CNORdt

CNORdt extends the CellNOptR framework to model and fit time-course signaling data in cellular networks using logic-based simulations.

Key Features:

• Time-Course Data Modeling: Supports analysis of temporal signaling datasets, enabling dynamic modeling of cellular signaling networks beyond steady-state conditions.
• Simulation Step Scaling: Scales simulation steps to enable comparison and fitting of model outputs to experimental time-course data.
• Integration with CellNOptR Framework: Expands the CellNOptR modeling environment for training signaling network models derived from prior knowledge against experimental signaling data such as phosphoproteomic measurements.

Scientific Applications:

• Cell Signaling Network Modeling: Enables context-specific modeling of signaling pathways trained on experimental time-course datasets.
• Perturbation Response Prediction: Supports prediction of cellular responses to individual or combined perturbations in signaling networks.
• Systems Biology Analysis: Facilitates dynamic analysis of signal transduction processes in different cellular conditions and contexts.

Methodology:

CNORdt trains prior-knowledge signaling network models using time-course experimental data by scaling simulation steps and applying logic-based formalisms ranging from Boolean models to differential equation frameworks.