A New Approach for Evaluating Electron Transfer Dynamics by Using Resonance Raman Microscopy and Chronoamperometry in Conjunction with a Dynamic Model.

is a good candidate as a chassis organism due to its ability to form thick, conductive biofilms, enabling long-distance extracellular electron transfer (EET). Due to the complexity of EET pathways in , a dynamic approach is required to study genetically modified EET rates in the biofilm. By coupling online resonance Raman microscopy with chronoamperometry, we were able to observe the dynamic discharge response in the biofilm's cytochromes to an increase in anode voltage. Measuring the heme redox state alongside the current allows for the fitting of a dynamic model using the current response and a subsequent validation of the model via the value of a reduced cytochrome Raman peak. The modeled reduced cytochromes closely fitted the Raman response data from the wild-type strain, showing the oxidation of heme groups in cytochromes until a new steady state was achieved. Furthermore, the use of a dynamic model also allows for the calculation of internal rates, such as acetate and NADH consumption rates. The Raman response of a mutant lacking OmcS showed a higher initial oxidation rate than predicted, followed by an almost linear decrease of the reduced mediators. The increased initial rate could be attributed to an increase in biofilm conductivity, previously observed in biofilms lacking OmcS. One explanation for this is that OmcS acts as a conduit between cytochromes; therefore, deleting the gene restricts the rate of electron transfer to the extracellular matrix. This could, however, be modeled assuming a linear oxidation rate of intercellular mediators. Bioelectrochemical systems can fill a vast array of application niches, due to the control of redox reactions that it offers. Although native microorganisms are preferred for applications such as bioremediation, more control is required for applications such as biosensors or biocomputing. The development of a chassis organism, in which the EET is well defined and readily controllable, is therefore essential. The combined approach in this work offers a unique way of monitoring and describing the reaction kinetics of a biofilm, as well as offering a dynamic model that can be used in conjunction with applications such as biosensors.