In silico engineering of metabolism reveals new biomarkers for increased biosurfactant production.

BACKGROUND

Rhamnolipids, biosurfactants with a wide range of biomedical applications, are amphiphilic molecules produced on the surfaces of or excreted extracellularly by bacteria including . However, is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications.

METHODS

We investigate in silico the metabolic capabilities of for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes ( and ) from into a genome-scale model of . This pipeline combines machine learning methods with multi-omic modelling, and drives the engineered model toward an optimal production and export of rhamnolipids out of the membrane.

RESULTS

We identify a substantial increase in synthesis of rhamnolipids by the engineered model compared to the control model. We apply statistical and machine learning techniques on the metabolic reaction rates to identify distinct features on the structure of the variables and individual components driving the variation of growth and rhamnolipids production. We finally provide a computational framework for integrating multi-omics data and identifying latent pathways and genes for the production of rhamnolipids in .

CONCLUSIONS

We anticipate that our results will provide a versatile methodology for integrating multi-omics data for topological and functional analysis of toward maximization of biosurfactant production.