Modelling microbial communities using biochemical resource allocation analysis.

Area of Science:

• Microbial ecology
• Systems biology
• Computational biology

Background:

• Understanding microbial community dynamics is crucial in biology.
• Current ecosystem simulations often use simplified microbial growth models.
• A gap exists between ecological and systems/synthetic biology descriptions of microbial metabolism.

Purpose of the Study:

• To demonstrate how resource allocation models can bridge the gap between ecological and systems biology approaches.
• To present a framework for mechanistically modeling microbial growth that is physiologically relevant and computationally efficient.
• To advance the simulation and parameterization of microbial ecosystems.

Main Methods:

• Formulating mechanistic microbial growth models based on quantitative resource allocation.
• Developing coarse-grained biochemical resource allocation models.
• Applying models to simulate cyanobacterial growth.

Main Results:

• Resource allocation models provide a physiologically meaningful alternative to traditional growth models (e.g., Michaelis-Menten, Monod).
• These models capture emergent properties and the plasticity of microbial growth.
• Demonstrated utility in addressing marine microbial ecosystem simulation challenges, including acclimation, nutrient co-limitation, and alternative nutrient utilization.

Conclusions:

• Biochemical resource allocation models offer significant advantages for microbial ecosystem simulations.
• These models enable a more accurate representation of microbial physiology and adaptation.
• The approach facilitates the integration of quantitative cell physiology into ecological modeling.