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Automated inference of Boolean models from molecular interaction maps using CaSQ.

Sara Sadat Aghamiri1, Vidisha Singh1, Aurélien Naldi2

  • 1GenHotel, Département de Biologie, Univ. èvry, Université Paris-Saclay, Genopole, èvry 91025, France.

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Summary

CaSQ infers Boolean rules from static molecular interaction maps, enabling dynamic simulations. This computational tool bridges the gap between static biological representations and dynamic system behavior analysis.

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Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Molecular interaction maps offer systematic biological mechanism representation but lack dynamic insights.
  • Static maps limit understanding of system behavior under varying conditions.
  • Computational modeling is crucial for studying dynamic biological properties via simulations.

Purpose of the Study:

  • To bridge the gap between static and dynamic biological system representations.
  • To develop CaSQ, a software tool for inferring Boolean rules from molecular interaction maps.
  • To enable dynamic analysis of biological systems through computational modeling.

Main Methods:

  • CaSQ infers Boolean models based on molecular map topology and semantics.
  • Conversion rules and logical formulas are defined for model inference.
  • The tool processes maps built with CellDesigner, with or without Systems Biology Graphical Notation (SBGN) standards.

Main Results:

  • CaSQ generates executable Boolean models in Systems Biology Marked Up Language-qualitative (SBML-qual) format.
  • The tool successfully processes large and complex molecular maps.
  • Inferred models retain references, annotations, and layout, ensuring interoperability and reusability.

Conclusions:

  • CaSQ facilitates dynamic analysis of biological systems from static interaction maps.
  • The tool enhances model reusability and interoperability in systems biology research.
  • CaSQ provides a valuable bridge between map representation and computational modeling.