Automate constraint-based metabolic modeling analyses with the COBRApy API.
npx skills add https://github.com/OwnLabAI/ownlab --skill cobrapy-ownlabai
Please help me install this Agent Skill. Skill: cobrapy Source: https://github.com/OwnLabAI/ownlab/tree/main/mart/skills/scientific-skills/cobrapy Command: npx skills add https://github.com/OwnLabAI/ownlab --skill cobrapy-ownlabai
COBRApy enables researchers to construct, simulate, and analyze genome-scale metabolic models using constraint-based methods like FBA, FVA, and gapfilling, empowering systematic exploration of metabolic networks.
Install COBRApy, load a model (e.g., ecoli), and run a basic FBA to obtain a growth prediction.
High-intent search queries and answers about installing and using this skill.
Flux balance analysis in Python automates constraint-based metabolic modeling by loading genome-scale models and running FBA to obtain growth predictions. You load an SBML or JSON model and execute FBA to calculate optimal metabolic flux distributions.
Gene deletion simulations are supported natively for single and double gene knockouts in Python. The Skill automates knockout workflows by removing target genes from genome-scale metabolic models and evaluating the resulting growth phenotypes.
Metabolic models can be loaded and saved in SBML, JSON, and YAML formats. This format compatibility ensures you can import standard genome-scale metabolic network files and persist modified models after performing analyses like gapfilling.
Flux variability analysis and parsimonious FBA are both supported alongside geometric FBA. These constraint-based methods evaluate the range of possible flux distributions and identify the most efficient metabolic network solutions.
Minimal media design and production envelope analysis are automated to explore production strains. You define environmental constraints on your metabolic model and generate production envelopes to identify optimal engineered strains.
Constraint-based metabolic modeling assumes steady-state conditions and does not capture kinetic parameters or dynamic regulatory effects. It is best suited for predicting growth yields and flux distributions rather than simulating time-dependent metabolic dynamics.
jaechang-hitsPerform COBRA analyses of genome-scale metabolic models with Python.
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dailycafiPerform constraint-based metabolic simulations on genome-scale models with cobrapy.
K-Dense-AIPlan and execute constraint-based metabolic modeling on genome-scale networks.
