Average phenotype consistency across all test organisms for models gap filled using the four evaluated algorithms.

Iterative gap filling greatly increased the sensitivity (more correct positive growth conditions) and reduced the specificity (more incorrect positive growth conditions) of Biolog simulations. The use of likelihoods did not have a significant effect on the specificity or sensitivity of Biolog simulations. The overall model accuracy for essentiality data was similar for all four algorithms because genes added due to likelihood-based gap filling represented only at most about 7% of the genes in the model. See Figure 6 for the results of knockout simulations using only genes added to gap filling solutions. “PP” means post-processed to add genes to gap filled reactions.Average phenotype consistency across all test organisms for models gap filled using the four evaluated algorithms.