B-NEM

B-NEM integrates downstream perturbation effects with Boolean network modelling to infer and reconstruct cellular signalling pathway structure and resolve signal flows.

Key Features:

• Integration of Downstream Effects and Boolean Networks: Uses Nested Effect Models to infer network structure from observed downstream effects of perturbations and Boolean Networks to provide high-resolution logical structure.
• Resolution of Complex Signalling Details: Reconstructs signalling complexes and proteins activated by multiple alternative input signals, addressing limitations of traditional Nested Effect Models.
• Demonstrated on Simulated and Experimental Data: Applied to simulated datasets and experimental data, including resolving BCR signalling via PI3K and TAK1 kinases in BL2 lymphoma cell lines.

Scientific Applications:

• Network reconstruction from perturbation data: Infers signalling pathway structure when direct measurement of protein activation is not feasible.
• BCR signalling analysis: Dissects PI3K and TAK1 kinase interactions in BL2 lymphoma cell lines.
• Investigation of signal flow and complex activation: Studies formation of signalling complexes and multi-input activation mechanisms relevant to molecular and cancer biology.

Methodology:

Combines Nested Effect Models—which infer network structures indirectly from observed downstream effects of pathway perturbations—with Boolean Networks to represent logical interactions and signal flow; applied to simulated and experimental datasets.