Candida albicans is the most common cause of invasive candidiasis, partly due to its ability to acquire drug
resistance. With the rise in frequency of multidrug resistant clinical isolates, therapeutic options are running
low. The identification of new drug targets and new drugs is crucial to overcome the increase in therapeutic
failure. Currently, genome-scale metabolic models can be considered established tools for drug targeting.
In this study, we propose the first genome-scale metabolic model for Candida albicans, iRV1930. The model
consists of 1556 reactions, 1344 metabolites, 1053 genes, and 5 compartments. This model, currently under
validation, proved accurate when predicting the capability of utilizing different carbon and nitrogen sources
when compared to experimental data. This model was reconstructed using open source software tool, merlin
3.9.6, and is provided in the well-established systems biology markup language (SBML) format, thus, it can
be used in most metabolic engineering platforms, such as OptFlux or Cobra.
Altogether, this model provides a promising platform for global elucidation of the metabolism of C. albicans,
currently being used to guide the identification of new drug targets to tackle human candidiasis.info:eu-repo/semantics/publishedVersio
