A computational framework for integration of lipidomics data into metabolic pathways.

Area of Science:

• Biochemistry
• Systems Biology
• Computational Biology

Background:

• Lipidomics studies rapidly identify new lipid structures, outpacing pathway representations.
• A gap exists between known lipid structures and their metabolic pathways.
• Lipids are crucial for human physiology and as renewable resources.

Purpose of the Study:

• To bridge the gap between lipid structures and metabolic pathways.
• To develop a computational approach for associating lipid structures with metabolic pathways.
• To postulate novel lipid metabolism pathways and integrate known lipids into biological networks.

Main Methods:

• Formulation of generalized enzymatic reaction rules for lipid metabolism.
• Application of generalized rules to predict novel lipid metabolic pathways.
• Integration of discovered lipids into networks of biosynthesis and biodegradation pathways using NICELips (Network Integrated Computational Explorer for Lipidomics).

Main Results:

• NICELips successfully proposed novel synthesis and degradation pathways for bis(monoacylglycero)phosphate (BMP).
• The approach identified alternative pathways for lipids with known metabolism.
• Several lipids with previously unknown metabolism had their pathways elucidated.

Conclusions:

• NICELips effectively links lipid structures to metabolic pathways, enabling the discovery of novel routes.
• The computational approach has potential applications in developing therapies for lipid-associated disorders.
• NICELips can aid in metabolic engineering for improved biobased production of lipid-derived fuels and chemicals.