Abstract:
:Biomanufacturing exhibits inherent variability that can lead to variation in performance attributes and batch failure. To help ensure process consistency and product quality the development of predictive models and integrated control strategies is a promising approach. In this study, a feedback controller was developed to limit excessive lactate production, a widespread metabolic phenomenon that is negatively associated with culture performance and product quality. The controller was developed by applying machine learning strategies to historical process development data, resulting in a forecast model that could identify whether a run would result in lactate consumption or accumulation. In addition, this exercise identified a correlation between increased amino acid consumption and low observed lactate production leading to the mechanistic hypothesis that there is a deficiency in the link between glycolysis and the tricarboxylic acid cycle. Using the correlative process parameters to build mechanistic insight and applying this to predictive models of lactate concentration, a dynamic model predictive controller (MPC) for lactate was designed. This MPC was implemented experimentally on a process known to exhibit high lactate accumulation and successfully drove the cell cultures towards a lactate consuming state. In addition, an increase in specific titer productivity was observed when compared with non-MPC controlled reactors.
journal_name
Biotechnol Bioengjournal_title
Biotechnology and bioengineeringauthors
Schmitt J,Downey B,Beller J,Russell B,Quach A,Lyon D,Curran M,Mulukutla BC,Chu Cdoi
10.1002/bit.27015subject
Has Abstractpub_date
2019-09-01 00:00:00pages
2223-2235issue
9eissn
0006-3592issn
1097-0290journal_volume
116pub_type
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