there is an interaction existing in a GPCR–drug pair.Methods: In this paper, the proposed model aims to improve the accuracy of predicting the interactions of GPCR–drug pairs. For GPCRs, the work extracts protein sequence features based on a novel bag-of-words (BOW) model improved with weighted Silhouette Coefficient and has been confirmed that it can extract more pattern information and limit the dimension of feature. For drug molecules, discrete wavelet transform (DWT) is used to extract features from the original molecular fingerprints. Subsequently, the above-mentioned two types of features are contacted, and SMOTE algorithm is selected to balance the training dataset. Then, artificial neural network is used to extract features further. Finally, a gradient boosting decision tree (GBDT) model is trained with the selected features. In this paper, the proposed model is named as BOW-GBDT.Results: D92M and Check390 are selected for testing BOW-GBDT. D92M is used for a cross-validation dataset which contains 635 interactive GPCR–drug pairs and 1,225 non-interactive pairs. Check390 is used for an independent test dataset which consists of 130 interactive GPCR–drug pairs and 260 non-interactive GPCR–drug pairs, and each element in Check390 cannot be found in D92M. According to the results, the proposed model has a better performance in generation ability compared with the existing machine learning models.Conclusion: The proposed predictor improves the accuracy of the interactions of GPCR–drug pairs. In order to facilitate more researchers to use the BOW-GBDT, the predictor has been settled into a brand-new server, which is available at http://www.jci-bioinfo.cn/bowgbdt.

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Progress and Prospects of Bioelectrochemical Systems: Electron Transfer and Its Applications in the Microbial Metabolism

Cited by 111 publications

References 105 publications

The Use of Electroactive Halophilic Bacteria for Improvements and Advancements in Environmental High Saline Biosensing

The Use of Electroactive Halophilic Bacteria for Improvements and Advancements in Environmental High Saline Biosensing

Microbial Electrochemical Systems: Principles, Construction and Biosensing Applications

BOW-GBDT: A GBDT Classifier Combining With Artificial Neural Network for Identifying GPCR–Drug Interaction Based on Wordbook Learning From Sequences

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