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Advanced Modeling of Biomanufacturing Processes

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

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 30539

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Special Issue Editors

Dr. Giannis Penloglou

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Chemical Process and Energy Resources Institute—CPERI/LPRE, Centre for Research and Technology Hellas—CERTH, Thessaloniki 57001, Greece
Interests: photobioreactors; fermentation; microalgae; biopolymers; biorefinery; biofuels; nanocellulose; bioprocess modeling; techno-economic analysis; biomass
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Dr. Alexandros Kiparissides

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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 ultimate goal of biotechnology is to reach a degree of maturity that would enable a fully integrated, sustainable, bio-based economy with a minimal non-recyclable waste and emissions footprint. At the core of this future manufacturing challenge lies our ability to reliably manipulate cellular function both at the metabolic and the bioprocess engineering level in order to design novel, robust and economically viable bioprocesses. Scientists and Engineers alike highlight the need for sophisticated and robust mathematical modelling approaches to expedite developments and facilitate new discoveries.

The aim of this special issue of Processes is to attract high-quality original research contributions and review articles in the field of advanced bioprocess modeling of bio-based manufacturing processes. This could include multi-scale modelling approaches and/or rigorous hybrid modelling approaches that combine two or more sub-models to capture experimental and/or mechanistic information at various scales (e.g. molecular/multi-omics, kinetic/polymerization, metabolic/pathway, population balance, macroscopic/mechanistic, computational fluid dynamics, bioreactor, plant-wide, techno-economic evaluation and life-cycle-analysis). The objective is to demonstrate the significance of existing and novel simulation tools to qualitatively comprehend and describe complex biochemical and biological mechanisms and quantitatively predict and optimize the production of valuable chemicals, proteins or enzymes. Specific topics for the special issue include but are not limited to:

Modeling and intensification of microbial, mammalian, algal and enzymatic production processes.
Upstream and downstream bioprocess simulation, control and optimization.
Innovative concepts for integrating multiple models at different scales.
Advanced numerical methods for model calibration and simulation of complex bio-based systems.
Analysis and validation of multi-scale models and simulations.
Integrated multi-scale simulation platforms for whole-process sustainability analysis.

Dr. Giannis Penloglou
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

Model-based design of bioprocesses
Bioprocess simulation and control
Life cycle analysis and sustainability of bioprocesses
Advanced multiscale modeling of bioprocesses
Model-based bioprocess intensification and optimization

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Published Papers (10 papers)

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Editorial

Jump to: Research, Review

4 pages, 168 KiB

Open AccessEditorial

Advanced Modeling of Biomanufacturing Processes

by Giannis Penloglou and Alexandros Kiparissides

Processes 2024, 12(2), 387; https://doi.org/10.3390/pr12020387 - 15 Feb 2024

Viewed by 961

Abstract

The multi-layered and complex nature of cellular regulation enhances the need for advanced computational methodologies that can serve as scaffolds for organizing experimental data to facilitate the inference of meaningful relationships [...] Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

Research

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13 pages, 2683 KiB

Open AccessArticle

Evaluation of the Impact of Buffer Management Strategies on Biopharmaceutical Manufacturing Process Mass Intensity

by Kevin Gibson, Jorge C. Oliveira and Denis Ring

Processes 2023, 11(8), 2242; https://doi.org/10.3390/pr11082242 - 26 Jul 2023

Viewed by 2279

Abstract

There is an increasing demand to improve the overall sustainability of the biopharmaceutical industry. A barrier to improvement has been the limited research undertaken in the area of environmental impact of key design decisions. The aim of this study was to perform a comprehensive evaluation of the impact of buffer management strategy and technology selection on overall process efficiency using process mass intensity (PMI) as a metric for comparison. The environmental impact of buffer management has yet to be fully understood, despite buffers being one of the most resource-intensive aspects of biopharmaceutical manufacturing. A detailed process model was used to evaluate the impact of buffer management on a monoclonal antibody (MAB) process at the 2000 L scale. This was achieved by means of a non-replicated full factorial design composed of six variables: product titre, quantity of unique buffers, preparation frequency, single-use threshold and equipment cleaning duration with two levels and buffer preparation strategy type with four levels. The study identified that buffer management has a critical impact on overall process mass intensity, demonstrating a possibility to achieve a reduction in PMI of up to 90% for the best scenario compared to the worst. The findings also indicated that single-use systems are greatly superior to stainless-steel systems in terms of overall process efficiency, which is consistent with established thinking. The results from this research represent a further significant step towards achieving a more sustainable biopharmaceutical industry, establishing buffer management as a critical focus area, quantifying the influence of key variables on process mass intensity and highlighting the benefits of using a process mass intensity metric as part of routine biopharmaceutical design. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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18 pages, 4421 KiB

Open AccessArticle

Designing an Efficient Surfactant–Polymer–Oil–Electrolyte System: A Multi-Objective Optimization Study

by Mohammed Nedjhioui, Noureddine Nasrallah, Mohammed Kebir, Hichem Tahraoui, Rachida Bouallouche, Aymen Amin Assadi, Abdeltif Amrane, Bassem Jaouadi, Jie Zhang and Lotfi Mouni

Processes 2023, 11(5), 1314; https://doi.org/10.3390/pr11051314 - 24 Apr 2023

Cited by 10 | Viewed by 1367

Abstract

This research aimed to study the effects of individual components on the physicochemical properties of systems composed of surfactants, polymers, oils, and electrolytes in order to maximize the recovery efficiency of kerosene while minimizing the impact on the environment and human health. Four independent factors, namely anionic surfactant sodium dodecylbenzene sulfonate (X₁) (SDBS), oil (X₂) (kerosene), water-soluble polymer poly(ethylene glycol) (X₃) (PEG), and sodium chloride (X₄) (NaCl), were studied using the full factorial design (FFD) model. Four output variables, namely conductivity (Y₁), turbidity (Y₂), viscosity (Y₃), and interfacial tension (IFT) (Y₄), were taken as the response variables. All four FFD models have high coefficients of determination and low errors. The developed models were used in a multi-objective optimization (MOO) framework to determine the optimal conditions. The obtained optimal conditions are X₁ = 0.01, X₂ = 50, X₃ = 5, and X₄ = 0.1, with an error of 0.9414 between the predicted and experimental objective function values. This result shows the efficiency of the model developed and the system used for the recovery of kerosene, while also having a positive effect on the protection of the environment. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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19 pages, 4626 KiB

Open AccessArticle

An Investigation into the Metabolic Differences between Conventional and High Seeding Density Fed-Batch Cell Cultures by Applying a Segmented Modeling Approach

by Teresa Laura Krumm, Alireza Ehsani, Jochen Schaub and Fabian Stiefel

Processes 2023, 11(4), 1094; https://doi.org/10.3390/pr11041094 - 4 Apr 2023

Cited by 2 | Viewed by 2311

Abstract

The conventional fed-batch process characterized by a low titer currently challenges pharmaceutical development. Process optimization by applying a perfusion process in the pre-stage and subsequent production phase at a high seeding density (HSD) can meet this challenge. In this study, we employed a simplified approach based on measured experiments, namely segmented modeling, to systematically analyze an HSD fed-batch process compared to a standard process. A comparison indicated that the metabolic phases of HSD processes are not only shifted in time, but metabolite trends show an altered metabolism. In an extended study, we integrated the intracellular fluxes determined by a metabolic flux analysis into the segmented modeling approach. Compared to using only extracellular rates, similar phases are identified, and this highlights the reliability of phase identification modeling using extracellular rates only. Furthermore, the segmented linear regression approach is used to create a model that describes cellular behavior and that can be used to predict potential improvements in the feeding strategy and in harvest viability. Here, overfeeding was eliminated and a significantly higher titer was achieved. This work provides insights into the overall metabolic changes in the HSD process and paves the way towards the optimization of the feeding regime. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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10 pages, 1359 KiB

Open AccessArticle

Best Conditions for the Production of Natural Isopentyl Acetate (Banana Aroma) from Cheese Industry Waste: An Experimental Precursor Approach

by Carlos Alberto Gómez-Aldapa, Javier Castro-Rosas, Antioco López-Molina, Carolina Conde-Mejía, Cuauhtémoc Francisco Pineda-Muñoz, Angélica Jiménez-González, Sergio Alejandro Medina-Moreno, Martha Patricia Falcón-León and Laura Conde-Báez

Processes 2021, 9(11), 1880; https://doi.org/10.3390/pr9111880 - 21 Oct 2021

Cited by 4 | Viewed by 3001

Abstract

In some fermentation systems, whey components (lactose, proteins and minerals) can produce isopentyl acetate (IA). An analysis of the best conditions for IA production with Kluyveromyces marxianus was developed in this work. The experiment design was two-factor and three-level design based on a response surface methodology (RSM) using Design-Expert^® software. The analysis of anomeric protons by nuclear magnetic resonance (1H-NMR) showed 81.25% of β lactose content. This characteristic favored the production of IA. The maximum output (Mp) of IA, determined by gas chromatography, was 9.52 g/L (p < 0.05). The central composite design (CCD) was used to perform the factor analysis. Results showed that concentrations of 0.03 (g/L) ammonium sulphate and 0.3 (v/v) of isoamyl alcohol are the best conditions for a maximum rate of IA production. The production of IA can reduce the discharge of whey, allowing its reuse and revaluation. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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15 pages, 1509 KiB

Open AccessFeature PaperEditor’s ChoiceArticle

Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning

by Martin F. Luna, Andrea M. Ochsner, Véronique Amstutz, Damian von Blarer, Michael Sokolov, Paolo Arosio and Manfred Zinn

Processes 2021, 9(9), 1560; https://doi.org/10.3390/pr9091560 - 1 Sep 2021

Cited by 20 | Viewed by 4369

Abstract

Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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26 pages, 6672 KiB

Open AccessFeature PaperArticle

A Novel Kinetic Modeling Framework for the Polycondensation of Sugars Using Monte Carlo and the Method of Moments

by Dimitrios Meimaroglou, Sandrine Hoppe and Baptiste Boit

Processes 2021, 9(5), 745; https://doi.org/10.3390/pr9050745 - 22 Apr 2021

Cited by 2 | Viewed by 2283

Abstract

The kinetics of the hydrolysis and polycondensation reactions of saccharides have made the subject of numerous studies, due to their importance in several industrial sectors. The present work, presents a novel kinetic modeling framework that is specifically well-suited to reacting systems under strict moisture control that favor the polycondensation reactions towards the formation of high-degree polysaccharides. The proposed model is based on an extended and generalized kinetic scheme, including also the presence of polyols, and is formulated using two different numerical approaches, namely a deterministic one in terms of the method of moments and a stochastic kinetic Monte Carlo approach. Accordingly, the most significant advantages and drawbacks of each technique are clearly demonstrated and the most fitted one (i.e., the Monte Carlo method) is implemented for the modeling of the system under different conditions, for which experimental data were available. Through these comparisons it is shown that the model can successfully follow the evolution of the reactions up to the formation of polysaccharides of very high degrees of polymerization. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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10 pages, 889 KiB

Open AccessArticle

Design of a Separation Process for Natural 2-Phenylethanol with Economic and Safety Considerations

by Luis E. Puga-Córdova, Zahira A. Pérez-Sánchez, Antíoco López-Molina, Laura Conde-Báez, Arturo Jiménez-Gutiérrez and Carolina Conde-Mejía

Processes 2020, 8(12), 1570; https://doi.org/10.3390/pr8121570 - 28 Nov 2020

Cited by 3 | Viewed by 3524

Abstract

The present work aimed to design a separation process for 2-phenylethanol (2-PEA) produced by whey fermentation and to evaluate its economic potential. The separation sequence consisted of a liquid–liquid extraction column followed by two distillation columns for 2-PEA purification and solvent recovery. In addition, the use of ethyl acetate as a solvent for the extraction process was analyzed. The results, aided by the Aspen Plus v.10 process simulator, showed that 2-PEA can be separated with a purity of 96% by weight. The operating cost of the process, estimated at USD 22.70 per kilogram, shows that the separation alternative developed in this work has a high economic potential. The use of ethyl acetate as a solvent was found to efficiently remove 2-PEA from the fermentation mixture. From a process safety analysis point of view, the use of a bioprocess safety index developed in this work identified the separation process sections that could require special attention as part of the safety engineering stage of the process implementation. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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Review

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17 pages, 2042 KiB

Open AccessEditor’s ChoiceReview

Techno-Economic Considerations on Nanocellulose’s Future Progress: A Short Review

by Giannis Penloglou, Aikaterini Basna, Alexandros Pavlou and Costas Kiparissides

Processes 2023, 11(8), 2312; https://doi.org/10.3390/pr11082312 - 1 Aug 2023

Cited by 9 | Viewed by 4133

Abstract

Nanocellulose (NC) is an emerging natural material that offers great potential for various applications due to its unique properties and renewable character. Nowadays, as NC production technologies are advancing, it is essential to evaluate their economic feasibility, technological maturity and commercialization potential using systematic techno-economic analysis (TEA). The present study considers both technical and economic aspects of NC production and analyzes them in two ways: first, by developing a new concept based on the production of different types of NC through the conversion of lignocellulosic biomass by chemical and mechanical technologies, and second, by a comparative review of existing TEA studies in the open literature. Three specific scenarios and two case studies are evaluated by comparing specific key performance indicators (KPIs), such as the production cost (PC) and minimum product selling price (MPSP) of NC. As a result, a short though comprehensive overview of the current state of NC production is provided, highlighting the main technical and economic challenges associated with it. Key areas for future research and innovation (R&I) are also identified to optimize the production processes and reduce relevant costs, in order to make NC competitive with existing materials and realize its full potential. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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23 pages, 1947 KiB

Open AccessReview

Bio-Electrochemical System Depollution Capabilities and Monitoring Applications: Models, Applicability, Advanced Bio-Based Concept for Predicting Pollutant Degradation and Microbial Growth Kinetics via Gene Regulation Modelling

by Argyro Tsipa, Constantina K. Varnava, Paola Grenni, Vincenzo Ferrara and Andrea Pietrelli

Processes 2021, 9(6), 1038; https://doi.org/10.3390/pr9061038 - 14 Jun 2021

Cited by 11 | Viewed by 4719

Abstract

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored. Full article

(This article belongs to the Special Issue Advanced Modeling of Biomanufacturing Processes)

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