Stochastic models allow improved inference of microbiome interactions from time series data.

Area of Science:

• Microbiology
• Computational Biology
• Systems Biology

Background:

• Microbiome research often uses deterministic models fitted to averaged data, losing crucial information.
• Deterministic models may inaccurately represent the inherent stochasticity of microbial systems.

Purpose of the Study:

• To develop a novel inference method for microbiomes that accounts for stochasticity in both models and experiments.
• To improve the identifiability and precision of microbial interaction parameter estimation.

Main Methods:

• Derived dynamical equations for statistical moments of microbial abundances from a stochastic model.
• Applied these equations to infer interaction parameter distributions from biological experimental data.
• Developed methods applicable to relative microbial abundance and replicate host data.

Main Results:

• The new method enhances the precision and identifiability of microbial interaction parameters.
• Inferred parameter distributions allow for predictions and assessment of parameter certainty.
• The approach is compatible with conventional metagenome data and tracking of replicate hosts.

Conclusions:

• Stochastic modeling provides a more accurate representation of microbiome dynamics than deterministic approaches.
• This method offers a powerful tool for dissecting complex microbial community interactions.
• Improved inference capabilities can advance our understanding of microbiome functions and host-microbe relationships.