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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

Concentration gradients that occur in large industrial‐scale bioreactors due to mass transfer limitations have significant effects on process efficiency. Hence, it is desirable to investigate the response of strains to such heterogeneities to reduce the risk of failure during process scale‐up. Although there are various scale‐down techniques to study these effects, scale‐down strategies are rarely applied in the early developmental phases of a bioprocess, as they have not yet been implemented on small‐scale parallel cultivation devices. In this study, we combine mechanistic growth models with a parallel mini‐bioreactor system to create a high‐throughput platform for studying the response of Escherichia coli strains to concentration gradients. As a scaled‐down approach, a model‐based glucose pulse feeding scheme is applied and compared with a continuous feed profile to study the influence of glucose and dissolved oxygen gradients on both cell physiology and incorporation of noncanonical amino acids into recombinant proinsulin. The results show a significant increase in the incorporation of the noncanonical amino acid norvaline in the soluble intracellular extract and in the recombinant product in cultures with glucose/oxygen oscillations. Interestingly, the amount of norvaline depends on the pulse frequency and is negligible with continuous feeding, confirming observations from large‐scale cultivations. Most importantly, the results also show that a larger number of the model parameters are significantly affected by the scale‐down scheme, compared with the reference cultivations. In this example, it was possible to describe the effects of oscillations in a single parallel experiment. The platform offers the opportunity to combine strain screening with scale‐down studies to select the most robust strains for bioprocess scale‐up.

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