Modeling of Nitrification Kinetics in a Respirometric Biosensor under Suboptimal Conditions

Abstract

Sensitive detection with cell biosensors requires optimization of their working conditions and standardization of the response in variable physicochemical conditions. The introduction of an analyte to a sensor, which contributes to this variability, may account for the modeling of microbial metabolism. We constructed a multiparameter model of a water toxicity sensor of Automatic Biodetector for Water Toxicity (ABTOW), developed by our group and based on nitrifying bacteria. The model describes the kinetics of nitrification as a function of four orthogonal parameters: temperature, pH, oxygen and ammonium concentration. Furthermore, we characterized the signal-to-noise ratio (SNR) of the ABTOW readout as a function of these parameters. Thus, a region of parameter space corresponding to optimal ABTOW operation is identified and its sensitivity quantified. We applied the model to describe the ABTOW performance in non-equilibrium conditions produced by rapid changes in pH and temperature. In sum, the model based on four physicochemical parameters describes changes in the biosensor’s activity, the biological element of which are nitrifying bacteria characterized by simple chemolithoautotrophic metabolism. The description of reaction kinetics through multiparameter modeling in combination with stability analysis can find application in process control in biotechnology, biodetection and environmental research.