MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using...

Area of Science:

• Biochemistry
• Systems Biology
• Bioinformatics

Background:

• Metabolomics reveals biochemical diversity, often analyzed using metabolic network models.
• Modeling nonmodel organisms is difficult due to gene homology errors in network construction.
• Existing methods struggle with poorly characterized species, limiting biological insights.

Purpose of the Study:

• To introduce MINNO, a web-based tool for refining metabolic networks using empirical metabolomics data.
• To demonstrate MINNO's utility in studying understudied organisms, specifically the Borrelia genus.
• To improve the accuracy of metabolic network reconstruction for nonmodel organisms.

Main Methods:

• Developed MINNO, a web tool integrating metabolomics data to refine metabolic networks.
• Applied a hybrid genomics-metabolomics approach to construct species-specific networks for three Borrelia species.
• Utilized MINNO for pathway visualization, modification, and analysis in single and multi-species contexts.

Main Results:

• MINNO successfully refined metabolic networks for Borrelia species using empirical data.
• Metabolically differentiated Borrelia species based on nucleotide metabolism, a distinction not evident from genomic data alone.
• Identified 18 novel reactions missing from the KEGG database, with nine supported by existing literature.

Conclusions:

• Metabolomics data can empirically refine genetically constructed metabolic networks.
• MINNO is effective for studying and reconstructing metabolic networks of nonmodel organisms.
• The refined networks provide novel insights into species-specific metabolism, like that of Borrelia.