scholarly journals Probabilistic Model by Bayesian Network for the Prediction of Antibody Glycosylation in Perfusion and Fed‐Batch Cell Cultures

2021 ◽  
Author(s):  
Liang Zhang ◽  
MingLiang Wang ◽  
Andreas Castan ◽  
Håkan Hjalmarsson ◽  
Veronique Chotteau
Keyword(s):  
Bayesian Network ◽  
Cell Cultures ◽  
Probabilistic Model ◽  
Fed Batch ◽  
Batch Cell

scholarly journals Metabolic Control in Mammalian Fed-Batch Cell Cultures for Reduced Lactic Acid Accumulation and Improved Process Robustness

2016 ◽  
Vol 3 (1) ◽  
pp. 5 ◽  
Author(s):  
Viktor Konakovsky ◽  
Christoph Clemens ◽  
Markus Müller ◽  
Jan Bechmann ◽  
Martina Berger ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Metabolic Control ◽  
Cell Cultures ◽  
Fed Batch ◽  
Process Robustness ◽  
Acid Accumulation ◽  
Batch Cell

Controlling glycation of recombinant antibody in fed-batch cell cultures

2011 ◽  
Vol 108 (11) ◽  
pp. 2600-2610 ◽  
Author(s):  
Inn H. Yuk ◽  
Boyan Zhang ◽  
Yi Yang ◽  
George Dutina ◽  
Kimberly D. Leach ◽  
...  
Keyword(s):  
Cell Cultures ◽  
Recombinant Antibody ◽  
Fed Batch ◽  
Batch Cell

Investigation of metabolic variability observed in extended fed batch cell culture

2013 ◽  
Vol 29 (6) ◽  
pp. 1519-1527 ◽  
Author(s):  
Alan Gilbert ◽  
Kyle McElearney ◽  
Rashmi Kshirsagar ◽  
Martin S. Sinacore ◽  
Thomas Ryll
Keyword(s):  
Cell Culture ◽  
Fed Batch ◽  
Batch Cell

scholarly journals Impact of aeration strategies on fed-batch cell culture kinetics in a single-use 24-well miniature bioreactor

2014 ◽  
Vol 82 ◽  
pp. 105-116 ◽  
Author(s):  
J.P.J. Betts ◽  
S.R.C. Warr ◽  
G.B. Finka ◽  
M. Uden ◽  
M. Town ◽  
...  
Keyword(s):  
Cell Culture ◽  
Fed Batch ◽  
Single Use ◽  
Batch Cell

scholarly journals A Bayesian network model for personalised COVID19 risk assessment and contact tracing

2020 ◽  
Author(s):  
Norman E Fenton ◽  
Scott McLachlan ◽  
Peter Lucas ◽  
Kudakwashe Dube ◽  
Graham A Hitman ◽  
...  
Keyword(s):  
Risk Factors ◽  
Risk Assessment ◽  
Bayesian Network ◽  
Social Interactions ◽  
Probabilistic Model ◽  
Personal Information ◽  
Contact Tracing ◽  
Smartphone App ◽  
Bayesian Network Model ◽  
The Uk

AbstractConcerns about the practicality and effectiveness of using Contact Tracing Apps (CTA) to reduce the spread of COVID19 have been well documented and, in the UK, led to the abandonment of the NHS CTA shortly after its release in May 2020. We present a causal probabilistic model (a Bayesian network) that provides the basis for a practical CTA solution that addresses some of the concerns and which has the advantage of minimal infringement of privacy. Users of the model can provide as much or little personal information as they wish about relevant risk factors, symptoms, and recent social interactions. The model then provides them feedback about the likelihood of the presence of asymptotic, mild or severe COVID19 (past, present and projected). When the model is embedded in a smartphone app, it can be used to detect new outbreaks in a monitored population and identify outbreak locations as early as possible. For this purpose, the only data needed to be centrally collected is the probability the user has COVID19 and the GPS location.

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