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Special Issue "Application of Systems Engineering Principles to Bioprocessing "
Deadline for manuscript submissions: 15 February 2020.
Interests: bioprocessing, recombinant protein production, protein glycosylation, modelling and optimization of cell culture systems
Interests: systems biology; recombinant protein production; modelling and optimization of cell culture systems; sensitivity analysis; parameter estimation; design of experiments; cell culture media and feeding strategies
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
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 papers will be 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 1400 CHF (Swiss Francs). Please note that for papers submitted after 30 June 2020 an APC of 1500 CHF applies. 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.
- bioprocess design
- process simulation
- metabolic modelling
- fermentation modelling
- model-based optimization
- online control
- first-principles modelling
- data-driven modelling