Predicting metabolomic profiles from microbial composition through neural ordinary differential equations.

Area of Science:

• Microbiome research
• Computational biology
• Metabolomics

Background:

• Microbial community metabolic profiling is vital for understanding biological functions.
• Metabolomics is experimentally challenging; sequencing-based microbiome composition is more accessible.
• Existing computational methods for predicting metabolomic profiles lack high prediction power, generalizability, and interpretability.

Purpose of the Study:

• To develop a novel computational method for predicting microbial metabolomic profiles from community composition.
• To improve the prediction accuracy, applicability, and interpretability of computational microbiome analysis.
• To investigate the relationship between microbiome, diet, and metabolome.

Main Methods:

• Developed mNODE (Metabolomic profile predictor using Neural Ordinary Differential Equations), a deep neural network model.
• Applied mNODE to predict metabolomic profiles of human and environmental microbiomes.
• Incorporated dietary information into mNODE for human gut microbiome analysis.
• Utilized susceptibility analysis to identify microbe-metabolite interactions.

Main Results:

• mNODE demonstrated superior performance in predicting metabolomic profiles compared to existing methods across various microbiomes.
• mNODE successfully integrated dietary data to enhance prediction accuracy for human gut microbiomes.
• Susceptibility analysis revealed significant microbe-metabolite interactions, validated with synthetic and real data.

Conclusions:

• mNODE is a powerful and interpretable tool for predicting microbial metabolomic profiles.
• The method facilitates the investigation of microbiome-diet-metabolome interactions.
• mNODE holds promise for advancing precision nutrition research.