Model-based optimization of cell-free enzyme cascades exemplified for the production of GDP-fucose

Cell-free production systems are increasingly used for the synthesis of industrially relevant chemicals and biopharmaceuticals. Cell-free systems often utilize cell lysates, but biocatalytic cascades based on recombinant enzymes have emerged as a promising alternative strategy. However, implementing efficient enzyme cascades is a non-trivial task and mathematical modeling and optimization has become a key tool to improve their performance. In this work, we introduce a generic framework for the model-based optimization of cell-free enzyme cascades based on a given kinetic model of the system. We first formulate and systematize seven optimization problems relevant in the context of cell-free production processes including, for example, the maximization of productivity or product yield and the minimization of overall costs. We then present an approach that accounts for parameter uncertainties, not only during model calibration and model analysis but also when performing the actual optimization. After constructing a kinetic model of the enzyme cascade, experimental data are used to generate an ensemble of kinetic parameter sets reflecting their variabilities. For every parameter set, systems optimization is then performed and the resulting solution subsequently cross-validated for all other parameterizations to identify the solution with the highest overall performance under parameter uncertainty. We exemplify our approach for the cell-free synthesis of GDP-fucose, an important sugar nucleotide with various applications. We selected and solved three optimization problems based on a constructed dynamic model and validated two of them experimentally leading to significant improvements of the process (e.g., 50% increase of titer under identical total enzyme load). Overall, our results demonstrate the potential of model-driven optimization for the rational design and improvement of cell-free production systems. The developed approach for systems optimization under parameter uncertainty could also be relevant for the metabolic design of cell factories.