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.

Background Lignocellulosic biomass is an abundant and sustainable feedstock, which represents a promising raw material for the production of lactic acid via microbial fermentation. However, toxic compounds that affect microbial growth and metabolism are released from the biomass upon thermochemical pre-treatment. So far, susceptibility of bacterial strains to biomass-derived inhibitors still represents a major barrier to lactic acid production from lignocellulose. Detoxification of the pre-treated lignocellulosic material by water washing is commonly performed to alleviate growth inhibition of the production microorganism and achieve higher production rates. Results In this study, we assessed the feasibility of replacing the washing step with integrated cellular adaptation during pre-culture of Bacillus coagulans MA-13 prior to simultaneous saccharification and lactic acid fermentation of steam exploded wheat straw. Using a seed culture pre-exposed to 30% hydrolysate led to 50% shorter process time, 50% higher average volumetric and 115% higher average specific productivity than when using cells from a hydrolysate-free seed culture. Conclusions Pre-exposure of B. coagulans MA-13 to hydrolysate supports adaptation to the actual production medium. This strategy leads to lower process water requirements and combines cost-effective seed cultivation with physiological pre-adaptation of the production strain, resulting in reduced lactic acid production costs. Electronic supplementary material The online version of this article (10.1186/s13068-019-1382-2) contains supplementary material, which is available to authorized users.

Bioprocess development, optimization, and control in mini-bioreactor systems require information about essential process parameters, high data densities, and the ability to dynamically change process conditions. We present an integration approach combining a parallel mini-bioreactor system integrated into a liquid handling station (LHS) with a second LHS for offline analytics. Non-invasive sensors measure pH and DO online. Offline samples are collected every 20 min and acetate, glucose, and OD 620 subsequently analyzed offline. All data are automatically collected, analyzed, formalized, and used for process control and optimization. Fed-batch conditions are realized via a slow enzymatic glucose release system. The integration approach was successfully used to apply an online experimental re-design method to eight Escherichia coli fed-batch cultivations. The method utilizes generated data to select the following experimental actions online in order to reach the optimization goal of estimating E. coli fed-batch model parameters with as high accuracy as possible. Optimal experimental designs were re-calculated online based on the experimental data and implemented by introducing pulses via the LHS to the running fermentations. The LHS control allows for various implementations of advanced control and optimization strategies in milliliter scale.

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