VSCode-Antimony
VSCode-Antimony facilitates development, analysis, and translation of biochemical models using the Antimony modeling language for systems biology.
Key Features:
- Autocompletion: Provides autocompletion for variable and species names derived from parsed model symbols.
- Hover annotations: Generates hover messages that deliver detailed information about chemical species within a model.
- SBML translation: Performs translation between Antimony and SBML XML representations.
- Antimony grammar analysis: Parses the Antimony grammar to identify model symbols and their types.
- External knowledge queries: Implements a query system that accesses external knowledge sources for chemical species and reactions.
- Automated conversion: Automates conversion between different model representations.
Scientific Applications:
- Systems biology modeling: Construction and refinement of biochemical reaction network models using the Antimony language.
- Computational simulation: Preparation and translation of models for computational simulation workflows.
- Hypothesis testing and data analysis: Support for hypothesis testing and analysis of biological system behavior through iterative model development.
Methodology:
Parses Antimony grammar to identify model symbols and types; provides autocompletion and hover annotations; implements queries to external knowledge sources for chemical species and reactions; and automates conversion between Antimony and SBML XML representations.
Topics
Details
- License:
- MIT
- Cost:
- Free of charge
- Tool Type:
- plugin
- Operating Systems:
- Mac, Linux, Windows
- Programming Languages:
- Python
- Added:
- 4/19/2024
- Last Updated:
- 4/19/2024
Operations
Publications
Ma S, Fan L, Konanki SA, Liu E, Gennari JH, Smith LP, Hellerstein JL, Sauro HM. VSCode-Antimony: a source editor for building, analyzing, and translating antimony models. Bioinformatics. 2023;39(12). doi:10.1093/bioinformatics/btad753. PMID:38096590. PMCID:PMC10753917.
PMID: 38096590
PMCID: PMC10753917
Funding: - National Science Foundation award: CMMI-1933453
- Data Science Environments project award from the Gordon and Betty Moore Foundation: 2013-10-29
- Alfred P. Sloan Foundation: 3835