Researchers led by Imperial College London's Oliver Ratmann developed a new model-based way to use Bayesian inference to study biological network data. In PLoS Computational Biology, they report that their approach uses the Approximate Bayesian Computation, or Likelihood-Free Inference and a MCMC algorithm to ascertain the distribution of the model's parameters. The researchers then used their approach to model gene duplication in Helicobacter pylori and Plasmodium falciparum and found that gene duplication plays more of a role in eukaryotic network evolution than in that of prokaryotes.