Biocatalysis has been attracting increasing interest in recent years. Nevertheless, most studies concerning biocatalysis have been carried out using single enzymes (soluble or immobilized). Currently, multiple enzyme mixtures are attractive for the production of many compounds at an industrial level. In this review, a classification of multienzyme-catalyzed processes is proposed. Special emphasis is placed on the description of multienzyme ex-vivo systems where several reactions are carried out by a combination of enzymes acting outside the cell. Furthermore, reaction and process considerations for mathematical modeling are discussed for the specific case where the synthetic reactions are carried out in a single reactor, the so-called multienzyme ‘in-pot’ process. In addition, options for multienzyme ‘in-pot’ process improvements via process engineering and enzyme immobilization technology are described. Finally, enzyme modification via protein engineering is also discussed, such that a better compatibility of the enzymes in the reactor is achieved as a means of assisting the implementation of multienzyme ‘in-pot’ processes.

Effective estimation of parameters in biocatalytic reaction kinetic expressions are very important when building process models to enable evaluation of process technology options and alternative biocatalysts. The kinetic models used to describe enzyme-catalyzed reactions generally include several parameters, which are strongly correlated with each other. State-of-the-art methodologies such as nonlinear regression (using progress curves) or graphical analysis (using initial rate data, for example, the Lineweaver-Burke plot, Hanes plot or Dixon plot) often incorporate errors in the estimates and rarely lead to globally optimized parameter values. In this article, a robust methodology to estimate parameters for biocatalytic reaction kinetic expressions is proposed. The methodology determines the parameters in a systematic manner by exploiting the best features of several of the current approaches. The parameter estimation problem is decomposed into five hierarchical steps, where the solution of each of the steps becomes the input for the subsequent step to achieve the final model with the corresponding regressed parameters. The model is further used for validating its performance and determining the correlation of the parameters. The final model with the fitted parameters is able to describe both initial rate and dynamic experiments. Application of the methodology is illustrated with a case study using the ω-transaminase catalyzed synthesis of 1-phenylethylamine from acetophenone and 2-propylamine.

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