MAMBO

MAMBO integrates genome-scale, constraint-based metabolic models (GSMMs) with microbial abundance profiles derived from shotgun sequencing using fBa and Optimization to infer community metabolomes and predict metabolic fluxes within defined metabolic environments.

Key Features:

• Integration of GSMMs and metagenomic abundances: Combines genome-scale, constraint-based metabolic models (GSMMs) with species relative abundances obtained from shotgun metagenomic data.
• Semiautomatic GSMM reconstruction: Semiautomatically reconstructs genome-scale metabolic models for microorganisms from genomic sequences using genome annotation and systems biology approaches.
• Growth prediction in defined environments: Predicts growth of GSMMs within defined metabolic environments to link individual microbial metabolic fluxes to community dynamics.
• Minimal training-data requirement: Operates without extensive training data, requiring only essential genome annotations and knowledge of environmental metabolites that support microbial growth.
• Abundance-informed metabolic inference: Uses relative abundances of bacterial species from metagenomic analysis combined with GSMMs to infer the metabolic status of biomes.
• Large-scale metabolome inference: Applied to over 1,500 human-associated GSMMs to infer distinct metabolomes for four different human body sites consistent with experimental data.

Scientific Applications:

• Predictive microbiome modeling: Links GSMM-derived metabolic fluxes with species abundances to predict how microbial communities interact with their metabolic environments.
• Host-associated metabolome inference: Infers site-specific metabolomes from metagenomic profiles, demonstrated on human-associated bacterial GSMM collections.
• Community functional capability assessment: Assesses community-level metabolic interactions and functional capabilities using constraint-based modeling informed by metagenomic abundances.

Methodology:

Semiautomatic reconstruction of GSMMs from genomic sequences using genome annotation and systems biology, integration of GSMMs with species relative abundances from shotgun metagenomic data, and prediction of GSMM growth and metabolic fluxes within defined metabolic environments using fBa and Optimization.