bletl ‐ A Python package for integrating BioLector microcultivation devices in the Design‐Build‐Test‐Learn cycle

• Published in: Engineering in life sciences Vol. 22; no. 3-4; pp. 242 - 259
• Main Authors: Osthege, Michael, Tenhaef, Niklas, Zyla, Rebecca, Müller, Carolin, Hemmerich, Johannes, Wiechert, Wolfgang, Noack, Stephan, Oldiges, Marco
• Format: Journal Article
• Language: English
• Published: Germany John Wiley & Sons, Inc 01.03.2022; John Wiley and Sons Inc
• Subjects: Automation; Bayesian analysis; BioLector; Bioreactors; Data analysis; Data processing; Datasets; Design; Embedding; Feature extraction; Growth rate; Learning algorithms; Machine learning; microbial phenotyping; Microorganisms; Phenotypes; Phenotyping; Random walk; Software; Tooling; uncertainty quantification; uncertainty quantification; growth rate; feature extraction; microbial phenotyping; BioLector
• ISSN: 1618-0240; 1618-2863; 1618-2863
• DOI: 10.1002/elsc.202100108

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 μ⃗t$\overrightarrow{\mu }_t$ 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.