CABERNET

CABERNET generates, simulates, and analyzes Boolean network models of gene regulatory networks (GRNs) to explore network dynamics and robustness when only partial topological and functional data are available.

Key Features:

• Boolean Model Generation: Generates Boolean network models representing binary gene activation states in gene regulatory networks (GRNs).
• Network Ensemble Generation: Creates large ensembles of networks by incorporating user-defined entities and relations into an existing core network to explore missing components.
• Simulation Capabilities: Simulates Boolean network dynamics to investigate processes such as cell differentiation and cancer development.
• Analysis Tools: Analyzes gene activation patterns to characterize cell types and differentiation pathways.
• Robustness Assessments: Performs robustness testing, including gene knockout simulations, to assess network resilience.
• Visualization: Visualizes Boolean models within the Cytoscape 3.2.0 framework.

Scientific Applications:

• Cell Differentiation and Cancer Research: Investigates dynamic behaviors underlying cell differentiation and cancer development via Boolean model simulations.
• Cell Type and Differentiation Characterization: Derives cell-type and differentiation pathway information from gene activation patterns.
• Augmented GRN Exploration: Tests hypotheses about missing GRN components by augmenting core networks with user-defined entities and relations.
• T-helper Cell GRN Analysis: Applies augmented-network analysis to an example T-helper cell gene regulatory network.

Methodology:

Generates Boolean network models and simulates their dynamics; creates large ensembles by adding user-defined entities and relations to an existing core network; analyzes gene activation patterns and performs robustness testing including gene knockout simulations within the Cytoscape 3.2.0 environment.