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

Dr Claudio Angione, Teesside University
Dr Pattanathu Rahman, TeeGene Biotech Ltd. & University of Portsmouth

 


“Integrating machine learning techniques with genome-scale metabolic models is a highly-promising technique, as it has the potential to achieve for the first time interpretable and mechanism-aware machine learning. With this POC we have shown that this can be used to infer the optimal metabolic engineering steps for overproduction of desired metabolites.”

Dr Claudio Angione, Teesside University


The Challenge

Rhamnolipids are bacterial biosurfactants which have several industrial applications including in biopharmaceuticals, cosmetics, detergents, and bioremediation. Rhamnolipids are predominantly produced by Pseudomonas aeruginosa. However, the model organism Pseudomonas putida has greater metabolic versatility and potential for industrial applications.

Computational methods for metabolic engineering are able to model and optimise biological models such as P. putida, leading to the improvement of a given biotechnological pipeline. The challenge is to combine machine learning tools with a metabolic metabolic model of P. putida for rhamnolipid production. This model can then be used to engineer Pseudomonas strains to efficiently manufacture these industrially relevant compounds.

The Research

Dr Claudio Angione is a Senior Lecturer at Teesside University. As a member of the Machine Intelligence Research Group and the Healthcare Innovation Centre, his research interests include systems biology and machine learning.

TeeGene Biotech is a spinout venture from Teesside University lead by Dr Pattanathu Rahman, which is pioneering the use of biosurfactants in a range of products, including household, environmental, cosmetic and biomedical applications.

Dr Angione applied for a CBMNet Proof-of-Concept award with TeeGene Biotech, building on work previously funded by a CBMNet Business Interaction Voucher. Their project aimed to build a multi-omic engineered model of Rhamnolipid production in Pseudomonas putida. This model will be used to make biosurfactant production more economically viable for TeeGene.

The Result

In a previous CBMNet-funded project, Dr Angione’s group built an engineered genome-scale model of P. putida for rhamnolipid production and transport through the cell membrane. In this project, the aim was to integrate poly-omics data into this model to produce a multi-omic engineered model of P. putida.

Using this model the researchers could apply multi-omics modelling, statistical, metabolic and biosynthetic engineering to predict the role of individual reactions and pathways and to investigate various growth conditions. Using rhamnolipid production and biomass as objectives, predictions could be made as to optimal growth conditions and these have been communicated to TeeGene Biotech. In addition, TeeGene now also has useful information on the mechanism of rhamnolipid production in Pseudomonas, and the ability to predict cause-effect mechanisms in the metabolic network before carrying out lab experiments.

An ongoing aspect of this project is to achieve an integration of machine learning techniques with metabolic modelling, with the aim to use the pipeline developed to build a genome-scale model of Pseudomonas teessidea and apply metabolic engineering steps for the overproduction of rhamnolipids.

The Future

The work described here has been published (Occhipinti et al. (2018) PeerJ 6:e6046) and the project will continue via a joint PhD studentship, a Special Research Technician and a PDRA based at the University of Portsmouth – Centre for Enzyme Innovation in collaboration with Teesside University. The long-term goal is the commercialisation of this technology by TeeGene. This project also formed the basis of a submission to the “Sustainable bio-based surfactants for everyone” grand-challenge by Nouryon.

The collaboration between Dr Angione and TeeGene has been strengthened and two related funding proposals have been submitted: (1) UKIERI-DST “Food waste as feedstock for surface active agents’ production for remediation of oil contaminated sites and Enhanced Oil Recovery”; (2) Indo/UK GCRF networking application: “A circular economy approach of developing biosurfactants and biochar from waste materials for industrial application”.


Teegene_logo“The computational predictions from this POC work are excellent and TeeGene would seek further expansion in this technology towards Pseudomonas engineered design for biosurfactants manufacturing and validation of the technology”

Dr Pattanathu Rahman, TeeGene Biotech Ltd. & University of Portsmouth