MetNet

MetNet infers metabolic network topologies from quantitative intensity data and high-resolution mass/charge information by combining statistical models with mass difference matching against enzymatic transformation data.

Key Features:

• Statistical Network Inference: Uses correlation, mutual information, regression, and Bayes statistics to infer adjacency matrices from quantitative feature intensity data that can be combined into a consensus matrix.
• Mass Difference Matching: Calculates mass differences between mass/charge values of features and matches them against supplied mass/charge differences referring to transformations of enzymatic activities.
• Multi-Source Integration: Combines adjacency matrices inferred from quantitative data with structural information to form a unified adjacency matrix.

Scientific Applications:

• Metabolic Network Reconstruction: Infers metabolic network topologies from untargeted high-resolution mass spectrometry data.
• Enzymatic Transformation Detection: Identifies potential enzymatic activity transformations through mass/charge difference matching.

Methodology:

MetNet employs a three-step approach: statistical models (correlation, mutual information, regression, and Bayes statistics) are applied to quantitative intensity data to infer adjacency matrices; mass differences between mass/charge values are matched against a data frame of supplied mass/charge differences for enzymatic transformations; the quantitative and structural information are combined into a consensus adjacency matrix.