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Microbial production of advanced biofuels

Abstract

Concerns over climate change have necessitated a rethinking of our transportation infrastructure. One possible alternative to carbon-polluting fossil fuels is biofuels produced by engineered microorganisms that use a renewable carbon source. Two biofuels, ethanol and biodiesel, have made inroads in displacing petroleum-based fuels, but their uptake has been limited by the amounts that can be used in conventional engines and by their cost. Advanced biofuels that mimic petroleum-based fuels are not limited by the amounts that can be used in existing transportation infrastructure but have had limited uptake due to costs. In this Review, we discuss engineering metabolic pathways to produce advanced biofuels, challenges with substrate and product toxicity with regard to host microorganisms and methods to engineer tolerance, and the use of functional genomics and machine learning approaches to produce advanced biofuels and prospects for reducing their costs.

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Fig. 1: Methods for producing biofuels.
Fig. 2: Metabolic pathways for advanced biofuel production.
Fig. 3: Major sources of toxicity and inhibition of growth and production.
Fig. 4: The phosphoketolase shunt.
Fig. 5: Three systematic approaches to increase TRY.
Fig. 6: Idealized ‘fuel properties-first approach’ for identification and screening of bio-advantaged fuels.

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Acknowledgements

The authors thank C. Scown (Lawrence Berkeley National Laboratory) for helpful discussions on life cycle and technoeconomic analyses of biofuel production. This work was performed as part of the US Department of Energy (DOE) Joint BioEnergy Institute (https://www.jbei.org) supported by the DOE, Office of Science, Office of Biological and Environmental Research, and by the DOE, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office, and as part of the Co-Optimization of Fuels & Engines project sponsored by the DOE, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office and Vehicle Technologies Office, under contract DEAC02-05CH11231 between the DOE and Lawrence Berkeley National Laboratory. The views and opinions of the authors expressed herein do not necessarily state or reflect those of the US Government or any agency thereof. Neither the US Government nor any agency thereof, nor any of their employees, makes any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness or usefulness of any information, apparatus, product or process disclosed, or represents that its use would not infringe privately owned rights. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of the manuscript, or allow others to do so, for US Government purposes. The DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Correspondence to Jay Keasling.

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J.K. has a financial interest in Amyris, Lygos, Demetrix, Napigen, Apertor Pharmaceuticals, Maple Bio, Ansa Biotechnologies, Berkeley Yeast and Zero Acre Farms. The other authors declare no competing interests.

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Glossary

Octane number

A standard measure of an engine or aviation fuel capability against compression.

Cetane number

An indicator of the combustion speed of diesel fuel and compression needed for ignition.

Ionic liquids

(ILs). A highly efficient set of reagents for the depolymerization and deconstruction of a range of feedstocks.

C1 substrates

One-carbon microbial substrates, including CO2, CH4, CO, HCO2 and CH3OH.

Synthesis gas

A mixture of CO, CO2 and H2.

Blendstocks

Single fuel components that are blended with additional components to produce a finished fuel.

Quadratic regression

Modelling approach that uses a polynomial of up to grade 2 to predict the response.

Design of experiments

Applied statistics techniques that deal with planning, conducting, analysing and interpreting controlled tests to evaluate the factors that control the experimental output under study.

Ensemble models

Modelling approach that takes the input of various different models and has them ‘vote’ for a particular prediction.

Off-gas

The exhaust gas stream exiting a bioreactor.

Host onboarding

Development of the genetic tools necessary to allow metabolic engineering of a previously unengineered microorganism.

Sooting propensity

The degree to which a fuel mixture generates black carbon soot when combusted.

E10 petrol

Petrol containing 10% ethanol by volume.

Cold flow

Fuel viscosity at low temperature; poor cold flow can lead to gelling and compromise engine operability in cold weather conditions.

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Keasling, J., Garcia Martin, H., Lee, T.S. et al. Microbial production of advanced biofuels. Nat Rev Microbiol (2021). https://doi.org/10.1038/s41579-021-00577-w

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