PORGY

PORGY models biochemical systems using port graphs and rule-based graph rewriting to simulate molecular interactions and analyze dynamic system behavior.


Key Features:

  • Port graph representation: Molecules and their interactions are represented as port graphs with nodes for molecular entities and edges for interactions or bindings.
  • Port graph rewrite rules: Interactions and transformations are expressed as port graph rewrite rules to enable dynamic simulation of biochemical processes.
  • Strategy language: A strategy language controls where, when, and how rewrite rules are applied during simulations.
  • State recording and parameter tracking: Model changes are recorded and simulation parameters are tracked over time.
  • Parameter plotting: Tracked parameters can be plotted to analyze temporal system dynamics quantitatively.

Scientific Applications:

  • Signalling pathway analysis: Simulation and analysis of complex signalling pathways such as the RAF/MEK/ERK cascade.
  • Scaffold protein studies: Investigation of scaffold protein roles within signalling networks using rule-based models.
  • Systems biology modelling: Modeling dynamic biochemical networks to study system-level behavior in systems biology and bioinformatics.
  • Drug discovery support: Analysis of pathway dynamics to inform hypothesis generation relevant to drug discovery.

Methodology:

Models use port graphs (nodes = molecular entities, edges = interactions), define transformations with port graph rewrite rules, control rule application via a strategy language, record state changes and parameters during simulation, and produce parameter plots.

Topics

Details

License:
LGPL-3.0
Maturity:
Mature
Cost:
Free of charge
Tool Type:
desktop application
Operating Systems:
Linux, Windows, Mac
Programming Languages:
C++
Added:
8/3/2019
Last Updated:
6/16/2020

Operations

Publications

Andrei O, Fernández M, Kirchner H, Pinaud B. Strategy-Driven Exploration for Rule-Based Models of Biochemical Systems with Porgy. Methods in Molecular Biology. 2019. doi:10.1007/978-1-4939-9102-0_3. PMID:30945242.

Documentation

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