libsbml-network-modeling
Build, read, validate, modify SBML biological network models via the libSBML Python API. SBML Levels 1–3, reactions/kinetic laws, species, rules, FBC extension for flux balance, conversion. Interoperates with COBRApy, Tellurium/RoadRunner, COPASI. Use when programmatically constructing ODE or constraint-based metabolic/signaling models in SBML.
npx skills add BioTender-max/awesome-bio-agent-skills --skill libsbml-network-modeling --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Conveys a Python-centric toolkit to create, read, validate, and modify SBML models. It covers constructing models from scratch with compartments, species, reactions, and kinetic laws; editing parameters, rules, and constraints; applying the FBC extension for flux bounds and COBRApy-style optimization; and exporting or converting SBML between levels/versions. It also demonstrates reading SBML files, validating consistency, and using auxiliary tools like Antimony/Tellurium for representation or conversion.
How it works
- Module 1: Reading and Validating SBML shows loading SBML documents, counting errors, checking SBML Level/Version, and reading from strings. It includes a pattern to verify parse errors and print error details.
- Module 2: Creating Models from Scratch builds a Level 3/Version 2 document, creates a model, compartments, and species with initial conditions, then reports counts.
- Module 3: Editing Reactions and Kinetic Laws adds reactions with reactants and products, then creates a KineticLaw using a MathML/Formula parsed via parseL3Formula, and saves the model.
- Module 4: Species and Compartments demonstrates inspecting species properties, modifying compartment sizes, and updating initial concentrations.
- Module 5: Rules and Constraints introduces AssignmentRule, RateRule, and Constraint usage, including setting variables, parsing math expressions, and reporting counts.
- Module 6: FBC Extension shows enabling the FBC package, creating a minimal model with flux bounds, adding species with FBC plugin data, and defining an objective to maximize a flux.
- Module 7: Exporting and Level Conversion covers writing SBML to string/files, converting between levels/versions via ConversionProperties, and optional Antimony export through Tellurium.
When to use it
- When you need to programmatically author SBML models (ODE or constraint-based) from Python.
- When modifying existing SBML models (parameters, species, stoichiometry, or kinetics) prior to simulation in COPASI, Tellurium, RoadRunner, or COBRApy.
- When preparing FBC-enabled metabolic models with flux bounds and objectives for FBA workflows.
- When converting models between SBML levels/versions or exporting to human-readable forms.
What it can touch
- Libraries and tools: libsbml Python API, numpy, pandas (optional), cobra, tellurium (optional), and Antimony export via Tellurium.
- File targets: SBML documents (.xml) and generated outputs like modified models and converted versions.
Caveats
- License declared: LGPL-2.1.
- The skill requires specific Python packages (python-libsbml, numpy, pandas) and optional packages (cobra, tellurium) for full functionality, as shown in prerequisites.
- It relies on parsing and constructing MathML/Formula strings; incorrect formulas can raise runtime errors (e.g., parseL3Formula failures).
# libsbml-network-modeling ## Overview libSBML is the reference library for reading, writing, creating, and validating SBML (Systems Biology Markup Language) models. SBML is the community standard for encoding biochemical reaction networks — ODE models, signaling cascades, and genome-scale metabolic models all use it. The Python API (`python-libsbml`) exposes a full object model covering compartments, species, reactions, kinetic laws, rules, constraints, and every SBML extension. Models saved as SBML `.xml` files are interoperable with COPASI, Tellurium, RoadRunner, COBRApy, and BioModels Database. ## When to Use - Building a new ODE-based biochemical model (enzyme kinetics, signaling pathway) from scratch in SBML format for simulation in COPASI or Tellurium - Reading and programmatically modifying an existing BioModels Database model — changing kinetic parameters, adding species, or patching reaction stoichiometry - Validating an SBML file against the specification before submitting to BioModels or sharing with collaborators - Converting SBML models between Level 1/2/3 for compatibility with older simulation tools - Constructing genome-scale metabolic models with flux bounds and o
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Reading and Validating SBML
- Module 2: Creating Models from Scratch
- Module 3: Editing Reactions and Kinetic Laws
- Module 4: Species and Compartments
- Module 5: Rules and Constraints
- Module 6: FBC Extension (Flux Balance Constraints)
- Module 7: Exporting and Level Conversion
- Key Concepts
- SBMLDocument, Model, and the Plugin Architecture
pip install python-libsbml numpy pandas Optional simulation/FBA integrations: pip install cobra tellurium
What does the libsbml-network-modeling skill do?
Build, read, validate, modify SBML biological network models via the libSBML Python API. SBML Levels 1–3, reactions/kinetic laws, species, rules, FBC extension for flux balance, conversion. Interoperates with COBRApy, Tellurium/RoadRunner, COPASI. Use when programmatically constructing ODE or constraint-based metabolic/signaling models in SBML.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill libsbml-network-modeling --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.