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
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

Links