Metabolic route computation in organism communities

Area of Science:

• Microbiology
• Metabolic Engineering
• Bioinformatics

Background:

• Microbiomes are intricate communities with interconnected metabolic networks.
• Understanding cross-organism metabolic transformations is a significant challenge.
• Identifying metabolic routes requires analyzing interactions between diverse microbial species.

Purpose of the Study:

• To develop an efficient computational tool for discovering multi-organism metabolic routes.
• To analyze the metabolic capabilities of microbiomes by tracing compound transformations.
• To identify cooperative metabolic pathways within microbial communities.

Main Methods:

• Developed a software tool for searching metabolic routes across multiple organisms.
• Utilized metabolic networks from BioCyc, inferred from annotated genomes.
• Optimized routes by minimizing reactions, maximizing atom conservation, and minimizing organism switches.
• Pre-computed organism-specific reaction sets for computational efficiency.

Main Results:

• The tool successfully identified plausible metabolic routes for complex microbiome problems.
• Optimal routes for indoxyl sulfate and trimethylamine N-oxide (TMAO) production matched literature findings.
• Generated routes visually represent metabolic pathways and list catalyzing organisms.

Conclusions:

• The developed tool efficiently finds multi-organism metabolic routes.
• These routes elucidate cooperative metabolic transformations in microbiomes.
• The tool aids scientists in exploring alternative pathways and identifying key microbial players.