Advances and insights into modeling extracellular electron transfer in anaerobic bioprocesses

Extracellular electron transfer (EET) plays an important role in maintaining redox balance in both natural and engineered anaerobic microbial systems, driving key biochemical processes such as energy generation, bioremediation, and waste degradation. While EET has been characterized in a limited number of microbes and applied in anaerobic digestion and bioelectrochemical systems, further research is needed to explore its mechanism across a broader range of microbial species and anaerobic processes. This review highlights advanced modeling frameworks that provide deeper insights into EET mechanisms and dynamics, aiming to optimize research efforts and minimize time and resource expenditure. Mechanistic models, encompassing thermodynamics and kinetics, are discussed for their utility in calculating conduction rates of electroactive microbes and assessing the energetics of medium chain carboxylic acids production. Genome-scale metabolic models are highlighted for elucidating the roles of cytochromes and conductive pili in the EET pathway. Machine learning is presented as a tool to improve model accuracy and predict EET mechanisms. Furthermore, the integration of quantum mechanics/molecular mechanics methods offers molecular-level insights into electron transfer, while quantum computing addresses limitations of classical computers by simulating complex electron transfer processes in multi-heme cytochromes. Developing advanced modeling techniques will complement experimental techniques, enabling precise predictions and optimization strategies for developing innovative and sustainable anaerobic biotechnologies.