Cruz Bournazou, Mariano NicolasBarz, TilmanNickel, D. B.Lopez Cárdenas, Diana CarolinaGlauche, FlorianKnepper, AndreasNeubauer, Peter2022-05-112022-05-112016-10-030006-3592https://depositonce.tu-berlin.de/handle/11303/16866http://dx.doi.org/10.14279/depositonce-15644We 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.en570 Biowissenschaften; BiologieE. collifed-batch processonline Optimal Experimental re-designmacro-kinetic growth modelHigh Throughput Bioprocess Developmentparallel mini bioreactorsParameter EstimationArticle1097-0290