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Teach Open PSE

Teach Open Process Systems Engineering

From the lab bench to FAIR data, models and processes

An open, hands-on learning resource for research data management in chemical and bioprocess engineering. Every module is a Jupyter notebook you can read here, download, run and adapt.

Browse the modules Start with the initial-rate track Contribute

Why Teach Open PSE?

Experimental process data in biocatalysis and bioprocess engineering are rich but fragmented: assay designs, instrument exports, reporting guidelines (STRENDA), exchange formats (EnzymeML), databases (STRENDA DB, SABIO-RK) and modelling tools exist, but rarely meet in one place where learners can practise the whole chain. Teach Open PSE connects them in reproducible, openly licensed notebooks, following the model of TeachOpenCADD for computer-aided drug design.

Research data management

Plan data before measuring, structure it with community standards (STRENDA, EnzymeML) and package data, code and provenance as RO-Crates.

Experiments & PAT

Real bench protocols and real instrument exports (cuvette and microplate UV-Vis), including the messy parts: calibration, pathlength, plate layouts.

Kinetic modelling

From non-linear regression in SciPy to ODE fitting and model discrimination with Catalax, read straight from the EnzymeML document.

Process systems engineering

Use the fitted kinetics in reactor models, from an enzyme-membrane reactor to sensitivity and process design (growing).

The first edition: one enzyme reaction, two tracks

All core modules follow the formate dehydrogenase (FDH) reaction NAD⁺ + HCOO⁻ → NADH + CO₂, so the numbers always mean the same thing. The course then splits into an initial-rate and a progress-curve study of that reaction, which share planning and the RO-Crate capstone.

flowchart LR
    DMP["RDM-01<br/>Mini-DMP"] --> WIR
    DMP --> WPC
    subgraph IR["Initial-rate track"]
        WIR["WET-IR-UVVis-01<br/>initial-rate assay"] --> RIR["RDM-IR-01<br/>EnzymeML"] --> KIR["KIN-IR-01a/b/c<br/>fit the rate law"]
    end
    subgraph PC["Progress-curve track"]
        WPC["WET-PC-UVVis-01<br/>plate-reader assay + EnzymeML"] --> KPC["KIN-PC-01/02<br/>fit and discriminate models"]
    end
    KIR --> CMP["RDM-02<br/>cross-method audit"]
    KPC --> CMP
    CMP --> X["RDM-03<br/>RO-Crate capstone"]
    style DMP fill:#F3E5F5,stroke:#4A148C
    style RIR fill:#F3E5F5,stroke:#4A148C
    style CMP fill:#F3E5F5,stroke:#4A148C
    style X fill:#fde68a,stroke:#b45309,stroke-width:2px
    style WIR fill:#E0F7FA,stroke:#006064
    style WPC fill:#E0F7FA,stroke:#006064
    style KIR fill:#FFF3E0,stroke:#E65100
    style KPC fill:#FFF3E0,stroke:#E65100

New to the lab? Begin with the primer LAB-01: a crash course in the wet lab.

Run it yourself

git clone https://github.com/NMEpping/TeachOpenBiocat.git
cd TeachOpenBiocat
uv sync
uv run jupyter lab

Every wet-lab module falls back to bundled reference data, so no bench access is needed. Step-by-step instructions for macOS, Windows and VS Code are in the setup guide.