FALCON

FALCON integrates logical models with biological measurements using a probabilistic framework to contextualize regulatory networks and analyze rule-based interactions.

Key Features:

• Probabilistic Contextualization: Employs a probabilistic framework to integrate logical models of regulatory networks with biological measurements, enabling representation of rule-based interactions.
• Automated Network Construction and Contextualization: Automates the construction and contextualization of networks from logical rules and empirical data.
• Parameter Analysis and Gene Knockouts: Provides pipelines for parameter analysis and simulation of gene knockouts to evaluate effects of perturbations on network behavior.
• Model Interrogation and Sensitivity Analysis: Enables interrogation of models and sensitivity analysis to assess the impact of parameters and perturbations on network dynamics.

Scientific Applications:

• Systems Biology: Contextualizing logical models within systems biology to reveal system-level properties of regulatory networks.
• Gene Regulation: Investigating gene regulation by integrating empirical measurements with logical rules.
• Signal Transduction Pathways: Contextualizing and analyzing signal transduction pathways and other molecular interactions.
• Hypothesis Generation and Experimental Prioritization: Supporting generation and prioritization of testable hypotheses and perturbations for experimental study.

Methodology:

Operates within the MATLAB environment (MathWorks) and requires the Optimization Toolbox; methodology is centered on a probabilistic description of rule-based interactions and integration of diverse biological measurements into logical models.