Titlebook: Digital Twins; Tools and Concepts f Christoph Herwig,Ralf P?rtner,Johannes M?ller Book 2021 The Editor(s) (if applicable) and The Author(s)

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When Is an In Silico Representation a Digital Twin? A Biopharmaceutical Industry Approach to the Di a brief retrospect on their history and expectations for their deployment in today’s business environment, and a detailed financial assessment of their attractive economic benefits are provided in this chapter. The argument that restrictive guidelines set forth by regulatory agencies would hinder t

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Digitalization and Bioprocessing: Promises and Challenges,While advances in synthetic biology and metabolic engineering have made it possible to produce thousands of new molecules from cells, few of these molecules have reached the market. The traditional methods of strain and bioprocess development that transform laboratory results to industrial processes

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Digital Seed Train Twins and Statistical Methods,oduction processes, which contain several challenging development and process steps. One of these steps is the time- and cost-intensive cell proliferation process (also called seed train) to increase cell number from cell thawing up to production scale. Challenges like complex cell metabolism, batch

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Mechanistic Mathematical Models as a Basis for Digital Twins,re detailed virtual representations of bioprocesses with predictive capabilities. In biotechnology, Digital Twins can be used to monitor processes and to provide data for process control and optimization. Central and crucial components of Digital Twins are mathematical process models, which are capa

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0724-6145 ficient methods for research and development, and smart manuThis is the first of two volumes that together provide an overview of the latest advances in the generation and application of digital twins in bioprocess design and optimization..Both processes have undergone significant changes over the p

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Book 2021cess design and optimization..Both processes have undergone significant changes over the past few decades, moving from data-driven approaches into the 21st-century digitalization of the bioprocess industry. Moreover, the high demand for biotechnological products calls for efficient methods during re

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