Dynamical model-guided SynCom design for sustainable agriculture

Area of Science:

• Agricultural Science
• Microbiology
• Systems Biology

Background:

• Synthetic microbial communities (SynComs) show promise for agriculture, surpassing single-strain inoculants with enhanced robustness and adaptability.
• Current challenges lie in translating SynCom research into scalable and engineerable agricultural practices.
• A shift from strain-centered assembly to system-level design is needed for effective SynCom development.

Purpose of the Study:

• To synthesize current research on plant SynComs.
• To propose a framework for system-level design of SynComs, moving beyond strain-centered approaches.
• To promote model-driven design and large-scale agricultural application of SynComs.

Main Methods:

• Review and synthesis of current research on plant SynComs.
• Framework development linking natural microbiome stability to plant-microbe-soil interactions.
• Integration of dynamical models (e.g., Lotka-Volterra, consumer-resource models) with multi-omics data.
• Focus on system-level constraints for SynCom design.

Main Results:

• Identification of stable coexisting communities in natural microbiomes as a basis for SynCom design.
• Elucidation of plant-microbe-soil interaction mechanisms is crucial for predicting community dynamics.
• Dynamical models integrated with multi-omics data provide a powerful tool for SynCom design.
• Platform-based design and production pipelines are essential for scalability.

Conclusions:

• A system-level, model-driven approach is necessary for engineering robust and scalable SynComs in agriculture.
• Integrating ecological principles with advanced modeling and multi-omics data advances SynCom design.
• This framework facilitates the transition of SynComs from conceptual studies to practical agricultural applications.