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Bacterial metabolic networks exhibit a power-law distribution in reaction popularity due to evolutionary processes. Our model explains how reaction recruitment and inheritance shape these diverse metabolic compositions across species.

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Area of Science:

  • * Evolutionary biology
  • * Systems biology
  • * Metabolic network analysis

Background:

  • * Cellular metabolism varies significantly across different species.
  • * Empirical data show bacterial species have similar numbers of metabolic reactions.
  • * Reaction popularity across species follows a heterogeneous power-law distribution.

Purpose of the Study:

  • * To develop an evolutionary model explaining the observed power-law distribution of metabolic reactions.
  • * To investigate the mechanisms driving the heterogeneity in reaction prevalence across bacterial species.
  • * To validate the model against empirical data on metabolic network structure and species phylogeny.

Main Methods:

  • * Stochastic recruitment model for chemical reactions into species' metabolisms.
  • * Simulation of reaction inheritance across descendant species.
  • * Analysis of metabolic network structure and species phylogeny in simulated data.

Main Results:

  • * The model successfully reproduces the empirically observed power-law distribution (exponent one) of reaction popularity.
  • * Exponential growth in species count per reaction and saturated recruitment of new reactions explain the distribution.
  • * Simulated metabolic network structures and phylogenies align with real-world observations.

Conclusions:

  • * Evolutionary dynamics, specifically stochastic reaction recruitment and inheritance, are key drivers of metabolic network diversity.
  • * The proposed model provides a mechanistic explanation for the universal power-law distribution of metabolic reactions.
  • * The findings offer insights into the evolution of cellular metabolism and its structural organization.