Online optimal experimental re‐design in robotic parallel fed‐batch cultivation facilities

ABSTRACT We present an integrated framework for the online optimal experimental re‐design applied to parallel nonlinear dynamic processes that aims to precisely estimate the parameter set of macro kinetic growth models with minimal experimental effort. This provides a systematic solution for rapid validation of a specific model to new strains, mutants, or products. In biosciences, this is especially important as model identification is a long and laborious process which is continuing to limit the use of mathematical modeling in this field. The strength of this approach is demonstrated by fitting a macro‐kinetic differential equation model for Escherichia coli fed‐batch processes after 6 h of cultivation. The system includes two fully‐automated liquid handling robots; one containing eight mini‐bioreactors and another used for automated at‐line analyses, which allows for the immediate use of the available data in the modeling environment. As a result, the experiment can be continually re‐designed while the cultivations are running using the information generated by periodical parameter estimations. The advantages of an online re‐computation of the optimal experiment are proven by a 50‐fold lower average coefficient of variation on the parameter estimates compared to the sequential method (4.83% instead of 235.86%). The success obtained in such a complex system is a further step towards a more efficient computer aided bioprocess development. Biotechnol. Bioeng. 2017;114: 610–619. © 2016 Wiley Periodicals, Inc.