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Fermentation
Special Issues
Digital Bioprocessing and Fermentation Technology
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Digital Bioprocessing and Fermentation Technology

A special issue of Fermentation (ISSN 2311-5637). This special issue belongs to the section "Fermentation Process Design".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4658

Special Issue Editors

Dr. Masih Karimi Alavijeh

E-Mail Website
Guest Editor
Department of Chemical Engineering, University of Melbourne, Parkville, Australia
Interests: bioreactor modelling; multivariate data analysis; machine learning; bioprocess; waste valorisation; enzyme technology; bioenergy; pharmaceutical products
Dr. Harini Narayanan

E-Mail Website
Guest Editor
The Love Lab, Massachusetts Institute of Technology, Cambridge, MA, USA
Interests: artificial intelligence; bioprocesses; machine learning; protein engineering

Special Issue Information

Dear Colleagues,

Biological systems are often complex and difficult to optimize, automate, control and scale. Over decades of excellent research, significant time, effort and costs have been invested in our understanding of biological processes, creating substantial bodies of expert knowledge. Nonetheless, the fail-safe, resourceful and time-efficient exploration of the biological interactions and interconnectivities among a plethora of (bio)chemical variables in a biological system, such as a bioreactor, is still a far from trivial task. With this in mind, in recent years, a great deal of attention has been paid to the application of digital tools, namely, artificial intelligence (AI) and machine learning (ML) algorithms, statistical design of experiment (DoE) tools, computational fluid dynamics (CFD), process simulators and multivariate data analysis (MVDA), to mitigate the complexity of biochemical processes. Digitization—specifically with respect to the Fourth Industrial Revolution, described as Industry 4.0—is a state-of-the-art approach that integrates domain expert knowledge, including scientific principles, mechanistic descriptions and biological big data, with computer-based mathematical models, substantially transforming the ways in which researchers and industries formulate, create and analyse bioprocess data.

This Special Issue seeks to collect research articles and review papers investigating emerging computational solutions to address biochemical problems. This broad scope includes, but is not limited to, the modelling and simulation of cell cultures, microbial fermentation, cellular agriculture, waste valorisation, and agri-food and algal processes. Contributions that broaden the scope of expert-augmented ML modelling, scale-up/-down techniques, bioprocess simulations at scale, techno-economic assessments and environmental analysis are also welcomed.

Dr. Masih Karimi Alavijeh
Dr. Harini Narayanan
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. Fermentation 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 2600 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

  • data analytics
  • machine learning
  • mechanistic models
  • hybrid models
  • computational fluid dynamics
  • bioprocess optimization
  • automation
  • process analytical technology (PAT)
  • multivariate data analysis
  • time-series forecasting

Published Papers (2 papers)

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Research

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19 pages, 3283 KiB  
Article
Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction
by José Pinto, João R. C. Ramos, Rafael S. Costa and Rui Oliveira
Fermentation 2023, 9(7), 643; https://doi.org/10.3390/fermentation9070643 - 8 Jul 2023
Cited by 2 | Viewed by 2026
Abstract
Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody [...] Read more.
Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition. Full article
(This article belongs to the Special Issue Digital Bioprocessing and Fermentation Technology)
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Review

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22 pages, 3535 KiB  
Review
From Shallow to Deep Bioprocess Hybrid Modeling: Advances and Future Perspectives
by Roshanak Agharafeie, João Rodrigues Correia Ramos, Jorge M. Mendes and Rui Oliveira
Fermentation 2023, 9(10), 922; https://doi.org/10.3390/fermentation9100922 - 23 Oct 2023
Cited by 2 | Viewed by 1857
Abstract
Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge [...] Read more.
Deep learning is emerging in many industrial sectors in hand with big data analytics to streamline production. In the biomanufacturing sector, big data infrastructure is lagging compared to other industries. A promising approach is to combine deep neural networks (DNN) with prior knowledge in hybrid neural network (HNN) workflows that are less dependent on the quality and quantity of data. This paper reviews published articles over the past 30 years on the topic of HNN applications to bioprocesses. It reveals that HNNs have been applied to various bioprocesses, including microbial cultures, animal cells cultures, mixed microbial cultures, and enzyme biocatalysis. HNNs have been applied for process analysis, process monitoring, development of software sensors, open- and closed-loop control, batch-to-batch control, model predictive control, intensified design of experiments, quality-by-design, and recently for the development of digital twins. Most previous HNN studies have combined shallow feedforward neural networks (FFNNs) with physical laws, such as macroscopic material balance equations, following the semiparametric design principle. Only recently, deep HNNs based on deep FFNNs, convolution neural networks (CNN), long short-term memory (LSTM) networks and physics-informed neural networks (PINNs) have been reported. The biopharma sector is currently a major driver but applications to biologics quality attributes, new modalities, and downstream processing are significant research gaps. Full article
(This article belongs to the Special Issue Digital Bioprocessing and Fermentation Technology)
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