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Synthetic biology for microbial lipids production from lignocellulosic biomass using multi-functional synthetic consortia

Project description

Microbial lipids production from lignocellulosic biomass using synthetic biology

Microbial lipids from bio-based industries are a possible sustainable alternative to petroleum-derived fuels and chemicals. The commercial development of microbial lipids from inexpensive feedstocks like lignocellulosic biomass is currently limited due to high production costs resulting from physicochemical pretreatments and the extraction of intracellular lipids. The EU-funded SynBioLipid project will therefore construct engineered organisms with improved metabolic capabilities, integrating pretreatment, fermentation, detoxification and secretion of lipids. The researchers will use fungus Yarrowia lipolytica as a model microorganism for microbial lipids production from lignocellulosic biomass and develop strategies to overcome the challenges associated with the expression of large heterologous pathways. The work will generate synthetic microbial consortia comprising specialist strains and use these communities for the production of microbial lipids from lignocellulose.

Objective

Global transition towards a climate-neutral economy demands for the sustainable use of renewable biological resources. Microbial lipids are potential products of bio-based industries and sustainable alternatives to petroleum-derived fuels and chemicals. The commercial development of microbial lipids from inexpensive feedstocks such as lignocellulosic biomass is so far limited mainly due to the elevated production costs imposed by physicochemical pre-treatments and extraction of intracellular lipids. In addition, degradation of lignocellulose releases compounds which are toxic for most of the microorganisms. One solution is to construct engineered organisms with improved metabolic capabilities integrating pre-treatment, fermentation, detoxification and secretion of lipids. Hitherto, there is no successful research on an engineered organism which is able to do all these tasks. In fact, efficient transferring large heterologous pathways into one single microorganism is quite challenging and leads to high metabolic burden and less productivity. SynBioLipid will combine my expertise in metabolic modelling and fermentation using mixed microbial communities with the host experience in synthetic biology. It is aimed at using Yarrowia lipolytica, as a model microorganism for microbial lipids production from lignocellulosic biomass by presenting an innovative and original strategy to overcome the challenges associated with expression of large heterologous pathways. I first will generate synthetic microbial consortia comprised specialist strains, and second, will use these communities for the optimized production of microbial lipids. Each specialist strain is engineered to deliver an optimum output for one or more specific tasks. The metabolic network modelling will be integrated with synthetic biology and metabolic engineering to design and build multifunctional synthetic consortia, enabling efficient lipid production from lignocellulose.

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MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)

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Call for proposal

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(opens in new window) H2020-MSCA-IF-2020

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Coordinator

IMPERIAL COLLEGE OF SCIENCE TECHNOLOGY AND MEDICINE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 337 400,64
Address
SOUTH KENSINGTON CAMPUS EXHIBITION ROAD
SW7 2AZ London
United Kingdom

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Region
London Inner London — West Westminster
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 337 400,64
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