Challenges in microbial ecology: building predictive understanding of community function and dynamics

Area of Science:

• Microbial Ecology
• Systems Biology
• Computational Biology

Background:

• Microbial communities (MCs) are essential for Earth's biogeochemical cycles and ecosystem functions.
• Current understanding and predictive capabilities of complex, dynamic MCs are limited.
• Predicting MC function from composition is a major challenge in microbial ecology.

Purpose of the Study:

• To advocate for the integration of experimental data collection and mathematical model building to address challenges in microbial ecology.
• To highlight the necessity of coordinating experimental and modeling approaches for advancing MC research.
• To provide practical suggestions for achieving better integration in MC studies.

Main Methods:

• Review and discussion of existing research on model-experiment integration in microbial ecology.
• Identification of key research questions requiring enhanced integration.
• Proposal of practical strategies for coordinating experimental and modeling efforts.

Main Results:

• Model-experiment integration has already yielded significant insights into MC structure and function.
• Specific examples demonstrate the success of combined approaches.
• Key research areas still require improved integration for substantial progress.

Conclusions:

• Close coordination between experimental data and mathematical model building is essential for predicting and managing microbial communities.
• Enhanced integration is critical for significant advancements in understanding MC dynamics and function.
• Practical suggestions are provided to facilitate this integration in future research.