In this age of technology, the vision of manufacturing industries built of smart factories is not a farfetched future. As a prerequisite for Industry 4.0, industrial sectors are moving towards digitalization and automation. Despite its tremendous growth reaching a sales value of worth $188 billion in 2017, the biopharmaceutical sector distinctly lags in this transition. Currently, the challenges are innovative market disruptions such as personalized medicine as well as increasing commercial pressure for faster and cheaper product manufacturing. Improvements in digitalization and data analytics have been identified as key strategic activities for the next years to face these challenges. Alongside, there is an emphasis by the regulatory authorities on the use of advanced technologies, proclaimed through initiatives such as Quality by Design (QbD) and Process Analytical Technology (PAT). In the manufacturing sector, the biopharmaceutical domain features some of the most complex and least understood processes. Thereby, process models that can transform process data into more valuable information, guide decision‐making, and support the creation of digital and automated technologies are key enablers. This review summarizes the current state of model‐based methods in different bioprocess related applications and presents the corresponding future vision for the biopharmaceutical industry to achieve the goals of Industry 4.0 while meeting the regulatory requirements.

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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Clavulanic acid (CA) is produced by Streptomyces clavuligerus (S. clavuligerus) as a secondary metabolite. Knowledge about the carbon flux distribution along the various routes that supply CA precursors would certainly provide insights about metabolic performance. In order to evaluate metabolic patterns and the possible accumulation of tricarboxylic acid (TCA) cycle intermediates during CA biosynthesis, batch and subsequent continuous cultures with steadily declining feed rates were performed with glycerol as the main substrate. The data were used to in silico explore the metabolic capabilities and the accumulation of metabolic intermediates in S. clavuligerus. While clavulanic acid accumulated at glycerol excess, it steadily decreased at declining dilution rates; CA synthesis stopped when glycerol became the limiting substrate. A strong association of succinate, oxaloacetate, malate, and acetate accumulation with CA production in S. clavuligerus was observed, and flux balance analysis (FBA) was used to describe the carbon flux distribution in the network. This combined experimental and numerical approach also identified bottlenecks during the synthesis of CA in a batch and subsequent continuous cultivation and demonstrated the importance of this type of methodologies for a more advanced understanding of metabolism; this potentially derives valuable insights for future successful metabolic engineering studies in S. clavuligerus.

A new set of mathematical equations describing overflow metabolism and acetate accumulation in E. coli cultivation is presented. The model is a significant improvement of already existing models in the literature, with modifications based on the more recent concept of acetate cycling in E. coli, as revealed by proteomic studies of overflow routes. This concept opens up new questions regarding the speed of response of the acetate production and its consumption mechanisms in E. coli. The model is formulated as a set of continuous differentiable equations, which significantly improves model tractability and facilitates the computation of dynamic sensitivities in all relevant stages of fermentation (batch, fed-batch, starvation). The model is fitted to data from a simple 2 L fed-batch cultivation of E. coli W3110M, where twelve (12) out of the sixteen (16) parameters were exclusively identified with relative standard deviation less than 10%. The framework presented gives valuable insight into the acetate dilemma in industrial fermentation processes, and serves as a tool for the development, optimization and control of E. coli fermentation processes.

BACKGROUND The impact of concentration gradients in large industrial‐scale bioreactors on microbial physiology can be studied in scale‐down bioreactors. However, scale‐down systems pose several challenges in construction, operation and footprint. Therefore, it is challenging to implement them in emerging technologies for bioprocess development, such as in high throughput cultivation platforms. In this study, a mechanistic model of a two‐compartment scale‐down bioreactor is developed. Simulations from this model are then used as bases for a pulse‐based scale‐down bioreactor suitable for application in parallel cultivation systems. RESULTS As an application, the pulse‐based system model was used to study the misincorporation of non‐canonical branched‐chain amino acids into recombinant pre‐proinsulin expressed in Escherichia coli, as a response to oscillations in glucose and dissolved oxygen concentrations. The results show significant accumulation of overflow metabolites, up to 18.3% loss in product yield and up to 10‐fold accumulation of the non‐canonical amino acids norvaline and norleucine in the product in the pulse‐based cultivation, compared with a reference cultivation. CONCLUSIONS Results indicate that the combination of a pulse‐based scale‐down approach with mechanistic models is a very suitable method to test strain robustness and physiological constraints at the early stages of bioprocess development. © 2018 Society of Chemical Industry

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.