In general, enzymes have their own optimum conditions for maximum activity and inherent structural stability. These unique characteristics also reflect in the most favorable conditions for use of enzymes in various applications and especially where multiple enzyme systems are required, e.g. coupled enzyme assays. When multiple enzyme systems are required the buffer and co-reagent conditions may be different for each enzyme present and optimisation is always necessary to maximize enzyme efficiency.

Also, there are no generic theoretical models to explain the performance of every enzyme system and in general successful optimisation is characterized by empirical experimentation, necessitating large design protocols and excessive amounts of time. In industrial contexts especially, research productivity is of crucial concern, therefore the use of statistical problem solving, including factorial design is beginning to be used increasingly.

Factorial design and related statistical techniques provide a set of tools, which can be used to maximize empirical problem solving with the minimum amount of time and resources, increasing the amount of information-rich data obtained from experiments [1].

This presentation demonstrates these tools to optimise the formulation of an analytical multi-enzyme system, containing Hexokinase and glucose-6-phosphate dehydrogenase. The optimisation of buffer types, buffer concentrations, pH, co-factor concentrations and stabilisation formulations will be shown as an example of the power of the factorial design technique.

[1] P. D. Haaland (1989). Experimental Design in Biotechnology New York: Marcel Dekker, Inc.