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.

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The online redesign of experiments for parameter determination of nonlinear dynamic systems has been studied recently by different research groups. In this article, this technique is assessed in a real case study for the first time. The presented algorithm adopts well-known concepts from model-based control. Compared to previous studies, special attention is given to the efficient treatment of the underlying nonlinear and possibly ill-conditioned parameter estimation and experiment design problems. These problems are solved with single shooting and gradient-based nonlinear programming (NLP) solvers. We use an initial value solver, which generates first- and second-order sensitivities to compute exact derivatives of the problem functions. As a special feature, we propose the integration of a local parameter identifiability analysis and a corresponding algorithm that generates well-conditioned problems. The practical applicability is demonstrated by experimental application to a chromatography column system where A, D, and E optimal experiments are performed

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In contrast to the majority of published works in the field of model-based optimal experimental design which focused on numerical studies so as to demonstrate the validity of the OED approach or the development of new criteria or numerical approaches, this work is mainly concerned with the experimental application and practical insights gained from the adaption of an optimal design framework. The presented work is discussed based on the determination of protein ion-exchange equilibrium parameters. For this purpose, special attention is paid to the explicit modeling of all laboratory steps so as to prepare, implement, and analyze experiments in order to have a realistic definition of the numeric design problem and to formally include experimental restrictions and sources of uncertainties in the problem formulation. Moreover, whereas the effect of erroneous assumptions in the initially assumed parameter values have been covered by various authors, in this work, uncertainties are considered in a more general way including those which arise during an imprecise implementation of optimal planned experiments. To compensate for uncertainty influences, a feed-back based approach to optimal design is adopted based on the combination of the parallel and sequential design approaches. Uncertainty identification is done by solution of an augmented parameter estimation problem, where deviations in the experimental design are detected and estimated together with the parameter values. It has been shown that uncertainty influences vanish along with the iterative refinement of the experiment design variables and estimated parameter values.