Bioprocess scale-up is a critical step in process development. However, loss of production performance upon scaling-up, including reduced titer, yield, or productivity, has often been observed, hindering the commercialization of biotech innovations. Recent developments in scale-down studies assisted by computational fluid dynamics (CFD) and powerful stimulus–response metabolic models afford better process prediction and evaluation, enabling faster scale-up with minimal losses. In the future, an ideal bioprocess design would be guided by an in silico model that integrates cellular physiology (spatiotemporal multiscale cellular models) and fluid dynamics (CFD models). Nonetheless, there are challenges associated with both establishing predictive metabolic models and CFD coupling. By highlighting these and providing possible solutions here, we aim to advance the development of a computational framework to accelerate bioprocess scale-up.

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The “lifelines” or “trajectories” of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.

During glucose-limited growth, a substantial input of adenosine triphosphate (ATP) is required for the production of β-lactams by the filamentous fungus Penicillium chrysogenum. Formate dehydrogenase has been confirmed in P. chrysogenum for formate oxidation allowing an extra supply of ATP, and coassimilation of glucose and formate has the potential to increase penicillin production and biomass yield. In this study, the steady-state metabolite levels and fluxes in response to cofeeding of formate as an auxiliary substrate in glucose-limited chemostat cultures at the dilution rates (D) of both 0.03 h⁻¹ and 0.05 h⁻¹ are determined to evaluate the quantitative impact on the physiology of a high-yielding P. chrysogenum strain. It is observed that an equimolar addition of formate is conducive to an increase in both biomass yield and penicillin production at D = 0.03 h⁻¹, while this is not the case at D = 0.05 h⁻¹. In addition, a higher cytosolic redox status (NADH/NAD⁺), a higher intracellular glucose level, and lower penicillin productivity are only observed upon formate addition at D = 0.05 h⁻¹, which are virtually absent at D = 0.03 h⁻¹. In conclusion, the results demonstrate that the effect of formate as an auxiliary substrate on penicillin productivity in the glucose-limited chemostat cultivations of P. chrysogenum is not only dependent on the formate/glucose ratio as published before but also on the specific growth rate. The results also imply that the overall process productivity and quality regarding the use of formate should be further explored in an actual industrial-scale scenario.

In Penicillium chrysogenum, it has been observed that turnover of storage carbohydrates (trehalose, mannitol, arabitol, erythritol and glycogen) resulting in an extra ATP expenditure might partly account for the reduced penicillin productivity under dynamic cultivation conditions. In this work, Penicillium chrysogenum mutants with altered trehalose metabolism were constructed using the Agrobacterium-mediated transformation method. It was observed that impaired trehalose biosynthesis did not result in growth arrest and change of glucose sensitivity to high glucose levels, but negatively influenced the sporulation. Compared with the original strain, in glucose-limited chemostat cultures, the biomass yield on glucose and energy efficiency were slightly enhanced; however, the penicillin productivity was significantly lowered in the trehalose mutant strains. Comparison with a high-yielding P. chrysogenum strain revealed that the original and mutant strains had a lower glucose uptake capacity but higher intracellular levels of free amino acids. Flux estimates through the central carbon metabolism showed distinctive difference in the upper part of the glycolysis and in the pentose phosphate pathway but comparable flux through the TCA cycle. Combining, the striking phenotypic effects observed in the trehalose mutants of P. chrysogenum indicated that trehalose metabolism plays an important role in metabolic regulation and is central to maintaining higher penicillin productivity under glucose-limited chemostat cultures.

Targeted, quantitative metabolomics can, in principle, provide precise information on intracellular metabolite levels, which can be applied to accurate modeling of intracellular processes required in systems biology and metabolic engineering. However, quantitative metabolite profiling is often hampered by biased mass spectrometry-based analyses caused by matrix effects, the degradation of metabolites and metabolite leakage during sample preparation, and unexpected variation in instrument responses. Isotope Dilution Mass Spectrometry (IDMS) has been proven as the most accurate method for high-throughput detection of intracellular metabolite concentrations, and the key has been the acquisition of the corresponding fully uniformly (U) - ¹³ C-labeled metabolites to be measured. Here, we have prepared U- ¹³ C-labeled cell extracts by cultivating P. chrysogenum in a fed-batch fermentation with fully U- ¹³ C-labeled substrates. Towards this goal, a dynamic fed-batch model describing P. chrysogenum growth and penicillin production was used to simulate the fermentation process and design the fed-batch fermentation media. Further, a case study with extensive intracellular metabolomics data from glucose-limited cultivation of Penicillium chrysogenum under both single and repetitive glucose pulses was illustrated by using the IDMS methods with the prepared U- ¹³ C-labeled cell extracts as internal standards. In conclusion, the IDMS method can be incorporated into well-established fast sampling and quenching protocols to obtain dynamic quantitative in vivo metabolome data at the timescales of (tens of) seconds and elucidate the underlying regulatory architecture. The case study revealed gross differences between single and repeated pulses, which suggests that single pulse studies have limited value for understanding of metabolic responses in large-scale bioreactors. Instead, intermittent feeding should be favored.

In the present work, by performing chemostat experiments at 400 and 600 RPM, two typical power inputs representative of industrial penicillin fermentation (P/V, 1.00 kW/m³ in more remote zones and 3.83 kW/m³ in the vicinity of the impellers, respectively) were scaled-down to bench-scale bioreactors. It was found that at 400 RPM applied in prolonged glucose-limited chemostat cultures, the previously reported degeneration of penicillin production using an industrial Penicillium chrysogenum strain was virtually absent. To investigate this, the cellular response was studied at flux (stoichiometry), residual glucose, intracellular metabolite and transcript levels. At 600 RPM, 20% more cell lysis was observed and the increased degeneration of penicillin production was accompanied by a 22% larger ATP gap and an unexpected 20-fold decrease in the residual glucose concentration (C_s,out). At the same time, the biomass specific glucose consumption rate (q_s) did not change but the intracellular glucose concentration was about sixfold higher, which indicates a change to a higher affinity glucose transporter at 600 RPM. In addition, power input differences cause differences in the diffusion rates of glucose and the calculated Batchelor diffusion length scale suggests the presence of a glucose diffusion layer at the glucose transporting parts of the hyphae, which was further substantiated by a simple proposed glucose diffusion-uptake model. By analysis of calculated mass action ratios (MARs) and energy consumption, it indicated that at 600 RPM glucose sensing and signal transduction in response to the low C_s,out appear to trigger a gluconeogenic type of metabolic flux rearrangement, a futile cycle through the pentose phosphate pathway (PPP) and a declining redox state of the cytosol. In support of the change in glucose transport and degeneration of penicillin production at 600 RPM, the transcript levels of the putative high-affinity glucose/hexose transporter genes Pc12g02880 and Pc06g01340 increased 3.5- and 3.3-fold, respectively, and those of the pcbC gene encoding isopenicillin N-synthetase (IPNS) were more than twofold lower in the time range of 100–200 hr of the chemostat cultures. Summarizing, changes at power input have unexpected effects on degeneration and glucose transport, and result in significant metabolic rearrangements. These findings are relevant for the industrial production of penicillin, and other fermentations with filamentous microorganisms.

A powerful approach for the optimization of industrial bioprocesses is to perform detailed simulations integrating large-scale computational fluid dynamics (CFD) and cellular reaction dynamics (CRD). However, complex metabolic kinetic models containing a large number of equations pose formidable challenges in CFD-CRD coupling and computation time afterward. This necessitates to formulate a relatively simple but yet representative model structure. Such a kinetic model should be able to reproduce metabolic responses for short-term (mixing time scale of tens of seconds) and long-term (fed-batch cultivation of hours/days) dynamics in industrial bioprocesses. In this paper, we used Penicillium chrysogenum as a model system and developed a metabolically structured kinetic model for growth and production. By lumping the most important intracellular metabolites in 5 pools and 4 intracellular enzyme pools, linked by 10 reactions, we succeeded in maintaining the model structure relatively simple, while providing informative insight into the state of the organism. The performance of this 9-pool model was validated with a periodic glucose feast–famine cycle experiment at the minute time scale. Comparison of this model and a reported black box model for this strain shows the necessity of employing a structured model under feast–famine conditions. This proposed model provides deeper insight into the in vivo kinetics and, most importantly, can be straightforwardly integrated into a computational fluid dynamic framework for simulating complete fermentation performance and cell population dynamics in large scale and small scale fermentors. Biotechnol. Bioeng. 2017;114: 1733–1743.