Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models

Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of c...

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Autores principales:	Barbara Pretzner, Rüdiger W. Maschke, Claudia Haiderer, Gernot T. John, Christoph Herwig, Peter Sykacek
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	particle filter shake flask Gompertz function logistic function time series forecasting critical event prediction Technology T Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/3fb6a5a988cd45ac8aa5ddcdfd64b500
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spelling	oai:doaj.org-article:3fb6a5a988cd45ac8aa5ddcdfd64b5002021-11-25T16:46:36ZPredictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models10.3390/bioengineering81101772306-5354https://doaj.org/article/3fb6a5a988cd45ac8aa5ddcdfd64b5002021-11-01T00:00:00Zhttps://www.mdpi.com/2306-5354/8/11/177https://doaj.org/toc/2306-5354Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of <i>Escherichia coli</i> (<i>E. coli</i>) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development.Barbara PretznerRüdiger W. MaschkeClaudia HaidererGernot T. JohnChristoph HerwigPeter SykacekMDPI AGarticleparticle filtershake flaskGompertz functionlogistic functiontime series forecastingcritical event predictionTechnologyTBiology (General)QH301-705.5ENBioengineering, Vol 8, Iss 177, p 177 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	particle filter shake flask Gompertz function logistic function time series forecasting critical event prediction Technology T Biology (General) QH301-705.5
spellingShingle	particle filter shake flask Gompertz function logistic function time series forecasting critical event prediction Technology T Biology (General) QH301-705.5 Barbara Pretzner Rüdiger W. Maschke Claudia Haiderer Gernot T. John Christoph Herwig Peter Sykacek Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models
description	Simplicity renders shake flasks ideal for strain selection and substrate optimization in biotechnology. Uncertainty during initial experiments may, however, cause adverse growth conditions and mislead conclusions. Using growth models for online predictions of future biomass (BM) and the arrival of critical events like low dissolved oxygen (DO) levels or when to harvest is hence important to optimize protocols. Established knowledge that unfavorable metabolites of growing microorganisms interfere with the substrate suggests that growth dynamics and, as a consequence, the growth model parameters may vary in the course of an experiment. Predictive monitoring of shake flask cultures will therefore benefit from estimating growth model parameters in an online and adaptive manner. This paper evaluates a newly developed particle filter (PF) which is specifically tailored to the requirements of biotechnological shake flask experiments. By combining stationary accuracy with fast adaptation to change the proposed PF estimates time-varying growth model parameters from iteratively measured BM and DO sensor signals in an optimal manner. Such proposition of inferring time varying parameters of Gompertz and Logistic growth models is to our best knowledge novel and here for the first time assessed for predictive monitoring of <i>Escherichia coli</i> (<i>E. coli</i>) shake flask experiments. Assessments that mimic real-time predictions of BM and DO levels under previously untested growth conditions demonstrate the efficacy of the approach. After allowing for an initialization phase where the PF learns appropriate model parameters, we obtain accurate predictions of future BM and DO levels and important temporal characteristics like when to harvest. Statically parameterized growth models that represent the dynamics of a specific setting will in general provide poor characterizations of the dynamics when we change strain or substrate. The proposed approach is thus an important innovation for scientists working on strain characterization and substrate optimization as providing accurate forecasts will improve reproducibility and efficiency in early-stage bioprocess development.
format	article
author	Barbara Pretzner Rüdiger W. Maschke Claudia Haiderer Gernot T. John Christoph Herwig Peter Sykacek
author_facet	Barbara Pretzner Rüdiger W. Maschke Claudia Haiderer Gernot T. John Christoph Herwig Peter Sykacek
author_sort	Barbara Pretzner
title	Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models
title_short	Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models
title_full	Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models
title_fullStr	Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models
title_full_unstemmed	Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models
title_sort	predictive monitoring of shake flask cultures with online estimated growth models
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3fb6a5a988cd45ac8aa5ddcdfd64b500
work_keys_str_mv	AT barbarapretzner predictivemonitoringofshakeflaskcultureswithonlineestimatedgrowthmodels AT rudigerwmaschke predictivemonitoringofshakeflaskcultureswithonlineestimatedgrowthmodels AT claudiahaiderer predictivemonitoringofshakeflaskcultureswithonlineestimatedgrowthmodels AT gernottjohn predictivemonitoringofshakeflaskcultureswithonlineestimatedgrowthmodels AT christophherwig predictivemonitoringofshakeflaskcultureswithonlineestimatedgrowthmodels AT petersykacek predictivemonitoringofshakeflaskcultureswithonlineestimatedgrowthmodels
_version_	1718412949182218240

Predictive Monitoring of Shake Flask Cultures with Online Estimated Growth Models

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