Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error prop...

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Autores principales:	Julian Kager, Christoph Herwig
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	generic error propagation Monte Carlo rate calculation regression analysis bioprocess evaluation interlinking of multiple methods Technology T Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/e5c10365d4c74c319849550aa1cdbc9e
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id	oai:doaj.org-article:e5c10365d4c74c319849550aa1cdbc9e
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spelling	oai:doaj.org-article:e5c10365d4c74c319849550aa1cdbc9e2021-11-25T16:46:26ZMonte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses10.3390/bioengineering81101602306-5354https://doaj.org/article/e5c10365d4c74c319849550aa1cdbc9e2021-10-01T00:00:00Zhttps://www.mdpi.com/2306-5354/8/11/160https://doaj.org/toc/2306-5354During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in <i>E. coli</i>, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.Julian KagerChristoph HerwigMDPI AGarticlegeneric error propagationMonte Carlorate calculationregression analysisbioprocess evaluationinterlinking of multiple methodsTechnologyTBiology (General)QH301-705.5ENBioengineering, Vol 8, Iss 160, p 160 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	generic error propagation Monte Carlo rate calculation regression analysis bioprocess evaluation interlinking of multiple methods Technology T Biology (General) QH301-705.5
spellingShingle	generic error propagation Monte Carlo rate calculation regression analysis bioprocess evaluation interlinking of multiple methods Technology T Biology (General) QH301-705.5 Julian Kager Christoph Herwig Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses
description	During process development, bioprocess data need to be converted into applicable knowledge. Therefore, it is crucial to evaluate the obtained data under the usage of transparent and reliable data reduction and correlation techniques. Within this contribution, we show a generic Monte Carlo error propagation and regression approach applied to two different, industrially relevant cultivation processes. Based on measurement uncertainties, errors for cell-specific growth, uptake, and production rates were determined across an evaluation chain, with interlinked inputs and outputs. These uncertainties were subsequently included in regression analysis to derive the covariance of the regression coefficients and the confidence bounds for prediction. The usefulness of the approach is shown within two case studies, based on the relations across biomass-specific rate control limits to guarantee high productivities in <i>E. coli</i>, and low lactate formation in a CHO cell fed-batch could be established. Besides the possibility to determine realistic errors on the evaluated process data, the presented approach helps to differentiate between reliable and unreliable correlations and prevents the wrong interpretations of relations based on uncertain data.
format	article
author	Julian Kager Christoph Herwig
author_facet	Julian Kager Christoph Herwig
author_sort	Julian Kager
title	Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses
title_short	Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses
title_full	Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses
title_fullStr	Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses
title_full_unstemmed	Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses
title_sort	monte carlo-based error propagation for a more reliable regression analysis across specific rates in bioprocesses
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e5c10365d4c74c319849550aa1cdbc9e
work_keys_str_mv	AT juliankager montecarlobasederrorpropagationforamorereliableregressionanalysisacrossspecificratesinbioprocesses AT christophherwig montecarlobasederrorpropagationforamorereliableregressionanalysisacrossspecificratesinbioprocesses
_version_	1718412990156374016

Monte Carlo-Based Error Propagation for a More Reliable Regression Analysis across Specific Rates in Bioprocesses

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