A new high throughput technique for enzyme characterization with specific attention to the long term stability, called "Enzyme Test Bench," is presented. The concept of the Enzyme Test Bench consists of short term enzyme tests in 96-well microtiter plates under partly extreme conditions to predict the enzyme long term stability under moderate conditions. The technique is based on the mathematical modeling of temperature dependent enzyme activation and deactivation. Adapting the temperature profiles in sequential experiments by optimal non-linear experimental design, the long term deactivation effects can be purposefully accelerated and detected within hours. During the experiment the enzyme activity is measured online to estimate the model parameters from the obtained data. Thus, the enzyme activity and long term stability can be calculated as a function of temperature. The engineered instrumentation provides for simultaneous automated assaying by fluorescent measurements, mixing and homogenous temperature control in the range of 10-85 +/- 0.5 degrees C. A universal fluorescent assay for online acquisition of ester hydrolysis reactions by pH-shift is developed and established. The developed instrumentation and assay are applied to characterize two esterases. The results of the characterization, carried out in microtiter plates applying short term experiments of hours, are in good agreement with the results of long term experiments at different temperatures in 1 L stirred tank reactors of a week. Thus, the new technique allows for both: the enzyme screening with regard to the long term stability and the choice of the optimal process temperature regarding such process parameters as turn over number, space time yield or optimal process duration. The comparison of the temperature dependent behavior of both characterized enzymes clearly demonstrates that the frequently applied estimation of long term stability at moderate temperatures by simple activity measurements after exposing the enzymes to elevated temperatures may lead to suboptimal enzyme selection. Thus, temperature dependent enzyme characterization is essential in primary screening to predict its long term behavior.

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Within industrial process development, powerful screening techniques are required to select the optimal biocatalyst regarding such process characteristics as cost effectiveness, turnover number or space time yield. Conventional measurement of the initial enzyme activity, which is the established high throughput screening technique, disregards the long-term stability of an enzyme. A new model based technique called "enzyme test bench" was recently presented before by our group which addresses this issue. It combines the high throughput screening approach with an extensive enzyme characterization, focusing especially on the long-term stability. The technique is based on modeling enzyme activation and deactivation as temperature dependent reactions in accordance with the Arrhenius law. Controlling these reactions by tailor made temperature profiles, the slow long-term deactivation effects are accelerated and characterizing models are parameterized. Thus, the process properties of an enzyme can be predicted and included into the screening procedure. Moreover, the optimum process temperature as function of the envisaged operation time can be found by these means. In this work, the technique is extended to the important class of oxygen consuming reactions. For this aim, a suitable assay and a defined oxygen supply were established. This extended technique was applied to characterize and to optimize a complex, multi-stage laccase-mediator system (LMS). For the variation and optimization of the enzyme to mediator to substrate ratio, experiments in microtiter plates were performed. Predictions from this high throughput characterization were compared to long-term experiments in a RAMOS device (Respiration Activity Monitoring System), a technique for on-line monitoring of the oxygen transfer rate in shake flasks. Within the limits of the model validity, the enzyme test bench predictions are in good agreement with the long-term experiments.

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