kboolnet

kboolnet implements analysis workflows for reaction-contingency (rxncon) models, enabling verification, validation, and visualization of Boolean-network representations of cell signaling networks.

Key Features:

• Integration with rxncon and software stack: Integrates with the Python-based rxncon software and is implemented as an R package with accompanying scripts.
• Verification (VerifyModel.R): The VerifyModel.R script checks model responsiveness to repeated stimulations and consistency of steady-state behavior.
• Validation (TruthTable.R, SensitivityAnalysis.R, ScoreNet.R): TruthTable.R generates truth tables, SensitivityAnalysis.R assesses input parameter effects on outputs, and ScoreNet.R compares predictions to a cloud-stored MIDAS-format experimental database to produce numerical scores.
• Visualization: Includes scripts for graphical representation of model topology and system behavior.
• Modularity: Supports extraction and analysis of user-defined modules within larger networks.
• Cloud-enabled architecture: Supports collaborative model sharing and access to cloud-stored MIDAS-format experimental datasets.

Scientific Applications:

• Large-scale signaling network modeling: Construction and analysis of cell signaling network models that can scale to thousands of components.
• Modeling with scarce kinetic parameters: Use of Boolean network representations to enable modeling when kinetic parameters are scarce or unavailable and to mitigate combinatorial explosion.

Methodology:

Models are encoded as rxncon reaction-contingency specifications and Boolean networks by splitting systems into reactions (state generators) and contingencies (reaction modifiers); analysis uses VerifyModel.R, TruthTable.R, SensitivityAnalysis.R, ScoreNet.R and visualization scripts.