Metabolizer

Metabolizer maps genomic and transcriptomic gene expression perturbations to changes in conserved metabolic module activities to analyze impacts on metabolite production, disease progression, and drug mechanisms of action (MoA).

Key Features:

• Modular metabolic pathway analysis: Analyzes the modular architecture of metabolic pathways by evaluating conserved metabolic modules that link specific substrates to defined products.
• Class prediction: Performs class prediction to categorize samples based on metabolic profiles derived from gene expression data.
• In silico knock-out (KO) simulation: Simulates knock-out (KO) effects to predict the impact of gene deletions on metabolic module activity and metabolite production.
• Optimal KO intervention prediction: Automatically predicts optimal KO interventions aimed at restoring diseased phenotypes to identify candidate therapeutic targets.
• Validation methods: Implements validation methods for predictions to assess reliability of outputs.
• Mechanistic model construction: Integrates available knowledge on metabolic processes to construct mechanistic models that connect gene expression perturbations with metabolic activity alterations.

Scientific Applications:

• Cancer metabolism analysis: Elucidates molecular mechanisms of metabolic alterations in diseases such as cancer by linking gene expression changes to shifts in metabolic activity.
• Drug mechanism of action (MoA) analysis: Assesses how genomic and transcriptomic perturbations induced by drugs affect metabolic modules and metabolite production.
• Therapeutic target identification: Identifies candidate drug targets via in silico KO simulations and optimal KO intervention predictions.
• Personalized treatment strategy support: Supports development of individualized treatment plans by predicting interventions that could restore diseased metabolic phenotypes.

Methodology:

Analyzes transcriptomic data to assess the impact of conserved metabolic modules—defined as pathway segments that begin with specific substrates and culminate in defined products—on metabolite production; algorithms integrate existing knowledge on metabolic processes to build mechanistic models linking gene expression perturbations with metabolic activity alterations.