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 membrane10. 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.
In metabolic engineering, mathematical and computational models can be successfully employed to help facilitate experimental design of microorganisms for optimal production of individually-relevant compounds. Genome-scale metabolic models are commonly used tools that capture all possible chemical reactions known to occur in a microorganism. Mathematical programming is then used to elucidate and evaluate the behaviour of the microorganism for biologically desirable outputs. More specifically, metabolic engineering aims to identify the best environmental conditions and determine target genes that support optimal microbial growth. This is achieved through the introduction of genetic changes for enhanced industrial production of chemicals. For example, one can generate models of a microorganism, use genetic engineering steps to design novel pathways producing high-value molecules, and transport those chemicals across the cell membrane.
In this project, we will use both transcriptomic and proteomic data to build and evaluate condition-specific models of Pseudomonas. On such models, we will apply genetic engineering steps needed to overproduce chemicals of interest and simultaneously minimise waste-product formation in a multi-target fashion. Based on our modelling, we will be able to engineer Pseudomonas to manufacture the most efficient way of producing industrially relevant compounds. The focus will be on rhamnolipids, which have a wide range of industrial applications including in bioremediation, biopharmaceuticals, cosmetics, and detergents. Using this pipeline, we will build the first model of Pseudomonas teessidea, which will be engineered towards overproduction of rhamnolipids.
This will make production of biosurfactants economically viable by reducing the cost of production and post-production expenses for our industrial partner TeeGene.