NICEpath: Finding metabolic pathways in large networks through atom-conserving substrate-product pairs

Area of Science:

• Metabolic Engineering
• Systems Biology
• Bioinformatics

Background:

• Identifying biosynthetic pathways is crucial for metabolic engineering, biodegradation prediction, and understanding metabolite production.
• Extracting novel pathways from large, complex biochemical networks presents significant analytical and navigational challenges.

Purpose of the Study:

• To develop a method for constructing searchable graph representations of metabolic networks.
• To enable efficient extraction and identification of biologically relevant metabolic pathways from large biochemical networks.

Main Methods:

• Metabolic networks are represented as graphs where reactions are decomposed into weighted reactant-product pairs.
• Weights are assigned based on conserved atoms between reactants and products.
• Graph search algorithms are applied to the network structure for pathway identification.

Main Results:

• The proposed method successfully extracts biologically relevant metabolic pathways from large biochemical networks.
• The graph structure facilitates efficient graph search algorithms, improving navigation and pathway identification.
• The approach demonstrates fast and reliable extraction of metabolic pathways from a network of 6546 reactions.

Conclusions:

• The developed method provides an efficient and reliable approach for navigating and extracting metabolic pathways from large biochemical networks.
• This facilitates advancements in metabolic engineering, bioproduction, and biodegradation prediction.