In silico engineering of Pseudomonas 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 Pseudomonas aeruginosa. However, Pseudomonas putida is a non-pathogenic model organism with greater metabolic versatility and potential for industrial applications. Methods: We investigate in silico the metabolic capabilities of P. putida for rhamnolipids biosynthesis using statistical, metabolic and synthetic engineering approaches after introducing key genes (RhlA and RhlB) from P. aeruginosa into a genome-scale model of P. putida. This pipeline combines machine learning methods with multi-omit modelling, and drives the engineered P. putida 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-omits data and identifying latent pathways and genes for the production of rhamnolipids in P. putida. Conclusions: We anticipate that our results will provide a versatile methodology for integrating multi-omits data for topological and functional analysis of P. putida toward maximization of biosurfactant production.