Dynamical Systems-Constrained Metabolic Modeling Enables Forecasting of Host-Microbiome Dynamics

Area of Science:

• Microbiome research
• Systems biology
• Computational biology

Background:

• Forecasting microbiome-host ecosystem dynamics at compositional and functional levels is challenging.
• Dynamical systems models (DSMs) predict community composition, while constraint-based metabolic models (CBMMs) estimate metabolic fluxes.
• Existing frameworks lack integration for time-resolved predictions of both microbial abundances and metabolite dynamics from ecological data alone.

Purpose of the Study:

• Introduce the Dynamical Systems Constrained Metabolic Modeling (DySCoMeMo) framework.
• Integrate ecological DSMs with CBMMs for mechanistically grounded, time-resolved forecasts.
• Predict temporal dynamics of biomass and metabolites in microbial communities and hosts.

Main Methods:

• DySCoMeMo integrates DSMs with CBMMs.
• It uses parameters from DSMs applied to microbiome time series data to constrain metabolic modeling over time.
• This bridges ecological interaction networks with genome-scale metabolic modeling.

Main Results:

• DySCoMeMo predicts in vitro community and metabolite dynamics with superior or on-par accuracy compared to existing methods.
• The framework generalizes to in vivo data, accurately forecasting dynamics during dietary perturbations, including host metabolism.
• DySCoMeMo uniquely identifies keystone species by quantifying their metabolic contributions.

Conclusions:

• DySCoMeMo establishes a generalizable, mechanistically grounded framework for time-resolved forecasting of microbiome-host microbial and metabolic dynamics.
• It bridges ecological interaction inference with genome-scale metabolism of communities.
• This work provides a novel approach for understanding and predicting complex ecosystem behaviors.