Biological Process Modelling, Monitoring and Control in a Rapidly Changing World

A special issue of Bioengineering (ISSN 2306-5354).

Deadline for manuscript submissions: closed (20 June 2021) | Viewed by 8028

Special Issue Editors


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Guest Editor
School of Engineering, Newcastle University, Newcastle-upon-Tyne, UK
Interests: mathematical modelling; process control; machine learning; wastewater treatment; water resource recovery; anaerobic digestion; bifurcation theory

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Guest Editor
Department of Chemical Engineering and Analytical Science, the University of Manchester, Manchester, UK
Interests: bioprocess systems engineering; machine learning; hybrid modelling; algal biotechnology; process analytical technology; bioreactor design and scale-up

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Guest Editor
Department of civil and water engineering, Université Laval, Québec, QC, Canada
Interests: instrumentation and control; integrated urban wastewater system; mathematical modelling; real-time control; stormwater management; urban drainage; water resource recovery Photo: attached

Special Issue Information

Dear Colleagues,

Process modelling for the purpose of monitoring, prediction and control of bioengineering systems, such as the activated sludge process, anaerobic digestion, and biopharmaceutical manufacturing, is well-established. Nevertheless, bioengineering technology and methods to understand and manipulate their underlying processes are still developing in response to challenges such as climate change, resource security and urbanization. Biological processes and bioengineering are at the vanguard of transformative change through the nexus of academic, industry, policy and good governance. As an example, water resource recovery inspires a broader approach to the design, management and operation of ‘waste’ water treatment and the added dimensionality of end-product value and quantity suggests the need for new models, measurements and control strategies.

A new frontier in modelling for bioengineering is being established at the intersection between complementary fields such as data analytics, machine learning, smart sensors, genomics, metabolic engineering and soft computing. The era of distinct disciplines working largely in silos has passed and the future of bioprocesses is truly innovative and multidisciplinary.

With the closer integration of science and engineering and an emphasis on practical solutions to tackle urgent environmental, energy, and public health challenges across scales, this Special Issue invites contributions that place bioprocess modelling, monitoring and control in the domain of transformative bioengineering. The scope covers biotechnologies, processes or sub-processes and across scales, but must be framed in one or more of the core topic areas (modelling, monitoring and control), and should ideally address current or anticipated societal challenges (e.g., Sustainable Development Goals), future-facing systems (e.g., water resource recovery facilities, fully continuous bio-manufacturing) or a major technical bottleneck related to bioprocessing.

Dr. Matthew Wade
Dr. Dongda Zhang
Dr. Sovanna Tik 
Guest Editors

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Keywords

  • Biological process modelling
  • Biological process control
  • Biological process monitoring
  • Process optimization
  • Advanced process control
  • Predictive modelling and control
  • Hybrid modelling
  • Plant-wide modelling
  • Process analytical technology and quality by design
  • Water resource recovery processes
  • Fermentation processes
  • Pharmaceutical bioprocesses
  • Metabolic engineering for bioprocesses

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Published Papers (2 papers)

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Research

32 pages, 2054 KiB  
Article
Microbial Interactions as Drivers of a Nitrification Process in a Chemostat
by Pablo Ugalde-Salas, Héctor Ramírez C., Jérôme Harmand and Elie Desmond-Le Quéméner
Bioengineering 2021, 8(3), 31; https://doi.org/10.3390/bioengineering8030031 - 25 Feb 2021
Cited by 2 | Viewed by 3059
Abstract
This article deals with the inclusion of microbial ecology measurements such as abundances of operational taxonomic units in bioprocess modelling. The first part presents the mathematical analysis of a model that may be framed within the class of Lotka–Volterra models fitted to experimental [...] Read more.
This article deals with the inclusion of microbial ecology measurements such as abundances of operational taxonomic units in bioprocess modelling. The first part presents the mathematical analysis of a model that may be framed within the class of Lotka–Volterra models fitted to experimental data in a chemostat setting where a nitrification process was operated for over 500 days. The limitations and the insights of such an approach are discussed. In the second part, the use of an optimal tracking technique (developed within the framework of control theory) for the integration of data from genetic sequencing in chemostat models is presented. The optimal tracking revisits the data used in the aforementioned chemostat setting. The resulting model is an explanatory model, not a predictive one, it is able to reconstruct the different forms of nitrogen in the reactor by using the abundances of the operational taxonomic units, providing some insights into the growth rate of microbes in a complex community. Full article
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18 pages, 5624 KiB  
Article
Automated Conditional Screening of Multiple Escherichia coli Strains in Parallel Adaptive Fed-Batch Cultivations
by Sebastian Hans, Benjamin Haby, Niels Krausch, Tilman Barz, Peter Neubauer and Mariano Nicolas Cruz-Bournazou
Bioengineering 2020, 7(4), 145; https://doi.org/10.3390/bioengineering7040145 - 11 Nov 2020
Cited by 13 | Viewed by 4310
Abstract
In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on [...] Read more.
In bioprocess development, the host and the genetic construct for a new biomanufacturing process are selected in the early developmental stages. This decision, made at the screening scale with very limited information about the performance in larger reactors, has a major influence on the efficiency of the final process. To overcome this, scale-down approaches during screenings that show the real cell factory performance at industrial-like conditions are essential. We present a fully automated robotic facility with 24 parallel mini-bioreactors that is operated by a model-based adaptive input design framework for the characterization of clone libraries under scale-down conditions. The cultivation operation strategies are computed and continuously refined based on a macro-kinetic growth model that is continuously re-fitted to the available experimental data. The added value of the approach is demonstrated with 24 parallel fed-batch cultivations in a mini-bioreactor system with eight different Escherichia coli strains in triplicate. The 24 fed-batch cultivations were run under the desired conditions, generating sufficient information to define the fastest-growing strain in an environment with oscillating glucose concentrations similar to industrial-scale bioreactors. Full article
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