MINNO

MINNO refines metabolic network models by integrating empirical metabolomics data with genomic information to improve representation of biochemical pathways in nonmodel organisms.

Key Features:

• Empirical Refinement: Leverages metabolomics data to empirically refine metabolic networks for more accurate representation of biochemical pathways.
• Hybrid Genomics–Metabolomics Modeling: Combines genomic information with empirical metabolomics data to construct refined metabolic networks.
• Species-Specific Networks: Facilitates construction of species-specific metabolic networks by integrating genomics and metabolomics data to differentiate closely related species.
• Metabolic Differentiation: Identifies unique metabolic characteristics, such as differences in nucleotide metabolism among species, that are not apparent from genomic analysis alone.
• Database Enhancement: Detects missing reactions in established databases such as KEGG, with several findings supported by primary literature.

Scientific Applications:

• Nonmodel organism analysis: Elucidates metabolic networks in poorly characterized organisms, including members of the Borrelia genus associated with Lyme disease and relapsing fever.
• Comparative metabolic differentiation: Enables differentiation of closely related species based on integrated metabolic and genomic evidence.
• Database curation: Supports identification and correction of missing reactions in metabolic databases such as KEGG.
• Research domains: Supports investigations in microbial ecology, evolutionary biology, and disease research by providing refined metabolic frameworks.

Methodology:

Combines genomic information with empirical metabolomics data in a hybrid modeling approach to construct and refine metabolic networks. Identifies metabolic differences (e.g., nucleotide metabolism) that are not predicted from genomes alone. Detects missing reactions in databases such as KEGG, with some findings corroborated by primary literature.