Biotechnology Data Analysis Training with Jupyter Notebooks.

Area of Science:

• Biotechnology
• Computational Biology
• Bioinformatics Education

Background:

• The rapid advancement of data analytics and computational methods in biotechnology outpaces current educational curricula.
• There is a critical need for updated teaching methodologies to equip future biotechnologists with robust data analysis skills.
• Existing curricula struggle to keep pace with the increasing volume and complexity of biological data.

Purpose of the Study:

• To develop and evaluate a novel computer-based instructional workflow for teaching data analysis in biotechnology.
• To create a virtual organism simulator (silvio) for generating realistic experimental data.
• To enhance biotechnological data literacy among Master's students through simulated experiments.

Main Methods:

• Development of the "silvio" Python package, a virtual organism simulator integrating microbial models.
• Construction of a computer-based instructional workflow using Jupyter Notebooks for data analysis simulations.
• Implementation of the workflow in distance learning Master's courses, assessing pedagogical effectiveness.

Main Results:

• The silvio package successfully simulated experimental data for growth curve analysis, promoter design, and expression rate measurement.
• The Jupyter Notebook workflow facilitated data analysis using Python or Excel within a cohesive learning environment.
• The instructional workflow demonstrated pedagogic efficiency in teaching data analysis to biology and biotechnology students.

Conclusions:

• Virtual organism simulations provide a consistent and motivating approach for developing biotechnological data literacy.
• The silvio package and associated workflow represent an effective tool for modernizing biotechnology education.
• This approach addresses the gap between technological advancements and curriculum adaptation in the life sciences.