Iterative reconstruction of a global metabolic model of Acinetobacter baylyi ADP1 using high-throughput growth phenotype and gene essentiality data
Résumé
Background: Genome-scale metabolic models are powerful tools to study global properties of
metabolic networks. They provide a way to integrate various types of biological information in a
single framework, providing a structured representation of available knowledge on the metabolism
of the respective species.
Results: We reconstructed a constraint-based metabolic model of Acinetobacter baylyi ADP1, a soil
bacterium of interest for environmental and biotechnological applications with large-spectrum
biodegradation capabilities. Following initial reconstruction from genome annotation and the
literature, we iteratively refined the model by comparing its predictions with the results of largescale
experiments: (1) high-throughput growth phenotypes of the wild-type strain on 190 distinct
environments, (2) genome-wide gene essentialities from a knockout mutant library, and (3) largescale
growth phenotypes of all mutant strains on 8 minimal media. Out of 1412 predictions, 1262
were initially consistent with our experimental observations. Inconsistencies were systematically
examined, leading in 65 cases to model corrections. The predictions of the final version of the
model, which included three rounds of refinements, are consistent with the experimental results
for (1) 91% of the wild-type growth phenotypes, (2) 94% of the gene essentiality results, and (3)
94% of the mutant growth phenotypes. To facilitate the exploitation of the metabolic model, we
provide a web interface allowing online predictions and visualization of results on metabolic maps.
Conclusion: The iterative reconstruction procedure led to significant model improvements,
showing that genome-wide mutant phenotypes on several media can significantly facilitate the
transition from genome annotation to a high-quality model.