Microbial bioprocesses need to be designed to be transferable from lab scale to production scale as well as between setups. Although substantial effort is invested to control technological parameters, usually the only true constant parameter is the actual producer of the product: the cell. Hence, instead of solely controlling technological process parameters, the focus should be increasingly laid on physiological parameters. This contribution aims at illustrating a workflow of data life cycle management with special focus on physiology. Information processing condenses the data into physiological variables, while information mining condenses the variables further into physiological descriptors. This basis facilitates data analysis for a physiological explanation for observed phenomena in productivity. Targeting transferability, we demonstrate this workflow using an industrially relevant Escherichia coli process for recombinant protein production and substantiate the following three points: (1) The postinduction phase is independent in terms of productivity and physiology from the preinduction variables specific growth rate and biomass at induction. (2) The specific substrate uptake rate during induction phase was found to significantly impact the maximum specific product titer. (3) The time point of maximum specific titer can be predicted by an easy accessible physiological variable: while the maximum specific titers were reached at different time points (19.8 ± 7.6 h), those maxima were reached all within a very narrow window of cumulatively consumed substrate dSn (3.1 ± 0.3 g/g). Concluding, this contribution provides a workflow on how to gain a physiological view on the process and illustrates potential benefits. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 33:261-270, 2017.
Keywords: bioprocess development; industrial biotechnology; physiologic descriptor; productivity.
© 2016 American Institute of Chemical Engineers.