Environmental flexibility does not explain metabolic robustness.

Area of Science:

• Systems Biology
• Metabolic Engineering
• Computational Biology

Background:

• Cells exhibit significant resilience to genetic and environmental changes, but the evolutionary basis of this robustness is unclear.
• Quantifying cellular robustness requires computationally accessible methods, particularly for genome-scale metabolic models (GSMMs).

Purpose of the Study:

• To develop and apply an unbiased metric for quantifying structural robustness in GSMMs using reliability engineering principles.
• To investigate the relationship between metabolic network robustness and environmental flexibility, and identify key metabolic pathways contributing to robustness.

Main Methods:

• Developed a metric for probability of failure (PoF) in GSMMs, approximating it using minimal cut sets (MCSs).
• Analyzed 489 microbial GSMMs (E. coli, Shigella, Salmonella, fungi) to assess network robustness.
• Correlated network robustness with environmental diversity and identified dominant metabolic pathways.

Main Results:

• The probability of failure (PoF) metric effectively approximates cellular robustness in large-scale models using low-cardinality minimal cut sets.
• No correlation was found between metabolic network robustness and the diversity of growth-supporting environments, challenging congruence theory.
• Amino acid synthesis pathways were identified as major contributors to metabolic robustness, surpassing carbon metabolism.

Conclusions:

• The developed PoF metric offers a computationally feasible approach to quantify structural robustness in GSMMs.
• Cellular robustness is primarily driven by intrinsic metabolic network structure, particularly amino acid synthesis, rather than adaptation to environmental variability.
• Findings provide new insights into the evolutionary origins and maintenance of cellular robustness.