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Processes
Special Issues
Modeling and Analysis of Bioprocesses
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Modeling and Analysis of Bioprocesses

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 November 2023) | Viewed by 4060

Special Issue Editors

Dr. Zhuangrong Huang

E-Mail Website
Guest Editor
Bristol-Myers Squibb, Devens, MA 01434, USA
Interests: bioprocess modeling; metabolic flux analysis; genome-scale model; omics analysis; cellular metabolism; multivariate data analysis
Dr. Yiran Wang

E-Mail Website
Guest Editor
Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, MA 01434, USA
Interests: protein chromatography; mechanistic modeling of chromatography; biopharmaceutical downstream process
Dr. Eric Hodgman

E-Mail Website
Guest Editor
Biologics Development, Global Product Development and Supply, Bristol Myers Squibb, Devens, MA 01434, USA
Interests: process analytical technology; process scale-up; small-scale models; process control

Special Issue Information

Dear Colleagues,

Bioprocesses are inherently complex, dynamic, and influenced by numerous variables. Modeling and analysis have become pivotal for unraveling the processes’ underlying mechanisms, optimizing the parameters, predicting behavior under different conditions, scaling up, guiding the control strategies, and improving productivity and product quality. This Special Issue allows researchers and engineers to showcase innovative work in this rapidly evolving field. The scope of this section encompasses various bioprocess applications, such as cell culture optimization, media formulation, bioreactor design, downstream processing, and the scaling up, quality assessment, and robustness of processes. We encourage contributions that demonstrate the impact of modeling and analysis on process optimization, scaling up, control, and sustainability.

This Special Issue on “Modeling and Analysis of Bioprocesses”  seeks high-quality research articles and reviews on novel insights, rigorous methodologies, and the practical implications of modeling and analysis tools.

Dr. Zhuangrong Huang
Dr. Yiran Wang
Dr. Eric Hodgman
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

  • the development of mathematical models for upstream and downstream bioprocesses
  • multi-scale modeling approaches in bioprocesses
  • process intensification and optimization
  • the application of artificial intelligence and machine learning in bioprocess modeling
  • model-based optimization and control strategies
  • process validation and quality assurance via modeling
  • process analytical technology (PAT) and multivariate data analysis
  • economic and environmental analysis of bioprocesses
  • the application of novel technologies mentioned above, including case studies

Published Papers (2 papers)

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Research

16 pages, 2136 KiB  
Article
Dynamic Optimisation of Fed-Batch Bioreactors for mAbs: Sensitivity Analysis of Feed Nutrient Manipulation Profiles
by Wil Jones and Dimitrios I. Gerogiorgis
Processes 2023, 11(11), 3065; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11113065 - 25 Oct 2023
Cited by 1 | Viewed by 1087
Abstract
Successful cultivation of mammalian cells must consider careful formulation of culture media consisting of a variety of substrates and amino acids. A widely cited method for quantifying metabolic networks of mammalian cultures is dynamic flux balance modelling. Application of in-silico techniques allows researchers [...] Read more.
Successful cultivation of mammalian cells must consider careful formulation of culture media consisting of a variety of substrates and amino acids. A widely cited method for quantifying metabolic networks of mammalian cultures is dynamic flux balance modelling. Application of in-silico techniques allows researchers to circumvent time-consuming and costly in-vivo experimentation. Dynamic simulation and optimisation of reliable models allows for the visualization of opportunities to improve throughputs of target protein products, such as monoclonal antibodies (mAbs). This study presents a sensitivity analysis comparing dynamic optimisation results for industrial-scale fed-batch bioreactors, considering a variety of initial conditions. Optimized feeding trajectories are computed via Nonlinear Programming (NLP) model, employing the established IPOPT solver. Glucose, then glutamine, then asparagine, can lead to improved mAb yields and viable cell counts. Full article
(This article belongs to the Special Issue Modeling and Analysis of Bioprocesses)
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22 pages, 12994 KiB  
Article
Automated Shape and Process Parameter Optimization for Scaling Up Geometrically Non-Similar Bioreactors
by Stefan Seidel, Fruhar Mozaffari, Rüdiger W. Maschke, Matthias Kraume, Regine Eibl-Schindler and Dieter Eibl
Processes 2023, 11(9), 2703; https://0-doi-org.brum.beds.ac.uk/10.3390/pr11092703 - 10 Sep 2023
Cited by 2 | Viewed by 2643
Abstract
Scaling bioprocesses remains a major challenge. Since it is physically impossible to increase all process parameters equally, a suitable scale-up strategy must be selected for a successful bioprocess. One of the most widely used criteria when scaling up bioprocesses is the specific power [...] Read more.
Scaling bioprocesses remains a major challenge. Since it is physically impossible to increase all process parameters equally, a suitable scale-up strategy must be selected for a successful bioprocess. One of the most widely used criteria when scaling up bioprocesses is the specific power input. However, this represents only an average value. This study aims to determine the Kolmogorov length scale distribution by means of computational fluid dynamics (CFD) and to use it as an alternative scale-up criterion for geometrically non-similar bioreactors for the first time. In order to obtain a comparable Kolmogorov length scale distribution, an automated geometry and process parameter optimization was carried out using the open-source tools OpenFOAM and DAKOTA. The Kolmogorov–Smirnov test statistic was used for optimization. A HEK293-F cell expansion (batch mode) from benchtop (Infors Minifors 2 with 4 L working volume) to pilot scale (D-DCU from Sartorius with 30 L working volume) was carried out. As a reference cultivation, the classical scale-up approach with constant specific power input (233 W m−3) was used, where a maximum viable cell density (VCDmax) of 5.02·106 cells mL−1 was achieved (VCDmax at laboratory scale 5.77·106 cells mL−1). Through the automated optimization of the stirrer geometry (three parameters), position and speed, comparable cultivation results were achieved as in the small scale with a maximum VCD of 5.60·106 cells mL−1. In addition, even on the pilot scale, cell aggregate size distribution was seen to strictly follow a geometric distribution and can be predicted with the help of CFD with the previously published correlation. Full article
(This article belongs to the Special Issue Modeling and Analysis of Bioprocesses)
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