bnem

bnem trains logical models from indirect measurements of perturbation experiments by combining Nested Effects Models with Boolean networks to infer Boolean regulation of downstream signalling nodes.

Key Features:

• Nested Effects Model Integration: Incorporates indirect measurements from the mnem package to analyse perturbation effects on global gene expression profiles.
• Boolean Network Framework: Utilises the Boolean network capabilities of CellNOptR to model signalling network topology.
• Logic Gate Inference: Distinguishes between OR-gate and AND-gate regulation patterns for downstream nodes based on parent-child relationships in the network.

Scientific Applications:

• Signalling Network Reconstruction: Analyses perturbation experiments of signalling nodes in cells to infer regulatory relationships from global gene expression data.
• Boolean Regulation Mapping: Determines whether multiple parent nodes activate a child node independently or jointly in biological signalling networks.

Methodology:

bnem combines indirect measurements of Nested Effects Models (package mnem) with the Boolean networks of CellNOptR to analyse perturbation experiments of signalling nodes and their effects on global gene expression profiles.