Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling

Processes

Constantina K. Varnava¹  Argyro Tsipa¹  Andrea Pietrelli²  Vincenzo Ferrara²  Paola Grenni³ 

[1] Department of Civil and Environmental Engineering, University of Cyprus, Kallipoleos 75, Nicosia 1678, Cyprus;Department of Information Engineering, Electronics and Telecommunications, University of Rome La Sapienza, Via Eudossiana 18, 00184 Rome, Italy;Water Research Institute, National Research Council, Via Salaria km 29.300, 00015 Rome, Italy;

关键词: bioelectrochemical systems;    microbial fuel cell;    depollution;    substrate biodegradation;    mathematical models;    microbial growth kinetic models;   

DOI  :  10.3390/pr9061038

来源: DOAJ

【 摘 要 】

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.

【 授权许可】

Unknown