MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.

Area of Science:

• Microbiome research
• Computational biology
• Systems biology

Background:

• Predicting host-microbial ecosystem dynamics is essential for developing effective bacteriotherapies.
• Current methods for inferring microbial community behavior from time-series data have limitations.

Purpose of the Study:

• To introduce MDSINE, a novel suite of algorithms for modeling and predicting microbiome temporal dynamics.
• To evaluate MDSINE's performance against existing methods using simulated and experimental data.

Main Methods:

• Development of MDSINE algorithms for inferring dynamical systems from microbiome time-series data.
• Validation using simulated datasets to compare MDSINE with existing inference techniques.
• Application of MDSINE to gnotobiotic mouse models with Clostridium difficile infection and probiotic administration.

Main Results:

• MDSINE significantly outperforms existing methods in inferring microbial dynamics from simulated data.
• MDSINE accurately forecasts temporal changes in complex microbial communities.
• The algorithm successfully predicts stable sub-communities that can suppress pathogen growth and identifies critical bacteria for community resilience.

Conclusions:

• MDSINE offers a powerful new tool for understanding and predicting host-microbial ecosystem behavior.
• This approach advances the rational design of bacteriotherapies by enabling accurate forecasting and identification of keystone taxa.
• MDSINE demonstrates significant potential for applications in microbiome-based diagnostics and therapeutics.