CellNOptR

CellNOptR constructs predictive logic models of cellular signaling networks by integrating prior knowledge with medium- and high-throughput proteomic perturbation data.

Key Features:

• R implementation: Provided as an R software package for model construction and analysis.
• Integration with prior knowledge: Builds models constrained by existing signaling network knowledge to produce context-specific representations.
• Support for perturbation data: Leverages medium- and high-throughput proteomic datasets from perturbation studies to train models.
• Flexible logic formalisms: Supports a range of formalisms from Boolean networks to differential equations within a unified framework.
• Predictive perturbation analysis: Enables prediction of effects of individual and combined perturbations on signaling outcomes.
• Cytoscape integration: Interfaces with Cytoscape via CytoCopteR for network exchange and downstream network-based analyses.

Scientific Applications:

• Perturbation effect prediction: Predicts cellular responses to single and combined perturbations using fitted logic models.
• Context-specific signaling analysis: Identifies how signaling processes differ across cell types and experimental conditions.
• Disease mechanism investigation: Investigates alterations in signal processing associated with disease states.
• Therapeutic hypothesis generation: Supports prediction of differential effects and potential side effects of targeted interventions based on cellular context.

Methodology:

Constructs logic models by integrating prior knowledge with medium- and high-throughput proteomic perturbation data using logic formalisms ranging from Boolean networks to differential equations, and can exchange networks with Cytoscape via CytoCopteR.