Bletl ‐ A Python package for integrating BioLector microcultivation devices in the Design‐Build‐Test‐Learn cycle. Issue 3 (1st March 2022)

• Record Type: Journal Article
• Authors: Osthege, Michael; Tenhaef, Niklas; Zyla, Rebecca; Müller, Carolin; Hemmerich, Johannes; Wiechert, Wolfgang; Noack, Stephan; Oldiges, Marco
• Other Names: Beutel Sascha guestEditor.
• Is Part Of: Engineering in life sciences. Volume 22:Issue 3/4(2022)
• Page Start: 242
• Page End: 259
• Publication Date: 2022-03-01
• Subjects: BioLector -- feature extraction -- growth rate -- microbial phenotyping -- uncertainty quantification; Bioengineering -- Periodicals; 660.605
• DOI: 10.1002/elsc.202100108
• Languages: English
• ISSNs: 1618-0240

Abstract

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 μ ⃗ t $\overrightarrow{\mu }_t$ 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.