Superessential reactions in metabolic networks

Proc Natl Acad Sci U S A. 2012 May 1;109(18):E1121-30. doi: 10.1073/pnas.1113065109. Epub 2012 Apr 16.


The metabolic genotype of an organism can change through loss and acquisition of enzyme-coding genes, while preserving its ability to survive and synthesize biomass in specific environments. This evolutionary plasticity allows pathogens to evolve resistance to antimetabolic drugs by acquiring new metabolic pathways that bypass an enzyme blocked by a drug. We here study quantitatively the extent to which individual metabolic reactions and enzymes can be bypassed. To this end, we use a recently developed computational approach to create large metabolic network ensembles that can synthesize all biomass components in a given environment but contain an otherwise random set of known biochemical reactions. Using this approach, we identify a small connected core of 124 reactions that are absolutely superessential (that is, required in all metabolic networks). Many of these reactions have been experimentally confirmed as essential in different organisms. We also report a superessentiality index for thousands of reactions. This index indicates how easily a reaction can be bypassed. We find that it correlates with the number of sequenced genomes that encode an enzyme for the reaction. Superessentiality can help choose an enzyme as a potential drug target, especially because the index is not highly sensitive to the chemical environment that a pathogen requires. Our work also shows how analyses of large network ensembles can help understand the evolution of complex and robust metabolic networks.

MeSH terms

  • Biomass
  • Carbon / metabolism
  • Computer Simulation
  • Drug Resistance / genetics
  • Escherichia coli / genetics
  • Escherichia coli / growth & development
  • Escherichia coli / metabolism
  • Evolution, Molecular
  • Genotype
  • Markov Chains
  • Metabolic Networks and Pathways / genetics*
  • Models, Biological
  • Models, Genetic
  • Monte Carlo Method
  • Systems Biology


  • Carbon