CaSQ

CaSQ converts CellDesigner molecular interaction maps into executable Boolean models encoded in SBML-Qual to enable dynamic analysis of biological systems.


Key Features:

  • Conversion from Static to Dynamic Models: Infers Boolean rules from the topology and semantics of CellDesigner molecular interaction maps to produce executable dynamic models.
  • Adherence to SBGN Standards: Processes molecular maps whether or not they adhere to Systems Biology Graphical Notation (SBGN) standards, enabling use with diverse map formats.
  • Output in SBML-Qual Format: Generates Boolean models encoded in Systems Biology Markup Language - qualitative (SBML-Qual) for interoperability with modeling tools.
  • Retention of Annotations and Layouts: Preserves references, annotations, and layout information from the original CellDesigner maps during conversion.

Scientific Applications:

  • In silico simulation and perturbation studies: Enables systems biologists to perform in silico simulations and perturbation analyses on networks derived from molecular interaction maps.
  • Gene regulatory network analysis: Supports exploration of gene regulatory network dynamics by providing executable Boolean representations.
  • Signaling pathway and emergent behavior analysis: Facilitates analysis of signaling pathways and emergent behaviors in complex biological networks.

Methodology:

Applies defined conversion rules and logical formulas that translate map topology and annotations into Boolean rules, with validation possible by comparing inferred models to manually constructed logical models.

Topics

Details

License:
GPL-3.0
Tool Type:
command-line tool
Programming Languages:
Python
Added:
1/18/2021
Last Updated:
5/4/2022

Operations

Data Inputs & Outputs

Mapping

Publications

Aghamiri SS, Singh V, Naldi A, Helikar T, Soliman S, Niarakis A. Automated inference of Boolean models from molecular interaction maps using CaSQ. Bioinformatics. 2020;36(16):4473-4482. doi:10.1093/bioinformatics/btaa484. PMID:32403123. PMCID:PMC7575051.

PMID: 32403123
PMCID: PMC7575051
Funding: - NIH: #5R35GM119770-04 - ANR BIOPSY: ANR-16-CE18-0029

Links