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Automated growth rate determination in high-throughput microbioreactor systems

OBJECTIVE: The calculation of growth rates provides basic metric for biological fitness and is standard task when using microbioreactors (MBRs) in microbial phenotyping. MBRs easily produce huge data at high frequency from parallelized high-throughput cultivations with online monitoring of biomass f...

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Autores principales: Hemmerich, Johannes, Wiechert, Wolfgang, Oldiges, Marco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702135/
https://www.ncbi.nlm.nih.gov/pubmed/29178966
http://dx.doi.org/10.1186/s13104-017-2945-6
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author Hemmerich, Johannes
Wiechert, Wolfgang
Oldiges, Marco
author_facet Hemmerich, Johannes
Wiechert, Wolfgang
Oldiges, Marco
author_sort Hemmerich, Johannes
collection PubMed
description OBJECTIVE: The calculation of growth rates provides basic metric for biological fitness and is standard task when using microbioreactors (MBRs) in microbial phenotyping. MBRs easily produce huge data at high frequency from parallelized high-throughput cultivations with online monitoring of biomass formation at high temporal resolution. Resulting high-density data need to be processed efficiently to accelerate experimental throughput. RESULTS: A MATLAB code is presented that detects the exponential growth phase from multiple microbial cultivations in an iterative procedure based on several criteria, according to the model of exponential growth. These were obtained with Corynebacterium glutamicum showing single exponential growth phase and Escherichia coli exhibiting diauxic growth with exponential phase followed by retarded growth. The procedure reproducibly detects the correct biomass data subset for growth rate calculation. The procedure was applied on data set detached from growth phenotyping of library of genome reduced C. glutamicum strains and results agree with previously reported results where manual effort was needed to pre-process the data. Thus, the automated and standardized method enables a fair comparison of strain mutants for biological fitness evaluation. The code is easily parallelized and greatly facilitates experimental throughout in biological fitness testing from strain screenings conducted with MBR systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13104-017-2945-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-57021352017-12-04 Automated growth rate determination in high-throughput microbioreactor systems Hemmerich, Johannes Wiechert, Wolfgang Oldiges, Marco BMC Res Notes Research Note OBJECTIVE: The calculation of growth rates provides basic metric for biological fitness and is standard task when using microbioreactors (MBRs) in microbial phenotyping. MBRs easily produce huge data at high frequency from parallelized high-throughput cultivations with online monitoring of biomass formation at high temporal resolution. Resulting high-density data need to be processed efficiently to accelerate experimental throughput. RESULTS: A MATLAB code is presented that detects the exponential growth phase from multiple microbial cultivations in an iterative procedure based on several criteria, according to the model of exponential growth. These were obtained with Corynebacterium glutamicum showing single exponential growth phase and Escherichia coli exhibiting diauxic growth with exponential phase followed by retarded growth. The procedure reproducibly detects the correct biomass data subset for growth rate calculation. The procedure was applied on data set detached from growth phenotyping of library of genome reduced C. glutamicum strains and results agree with previously reported results where manual effort was needed to pre-process the data. Thus, the automated and standardized method enables a fair comparison of strain mutants for biological fitness evaluation. The code is easily parallelized and greatly facilitates experimental throughout in biological fitness testing from strain screenings conducted with MBR systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13104-017-2945-6) contains supplementary material, which is available to authorized users. BioMed Central 2017-11-25 /pmc/articles/PMC5702135/ /pubmed/29178966 http://dx.doi.org/10.1186/s13104-017-2945-6 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Note
Hemmerich, Johannes
Wiechert, Wolfgang
Oldiges, Marco
Automated growth rate determination in high-throughput microbioreactor systems
title Automated growth rate determination in high-throughput microbioreactor systems
title_full Automated growth rate determination in high-throughput microbioreactor systems
title_fullStr Automated growth rate determination in high-throughput microbioreactor systems
title_full_unstemmed Automated growth rate determination in high-throughput microbioreactor systems
title_short Automated growth rate determination in high-throughput microbioreactor systems
title_sort automated growth rate determination in high-throughput microbioreactor systems
topic Research Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5702135/
https://www.ncbi.nlm.nih.gov/pubmed/29178966
http://dx.doi.org/10.1186/s13104-017-2945-6
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