Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.
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Simulates biochemical kinetic models from SBML or Antimony with Tellurium and libRoadRunner, checks model units, compares deterministic parameter perturbations, and exports and replays SBML plus SED-ML COMBINE archives. Use for reaction-network time courses, kinetic parameters, concentration dynamics and reproducible simulation experiments; steady-state constraint-based metabolic flux analysis belongs to cobrapy.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
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.
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.
The six pinned releases were rechecked against official PyPI metadata on 2026-10-01. RoadRunner's documentation site still displays an old version banner; the solver settings below were also checked against released 2.10.0 source and the installed native runtime.
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.hasOnlySubstanceUnits=false;
it rejects amount-only selections to avoid changing their meaning during SED-ML replay.
Zero-dimensional compartments and rate-rule models are also rejected; the latter need a separate
tolerance workflow because RoadRunner 2.10.0 can order scalar tolerances differently from states.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.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.
Additional checks use a 5-L compartment and an initial amount of 10 mol (2 mol/L), resolve every
SED-ML species XPath against its actual SBML file, and verify the solver tolerance scaling.
That verifies this controlled example; arbitrary SBML packages, events, delays or stochastic models
are not covered by those tests.
| File | Contents |
|---|---|
baseline.csv, other scenario CSVs | Time and selected concentrations, with bracketed species headers |
model_<scenario>.xml | Exact independent SBML condition used by both execution routes |
experiment.sedml | Uniform time course, CVODE/tolerances, models, tasks and output selections |
experiment.omex | Those SBML files plus the master SED-ML and archive manifest |
report.json | Versions, input/archive checksums, parameters, units, validation findings, initial state tolerance vectors, minimum concentrations and replay differences |
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.
In RoadRunner 2.10.0, the JSON absolute_tolerance value is a scalar adjustment factor for
state/amount tolerances, not a uniform concentration error bound. The archive records that meaning
as KISAO:0000571; inspect initial_state_absolute_tolerances and the detailed explanation in
references/experiments.md. Declared SBML XPath namespaces and explicit
stiff/uniform-output settings prevent successful Tellurium replay from hiding missing archive context.
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Estimate intracellular reaction rates from steady-state carbon-13 tracing measurements. Validate carbon atom mappings and measurement inputs, simulate labeling, fit constrained fluxes, and diagnose which pathway rates the data can meaningfully constrain.
中文简介正在准备中。当前显示英语简介。
Plan protein characterization experiments with Adaptyv's cloud laboratory, estimate costs, submit sequences, track progress, and retrieve measured results. Supports binding, thermal stability, expression, fluorescence, and enzyme activity workflows through the API and Python SDK.
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Analyze time series with aeon, a Python toolkit for data that changes over time. Supports classification, regression, clustering, forecasting, anomaly detection, and pattern search, with guidance on preprocessing, algorithm selection, and evaluation.
中文简介正在准备中。当前显示英语简介。
Investigate how DNA variants may affect gene activity using precomputed AlphaGenome Atlas results and model predictions. Rank candidate variants, examine tissue-specific effects, and inspect the features behind scores to develop research hypotheses about biological mechanisms.
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Plan and document studies that assess whether an analytical procedure suits its intended use. Select the governing framework, evaluate calibration, accuracy, precision, and detection limits, and compare methods using acceptance criteria defined before data collection.
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Organize single-cell measurements with cell and gene metadata, read and write h5ad or Zarr files, and prepare subsets or combined datasets. Includes guidance on handling large annotated matrices without loading all measurement values into memory.
中文简介正在准备中。当前显示英语简介。