Differential metabolic activity and discovery of therapeutic targets using summarized metabolic pathway models

Area of Science:

• Systems biology
• Metabolic modeling
• Computational biology

Background:

• Gene expression data is abundant, but linking it to disease phenotypes and molecular mechanisms remains challenging.
• Metabolic alterations are central to many diseases, including cancer, highlighting the need to integrate metabolic knowledge.
• Pathway modules offer a suitable framework for modeling the link between gene expression and metabolic activity.

Purpose of the Study:

• To present Metabolizer, a web application for analyzing metabolic pathway module activities.
• To enable the understanding of molecular mechanisms underlying diseases and drug modes of action (MoA).
• To facilitate in silico prediction of gene knock-out (KO) effects and identify optimal KO interventions for disease phenotype restoration.

Main Methods:

• Utilizing gene expression measurements to analyze differences in pathway metabolic module activities.
• Developing a web-based application with an interactive interface.
• Implementing class prediction and in silico KO effect prediction functionalities.
• Automating the prediction of optimal KO interventions.

Main Results:

• Metabolizer provides an intuitive platform for exploring metabolic pathway alterations.
• The tool successfully predicts disease mechanisms and drug MoA.
• Validations confirm the accuracy of Metabolizer's predictions for KO interventions.
• Metabolizer identifies potential therapeutic targets for personalized interventions.

Conclusions:

• Metabolizer bridges the gap between gene expression data and functional metabolic insights.
• The application aids in understanding disease mechanisms and drug action through metabolic context.
• Metabolizer offers a valuable resource for identifying therapeutic strategies and personalized treatments.