ArticleBMC research notes2017
Automated growth rate determination in high-throughput microbioreactor systems.
Article in BMC research notes, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Online Monitoring of Oxygen Transfer Rate as a Versatile Tool for Fermentation Characterization and Optimization.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Automation of a Capillary-Wave Microbioreactor Platform to Enhance Phage Sensitivity Screen Efficiency.Engineering in life sciences · 2025Article
- gcplyr: an R package for microbial growth curve data analysis.BMC bioinformatics · 2024Article
- A genetic toolbox to empower Paracoccus pantotrophus DSM 2944 as a metabolically versatile SynBio chassis.Microbial cell factories · 2024Article
- bletl - A Python package for integrating BioLector microcultivation devices in the Design-Build-Test-Learn cycle.Engineering in life sciences · 2022Article
- Ustilaginaceae Biocatalyst for Co-Metabolism of COJournal of fungi (Basel, Switzerland) · 2021Article
- Screening of a genome-reduced Corynebacterium glutamicum strain library for improved heterologous cutinase secretion.Microbial biotechnology · 2020Article
- Microbial single-cell growth response at defined carbon limiting conditions.RSC advances · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectiveThe 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.
resultsA 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.