Kinetic models for detection of toxicity in a microbial fuel cell based biosensor

• Published in: Biosensors & bioelectronics 2011-03, Vol.26 (7), p.3115-3120
• Main Authors: Stein, Nienke E., Keesman, Karel J., Hamelers, Hubertus V.M., van Straten, Gerrit
• Format: Article
• Language: English
• Subjects: Bacteria; Biochemical fuel cells; Bioelectric Energy Sources - microbiology; biofilm anode; Biological and medical sciences; Biosensing Techniques - instrumentation; Biosensor; Biosensors; Biotechnology; Computer Simulation; Fundamental and applied biological sciences. Psychology; Kinetics; Mathematical models; Methods. Procedures. Technologies; Microbial fuel cell; Microorganisms; Models, Biological; Overpotential; Polarization; Rate constants; Sensitivity; Sensors; system; Toxicity; Various methods and equipments; Water Pollutants, Chemical - analysis
• ISSN: 0956-5663; 1873-4235
• DOI: 10.1016/j.bios.2010.11.049

Currently available models describing microbial fuel cell (MFC) polarization curves, do not describe the effect of the presence of toxic components. A bioelectrochemical model combined with enzyme inhibition kinetics, that describes the polarization curve of an MFC-based biosensor, was modified to describe four types of toxicity. To get a stable and sensitive sensor, the overpotential has to be controlled. Simulations with the four modified models were performed to predict the overpotential that gives the most sensitive sensor. These simulations were based on data and parameter values from experimental results under non-toxic conditions. Given the parameter values from experimental results, controlling the overpotential at 250 mV leads to a sensor that is most sensitive to components that influence the whole bacterial metabolism or that influence the substrate affinity constant ( Km). Controlling the overpotential at 105 mV is the most sensitive setting for components influencing the ratio of biochemical over electrochemical reaction rate constants ( K1), while an overpotential of 76 mV gives the most sensitive setting for components that influence the ratio of the forward over backward biochemical rate constants ( K2). The sensitivity of the biosensor was also analyzed for robustness against changes in the model parameters other than toxicity. As an example, the tradeoff between sensitivity and robustness for the model describing changes on K1 (I K1) is presented. The biosensor is sensitive for toxic components and robust for changes in model parameter K2 when overpotential is controlled between 118 and 140 mV under the simulated conditions.