Journal Article

FZJ-2022-01544

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png bletl ‐ A Python package for integrating BioLector microcultivation devices in the Design‐Build‐Test‐Learn cycle

Osthege, M.FZJ* ; Tenhaef, N.FZJ* ; Zyla, R. ; Müller, C.FZJ* ; Hemmerich, J. ; Wiechert, W.FZJ* ; Noack, S.FZJ* ; Oldiges, M. (Corresponding author)FZJ*

2022
Wiley-VCH Weinheim

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Please use a persistent id in citations: http://hdl.handle.net/2128/30973  doi:10.1002/elsc.202100108

Abstract: Microbioreactor (MBR) devices have emerged as powerful cultivation tools for tasks of microbial phenotyping and bioprocess characterization and provide a wealth of online process data in a highly parallelized manner. Such datasets are difficult to interpret in short time by manual workflows. In this study, we present the Python package bletl and show how it enables robust data analyses and the application of machine learning techniques without tedious data parsing and preprocessing. bletl reads raw result files from BioLector I, II and Pro devices to make all the contained information available to Python-based data analysis workflows. Together with standard tooling from the Python scientific computing ecosystem, interactive visualizations and spline-based derivative calculations can be performed. Additionally, we present a new method for unbiased quantification of time-variable specific growth rate 𝜇⃗ 𝑡 based on unsupervised switchpoint detection with Student-t distributed random walks. With an adequate calibration model, this method enables practitioners to quantify time-variable growth rate with Bayesian uncertainty quantification and automatically detect switch-points that indicate relevant metabolic changes. Finally, we show how time series feature extraction enables the application of machine learning methods to MBR data, resulting in unsupervised phenotype characterization. As an example, Neighbor Embedding (t-SNE) is performed to visualize datasets comprising a variety of growth/DO/pH phenotypes

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Contributing Institute(s):

1. Biotechnologie (IBG-1)

Research Program(s):

1. 2172 - Utilization of renewable carbon and energy sources and engineering of ecosystem functions (POF4-217) (POF4-217)

Appears in the scientific report 2022

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Database coverage:
; ; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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