Thin Layers for Kinetic Modeling

What Are Thin Layers?

Thin layers are PyEnzyme’s interface for connecting experimental data to powerful kinetic modeling tools. They function as translators that bridge EnzymeML (the experimental data format) and the language of modeling software.

For experimentalists who have collected time-course data showing how concentrations change over time, thin layers enable:

1. Simulation of model predictions under different conditions
2. Parameter fitting of models to experimental data to determine kinetic parameters
3. Comparison of model predictions with experimental measurements

PyEnzyme handles all translation automatically. Manual data conversion or learning complex modeling software interfaces is not required.

The Workflow

The typical workflow is straightforward:

1. Prepare an EnzymeML document with reactions, equations, and measurements
2. Choose a thin layer (PySCeS or COPASI) based on requirements
3. Simulate to observe model predictions
4. Optimize to fit parameters to experimental data
5. Export the fitted parameters back to the EnzymeML document

Available Thin Layers

PyEnzyme provides thin layers for two popular modeling tools. Both provide the same interface, enabling easy switching between them:

PySCeS Thin Layer

COPASI Thin Layer