Tellurium kinetic experiments

When to use

Use this skill for deterministic reaction-network trajectories and independent parameter conditions from a local model. The helper performs SBML consistency checks, CVODE integration and an actual COMBINE archive replay. It exports each condition's exact SBML and the SED-ML experiment rather than handing off an unrecorded notebook state.

Runtime

uv venv --python 3.11 kinetic-env
uv pip install --python kinetic-env/bin/python tellurium==2.2.13.1 libroadrunner==2.10.0 \
  antimony==3.2.0 python-libsbml==5.21.2 python-libsedml==2.0.34 python-libcombine==0.2.20

The full workflow ran with these packages on macOS ARM64. It constructs SED-ML with libSEDML and archives with Tellurium/libCombine; PhraSEDML is not required by this helper. Headless runs can set MPLBACKEND=Agg. No plotting window is opened by the helper.

工作流程

1. Inspect the supplied model's compartments, species, initial conditions, boundary species, reactions, parameter definitions and rules/events. Identify the scientific question and distinguish a mechanistic kinetic model from a flux-balance reconstruction. Record the source model, version and any literature parameters; do not treat an example model as experimentally calibrated.
2. Check units before interpreting a trajectory. SBML reaction rates have amount/time units; species may have concentration or amount semantics. In a fixed-volume first-order model, k*A*cell converts concentration dependence into amount/time. The helper checks SBML consistency and retains every warning, including undefined units. Undefined units are reported as empty/indeterminable, not silently assumed to mean SI.
3. Select concentration outputs and an experiment in the JSON format described in references/experiments.md. Time values use the model's own time units. The tested helper outputs concentration for species with hasOnlySubstanceUnits=false; it rejects amount-only selections to avoid changing their meaning during SED-ML replay.
4. Run baseline and desired constant-global-parameter changes. Every scenario starts from a fresh SBML model, so previous final concentrations cannot leak into the next condition. Changes to species initial values, compartment volume, assignment rules or time-varying inputs require explicit model changes and corresponding tests; they are not parameter mutations hidden in this helper.
5. Examine finite outputs, signs, relevant conservation relations and timescales. Check solver sensitivity by repeating at stricter tolerances when the scientific interpretation depends on small differences. A smooth curve or zero archive-replay error does not establish model validity or parameter identifiability. Never clip negative concentrations to hide solver or model problems.
6. Review the COMBINE replay comparison, model warnings and units in report.json. The helper replays the archive it generated and compares every selected value against the direct trajectories. Deliver the archive, report, source model, experiment config and CSV curves.

Run the executable reference

assets/first-order.ant defines the closed reaction A → B in a constant 1-L compartment, initially A=1 and B=0 mol/L, with k=0.2 per second. assets/experiment.json runs baseline and k=0.4 per second from 0 to 10 s. From the skill directory, point the interpreter to the environment created above:

MPLBACKEND=Agg kinetic-env/bin/python scripts/kinetic_experiment.py \
  --model assets/first-order.ant --format antimony --experiment assets/experiment.json \
  --output kinetic-reference

# The SBML branch was also exercised; replace these filenames with actual user inputs.
MPLBACKEND=Agg kinetic-env/bin/python scripts/kinetic_experiment.py \
  --model model.xml --format sbml --experiment experiment.json --output kinetic-analysis

Output directories must be new. The reference was executed, including Antimony-to-SBML conversion, libSBML checks, both direct integrations, SED-ML creation and COMBINE replay. Both conditions matched the analytical A(t)=exp(-k*t), B(t)=1-A(t) within 2e-8 absolute/relative tolerance; A+B was conserved within 1e-10, and archive replay matched direct output exactly on the tested stack. That verifies this controlled example; arbitrary SBML packages, events, delays or stochastic models are not covered by those tests.

Artifacts

The libSEDML findings in the report are parse diagnostics. Successful execution and equality provide additional evidence that this generated uniform-course experiment works in Tellurium; they do not certify every SED-ML feature or every simulator's compatibility.

Primary references

• Tellurium model loading and export methods
• Antimony unit and reaction semantics
• COMBINE and SED-ML examples
• libRoadRunner documentation