PT  - JOURNAL ARTICLE
AU  - Ulf W. Liebal
AU  - Rafael Schimassek
AU  - Iris Broderius
AU  - Nicole Maaßen
AU  - Alina Vogelgesang
AU  - Philipp Weyers
AU  - Lars M. Blank
TI  - Biotechnology data analysis training with Jupyter Notebooks
AID  - 10.1101/2021.09.28.462133
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.09.28.462133
4099  - http://biorxiv.org/content/early/2021/09/30/2021.09.28.462133.short
4100  - http://biorxiv.org/content/early/2021/09/30/2021.09.28.462133.full
AB  - Biotechnology has experienced innovations in analytics and data processing. As the volume of data and its complexity grows, new computational procedures for extracting information are developed. However, the rate of change outpaces the adaptation of biotechnology curricula, necessitating new teaching methodologies to equip biotechnologists with data analysis abilities. To simulate experimental data, we created a virtual organism simulator (silvio) by combining diverse cellular and sub-cellular microbial models. silvio was utilized to construct a computer-based instructional workflow with important steps during strain characterization and recombinant protein expression. The instructional workflow is provided as a Jupyter Notebook with comprehensive explanatory text of biotechnological facts and experiment simulations using silvio tools. The students conduct data analysis in Python or Excel. This instructional workflow was separately implemented in two distance courses for Master’s students in biology and biotechnology. The concept of using virtual organism simulations that generate coherent results across different experiments can be used to construct consistent and motivating case studies for biotechnological data literacy.Competing Interest StatementThe authors have declared no competing interest.