In Silico genome-wide modelling and metabolic engineering of Pseudomonas strains for rhamnolipid synthesis

Rhamnolipids, bacterial biosurfactants, have several industrial applications including biopharmaceuticals, cosmetics, detergents, and bioremediation of heavy metal contaminated environments. For enhanced production of rhamnolipids, computational tools can be used based on metabolic engineering. Genome-scale metabolic models contain all known biochemical reactions in a microorganism, and are arguably the best tool for efficient prediction and to guide experimental metabolomics and lipidomics design.
We recently built an engineered genome-scale model of Pseudomonas putida for rhamnolipid production and transport through the cell membrane. This project aims to build upon our previous achievements by integrating poly-omics data with our model, achieving a multi-omic engineered model of P. putida. We will apply multi-level optimisation algorithms to achieve optimal rhamnolipid biosynthesis in the engineered model. This pipeline will then be used to build a genome-scale model of Pseudomonas teessidea, a first of its kind, and apply metabolic engineering steps for overproduction of rhamnolipids and their transport out of the cell membrane.