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SemGen: a tool for semantics-based annotation and composition of biosimulation models.

Maxwell L Neal1, Christopher T Thompson2, Karam G Kim3

  • 1Seattle Children's Research Institute, Center for Global Infectious Disease Research, Seattle, WA, USA.

Bioinformatics (Oxford, England)
|September 27, 2018
PubMed
Summary
This summary is machine-generated.

SemGen is a new tool that uses semantic annotations to help users understand and combine biosimulation models. It supports model integration across formats like SBML and CellML, improving accuracy.

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Area of Science:

  • Computational Biology
  • Systems Biology

Background:

  • Increasing complexity of biosimulation models necessitates advanced tools for understanding and integration.
  • Existing model exchange formats like SBML and CellML present challenges for seamless integration.

Purpose of the Study:

  • To introduce SemGen, a novel tool for semantics-based annotation and composition of biosimulation models.
  • To address the growing demand for tools that facilitate model comprehension and system integration.

Main Methods:

  • Utilizing semantic annotations to explicitly define the biological and physical meanings of modeled entities and processes.
  • Developing SemGen to decompose and integrate models across different exchange formats (SBML, CellML).

Main Results:

  • SemGen effectively exposes the biological and computational architecture of biosimulation models.
  • The tool automates aspects of model composition through semantic annotations.

Conclusions:

  • SemGen provides a robust solution for understanding and integrating complex biosimulation models.
  • The semantics-based approach enhances model accuracy and facilitates the creation of comprehensive biological systems.