Characterization of reactions and growth in automated continuous flow and bioreactor platforms—From linear DoE to model-based approaches

Laboratory automation that drives integrated miniaturized and parallel reactor systems incorporating analytical devices for online reaction monitoring has reached a remarkable level of sophistication. The result is a significant increase in experimental throughput allowing the generation of large amounts of data under complex experimental settings and dynamic conditions, making the design of experiments a very challenging task. Model-based optimal experimental design method is a systematic approach for the most effective exploration of the experimental design space toward a consistent characterization of nonlinear dynamic processes, reactions, catalysts, hosts, model candidates, etc. This contribution presents a critical examination of recent experimental applications performed in automated platforms in the field of classical DoE, model-free, and model-based approaches for the identification and optimization of biochemical reactions. The comparison of applications in continuous flow and bioreactor platforms reveals significant differences in the level of maturity of developed solutions toward an autonomous operation for the generation and analysis of the most informative data.