Bioprocess Research Articles

Techno-economic analysis as a valuable tool for the selection of high-potential extractive fermentation approaches for large-scale prodigiosin production

Prodigiosin is a promising microbial red pigment with several therapeutic properties. Unfortunately, production through fermentation is hampered by product inhibition, resulting in low titers, high production costs, and an elevated price of the final product. It is of the upmost importance to develop cost-effective strategies for the intensification of this metabolite. Previously, we developed a strategy for prodigiosin production using a low-cost medium successfully coupled to a mineral oil extractive phase. In the present work, we analyze through economical modelling the developed strategies, while considering the upstream and downstream processing, aiming to evaluate the economic feasibility of each and determine the best approach for large scale production. As a production benchmark, a reported method for prodigiosin extractive fermentation using micellar ATPS (81 % recovery) was included. From an economic perspective, the best approach resulted in processing only the mineral oil fraction at continuous operation, obtaining a 4.73-fold and 16.77-fold cost per mg decrease when compared to batch and the micellar approach, respectively. The optimum operational parameters to generate 1 kg of prodigiosin at continuous scale were 2,431 L reactor volume with a 91.02 L/h flow. Based on the obtained results, it is possible to propose the mineral oil approach as a cost-effective strategy for simultaneous prodigiosin production and direct extraction. This approach yields higher upstream yields, a relatively high product purity level (∼90 %), and results in a competitive production cost of USD $25 x 10-4 per mg.

Mulberry silkworm biotechnology as the basis of a bioindustrial platform. Improvements on the upstream processing stage (USP)

Molecular engineering is an upstream engineering method for creating functional materials and devices using molecules, and atoms as building blocks. In 2000, Japan became the first country to genetically modify silkworms (Bombyx mori, hereinafter referred to as B. mori). The subsequent research in the development of new materials expanded the possibilities of using sericulture products, characterizing this situation as a "sericulture revolution". In the Russian Federation, molecular engineering in life sciences is aimed at solving the tasks of developing world-class technological platforms to create tools to produce new molecules (biopolymers, proteins, enzymes), bioproducts, cells and organisms. The main "bottom-up" approaches applied in the upstream process (USP) stage of silk production to improve the production and economic performance and quality characteristics of raw materials have been studied. The variety of improvement methods includes: the possibility of using artificial nutrient medium, molecular engineering based on transient expression techniques or stable germline transformation, genetic selection methods, breeding management, etc. The advantages of the mulberry silkworm (B. mori) such as low breeding costs, significantly higher production yield compared to other protein expression systems, favor its use as an effective producer of recombinant proteins, antimicrobial peptides and biologically active substances. USP stage biotechnologies enable the production of new raw materials for downstream processing (DSP) to produce a wide range of products that contribute to improving the quality of human life. A set of biotechnological solutions forms the modern basis of the mulberry silkworm bioindustrial platform.

Continuous biomanufacturing in upstream and downstream processing

Abstract Continuous bioprocesses have become a significant technological change in regulated industries, with process analytical technology (PAT) and quality-by-design (QbD) being essential for enabling continuous biomanufacturing. PAT and QbD are associated with process automation and control, providing real-time key process information. Continuous manufacturing eliminates hold times and reduces processing times, providing benefits such as improved product quality, reduced waste, lower costs, and increased manufacturing flexibility and agility. Over the past decade, advancements in science and engineering, along with the adoption of QbD and the advancement of PAT, have progressed the scientific and regulatory readiness for continuous manufacturing. Regulatory authorities support the implementation of continuous manufacturing using science- and risk-based approaches, providing a great deal of potential to address issues of agility, flexibility, cost, and robustness in the development of pharmaceutical manufacturing processes.

Astaxanthin from microalgae: A review on structure, biosynthesis, production strategies and application

Astaxanthin is a red-colored secondary metabolite with excellent antioxidant properties, typically finds application as foods, feed, cosmetics, nutraceuticals, and medications. Astaxanthin is usually produced synthetically using chemicals and costs less as compared to the natural astaxanthin obtained from fish, shrimps, and microorganisms. Over the decades, astaxanthin has been naturally synthesized from Haematococcus pluvialis in commercial scales and remains exceptional, attributed to its higher bioactive properties as compared to synthetic astaxanthin. However, the production cost of algal astaxanthin is still high due to several bottlenecks prevailing in the upstream and downstream processes. To that end, the present study intends to review the recent trends and advancements in astaxanthin production from microalgae. The structure of astaxanthin, sources, production strategies of microalgal astaxanthin, and factors influencing the synthesis of microalgal astaxanthin were discussed while detailing the pathway involved in astaxanthin biosynthesis. The study also discusses the relevant downstream process used in commercial scales and details the applications of astaxanthin in various health related issues.

Challenges and Opportunities in the Process Development of Chimeric Vaccines.

Vaccines are integral to human life to protect them from life-threatening diseases. However, conventional vaccines often suffer limitations like inefficiency, safety concerns, unavailability for non-culturable microbes, and genetic variability among pathogens. Chimeric vaccines combine multiple antigen-encoding genes of similar or different microbial strains to protect against hyper-evolving drug-resistant pathogens. The outbreaks of dreadful diseases have led researchers to develop economical chimeric vaccines that can cater to a large population in a shorter time. The process development begins with computationally aided omics-based approaches to design chimeric vaccines. Furthermore, developing these vaccines requires optimizing upstream and downstream processes for mass production at an industrial scale. Owing to the complex structures and complicated bioprocessing of evolving pathogens, various high-throughput process technologies have come up with added advantages. Recent advancements in high-throughput tools, process analytical technology (PAT), quality-by-design (QbD), design of experiments (DoE), modeling and simulations, single-use technology, and integrated continuous bioprocessing have made scalable production more convenient and economical. The paradigm shift to innovative strategies requires significant attention to deal with major health threats at the global scale. This review outlines the challenges and emerging avenues in the bioprocess development of chimeric vaccines.

Producing protein-based products from microalgae cultivated on anaerobically digested abattoir effluent: Process integration and techno-economic analysis

The integration of microalgae production with anaerobically digested effluents might result in development of sustainable processes not only to remove nitrogen but also to produce value-added products rich in protein. This work analyses the feasibility of producing protein-based products derived from microalgae grown on anaerobically digested wastewater by performing techno-economic evaluation. The results show that it would be possible to propose an upstream process flow diagram producing 2.7 kg microalgae from 1 m−3 wastewater with possible production cost of 1.8–8.1 USD kg−1 depending on the scale of anaerobic digested effluent. It would be possible to produce protein isolate, concentrate, and animal feed from anaerobically digested effluents with possible production cost of 33 USD kg−1, 28 USD kg−1, and 6 USD kg−1 respectively at the scale of 1600 m3 d−1 effluent. Although currently the production of protein-based products might not be feasible, increasing the scale of production might result in some feasible outcomes. However, the associated costs regarding collecting more AD wastewater from different sources should be evaluated to increase the scale of production.

Online multimethod platform for comprehensive characterization of monoclonal antibodies in cell culture fluid from a single sample injection - Intact protein workflow

BackgroundTherapeutic monoclonal antibodies (mAbs) comprise a large structural variability with respect to charge, size and post-translational modifications. These critical quality attributes (CQAs) need to be assessed during and after the production of mAbs. This normally requires off-line purification and sample preparation as well as several chromatographic selectivities, which makes the whole process time-consuming and error-prone. To improve on this, we developed an integrated and automated multi-dimensional analytical platform for the simultaneous assessment of multiple CQAs of mAbs in cell culture fluid (CCF) from upstream processes. ResultsThe on-line system allows mAb characterization at the intact level, combining protein A affinity chromatography (ProtA) with size-exclusion, ion-exchange, and reversed-phase liquid chromatographic modes with UV and mass spectrometric detection. Multiple heart cuts of a single mAb elution band from ProtA are stored in 20-μL loops and successively sent to the multimethod options in the second dimension. ProtA loading and elution conditions and their compatibility with second-dimension LC modes were studied and optimized. Subsequently, heart-cutting and valve-switching schemes were investigated to achieve effective and reproducible analyses. The applicability of the developed workflow was demonstrated by the direct analysis (i.e. not requiring off-line sample preparation) of a therapeutic mAb in CCF, obtaining useful information on accurate molecular mass, glycosylation, and charge and size variants of the mAb product at the same time and in just over 1 h. SignificanceThe developed multidimensional platform is the first system that allows for multiple fractions from a single ProtA band to be characterized using different chromatographic selectivities in a single run allowing direct correlation between CQAs. The performance of the system is comparable to established off-line methods, fully compatible with upstream process samples, and provides a significant time-reduction of the characterization procedure.

Simplified Synthesis of Renieramycin T Derivatives to Target Cancer Stem Cells via β-Catenin Proteasomal Degradation in Human Lung Cancer.

Cancer stem cells (CSCs) found within cancer tissue play a pivotal role in its resistance to therapy and its potential to metastasize, contributing to elevated mortality rates among patients. Significant strides in understanding the molecular foundations of CSCs have led to preclinical investigations and clinical trials focused on CSC regulator β-catenin signaling targeted interventions in malignancies. As part of the ongoing advancements in marine-organism-derived compound development, it was observed that among the six analogs of Renieramycin T (RT), a potential lead alkaloid from the blue sponge Xestospongia sp., the compound DH_32, displayed the most robust anti-cancer activity in lung cancer A549, H23, and H292 cells. In various lung cancer cell lines, DH_32 exhibited the highest efficacy, with IC50 values of 4.06 ± 0.24 μM, 2.07 ± 0.11 μM, and 1.46 ± 0.06 μM in A549, H23, and H292 cells, respectively. In contrast, parental RT compounds had IC50 values of 5.76 ± 0.23 μM, 2.93 ± 0.07 μM, and 1.52 ± 0.05 μM in the same order. Furthermore, at a dosage of 25 nM, DH_32 showed a stronger ability to inhibit colony formation compared to the lead compound, RT. DH_32 was capable of inducing apoptosis in lung cancer cells, as demonstrated by increased PARP cleavage and reduced levels of the proapoptotic protein Bcl2. Our discovery confirms that DH_32 treatment of lung cancer cells led to a reduced level of CD133, which is associated with the suppression of stem-cell-related transcription factors like OCT4. Moreover, DH_32 significantly suppressed the ability of tumor spheroids to form compared to the original RT compound. Additionally, DH_32 inhibited CSCs by promoting the degradation of β-catenin through ubiquitin-proteasomal pathways. In computational molecular docking, a high-affinity interaction was observed between DH_32 (grid score = -35.559 kcal/mol) and β-catenin, indicating a stronger binding interaction compared to the reference compound R9Q (grid score = -29.044 kcal/mol). In summary, DH_32, a newly developed derivative of the right-half analog of RT, effectively inhibited the initiation of lung cancer spheroids and the self-renewal of lung cancer cells through the upstream process of β-catenin ubiquitin-proteasomal degradation.

Optimum formulation design and properties of drilling fluids incorporated with green uncoated and polymer-coated magnetite nanoparticles

Nanomaterials are materials that possess unique properties due to their high specific surface area and quantum effects. Nanomaterials have diverse applications in different fields including the petroleum and gas industry as additives. One of the classes of nanomaterials that currently have potential usage in the downstream, midstream, and upstream processes of the petroleum industry is nanoparticles (NPs). Among the upstream processes in the petroleum and gas industry is the drilling operations. It is popular that the most critical features that ensure the success of a drilling operation are the rheological and filtration loss characteristics of the drilling fluid. The current work deals with the synthesis of green uncoated and polymer-coated green magnetite nanoparticles (MNPs). The MNPs were characterized and assessed as rheology and filtration loss modifiers for water-based drilling fluids. The optimum formulation design of drilling fluids incorporated the MNPs for high-performance drilling fluids in terms of density, rheological, filtration loss, and sagging properties was identified. The effect of temperature (ambient − 80 °C), and aging time (6–248 h) on the investigated properties were evaluated. The results confirmed that optimum values for plastic viscosity, apparent viscosity, yield Point, gel strength (10sec), gel strength (10 min), mud thickness, and sag index were 13.77 cP, 69.69 cP, 89.87 lb/100ft2, 86.75 lb/100ft2, 128.38 lb/100ft2, ≤ 1 mm, and 0.511, respectively. Most of those values could be reached using an optimum formulation involving 0.92 % MNPs at ambient temperature. Increasing the temperature displays a decrease in the values while increasing the aging time displays an increase in the values. Drilling fluids with MNPs showed insignificant changes in the investigated properties with increasing temperature in particular those incorporated with polymer-coated MNPs compared to the water-based drilling fluids.

A Novel Temperature Anomaly Source Diagnostic: Method and Application to the 2021 Heatwave in the Pacific Northwest

AbstractQuantitative methods to pinpoint the origin of atmospheric temperature anomalies (T′) associated with heatwaves are pivotal for the construction of physically plausible synoptic storylines of heatwave formation and their evaluation in models. Here, we combine a Lagrangian T′ decomposition with concepts from moisture tracking techniques to identify where and when the principal physical processes generate T′ and to attribute these sources to synoptic weather systems. Applying this framework to near‐surface and free‐tropospheric T′ associated with the record‐shattering 2021 heatwave in the Pacific Northwest shows that ascending, diabatic air streams in North Pacific cyclones contribute more than 50% of free‐tropospheric T′, whereas near‐surface T′ is mainly produced by local subsidence and diabatic heating with only marginal upstream contributions. Since free‐tropospheric T′ facilitates near‐surface accumulation of locally produced T′ by rendering the atmosphere stable to moist convection, our findings corroborate the notion of top‐down induced heatwave formation fueled by upstream diabatic processes.

Spatiotemporal tracking of building materials and their related environmental impacts

Urban development will increase the demand for new buildings expected to cause significant environmental impacts in the coming decades. Spatiotemporal prediction for new buildings, their typologies, resource quantities and types required for construction, and the associated impacts are crucial to effectively tackle strategies to reduce the related greenhouse gas emissions. Within the context of Denmark, this study establishes a prognosis of expected yearly embedded impacts across the country towards 2050 based on Business as Usual (frozen policy) trends. Through the Holt-Winters method's additive version, the study forecasted the future amount of building types in each Danish municipality. The embedded impacts disaggregated into building types, components, materials, and life cycle stages are calculated from the material intensity coefficients of real projects. Considering a ‘business as usual’ scenario, the prediction shows an increase in demand by 6.5 % for new gross floor areas compared to the number of current buildings constructed in the past years. The GHGs from the upstream processing of materials correspond to 7 % of current consumption-based yearly emissions in Denmark. To strive for sustainable development, the findings of the study help inform stakeholders in the built environment to better correlate the material mechanism ‘supply-demand’ for circularity and where efforts to minimize the impacts should be prioritized.

Forecasting meteorological impacts on the environmental sustainability of a large-scale solar plant via artificial intelligence-based life cycle assessment

Although large-scale solar (LSS) is a promising renewable energy technology, it causes adverse impacts on the ecosystem, human health, and resource depletion throughout its upstream (i.e., raw material extraction to solar panel production) and downstream (i.e., plant demolition and waste management) processes. The LSS operational performance also fluctuates due to meteorological conditions, leading to uncertainty in electricity generation and raising concerns about its overall environmental performance. Hitherto, there has been no evidence-backed study that evaluates the ecological sustainability of LSS with the consideration of meteorological uncertainties. In this study, a novel integrated Life Cycle Assessment (LCA) and Artificial Neural Network (ANN) framework is developed to forecast the meteorological impacts on LSS's electricity generation and its life cycle environmental sustainability. For LCA, 18 impact categories and three damage categories are characterised and assessed by ReCiPe 2016 via SimaPro v. 9.1. For ANN, a feedforward neural network is applied via Neural Designer 5.9.3. Taking an LSS plant in Malaysia as a case study, the photovoltaic panel production stage contributes the highest environmental impact in LSS (30 % of human health, 30 % of ecosystem quality, and 34 % of resource scarcity). Aluminium recycling reduces by 10 % for human health, 10 % for ecosystem quality, and 9 % for resource scarcity. The emissions avoided by the forecasted LSS-generated electricity offset the environmental burden for human health, ecosystem quality, and resource scarcity 12–68 times, 13–73 times, and 18–98 times, respectively. The developed ANN-LCA framework can provide LSS stakeholders with data-backed insights to effectively design an environmentally conscious LSS facility, considering meteorological influences.

Effectiveness of Retrieval Practice Learning Strategy in the Memory Storage Process to Improve Students Learning Outcomes on Cell Structure and Bioprocesses

This study aims to determine the effect of the implementation of retrieval practice learning strategies in students learning outcomes on the material Structure and Cell Bioprocesses. The research method used was quasi experimental. The research subject were students of XI IPA, public senior high school in Palembang. The research instruments used were test questions and observation sheets. Learning outcomes data were analyzed with the independent sample t-test on the Statistical Program for Social Science 26 (SPSS version 26). The results showed that both the experimental class and the control class experienced an increase in learning outcomes from the initial test results. The average learning outcomes of the experimental class (retrieval practice) (41.11) are proven to be higher than the control class (32.24). In addition, the N-gain obtained by the experimental class is 0.05 and the control class is 0.10 which is in the low category. Moreover, the results also show that the experimental and control class students have not reached the master learning. These results show that both experimental and control classes get an increase in learning outcomes that are not optimal. However, the retrieval practice learning strategy still provides benefits for improving student learning outcomes.

A Comprehensive Study on Characterization of Residual Stress of Build-Up Layer and Prediction of Chip Warpage

Abstract Better understanding and control of residual stress in the chip build-up layer are becoming more and more important for the assembly process. To estimate the chip warpage and characterize the residual stress, different methods are proposed. However, most of them have high cost or some limitations for the upper build-up material. In this study, an innovative method is proposed to characterize the residual stress and predict the chip warpage behavior of different size chips at different temperatures. The method combines experimental inspection of chip warpage and finite element analysis. By reducing the silicon die thickness, the influence of residual stress in the build-up layer can be amplified. The residual stress can be obtained by inspecting the increased warpage when the silicon dies are reduced to different thicknesses. Correlating the thermal increase warpages of thinner chips can help characterize the effective modulus and coefficient of thermal expansion (CTE) of the build-up layer. This study can help better understand the commonly classified build-up layer information. The results show good agreements between two types of samples under the same upstream process flow.

Comparative Analysis of Protein Surface Hydrophobicity Maps Determined by Sparse Sampling INDUS and Spatial Aggregation Propensity.

Protein surface hydrophobicity plays a central role in various biological processes such as protein folding and aggregation, as well as in the design and manufacturing of biotherapeutics. While the hydrophobicity of protein surface patches has been linked to their constituent residue hydropathies, recent research has shown that protein surface hydrophobicity is more complex and characterized by the response of water to these surfaces. In this work, we employ water density perturbations to map the surface hydrophobicity of a set of model proteins using sparse indirect umbrella sampling simulations (SSI). This technique is used to identify hydrophobic surface patches for the set of model proteins, and the results are compared to those obtained from the widely adopted spatial aggregation propensity (SAP) technique. While SAP-based calculations show agreement with SSI in some cases, there are several examples of disagreement. We identify four general classes of difference in behavior and study factors that contribute to these differences. We find that the SAP method can sometimes mask the effect of weakly nonpolar or isolated nonpolar residues that can lead to strong hydrophobic patches on the protein surface. In addition, hydrophobic patches identified by SAP can exhibit shifts in both position and strength on the SSI map. Our results demonstrate that the combination of topography and chemical context controls the hydrophobicity of a given patch above and beyond the intrinsic polarity of the residues present on the patch surface. The availability of more accurate protein hydrophobicity maps in concert with new classes of hydrophobic molecular descriptors may create significant opportunities for in silico prediction of protein behavior for a range of applications, such as protein design, biomanufacturability, and downstream bioprocessing.

Environmental Footprint of Inland Fisheries: Integrating LCA Analysis to Assess the Potential of Wastewater-Based Microalga Cultivation as a Promising Solution for Animal Feed Production

This study evaluated the environmental impacts of producing 1 kg of biomass for animal feed grown in inland fisheries effluents as a culture medium using the ReCiPe method. Four scenarios with two downstream alternatives were modeled using the life cycle assessment method: Algal Life Feed (ALF), Algal Life Feed with Recycled nutrients (ALF+Rn), Pelletized Biomass (PB), and Pelletized Biomass with Recycled nutrients (PB+Rn). The findings reveal a substantial reduction in environmental impacts when wastewater is employed as a water source and nutrient reservoir. However, the eutrophication and toxicity-related categories reported the highest normalized impacts. ALF+Rn emerges as the most promising scenario due to its reduced energy consumption, highlighting the potential for further improvement through alternative energy sources in upstream and downstream processes. Therefore, liquid waste from fish production is a unique opportunity to implement strategies to reduce the emission of nutrients and pollutants by producing microalgae rich in various high-value-added metabolites.

Scaling strategy for cell and gene therapy bioreactors based on turbulent parameters.

So far, power input has been used as the main parameter for bioreactor scale-up/-down in upstream process development and manufacturing. The rationale is that maintaining a consistent power input per unit volume should result in comparable mixing times at different scales. However, shear generated from turbulent flow may compromise the integrity of non-robust cells such as those used during the production of cell and gene therapies, which may lead to low product quality and yield. Of particular interest is the Kolmogorov length parameter that characterizes the smallest turbulent eddies in a mixture. To understand its impact on scale-up/-down decisions, the distribution of Kolmogorov length along the trajectory flow of individual particles in bioreactors was estimated in silico with the help of computational fluid dynamics simulations. Specifically, in this study the scalability of iPSC-derived lymphocyte production and the impact of shear stress across various differentiation stages were investigated. The study used bioreactors of volumes from 0.1 to 10 L, which correspond to the scales most used for parameter optimization. Our findings, which align with in vitro runs, help determine optimal agitation speed and shear stress adjustments for process transfer between scales and bioreactor types, using vertically-oriented wheel and pitched-blade impellers. In addition, empirical models specific to the bioreactors used in this study were developed. The provided computational analysis in combination with experimental data supports selection of appropriate bioreactors and operating conditions for various cell and gene therapy process steps.

Dynamic risk assessment of chemical process systems using the System-Theoretic accident model and process approach (STAMP) in combination with cascading failure propagation model (CFPM)

To maintain continuous production, chemical plant operators may ignore faults or handle faults online rather than shutting down process systems. However, interaction and interdependence links between components in a digitalized process system are substantial. Thus, faults will be propagated to downstream nodes, potentially leading to risk accumulation and major accidents. However, limited attention has been paid to this type of risk. To model the risk accumulation process, a dynamic risk assessment method is proposed by integrating the system-theoretic accident model and process approach (STAMP) and the cascading failure propagation model (CFPM). Firstly, STAMP is used to model and analyze the system safety of a process system. Two CFPMs are then proposed to measure risk accumulation under two different engineering situations. The proposed method is applied to the Chevron Richmond refinery crude unit and its associated upstream process. The results show that the proposed approach can effectively quantify the process of risk accumulation. This method can generate a real-time dynamic risk profile to support auxiliary decision-making.

Dynamic modelling of cross-flow printed circuit heat exchangers for multistage reactor intercooling

A reduced-order dynamic model of a cross-flow printed circuit heat exchanger was developed to study both steady-state and transient performance under conditions that would be encountered in multistage catalytic combustion reactors to better understand how they can be used to minimize fluctuations in heat-integrated reactors and intensified processes. Previous transient models for cross-flow heat exchangers rely on an initial steady state and apply only for changes in inlet temperature, but this work presents a model that can be applied to intermediate states or multiple concurrent upstream process changes. Steady-state simulations showed that larger channels reduce volumetric heat transfer performance and effectiveness due to convective resistance. Furthermore, larger channel spacing increases effectiveness but also reduces compactness. Operating temperature was found to have a minimal effect on the overall heat transfer coefficient and effectiveness within the range of conditions studied, and operating pressure was found to have no effect on heat transfer. Transient analyses showed that the response to a unit step in temperature matches closely with an analytical infinite gas velocity model, both for time constants and the overall thermal response, with initial variations in outlet temperatures from ideal-case models below 3.8 % of unit step magnitudes, and average differences below 0.75 %. Transient analyses also show the effects of a finite fluid velocity which is critical in the transient response of large heat exchangers. The presented numerical model can also be more easily extended to complex configurations and can be applied to other changes in inlet conditions such as changing composition and flow rates as are found in combustion applications. A three-fluid thermal management system for a multistage packed-bed microreactor using printed circuit heat exchangers is proposed and modelled to provide a method for temperature regulation and pre-heating of fresh reactor feed.

Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing.

To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.