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Processes
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Application of Systems Engineering Principles to Bioprocessing...
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Application of Systems Engineering Principles to Bioprocessing

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Biological Processes and Systems".

Deadline for manuscript submissions: closed (15 June 2020) | Viewed by 18053

Special Issue Editors

Dr. Cleo Kontoravdi

E-Mail Website
Guest Editor
Department of Chemical Engineering, Imperial College London, South Kensington Campus, London SW7 2AZ, UK
Interests: bioprocessing; recombinant protein production; protein glycosylation; modelling and optimization of cell culture systems
Dr. Alexandros Kiparissides

E-Mail Website
Guest Editor
Department of Chemical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: systems biology; recombinant protein production; modeling and optimization of cell and microbial culture systems; sensitivity analysis; parameter estimation; design of experiments; culture media and feeding strategies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Quality by Design initiative has supported the application of model-based tools in the biopharmaceutical industry with the aim of improving process understanding and moving towards the implementation of optimisation and control principles using process analytical technologies. In the past decade we have seen significant progress in the development of data-driven, knowledge-driven and hybrid models of both upstream and downstream processing, which have enhanced the exploration of the design space as well as quantifying relationships between process parameters and critical quality attributes for specific unit operations. At the same time, we have seen the emergence of Synthetic Biology, which has expanded our toolset for genetic modification of hosts with an increased degree of specificity, accuracy and control. This opens up new possibilities for model-based design of pathways and functionalities that can enhance bioprocess performance.

This special issue on “Application of Systems Engineering Principles to Bioprocessing” aims to curate novel advances in the development and application of computational modeling and model-based applications to address longstanding challenges in Bioprocessing. Topics include, but are not limited to:

  • Modelling (mechanistic and data-driven) and optimisation of upstream and downstream unit operations;
  • Implementation of online control strategies;
  • Model-driven process design;
  • Whole process simulation and analysis, including flowsheeting of novel manufacturing processes for new modalities or modular production;
  • Model-based approaches to enhance the understanding and performance of cellular behaviour under industrial bioprocessing conditions, such as design of genetic and metabolic engineering strategies, and/or enable the analysis of large datasets, such as systems biology, machine learning and artificial intelligence appraoches.

Dr. Cleo Kontoravdi
Dr. Alexandros Kiparissides
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • bioprocess design
  • process simulation
  • metabolic modelling
  • fermentation modelling
  • model-based optimization
  • online control
  • first-principles modelling
  • data-driven modelling

Published Papers (4 papers)

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Research

27 pages, 4249 KiB  
Article
Dynamic Metabolic Analysis of Cupriavidus necator DSM545 Producing Poly(3-hydroxybutyric acid) from Glycerol
by Chenhao Sun, Cristina Pérez-Rivero, Colin Webb and Constantinos Theodoropoulos
Processes 2020, 8(6), 657; https://doi.org/10.3390/pr8060657 - 1 Jun 2020
Cited by 9 | Viewed by 4549
Abstract
Cupriavidus necator DSM 545 can utilise glycerol to synthesise poly(3-hydroxybutyric acid) under unbalanced growth conditions, i.e., nitrogen limitation. To improve poly(3-hydroxybutyric acid) (PHB) batch production by C. necator through model-guided bioprocessing or genetic engineering, insights into the dynamic effect of the fermentation conditions [...] Read more.
Cupriavidus necator DSM 545 can utilise glycerol to synthesise poly(3-hydroxybutyric acid) under unbalanced growth conditions, i.e., nitrogen limitation. To improve poly(3-hydroxybutyric acid) (PHB) batch production by C. necator through model-guided bioprocessing or genetic engineering, insights into the dynamic effect of the fermentation conditions on cell metabolism are crucial. In this work, we have used dynamic flux balance analysis (DFBA), a constrained-based stoichiometric modelling approach, to study the metabolic change associated with PHB synthesis during batch cultivation. The model employs the ‘minimisation of all fluxes’ as cellular objectives and measured extracellular fluxes as additional constraints. The mass balance constraints are further adjusted based on thermodynamic considerations. The resultant flux distribution profiles characterise the evolution of metabolic states due to adaptation to dynamic extracellular conditions and provide further insights towards improvements that can be implemented to enhance PHB productivity. Full article
(This article belongs to the Special Issue Application of Systems Engineering Principles to Bioprocessing )
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14 pages, 1248 KiB  
Article
Techno-Economic Assessment of Cell-Free Synthesis of Monoclonal Antibodies Using CHO Cell Extracts
by Vaishali Thaore, Dimitrios Tsourapas, Nilay Shah and Cleo Kontoravdi
Processes 2020, 8(4), 454; https://doi.org/10.3390/pr8040454 - 12 Apr 2020
Cited by 6 | Viewed by 5323
Abstract
Cell-free protein synthesis (CFPS) is an emerging tool for the rapid production of difficult-to-express proteins as well as for identifying protein synthesis bottlenecks. In CFPS, the biotic phase is substituted by extracts of living cells devoid of any of their own genetic material. [...] Read more.
Cell-free protein synthesis (CFPS) is an emerging tool for the rapid production of difficult-to-express proteins as well as for identifying protein synthesis bottlenecks. In CFPS, the biotic phase is substituted by extracts of living cells devoid of any of their own genetic material. The main advantage is that these systems delineate cell growth from recombinant protein production, enabling the expression of targets that would otherwise place too big a burden on living cells. We have conducted a techno-economic analysis of a CFPS system to produce monoclonal antibodies (mAbs) using extracts of Chinese hamster ovary (CHO) cells. We compare the performance of the CFPS system with two alternative production strategies: stable and transient gene expression in CHO cells. Our assessment shows that the viability of CFPS for mAb production requires a significant increase in the product yield and the recycling of high-cost components such as DNA. Nevertheless, CFPS shows significant promise for personalized medicine applications, providing a platform for on-demand production and simplified supply chains. Full article
(This article belongs to the Special Issue Application of Systems Engineering Principles to Bioprocessing )
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14 pages, 4930 KiB  
Article
Integration of Time-Series Transcriptomic Data with Genome-Scale CHO Metabolic Models for mAb Engineering
by Zhuangrong Huang and Seongkyu Yoon
Processes 2020, 8(3), 331; https://doi.org/10.3390/pr8030331 - 11 Mar 2020
Cited by 10 | Viewed by 4965
Abstract
Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required [...] Read more.
Chinese hamster ovary (CHO) cells are the most commonly used cell lines in biopharmaceutical manufacturing. Genome-scale metabolic models have become a valuable tool to study cellular metabolism. Despite the presence of reference global genome-scale CHO model, context-specific metabolic models may still be required for specific cell lines (for example, CHO-K1, CHO-S, and CHO-DG44), and for specific process conditions. Many integration algorithms have been available to reconstruct specific genome-scale models. These methods are mainly based on integrating omics data (i.e., transcriptomics, proteomics, and metabolomics) into reference genome-scale models. In the present study, we aimed to investigate the impact of time points of transcriptomics integration on the genome-scale CHO model by assessing the prediction of growth rates with each reconstructed model. We also evaluated the feasibility of applying extracted models to different cell lines (generated from the same parental cell line). Our findings illustrate that gene expression at various stages of culture slightly impacts the reconstructed models. However, the prediction capability is robust enough on cell growth prediction not only across different growth phases but also in expansion to other cell lines. Full article
(This article belongs to the Special Issue Application of Systems Engineering Principles to Bioprocessing )
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18 pages, 3270 KiB  
Article
Adaptive Control of Biomass Specific Growth Rate in Fed-Batch Biotechnological Processes. A Comparative Study
by Vytautas Galvanauskas, Rimvydas Simutis and Vygandas Vaitkus
Processes 2019, 7(11), 810; https://doi.org/10.3390/pr7110810 - 4 Nov 2019
Cited by 10 | Viewed by 2745
Abstract
This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. [...] Read more.
This article presents a comparative study on the development and application of two distinct adaptive control algorithms for biomass specific growth rate control in fed-batch biotechnological processes. A typical fed-batch process using Escherichia coli for recombinant protein production was selected for this research. Numerical simulation results show that both developed controllers, an adaptive PI controller based on the gain scheduling technique and a model-free adaptive controller based on the artificial neural network, delivered a comparable control performance and are suitable for application when using the substrate limitation approach and substrate feeding rate manipulation. The controller performance was tested within the realistic ranges of the feedback signal sampling intervals and measurement noise intensities. Considering the efforts for controller design and tuning, including development of the adaptation/learning algorithms, the model-free adaptive control algorithm proves to be more attractive for industrial applications, especially when only limited knowledge of the process and its mathematical model is available. The investigated model-free adaptive controller also tended to deliver better control quality under low specific growth rate conditions that prevail during the recombinant protein production phase. In the investigated simulation runs, the average tracking error did not exceed 0.01 (1/h). The temporary overshoots caused by the maximal disturbances stayed within the range of 0.025–0.11 (1/h). Application of the algorithm can be further extended to specific growth rate control in other bacterial and mammalian cell cultivations that run under substrate limitation conditions. Full article
(This article belongs to the Special Issue Application of Systems Engineering Principles to Bioprocessing )
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