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Metab Eng. 2014 May;23:1-8. doi: 10.1016/j.ymben.2013.12.007. Epub 2014 Jan 4.

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

Author information

1
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. Electronic address: noushin.hadadi@epfl.ch.
2
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. Electronic address: kengcher.soh@epfl.ch.
3
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. Electronic address: marianne.seijo@epfl.ch.
4
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. Electronic address: aikaterini.zisaki@epfl.ch.
5
Department of Biological Sciences, National University of Singapore, Singapore 117543, Singapore. Electronic address: xueli.guan@unibas.ch.
6
Department of Biological Sciences, National University of Singapore, Singapore 117543, Singapore; Swiss Tropical and Public Health Institute, 4002 Basel, Switzerland. Electronic address: bchmrw@nus.edu.sg.
7
Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, CH 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland. Electronic address: vassily.hatzimanikatis@epfl.ch.

Abstract

Lipids are important compounds for human physiology and as renewable resources for fuels and chemicals. In lipid research, there is a big gap between the currently available pathway-level representations of lipids and lipid structure databases in which the number of compounds is expanding rapidly with high-throughput mass spectrometry methods. In this work, we introduce a computational approach to bridge this gap by making associations between metabolic pathways and the lipid structures discovered increasingly thorough lipidomics studies. Our approach, called NICELips (Network Integrated Computational Explorer for Lipidomics), is based on the formulation of generalized enzymatic reaction rules for lipid metabolism, and it employs the generalized rules to postulate novel pathways of lipid metabolism. It further integrates all discovered lipids in biological networks of enzymatic reactions that consist their biosynthesis and biodegradation pathways. We illustrate the utility of our approach through a case study of bis(monoacylglycero)phosphate (BMP), a biologically important glycerophospholipid with immature synthesis and catabolic route(s). Using NICELips, we were able to propose various synthesis and degradation pathways for this compound and several other lipids with unknown metabolism like BMP, and in addition several alternative novel biosynthesis and biodegradation pathways for lipids with known metabolism. NICELips has potential applications in designing therapeutic interventions for lipid-associated disorders and in the metabolic engineering of model organisms for improving the biobased production of lipid-derived fuels and chemicals.

KEYWORDS:

Bioinformatics; Bis(monoacylglycero)phosphate (Bmp); Database; Phospholipids

PMID:
24395008
DOI:
10.1016/j.ymben.2013.12.007
[Indexed for MEDLINE]

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