Genome-scale metabolic models consistently predict in vitro characteristics of Corynebacterium striatum

Abstract

Genome-scale metabolic models (GEMs) are organism specific knowledge bases which can be used to unravel pathogenicity or improve production of specific metabolites in biotechnology applications. The present work combines in silico and in vitro approaches to create and curate strain-specific genome-scale metabolic models of Corynebacterium striatum. Approaches towards modeling rarely studied organisms are scarce. We introduce a cost-effective and easy experimental protocol which can be adapted to other organisms as well. Furthermore, the comparability of growth kinetics and in silico growth rates is discussed. This work introduces five newly created strain-specific genome-scale metabolic models (GEMs) of high quality, satisfying all contemporary standards and requirements. All these models have been benchmarked using the community standard test suite Metabolic Model Testing (MEMOTE) and experimentally validated by laboratory experiments conducted specifically for this purpose. For the curation of those models, the software infrastructure refineGEMs was developed to work on these models in parallel and to comply with the quality standards for GEMs. The model predictions were confirmed by experimental data and a new comparison metric based on the doubling time was developed to quantify bacterial growth. Future modeling projects can rely on the proposed software, which is independent of specific environmental conditions. The validation approach based on the growth rate calculation is now accessible and closely aligned with biological questions. The curated models are freely available via BioModels and a GitHub repository and can be used. The open-source software refineGEMs is available from https://github.com/draeger-lab/refinegems.