Assumptions on decision making and environment can yield multiple steady states in microbial community models

Area of Science:

• Microbiology
• Systems Biology
• Computational Biology

Background:

• Genome-scale metabolic networks (GSMs) are crucial for simulating microbial communities, particularly in human microbiome research.
• Current simulations often make unexamined assumptions regarding culturing environments and microbial decision-making strategies (individual vs. community benefit).
• The systematic impact of these assumptions on community simulation outcomes remains largely unexplored.

Purpose of the Study:

• To systematically investigate the impact of different assumption combinations on microbial community simulations.
• To provide novel mathematical formulations for simulating these assumptions.
• To elucidate how these assumptions influence predictions of microbial coexistence and cooperation.

Main Methods:

• Investigated four distinct combinations of environmental and decision-making assumptions.
• Developed novel mathematical formulations for simulating these assumption combinations.
• Analyzed predictions of microbial coexistence based on differential substrate utilization.
• Examined a synthetic community where strains grow only in cooperation.

Main Results:

• Different assumption combinations yield qualitatively different predictions regarding microbial coexistence through differential substrate utilization.
• The study highlights that steady-state GSM literature often overlooks differential substrate utilization in favor of crossfeeding mechanisms.
• Simulations of a synthetic community predicted multiple cooperation modes, even without explicit cooperation mechanisms.

Conclusions:

• Steady-state GSM modeling of microbial communities is sensitive to assumptions about decision-making principles and environmental conditions.
• While dynamic flux balance analysis can address these factors, direct steady-state methods may be preferable for communities exhibiting multiple steady states.