CRA toolbox

CRA toolbox performs conditional robustness analysis of biological network models by evaluating how parameter perturbations affect the temporal behavior of nodes in ordinary differential equation (ODE) systems.

Key Features:

• Conditional Robustness Algorithm Implementation: Implements the Conditional Robustness Algorithm (CRA) to quantify the influence of parameter perturbations on model output variables.
• ODE Model Analysis: Supports analysis of biological networks modeled using ordinary differential equation (ODE) systems.
• SBML Model Import: Imports mathematical models encoded in Systems Biology Markup Language (SBML) format.
• Parameter Perturbation Analysis: Perturbs model parameters to evaluate robustness and sensitivity of biological network outputs.
• Application to Nonlinear Network Models: Supports robustness analysis of nonlinear ODE models including the Pten-/- prostate mouse model, Pulse Generator Network, and EGFR–IGF1R signaling pathway.

Scientific Applications:

• Cancer Systems Biology Modeling: Analyzes robustness of signaling networks associated with cancer proliferation.
• Biological Network Sensitivity Analysis: Identifies parameters and nodes that significantly influence network dynamics.
• Therapeutic Target Discovery: Supports identification of critical network components that may serve as targets for cancer therapies.

Methodology:

CRA toolbox imports SBML-encoded biological network models, perturbs model parameters, and applies the Conditional Robustness Algorithm to quantify the impact of parameter variations on node dynamics in ODE-based systems.