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Thermal Sigmatropic Reactions: Overview

Thermal Sigmatropic Reactions: Overview

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Related Experiment Video

Updated: Jun 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

SBML2TikZ: supporting the SBML render extension in LaTeX.

Si Yuan Shen¹, Frank Bergmann, Herbert M Sauro

¹Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.

Bioinformatics (Oxford, England)

|September 11, 2010

Summary

SBML2TikZ automatically generates scalable vector graphics for biochemical models using TeX. This tool enhances documentation and presentations by rendering Systems Biology Markup Language (SBML) graphical information.

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Last Updated: Jun 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Area of Science:

Systems Biology
Computational Biology
Bioinformatics

Background:

The Systems Biology Markup Language (SBML) Render Extension allows graphical information of biochemical models to be stored.
Rendering SBML graphical data in portable systems like TeX is valuable for documentation and presentations.
Extending rendering functionality to CellDesigner annotations is beneficial due to limited application support for the SBML Render Extension.

Purpose of the Study:

To develop a tool for automatic generation of graphics for biochemical models.
To enable rendering of SBML graphical information in the TeX typesetting system.
To support rendering for both SBML Render Extension and CellDesigner annotations.

Main Methods:

Developed the SBML2TikZ library.
The library generates TeX macro commands for PGF/TikZ vector graphics.
The generated scripts can be compiled into scalable vector graphics.

Main Results:

SBML2TikZ successfully supports automatic graphics generation for biochemical models in TeX.
The library produces PGF/TikZ scripts that render SBML graphical information.
Scalable vector graphics of biochemical models can be generated.

Conclusions:

SBML2TikZ provides a valuable tool for researchers to visualize biochemical models.
The library facilitates the creation of high-quality graphics for publications and presentations.
It bridges the gap in rendering capabilities for SBML and CellDesigner annotations.