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

Engineering in Life Sciences, vol. 22, pp. 242–259

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 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.

Authors 8

1. Michael Osthege Aachen Institute of Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

   Institute of Biotechnology RWTH Aachen University Aachen Germany

   Institute of Biotechnology, RWTH Aachen University, Aachen, Germany

2. Niklas Tenhaef

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

3. Rebecca Zyla

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

4. Carolin Müller Aachen Institute of Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

   Institute of Biotechnology RWTH Aachen University Aachen Germany

   Institute of Biotechnology, RWTH Aachen University, Aachen, Germany

5. Johannes Hemmerich

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

6. Wolfgang Wiechert Aachen Computational Systems Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Computational Systems Biotechnology (AVT.CSB) RWTH Aachen University Aachen Germany

   Forschungszentrum Jülich GmbH Jülich Germany

   Computational Systems Biotechnology (AVT.CSB), RWTH Aachen University, Aachen, Germany

7. Stephan Noack

   Forschungszentrum Jülich

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

8. Marco Oldiges corresponding Aachen Institute of Biotechnology

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Forschungszentrum Jülich GmbH Jülich Germany

   Institute of Biotechnology RWTH Aachen University Aachen Germany

   Institute of Biotechnology, RWTH Aachen University, Aachen, Germany