Insights into pH-induced metabolic switch by flux balance analysis

ATP biosensor reveals microbial energetic dynamics and facilitates bioproduction

Adenosine-5'-triphosphate (ATP), the primary energy currency in cellular processes, drives metabolic activities and biosynthesis. Despite its importance, understanding intracellular ATP dynamics' impact on bioproduction and exploiting it for enhanced bioproduction remains largely unexplored. Here, we harness an ATP biosensor to dissect ATP dynamics across different growth phases and carbon sources in multiple microbial strains. We find transient ATP accumulations during the transition from exponential to stationary growth phases in various conditions, coinciding with fatty acid (FA) and polyhydroxyalkanoate (PHA) production in Escherichia coli and Pseudomonas putida, respectively. We identify carbon sources (acetate for E. coli, oleate for P. putida) that elevate steady-state ATP levels and boost FA and PHA production. Moreover, we employ ATP dynamics as a diagnostic tool to assess metabolic burden, revealing bottlenecks that limit limonene bioproduction. Our results not only elucidate the relationship between ATP dynamics and bioproduction but also showcase its value in enhancing bioproduction in various microbial species.

Building blocks needed for mechanistic modeling of bioprocesses: A critical review based on protein production by CHO cells

This paper reviews the key building blocks needed to develop a mechanistic model for use as an operational production tool. The Chinese Hamster Ovary (CHO) cell, one of the most widely used hosts for antibody production in the pharmaceutical industry, is considered as a case study. CHO cell metabolism is characterized by two main phases, exponential growth followed by a stationary phase with strong protein production. This process presents an appropriate degree of complexity to outline the modeling strategy. The paper is organized into four main steps: (1) CHO systems and data collection; (2) metabolic analysis; (3) formulation of the mathematical model; and finally, (4) numerical solution, calibration, and validation. The overall approach can build a predictive model of target variables. According to the literature, one of the main current modeling challenges lies in understanding and predicting the spontaneous metabolic shift. Possible candidates for the trigger of the metabolic shift include the concentration of lactate and carbon dioxide. In our opinion, ammonium, which is also an inhibiting product, should be further investigated. Finally, the expected progress in the emerging field of hybrid modeling, which combines the best of mechanistic modeling and machine learning, is presented as a fascinating breakthrough. Note that the modeling strategy discussed here is a general framework that can be applied to any bioprocess.

Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

Computational fluid dynamics simulation improves the design and characterization of a plug-flow-type scale-down reactor for microbial cultivation processes

The scale-up of bioprocesses remains one of the major obstacles in the biotechnology industry. Scale-down bioreactors have been identified as valuable tools to investigate the heterogeneities observed in large-scale tanks at the laboratory scale. Additionally, computational fluid dynamics (CFD) simulations can be used to gain information about fluid flow in tanks used for production. Here, we present the rational design and comprehensive characterization of a scale-down setup, in which a flexible and modular plug-flow reactor was connected to a stirred-tank bioreactor. With the help of CFD using the realizable k-ε model, the mixing time difference between a 20 and 4000 L bioreactor was evaluated and used as scale-down criterion. CFD simulations using a shear stress transport (SST) k-ω turbulence model were used to characterize the plug-flow reactor in more detail, and the model was verified using experiments. Additionally, the model was used to simulate conditions where experiments technically could not be performed due to sensor limitations. Nevertheless, verification is difficult in this case as well. This was the first time a scale-down setup was tested on high-cell-density Escherichia coli cultivations to produce industrially relevant antigen-binding fragments (Fab). Biomass yield was reduced by 11% and specific product yield was reduced by 20% during the scale-down cultivations. Additionally, the intracellular Fab fraction was increased by using the setup. The flexibility of the introduced scale-down setup in combination with CFD simulations makes it a valuable tool for investigating scale effects at the laboratory scale. More information about the large scale is still necessary to further refine the setup and to speed up bioprocess scale-up in the future.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

Proteomic Profiling of IgG1 Producing CHO Cells Using LC/LC-SPS-MS3: The Effects of Bioprocessing Conditions on Productivity and Product Quality

The biopharmaceutical market is dominated by monoclonal antibodies, the majority of which are produced in Chinese hamster ovary (CHO) cell lines. Intense cell engineering, in combination with optimization of various process parameters results in increasing product titers. To enable further improvements in manufacturing processes, detailed information about how certain parameters affect cellular mechanisms in the production cells, and thereby also the expressed drug substance, is required. Therefore, in this study the effects of commonly applied changes in bioprocessing parameters on an anti-IL8 IgG1 producing CHO DP-12 cell line were investigated on the level of host cell proteome expression combined with product quality assessment of the expressed IgG1 monoclonal antibody. Applying shifts in temperature, pH and dissolved oxygen concentration, respectively, resulted in altered productivity and product quality. Furthermore, analysis of the cells using two-dimensional liquid chromatography-mass spectrometry employing tandem mass tag based isotopic quantitation and synchronous precursor selection-MS³ detection revealed substantial changes in the protein expression profiles of CHO cells. Pathway analysis indicated that applied bioprocessing conditions resulted in differential activation of oxidative phosphorylation. Additionally, activation of ERK5 and TNFR1 signaling suggested an affected cell cycle. Moreover, in-depth product characterization by means of charge variant analysis, peptide mapping, as well as structural and functional analysis, revealed posttranslational and structural changes in the expressed drug substance. Taken together, the present study allows the conclusion that, in anti-IL8 IgG1 producing CHO DP-12 cells, an improved energy metabolism achieved by lowering the cell culture pH is favorable when aiming towards high antibody production rates while maintaining product quality.

Lactoyl leucine and isoleucine are bioavailable alternatives for canonical amino acids in cell culture media

Increasing demands for protein-based therapeutics such as monoclonal antibodies, fusion proteins, bispecific molecules, and antibody fragments require researchers to constantly find innovative solutions. To increase yields and decrease costs of next generation bioprocesses, highly concentrated cell culture media formulations are developed but often limited by the low solubility of amino acids such as tyrosine, cystine, leucine, and isoleucine, in particular at physiological pH. This study sought to investigate highly soluble and bioavailable derivatives of leucine and isoleucine that are applicable for fed-batch processes. N-lactoyl-leucine and N-lactoyl-isoleucine sodium salts were tested in cell culture media and proved to be beneficial to increase the overall solubility of cell culture media formulations. These modified amino acids proved to be bioavailable for various Chinese hamster ovary (CHO) cells and were suitable for replacement of canonical amino acids in cell culture feeds. The quality of the final recombinant protein was studied in bioprocesses using the derivatives, and the mechanism of cleavage was investigated in CHO cells. Altogether, both N-lactoyl amino acids represent an advantageous alternative to canonical amino acids to develop highly concentrated cell culture media formulations to support next generation bioprocesses.

Regulation of pyruvate dehydrogenase complex related to lactate switch in CHO cells

The metabolism of Chinese hamster ovary (CHO) cell lines is typically characterized by high rates of aerobic glycolysis with increased lactate formation, known as the "Warburg" effect. Although this metabolic state can switch to lactate consumption, the involved regulations of the central metabolism have only been partially studied so far. An important reaction transferring the lactate precursor, pyruvate, into the tricarboxylic acid cycle is the decarboxylation reaction catalyzed by the pyruvate dehydrogenase enzyme complex (PDC). Among other mechanisms, PDC is mainly regulated by phosphorylation-dephosphorylation at the three sites Ser232, Ser293, and Ser300. In this work, the PDC phosphorylation in antibody-producing CHO DP-12 cell culture is investigated during the lactate switch. Batch cultivations were carried out with frequent sampling (every 6 h) during the transition from lactate formation to lactate uptake, and the PDC phosphorylation levels were quantified using a novel indirect flow cytometry protocol. Contrary to the expected activation of PDC (i.e., reduced PDC phosphorylation) during lactate consumption, Ser293 and Ser300 phosphorylation levels were 33% higher compared to the phase of glucose excess. At the same time, the relative phosphorylation level of Ser232 increased steadily throughout the cultivation (66% increase overall). The intracellular pyruvate was found to accumulate only during the period of high lactate production, while acetyl-CoA showed nearly no accumulation. These results indicate a deactivation of PDC and reduced oxidative metabolism during lactate switch even though the cells undergo a metabolic transition to lactate-based cell growth and metabolism. Overall, this study provides a unique view on the regulation of PDC during the lactate switch, which contributes to an improved understanding of PDC and its interaction with the bioprocess.

Application of metabolic modeling for targeted optimization of high seeding density processes

Process intensification by application of perfusion mode in pre‐stage bioreactors and subsequent inoculation of cell cultures at high seeding densities (HSD) has the potential to meet the increasing requirements of future manufacturing demands. However, process development is currently restrained by a limited understanding of the cell's requirements under these process conditions. The goal of this study was to use extended metabolite analysis and metabolic modeling for targeted optimization of HSD cultivations. The metabolite analysis of HSD N‐stage cultures revealed accumulation of inhibiting metabolites early in the process and flux balance analysis led to the assumption that reactive oxygen species (ROS) were contributing to the fast decrease in cell viability. Based on the metabolic analysis an optimized feeding strategy with lactate and cysteine supplementation was applied, resulting in an increase in antibody titer of up to 47%. Flux balance analysis was further used to elucidate the surprisingly strong synergistic effect of lactate and cysteine, indicating that increased lactate uptake led to reduced ROS formation under these conditions whilst additional cysteine actively reduced ROS via the glutathione pathway. The presented results finally demonstrate the benefit of modeling approaches for process intensification as well as the potential of HSD cultivations for biopharmaceutical manufacturing.

Induced pluripotent stem cells can utilize lactate as a metabolic substrate to support proliferation

Human-induced pluripotent stem cells (iPSCs) hold the promise to improve cell-based therapies. Yet, to meet rising demands and become clinically impactful, sufficient high-quality iPSCs in quantity must be generated, a task that exceeds current capabilities. In this study, K3 iPSCs cultures were examined using parallel-labeling metabolic flux analysis (¹³ C-MFA) to quantify intracellular fluxes at relevant bioprocessing stages: glucose concentrations representative of initial media concentrations and high lactate concentrations representative of fed-batch culture conditions, prior to and after bolus glucose feeds. The glucose and lactate concentrations are also representative of concentrations that might be encountered at different locations within 3D cell aggregates. Furthermore, a novel method was developed to allow the isotopic tracer [U-¹³ C₃ ] lactate to be used in the ¹³ C-MFA model. The results indicated that high extracellular lactate concentrations decreased glucose consumption and lactate production, while glucose concentrations alone did not affect rates of aerobic glycolysis. Moreover, for the high lactate cultures, lactate was used as a metabolic substrate to support oxidative mitochondrial metabolism. These results demonstrate that iPSCs have metabolic flexibility and possess the capacity to metabolize lactate to support exponential growth, and that high lactate concentrations alone do not adversely impact iPSC proliferation.

Systems biology approach in the formulation of chemically defined media for recombinant protein overproduction

The cell culture medium is an intricate mixture of components which has a tremendous effect on cell growth and recombinant protein production. Regular cell culture medium includes various components, and the decision about which component should be included in the formulation and its optimum amount is an underlying issue in biotechnology industries. Applying conventional techniques to design an optimal medium for the production of a recombinant protein requires meticulous and immense research. Moreover, since the medium formulation for the production of one protein could not be the best choice for another protein, hence, the most suitable media should be determined for each recombinant cell line. Accordingly, medium formulation becomes a laborious, time-consuming, and costly process in biomanufacturing of recombinant protein, and finding alternative strategies for medium development seems to be crucial. In silico modeling is an attractive concept to be adapted for medium formulation due to its high potential to supersede laboratory examinations. By emerging the high-throughput datasets, scientists can disclose the knowledge about the effect of medium components on cell growth and metabolism, and via applying this information through systems biology approach, medium formulation optimization could be accomplished in silico with no need of significant amount of experimentation. This review demonstrates some of the applications of systems biology as a powerful tool for medium development and illustrates the effect of medium optimization with system-level analysis on the production of recombinant proteins in different host cells.

The Less the Better: How Suppressed Base Addition Boosts Production of Monoclonal Antibodies With Chinese Hamster Ovary Cells

Biopharmaceutical production processes strive for the optimization of economic efficiency. Among others, the maximization of volumetric productivity is a key criterion. Typical parameters such as partial pressure of CO2 (pCO2) and pH are known to influence the performance although reasons are not yet fully elucidated. In this study the effects of pCO2 and pH shifts on the phenotypic performance were linked to metabolic and energetic changes. Short peak performance of qmAb (23 pg/cell/day) was achieved by early pCO2 shifts up to 200 mbar but followed by declining intracellular ATP levels to 2.5 fmol/cell and 80% increase of qLac. On the contrary, steadily rising qmAb could be installed by slight pH down-shifts ensuring constant cell specific ATP production (qATP) of 27 pmol/cell/day and high intracellular ATP levels of about 4 fmol/cell. As a result, maximum productivity was achieved combining highest qmAb (20 pg/cell/day) with maximum cell density and no lactate formation. Our results indicate that the energy availability in form of intracellular ATP is crucial for maintaining antibody synthesis and reacts sensitive to pCO2 and pH-process parameters typically responsible for inhomogeneities after scaling up.

Perfusion cultures require optimum respiratory ATP supply to maximize cell-specific and volumetric productivities

Perfusion processes are an emerging alternative to common fed-batch processes in the growing biopharmaceutical industry. However, the challenge of maintaining high cell-specific productivities remains. In this study, glucose limitation was applied to two perfusion steady states and compared with a third steady state without any detectable limitation. The metabolic phenotype was enhanced under glucose limitation with a decrease of 30% in glucose uptake and 75% in lactate formation. Cell-specific productivities were substantially improved by 50%. Remarkably, the productivities showed a strong correlation to respiratory adenosine triphosphate (ATP) supply. As less reduced nicotinamide adenine dinucleotide (NADH) remained in the cytosol, the ATP generation from oxidative phosphorylation was increased by almost 30%. Consequently, the efficiency of carbon metabolism and the resulting respiratory ATP supply was crucial for maintaining the highly productive cellular state. This study highlights that glucose limitation can be used for process intensification in perfusion cultures as ATP generation via respiration is significantly increased, leading to elevated productivities.

A new unbiased and highly automated approach to find new prognostic markers in preclinical research

Data acquisition in (pre)clinical studies is often based on a hypothesis. Numerical algorithms, however, may help to find biomarkers from existing data without formulating any hypothesis. By simply assessing whether a statistical relationship exists between two parameters from a (unlimited) database, every (in)conceivable combination of data becomes a hypothesis. The aim was to create an unbiased and highly automated approach for secondary analysis of (pre)clinical research, including the possibility of a non-linear functional relationship. In our example, an almost homogeneous database was formed by overall 45 parameters (vital, blood and plasma parameters) measured in 11 individual experimental studies at 6 different time points using 57 rats without and 63 rats with systemic inflammation following lipopolysaccharide infusion. For each rat, four group classifiers (treatment, survival, study, ID) were used to get valid samples by a later filtering of the statistical base. Any information about the hypothesis leading to the respective studies was suppressed. In order to assess whether a statistical relationship exists, a total of six different functional prototypes (linear and non-linear) were postulated and examined for their regression. Regression quality, correlation and significance were obtained in form of matrices. In our example, ultimately 510 300 regressions were optimized, automatically evaluated and filtered. The developed algorithm is able to reveal statistical relationships from a nearly crude database with low effort by systematic and unbiased analysis. The finding of well-known correlations proves its reliability, whose validity could be increased by clean aggregation of different studies. In addition, new interesting hints for future research could be gained. Thus, unknown markers can be found which are associated with an increased risk of death during systemic inflammation and sepsis. A further development of the program is planned including multiple regressions (more than two parameters could be related to each other) or cluster analysis.

Proteomic analysis of micro-scale bioreactors as scale-down model for a mAb producing CHO industrial fed-batch platform

The pharmaceutical production of recombinant proteins, such as monoclonal antibodies, is rather complex and requires proper development work. Accordingly, it is essential to develop appropriate scale-down models, which can mimic the corresponding production scale. In this work, we investigated the impact of the bioreactor scale on intracellular micro-heterogeneities of a CHO cell line producing monoclonal antibodies in fed-batch mode, using a 10 mL micro-bioreactor (ambr™) scale-down model and the corresponding 300 L pilot-scale bioreactor. For each scale, we measured the time evolution of the proteome, which enabled us to compare the impact of the bioreactor scale on the intracellular processes. Nearly absolute accordance between the scales was verified by data mining methods, such as hierarchical clustering and in-detail analysis on a single protein base. The time response of principal enzymes related to N-glycosylation was discussed, emphasizing major dissimilarities between the glycan fractions adorning the heavy chain and the corresponding protein abundance. The enzyme expression displayed mainly a constant profile, whereas the resulting glycan pattern changed over time. It is concluded that the enzymatic activity is influenced by the changing environmental conditions present in the fed-batch processes leading to the observed time-dependent variation.

Elevated pCO2 affects the lactate metabolic shift in CHO cell culture processes

The shift from lactate production to consumption in CHO cell metabolism is a key event during cell culture cultivations and is connected to increased culture longevity and final product titers. However, the mechanisms controlling this metabolic shift are not yet fully understood. Variations in lactate metabolism have been mainly reported to be induced by process pH and availability of substrates like glucose and glutamine. The aim of this study was to investigate the effects of elevated pCO₂ concentrations on the lactate metabolic shift phenomena in CHO cell culture processes. In this publication, we show that at elevated pCO₂ in batch and fed-batch cultures, the lactate metabolic shift was absent in comparison to control cultures at lower pCO₂ values. Furthermore, through metabolic flux analysis we found a link between the lactate metabolic shift and the ratio of NADH producing and regenerating intracellular pathways. This ratio was mainly affected by a reduced oxidative capacity of cultures at elevated pCO₂. The presented results are especially interesting for large-scale and perfusion processes where increased pCO₂ concentrations are likely to occur. Our results suggest, that so far unexplained metabolic changes may be connected to increased pCO₂ accumulation in larger scale fermentations. Finally, we propose several mechanisms through which increased pCO₂ might affect the cell metabolism and briefly discuss methods to enable the lactate metabolic shift during cell cultivations.

Modelling microbial metabolic rewiring during growth in a complex medium

Background

In their natural environment, bacteria face a wide range of environmental conditions that change over time and that impose continuous rearrangements at all the cellular levels (e.g. gene expression, metabolism). When facing a nutritionally rich environment, for example, microbes first use the preferred compound(s) and only later start metabolizing the other one(s). A systemic re-organization of the overall microbial metabolic network in response to a variation in the composition/concentration of the surrounding nutrients has been suggested, although the range and the entity of such modifications in organisms other than a few model microbes has been scarcely described up to now.

Results

We used multi-step constraint-based metabolic modelling to simulate the growth in a complex medium over several time steps of the Antarctic model organism Pseudoalteromonas haloplanktis TAC125. As each of these phases is characterized by a specific set of amino acids to be used as carbon and energy source our modelling framework describes the major consequences of nutrients switching at the system level. The model predicts that a deep metabolic reprogramming might be required to achieve optimal biomass production in different stages of growth (different medium composition), with at least half of the cellular metabolic network involved (more than 50% of the metabolic genes). Additionally, we show that our modelling framework is able to capture metabolic functional association and/or common regulatory features of the genes embedded in our reconstruction (e.g. the presence of common regulatory motifs).

Finally, to explore the possibility of a sub-optimal biomass objective function (i.e. that cells use resources in alternative metabolic processes at the expense of optimal growth) we have implemented a MOMA-based approach (called nutritional-MOMA) and compared the outcomes with those obtained with Flux Balance Analysis (FBA). Growth simulations under this scenario revealed the deep impact of choosing among alternative objective functions on the resulting predictions of fluxes distribution.

Conclusions

Here we provide a time-resolved, systems-level scheme of PhTAC125 metabolic re-wiring as a consequence of carbon source switching in a nutritionally complex medium. Our analyses suggest the presence of a potential efficient metabolic reprogramming machinery to continuously and promptly adapt to this nutritionally changing environment, consistent with adaptation to fast growth in a fairly, but probably inconstant and highly competitive, environment. Also, we show i) how functional partnership and co-regulation features can be predicted by integrating multi-step constraint-based metabolic modelling with fed-batch growth data and ii) that performing simulations under a sub-optimal objective function may lead to different flux distributions in respect to canonical FBA.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3311-0) contains supplementary material, which is available to authorized users.

Investigation of the interactions of critical scale-up parameters (pH, pO2 and pCO2) on CHO batch performance and critical quality attributes

Understanding process parameter interactions and their effects on mammalian cell cultivations is an essential requirement for robust process scale-up. Furthermore, knowledge of the relationship between the process parameters and the product critical quality attributes (CQAs) is necessary to satisfy quality by design guidelines. So far, mainly the effect of single parameters on CQAs was investigated. Here, we present a comprehensive study to investigate the interactions of scale-up relevant parameters as pH, pO₂ and pCO₂ on CHO cell physiology, process performance and CQAs, which was based on design of experiments and extended product quality analytics. The study used a novel control strategy in which process parameters were decoupled from each other, and thus allowed their individual control at defined set points. Besides having identified the impact of single parameters on process performance and product quality, further significant interaction effects of process parameters on specific cell growth, specific productivity and amino acid metabolism could be derived using this method. Concerning single parameter effects, several monoclonal antibody (mAb) charge variants were affected by process pCO₂ and pH. N-glycosylation analysis showed positive correlations between mAb sialylation and high pH values as well as a relationship between high mannose variants and process pH. This study additionally revealed several interaction effects as process pH and pCO₂ interactions on mAb charge variants and N-glycosylation pattern. Hence, through our process control strategy and multivariate investigation, novel significant process parameter interactions and single effects were identified which have to be taken into account especially for process scale-up.

Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

Biomass and cell-specific metabolic rates usually change dynamically over time, making the "feed according to need" strategy difficult to realize in a commercial fed-batch process. We here demonstrate a novel feeding strategy which is designed to hold a particular metabolic state in a fed-batch process by adaptive feeding in real time. The feed rate is calculated with a transferable biomass model based on capacitance, which changes the nutrient flow stoichiometrically in real time. A limited glucose environment was used to confine the cell in a particular metabolic state. In order to cope with uncertainty, two strategies were tested to change the adaptive feed rate and prevent starvation while in limitation: (i) inline pH and online glucose concentration measurement or (ii) inline pH alone, which was shown to be sufficient for the problem statement. In this contribution, we achieved metabolic control within a defined target range. The direct benefit was two-fold: the lactic acid profile was improved and pH could be kept stable. Multivariate Data Analysis (MVDA) has shown that pH influenced lactic acid production or consumption in historical data sets. We demonstrate that a low pH (around 6.8) is not required for our strategy, as glucose availability is already limiting the flux. On the contrary, we boosted glycolytic flux in glucose limitation by setting the pH to 7.4. This new approach led to a yield of lactic acid/glucose (Y L/G) around zero for the whole process time and high titers in our labs. We hypothesize that a higher carbon flux, resulting from a higher pH, may lead to more cells which produce more product. The relevance of this work aims at feeding mammalian cell cultures safely in limitation with a desired metabolic flux range. This resulted in extremely stable, low glucose levels, very robust pH profiles without acid/base interventions and a metabolic state in which lactic acid was consumed instead of being produced from day 1. With this contribution, we wish to extend the basic repertoire of available process control strategies, which will open up new avenues in automation technology and radically improve process robustness in both process development and manufacturing.

ATP regulation in bioproduction

Adenosine-5'-triphosphate (ATP) is consumed as a biological energy source by many intracellular reactions. Thus, the intracellular ATP supply is required to maintain cellular homeostasis. The dependence on the intracellular ATP supply is a critical factor in bioproduction by cell factories. Recent studies have shown that changing the ATP supply is critical for improving product yields. In this review, we summarize the recent challenges faced by researchers engaged in the development of engineered cell factories, including the maintenance of a large ATP supply and the production of cell factories. The strategies used to enhance ATP supply are categorized as follows: addition of energy substrates, controlling pH, metabolic engineering of ATP-generating or ATP-consuming pathways, and controlling reactions of the respiratory chain. An enhanced ATP supply generated using these strategies improves target production through increases in resource uptake, cell growth, biosynthesis, export of products, and tolerance to toxic compounds.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling

Eukaryotic cell lines, including Chinese hamster ovary cells, yeast, and insect cells, are invaluable hosts for the production of many recombinant proteins. With the advent of genomic resources, one can now leverage genome-scale computational modeling of cellular pathways to rationally engineer eukaryotic host cells. Genome-scale models of metabolism include all known biochemical reactions occurring in a specific cell. By describing these mathematically and using tools such as flux balance analysis, the models can simulate cell physiology and provide targets for cell engineering that could lead to enhanced cell viability, titer, and productivity. Here we review examples in which metabolic models in eukaryotic cell cultures have been used to rationally select targets for genetic modification, improve cellular metabolic capabilities, design media supplementation, and interpret high-throughput omics data. As more comprehensive models of metabolism and other cellular processes are developed for eukaryotic cell culture, these will enable further exciting developments in cell line engineering, thus accelerating recombinant protein production and biotechnology in the years to come.