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Analysis of Transformed Upstream Bioprocess Data Provides Insights into Biological System Variation
Author(s) -
Richelle Anne,
Lee Boung Wook,
Portela Rui M. C.,
Raley Jonathan,
Stosch Moritz
Publication year - 2020
Publication title -
biotechnology journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.144
H-Index - 84
eISSN - 1860-7314
pISSN - 1860-6768
DOI - 10.1002/biot.202000113
Subject(s) - bioprocess , upstream (networking) , biochemical engineering , computer science , scale (ratio) , upstream and downstream (dna) , systems biology , multivariate statistics , downstream (manufacturing) , process engineering , biological system , computational biology , biology , engineering , machine learning , computer network , paleontology , operations management , physics , quantum mechanics
In recent years, multivariate data analysis (MVDA) and modeling approaches have found increasing applications for upstream bioprocess studies (e.g., monitoring, development, optimization, scale‐up, etc.). Many of these studies look at variations in the concentrations of metabolites and cell‐based measurements. However, these measures are subject to system inherent variations (e.g., changes in metabolic activity) but also intentional operational changes. It is proposed to perform MVDA and modeling on data representative of the underlying biological system operation, that is, the specific rates, which are per se independent of the scale, operational strategy (e.g., batch, fed‐batch), and biomass content. Two industrial case studies are highlighted to showcase the approach: one is HEK medium performance comparison study and the other is CHO scale‐up/‐down study. It is shown that analyzing processes in this way reveals insights into behavior of the underlying biological system, which cannot to the same degree be deducted from the analysis of concentrations.