MOOMIN

MOOMIN maps differential expression results onto genome-scale metabolic networks to infer qualitative changes in metabolic reaction activity associated with biological perturbations.

Key Features:

• Input Requirements: Accepts a metabolic network and differential expression (DE) results that include posterior probabilities of differential expression and the logarithm of fold change for genes.
• Reaction-level Inference: Evaluates each reaction in the metabolic network and assigns a status of "increased flux," "decreased flux," or "no change."
• Output Representation: Reports reaction statuses and can represent them using colors (red for increased flux, blue for decreased flux, grey for no change).
• Computational Framework: Operates within a genome-scale metabolic reconstruction framework using constraint-based analysis.
• No Objective Function Requirement: Infers qualitative metabolic changes without relying on predefined biological objective functions.
• Non-binary Gene Treatment: Does not assign binary on/off states to genes and can infer reaction changes even when corresponding genes are not differentially expressed.

Scientific Applications:

• Systems biology interpretation: Interprets how gene expression alterations translate into changes in metabolic network activity under perturbations.
• Hypothesis generation and testing: Generates and evaluates hypotheses about metabolic shifts suggested by differential expression data.
• Integrative omics analysis: Integrates DE measurements with metabolic reconstructions to provide a network-level view of transcriptomic effects on metabolism.

Methodology:

Uses a genome-scale metabolic reconstruction framework combined with constraint-based analysis, taking DE inputs (posterior probabilities and log fold change) to evaluate reactions and assign qualitative statuses (increased/decreased/no change) without predefined objective functions or binary gene assignments.