Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling
Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single ce...
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doaj-d179ba75a50d4db78ada60cc7a8d53362020-11-25T00:14:01ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2017-09-01810.3389/fmicb.2017.01813291259Heterogeneity in Pure Microbial Systems: Experimental Measurements and ModelingRebeca González-Cabaleiro0Anca M. Mitchell1Wendy Smith2Anil Wipat3Irina D. Ofiţeru4School of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United KingdomSchool of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United KingdomInterdisciplinary Computing and Complex BioSystems (ICOS), School of ComputingNewcastle University, Newcastle upon Tyne, United KingdomInterdisciplinary Computing and Complex BioSystems (ICOS), School of ComputingNewcastle University, Newcastle upon Tyne, United KingdomSchool of Engineering, Chemical Engineering, Newcastle UniversityNewcastle upon Tyne, United KingdomCellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale.http://journal.frontiersin.org/article/10.3389/fmicb.2017.01813/fullpopulation heterogeneitysingle cell analysisflow cytometrypopulation balance modelsindividual based models |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rebeca González-Cabaleiro Anca M. Mitchell Wendy Smith Anil Wipat Irina D. Ofiţeru |
spellingShingle |
Rebeca González-Cabaleiro Anca M. Mitchell Wendy Smith Anil Wipat Irina D. Ofiţeru Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling Frontiers in Microbiology population heterogeneity single cell analysis flow cytometry population balance models individual based models |
author_facet |
Rebeca González-Cabaleiro Anca M. Mitchell Wendy Smith Anil Wipat Irina D. Ofiţeru |
author_sort |
Rebeca González-Cabaleiro |
title |
Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling |
title_short |
Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling |
title_full |
Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling |
title_fullStr |
Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling |
title_full_unstemmed |
Heterogeneity in Pure Microbial Systems: Experimental Measurements and Modeling |
title_sort |
heterogeneity in pure microbial systems: experimental measurements and modeling |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Microbiology |
issn |
1664-302X |
publishDate |
2017-09-01 |
description |
Cellular heterogeneity influences bioprocess performance in ways that until date are not completely elucidated. In order to account for this phenomenon in the design and operation of bioprocesses, reliable analytical and mathematical descriptions are required. We present an overview of the single cell analysis, and the mathematical modeling frameworks that have potential to be used in bioprocess control and optimization, in particular for microbial processes. In order to be suitable for bioprocess monitoring, experimental methods need to be high throughput and to require relatively short processing time. One such method used successfully under dynamic conditions is flow cytometry. Population balance and individual based models are suitable modeling options, the latter one having in particular a good potential to integrate the various data collected through experimentation. This will be highly beneficial for appropriate process design and scale up as a more rigorous approach may prevent a priori unwanted performance losses. It will also help progressing synthetic biology applications to industrial scale. |
topic |
population heterogeneity single cell analysis flow cytometry population balance models individual based models |
url |
http://journal.frontiersin.org/article/10.3389/fmicb.2017.01813/full |
work_keys_str_mv |
AT rebecagonzalezcabaleiro heterogeneityinpuremicrobialsystemsexperimentalmeasurementsandmodeling AT ancammitchell heterogeneityinpuremicrobialsystemsexperimentalmeasurementsandmodeling AT wendysmith heterogeneityinpuremicrobialsystemsexperimentalmeasurementsandmodeling AT anilwipat heterogeneityinpuremicrobialsystemsexperimentalmeasurementsandmodeling AT irinadofiteru heterogeneityinpuremicrobialsystemsexperimentalmeasurementsandmodeling |
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1725391974751535104 |