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  • Review Article
  • Published:

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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References

  1. US Energy Information Administration. International energy outlook 2019: with projections to 2050 (EIA, 2019).

  2. US Environmental Protection Agency. Sources of greenhouse gas emissions. EPA https://www.epa.gov/ghgemissions/sources-greenhouse-gas-emissions (2020).

  3. Kircher, M. Sustainability of biofuels and renewable chemicals production from biomass. Curr. Opin. Chem. Biol. 29, 26–31 (2015).

    Article  CAS  PubMed  Google Scholar 

  4. Hughes, S. R. & Jones, M. A. in Green Energy to Sustainability: Strategies for Global Industries (eds Vertès, A. A., Qureshi, N., Blaschek, H. P. & Yukawa, H.) 137–156 (Wiley, 2020).

  5. Liu, Y. et al. Biofuels for a sustainable future. Cell 184, 1636–1647 (2021).

    Article  CAS  PubMed  Google Scholar 

  6. Field, J. L. et al. Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels. Proc. Natl Acad. Sci. USA 117, 21968–21977 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Baral, N. et al. Techno-economic analysis and life-cycle greenhouse gas mitigation cost of five routes to bio-jet fuel blendstocks. Energy Environ. Sci. 12, 807–824 (2018).

    Article  Google Scholar 

  8. Hannon, J. R. et al. Technoeconomic and life-cycle analysis of single-step catalytic conversion of wet ethanol into fungible fuel blendstocks. Proc. Natl Acad. Sci. USA 117, 12576–12583 (2020).

    Article  CAS  PubMed  Google Scholar 

  9. Baral, N. R. et al. Approaches for more efficient biological conversion of lignocellulosic feedstocks to biofuels and bioproducts. ACS Sustain. Chem. Eng. 7, 9062–9079 (2019).

    Article  CAS  Google Scholar 

  10. Yang, M., Baral, N. R., Anastasopoulou, A., Breunig, H. M. & Scown, C. D. Cost and life-cycle greenhouse gas implications of integrating biogas upgrading and carbon capture technologies in cellulosic biorefineries. Environ. Sci. Technol. 54, 12810–12819 (2020).

    Article  CAS  PubMed  Google Scholar 

  11. Langholtz, M. H., Stokes, B. J. & Eaton, L. M. Billion-ton report: advancing domestic resources for a thriving bioeconomy, volume 1: economic availability of feedstocks (Oak Ridge National Laboratory, 2016).

  12. Global Bioenergy Association. Global Bioenergy Statistics 2019. http://www.worldbioenergy.org/uploads/191129%20WBA%20GBS%202019_HQ.pdf (Global Bioenergy Association, 2019).

  13. Pattrick, C. A. et al. Proteomic profiling, transcription factor modeling, and genomics of evolved tolerant strains elucidate mechanisms of vanillin toxicity in Escherichia coli. mSystems 4, e00163-19 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Carroll, A. & Somerville, C. Cellulosic biofuels. Annu. Rev. Plant Biol. 60, 165–182 (2009).

    Article  CAS  PubMed  Google Scholar 

  15. Blanch, H. W., Simmons, B. A. & Klein-Marcuschamer, D. Biomass deconstruction to sugars. Biotechnol. J. 6, 1086–1102 (2011).

    Article  CAS  PubMed  Google Scholar 

  16. Dale, B. E. & Ong, R. G. Energy, wealth, and human development: why and how biomass pretreatment research must improve. Biotechnol. Prog. 28, 893–898 (2012).

    Article  CAS  PubMed  Google Scholar 

  17. Klein-Marcuschamer, D., Oleskowicz-Popiel, P., Simmons, B. A. & Blanch, H. W. The challenge of enzyme cost in the production of lignocellulosic biofuels. Biotechnol. Bioeng. 109, 1083–1087 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Lee, S. Y., Kim, H. M. & Cheon, S. Metabolic engineering for the production of hydrocarbon fuels. Curr. Opin. Biotechnol. 33, 15–22 (2015). A comprehensive review of metabolic engineering for biofuel production.

    Article  CAS  PubMed  Google Scholar 

  19. Adom, F., Dunn, J. B., Han, J. & Sather, N. Life-cycle fossil energy consumption and greenhouse gas emissions of bioderived chemicals and their conventional counterparts. Environ. Sci. Technol. 48, 14624–14631 (2014).

    Article  CAS  PubMed  Google Scholar 

  20. Biddy, M. J. et al. The techno-economic basis for coproduct manufacturing to enable hydrocarbon fuel production from lignocellulosic biomass. ACS Sustain. Chem. Eng. 4, 3196–3211 (2016).

    Article  CAS  Google Scholar 

  21. Brooks, K. P. et al. in Biofuels for Aviation: Feedstocks, Technology and Implementation (ed. Chuck, C.) 109–150 (Academic, 2016).

  22. George, K. W., Alonso-Gutierrez, J., Keasling, J. D. & Lee, T. S. Isoprenoid drugs, biofuels, and chemicals-artemisinin, farnesene, and beyond. Adv. Biochem. Eng. Biotechnol. 148, 355–389 (2015).

    CAS  PubMed  Google Scholar 

  23. Li, M. et al. Recent advances of metabolic engineering strategies in natural isoprenoid production using cell factories. Nat. Prod. Rep. 37, 80–99 (2020).

    Article  CAS  PubMed  Google Scholar 

  24. Rodríguez-Concepción, M. Plant isoprenoids: a general overview. Methods Mol. Biol. 1153, 1–5 (2014).

    Article  PubMed  CAS  Google Scholar 

  25. Gao, Y., Honzatko, R. B. & Peters, R. J. Terpenoid synthase structures: a so far incomplete view of complex catalysis. Nat. Prod. Rep. 29, 1153–1175 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Peralta-Yahya, P. P. et al. Identification and microbial production of a terpene-based advanced biofuel. Nat. Commun. 2, 483 (2011).

    Article  PubMed  CAS  Google Scholar 

  27. Harrison, K. W. & Harvey, B. G. Renewable high density fuels containing tricyclic sesquiterpanes and alkyl diamondoids. Sustain. Energy Fuels 1, 467–473 (2017).

    Article  CAS  Google Scholar 

  28. Zebec, Z. et al. Towards synthesis of monoterpenes and derivatives using synthetic biology. Curr. Opin. Chem. Biol. 34, 37–43 (2016).

    Article  CAS  PubMed  Google Scholar 

  29. George, K. W. et al. Correlation analysis of targeted proteins and metabolites to assess and engineer microbial isopentenol production. Biotechnol. Bioeng. 111, 1648–1658 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Meadows, A. L. et al. Rewriting yeast central carbon metabolism for industrial isoprenoid production. Nature 537, 694–697 (2016). A good demonstration of microbial engineering for biofuel-producing yeast, especially at industrial scale.

    Article  CAS  PubMed  Google Scholar 

  31. Kang, A. et al. Isopentenyl diphosphate (IPP)-bypass mevalonate pathways for isopentenol production. Metab. Eng. 34, 25–35 (2016).

    Article  CAS  PubMed  Google Scholar 

  32. Kang, A. et al. Optimization of the IPP-bypass mevalonate pathway and fed-batch fermentation for the production of isoprenol in Escherichia coli. Metab. Eng. 56, 85–96 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Lennen, R. M. & Pfleger, B. F. Engineering Escherichia coli to synthesize free fatty acids. Trends Biotechnol. 30, 659–667 (2012).

    Article  CAS  PubMed  Google Scholar 

  34. Budin, I. et al. Viscous control of cellular respiration by membrane lipid composition. Science 362, 1186–1189 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Marella, E. R., Holkenbrink, C., Siewers, V. & Borodina, I. Engineering microbial fatty acid metabolism for biofuels and biochemicals. Curr. Opin. Biotechnol. 50, 39–46 (2018).

    Article  CAS  PubMed  Google Scholar 

  36. Qiao, K. et al. Engineering lipid overproduction in the oleaginous yeast Yarrowia lipolytica. Metab. Eng. 29, 56–65 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Steen, E. J. et al. Microbial production of fatty-acid-derived fuels and chemicals from plant biomass. Nature 463, 559–562 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Zhou, Y. J. et al. Production of fatty acid-derived oleochemicals and biofuels by synthetic yeast cell factories. Nat. Commun. 7, 11709 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Krivoruchko, A., Zhang, Y., Siewers, V., Chen, Y. & Nielsen, J. Microbial acetyl-CoA metabolism and metabolic engineering. Metab. Eng. 28, 28–42 (2015).

    Article  CAS  PubMed  Google Scholar 

  40. Qiao, K., Wasylenko, T. M., Zhou, K., Xu, P. & Stephanopoulos, G. Lipid production in Yarrowia lipolytica is maximized by engineering cytosolic redox metabolism. Nat. Biotechnol. 35, 173–177 (2017).

    Article  CAS  PubMed  Google Scholar 

  41. Zhu, Z. et al. Expanding the product portfolio of fungal type I fatty acid synthases. Nat. Chem. Biol. 13, 360–362 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Schirmer, A., Rude, M. A., Li, X., Popova, E. & del Cardayre, S. B. Microbial biosynthesis of alkanes. Science 329, 559–562 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Rude, M. A. et al. Terminal olefin (1-alkene) biosynthesis by a novel p450 fatty acid decarboxylase from Jeotgalicoccus species. Appl. Environ. Microbiol. 77, 1718–1727 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Youngquist, J. T. et al. Production of medium chain length fatty alcohols from glucose in Escherichia coli. Metab. Eng. 20, 177–186 (2013).

    Article  CAS  PubMed  Google Scholar 

  45. Goh, E.-B., Baidoo, E. E. K., Keasling, J. D. & Beller, H. R. Engineering of bacterial methyl ketone synthesis for biofuels. Appl. Environ. Microbiol. 78, 70–80 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Javidpour, P. et al. Investigation of proposed ladderane biosynthetic genes from anammox bacteria by heterologous expression in E. coli. PLoS ONE 11, e0151087 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Czerwiec, Q. et al. Optimization of cyclopropane fatty acids production in Yarrowia lipolytica. Yeast 36, 143–151 (2019).

    CAS  PubMed  Google Scholar 

  48. Rabinovitch-Deere, C. A., Oliver, J. W. K., Rodriguez, G. M. & Atsumi, S. Synthetic biology and metabolic engineering approaches to produce biofuels. Chem. Rev. 113, 4611–4632 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Bond-Watts, B. B., Bellerose, R. J. & Chang, M. C. Y. Enzyme mechanism as a kinetic control element for designing synthetic biofuel pathways. Nat. Chem. Biol. 7, 222–227 (2011).

    Article  CAS  PubMed  Google Scholar 

  50. Atsumi, S., Hanai, T. & Liao, J. C. Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451, 86–89 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Sherkhanov, S. et al. Isobutanol production freed from biological limits using synthetic biochemistry. Nat. Commun. 11, 4292 (2020). A good demonstration of the cell-free system for biofuel (isobutanol) production with high TRY.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Rodriguez, G. M., Tashiro, Y. & Atsumi, S. Expanding ester biosynthesis in Escherichia coli. Nat. Chem. Biol. 10, 259–265 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Lee, J.-W. & Trinh, C. T. Microbial biosynthesis of lactate esters. Biotechnol. Biofuels 12, 226 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Yuzawa, S., Keasling, J. D. & Katz, L. Insights into polyketide biosynthesis gained from repurposing antibiotic-producing polyketide synthases to produce fuels and chemicals. J. Antibiot. 69, 494–499 (2016).

    Article  CAS  Google Scholar 

  55. Cai, W. & Zhang, W. Engineering modular polyketide synthases for production of biofuels and industrial chemicals. Curr. Opin. Biotechnol. 50, 32–38 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Liu, Q. et al. Engineering an iterative polyketide pathway in Escherichia coli results in single-form alkene and alkane overproduction. Metab. Eng. 28, 82–90 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Poust, S. et al. Divergent mechanistic routes for the formation of gem-dimethyl groups in the biosynthesis of complex polyketides. Angew. Chem. Int. Ed. 54, 2370–2373 (2015).

    Article  CAS  Google Scholar 

  58. Srirangan, K. et al. Engineering Escherichia coli for Microbial Production of Butanone. Appl. Environ. Microbiol. 82, 2574–2584 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Yuzawa, S. et al. Short-chain ketone production by engineered polyketide synthases in Streptomyces albus. Nat. Commun. 9, 4569 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Zargar, A. et al. Leveraging microbial biosynthetic pathways for the generation of “drop-in” biofuels. Curr. Opin. Biotechnol. 45, 156–163 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Smith, K. M., Cho, K.-M. & Liao, J. C. Engineering Corynebacterium glutamicum for isobutanol production. Appl. Microbiol. Biotechnol. 87, 1045–1055 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lin, P. P. et al. Consolidated bioprocessing of cellulose to isobutanol using Clostridium thermocellum. Metab. Eng. 31, 44–52 (2015).

    Article  CAS  PubMed  Google Scholar 

  63. Yan, Q. & Pfleger, B. F. Revisiting metabolic engineering strategies for microbial synthesis of oleochemicals. Metab. Eng. 58, 35–46 (2020).

    Article  CAS  PubMed  Google Scholar 

  64. Hanko, E. K. R. et al. Engineering β-oxidation in Yarrowia lipolytica for methyl ketone production. Metab. Eng. 48, 52–62 (2018).

    Article  CAS  PubMed  Google Scholar 

  65. Kim, H. M., Chae, T. U., Choi, S. Y., Kim, W. J. & Lee, S. Y. Engineering of an oleaginous bacterium for the production of fatty acids and fuels. Nat. Chem. Biol. 15, 721–729 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Sasaki, Y. et al. Engineering Corynebacterium glutamicum to produce the biogasoline isopentenol from plant biomass hydrolysates. Biotechnol. Biofuels 12, 41 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Yaegashi, J. et al. Rhodosporidium toruloides: a new platform organism for conversion of lignocellulose into terpene biofuels and bioproducts. Biotechnol. Biofuels 10, 241 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Sundstrom, E. et al. Demonstrating a separation-free process coupling ionic liquid pretreatment, saccharification, and fermentation with Rhodosporidium toruloides to produce advanced biofuels. Green Chem. 20, 2870–2879 (2018).

    Article  CAS  Google Scholar 

  69. Zhuang, X. et al. Monoterpene production by the carotenogenic yeast Rhodosporidium toruloides. Microb. Cell Fact. 18, 54 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Miao, R., Xie, H. & Lindblad, P. Enhancement of photosynthetic isobutanol production in engineered cells of Synechocystis PCC 6803. Biotechnol. Biofuels 11, 267 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Nguyen, A. D., Kim, D. & Lee, E. Y. Unlocking the biosynthesis of sesquiterpenoids from methane via the methylerythritol phosphate pathway in methanotrophic bacteria, using α-humulene as a model compound. Metab. Eng. 61, 69–78 (2020).

    Article  CAS  PubMed  Google Scholar 

  72. Krieg, T., Sydow, A., Faust, S., Huth, I. & Holtmann, D. CO2 to terpenes: autotrophic and electroautotrophic α-humulene production with Cupriavidus necator. Angew. Chem. Int. Ed. 57, 1879–1882 (2018).

    Article  CAS  Google Scholar 

  73. Grenz, S. et al. Exploiting Hydrogenophaga pseudoflava for aerobic syngas-based production of chemicals. Metab. Eng. 55, 220–230 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. Mukhopadhyay, A. Tolerance engineering in bacteria for the production of advanced biofuels and chemicals. Trends Microbiol. 23, 498–508 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Niu, F.-X., He, X., Wu, Y.-Q. & Liu, J.-Z. Enhancing production of pinene in Escherichia coli by using a combination of tolerance, evolution, and modular co-culture engineering. Front. Microbiol. 9, 1623 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Mendez-Perez, D. et al. Production of jet fuel precursor monoterpenoids from engineered Escherichia coli. Biotechnol. Bioeng. 114, 1703–1712 (2017).

    Article  CAS  PubMed  Google Scholar 

  77. Chong, H. et al. Enhancing E. coli isobutanol tolerance through engineering its global transcription factor cAMP receptor protein (CRP). Biotechnol. Bioeng. 111, 700–708 (2014).

    Article  CAS  PubMed  Google Scholar 

  78. Mukhopadhyay, A., Hillson, N. J. & Keasling, J. D. in Microbial Stress Tolerance for Biofuels (ed. Liu, Z. L.) 209–238 (Springer, 2012).

  79. Park, J. I. et al. A thermophilic ionic liquid-tolerant cellulase cocktail for the production of cellulosic biofuels. PLoS ONE 7, e37010 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Yu, C., Simmons, B. A., Singer, S. W., Thelen, M. P. & VanderGheynst, J. S. Ionic liquid-tolerant microorganisms and microbial communities for lignocellulose conversion to bioproducts. Appl. Microbiol. Biotechnol. 100, 10237–10249 (2016).

    Article  CAS  PubMed  Google Scholar 

  81. Thorwall, S., Schwartz, C., Chartron, J. W. & Wheeldon, I. Stress-tolerant non-conventional microbes enable next-generation chemical biosynthesis. Nat. Chem. Biol. 16, 113–121 (2020).

    Article  CAS  PubMed  Google Scholar 

  82. Sandoval, N. R. & Papoutsakis, E. T. Engineering membrane and cell-wall programs for tolerance to toxic chemicals: beyond solo genes. Curr. Opin. Microbiol. 33, 56–66 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Basler, G., Thompson, M., Tullman-Ercek, D. & Keasling, J. A Pseudomonas putida efflux pump acts on short-chain alcohols. Biotechnol. Biofuels 11, 136 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Chen, B., Ling, H. & Chang, M. W. Transporter engineering for improved tolerance against alkane biofuels in Saccharomyces cerevisiae. Biotechnol. Biofuels 6, 21 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Chen, Y. et al. Reverse engineering of fatty acid-tolerant Escherichia coli identifies design strategies for robust microbial cell factories. Metab. Eng. 61, 120–130 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Otoupal, P. B. & Chatterjee, A. CRISPR gene perturbations provide insights for improving bacterial biofuel tolerance. Front. Bioeng. Biotechnol. 6, 122 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Kurgan, G. et al. Bioprospecting of native efflux pumps to enhance furfural tolerance in ethanologenic Escherichia coli. Appl. Environ. Microbiol. 85, e02985-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Song, H.-S. et al. Increase in furfural tolerance by combinatorial overexpression of NAD salvage pathway enzymes in engineered isobutanol-producing E. coli. Bioresour. Technol. 245, 1430–1435 (2017).

    Article  CAS  PubMed  Google Scholar 

  89. Frederix, M. et al. Development of an E. coli strain for one-pot biofuel production from ionic liquid pretreated cellulose and switchgrass. Green Chem. 18, 4189–4197 (2016).

    Article  CAS  Google Scholar 

  90. Eng, T. et al. Restoration of biofuel production levels and increased tolerance under ionic liquid stress is enabled by a mutation in the essential Escherichia coli gene cydC. Microb. Cell Fact. 17, 159 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Ruegg, T. L. et al. Jungle Express is a versatile repressor system for tight transcriptional control. Nat. Commun. 9, 3617 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Wang, S. et al. NaCl enhances Escherichia coli growth and isoprenol production in the presence of imidazolium-based ionic liquids. Bioresour. Technol. Rep. 6, 1–5 (2019).

    Article  Google Scholar 

  93. Nikel, P. I. & de Lorenzo, V. Pseudomonas putida as a functional chassis for industrial biocatalysis: from native biochemistry to trans-metabolism. Metab. Eng. 50, 142–155 (2018).

    Article  CAS  PubMed  Google Scholar 

  94. Yang, S. et al. Zymomonas mobilis as a model system for production of biofuels and biochemicals. Microb. Biotechnol. 9, 699–717 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Stella, R. G., Wiechert, J., Noack, S. & Frunzke, J. Evolutionary engineering of Corynebacterium glutamicum. Biotechnol. J. 14, e1800444 (2019).

    Article  PubMed  CAS  Google Scholar 

  96. Castro, A. R., Rocha, I., Alves, M. M. & Pereira, M. A. Rhodococcus opacus B4: a promising bacterium for production of biofuels and biobased chemicals. AMB. Express 6, 35 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  97. Thompson, M. G. et al. Fatty acid and alcohol metabolism in Pseudomonas putida: functional analysis using random barcode transposon sequencing. Appl. Environ. Microbiol. 86, e01665-20 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Sandberg, T. E., Salazar, M. J., Weng, L. L., Palsson B. O. & Feist, A. M. The emergence of adaptive laboratory evolution as an efficient tool for biological discovery and industrial biotechnology. Metab. Eng. 56, 1–16 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Lim, H. G. et al. Generation of ionic liquid tolerant Pseudomonas putida KT2440 strains via adaptive laboratory evolution. Green Chem. 22, 5677–5690 (2020).

    Article  CAS  Google Scholar 

  100. Price, M. N. et al. Mutant phenotypes for thousands of bacterial genes of unknown function. Nature 557, 503–509 (2018). A study showing the power of the RB-TnSeq approach in elucidating gene function in a vast number of microorganisms, which is not only valuable for identifying gene targets for strain engineering but is also broadly useful for improving GSMMs.

    Article  CAS  PubMed  Google Scholar 

  101. Li, W.-J. et al. Unraveling 1,4-butanediol metabolism in Pseudomonas putida KT2440. Front. Microbiol. 11, 382 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  102. Phaneuf, P. V., Gosting, D., Palsson, B. O. & Feist, A. M. ALEdb 1.0: a database of mutations from adaptive laboratory evolution experimentation. Nucleic Acids Res. 47, D1164–D1171 (2019).

    Article  PubMed  Google Scholar 

  103. Zhang, F., Carothers, J. M. & Keasling, J. D. Design of a dynamic sensor-regulator system for production of chemicals and fuels derived from fatty acids. Nat. Biotechnol. 30, 354–359 (2012).

    Article  CAS  PubMed  Google Scholar 

  104. DeLoache, W. C., Russ, Z. N. & Dueber, J. E. Towards repurposing the yeast peroxisome for compartmentalizing heterologous metabolic pathways. Nat. Commun. 7, 11152 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Hu, T. et al. Engineering chimeric diterpene synthases and isoprenoid biosynthetic pathways enables high-level production of miltiradiene in yeast. Metab. Eng. 60, 87–96 (2020).

    Article  CAS  PubMed  Google Scholar 

  106. McCloskey, D. et al. Adaptation to the coupling of glycolysis to toxic methylglyoxal production in tpiA deletion strains of Escherichia coli requires synchronized and counterintuitive genetic changes. Metab. Eng. 48, 82–93 (2018).

    Article  CAS  PubMed  Google Scholar 

  107. Gubellini, F. et al. Physiological response to membrane protein overexpression in E. coli. Mol. Cell. Proteom. 10, M111.007930 (2011).

    Article  CAS  Google Scholar 

  108. Baumgarten, T., Ytterberg, A. J., Zubarev, R. A. & de Gier, J.-W. Optimizing recombinant protein production in the Escherichia coli periplasm alleviates stress. Appl. Environ. Microbiol. 84, e00270-18 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Boyarskiy, S., Davis López, S., Kong, N. & Tullman-Ercek, D. Transcriptional feedback regulation of efflux protein expression for increased tolerance to and production of n-butanol. Metab. Eng. 33, 130–137 (2016).

    Article  CAS  PubMed  Google Scholar 

  110. Henard, C. A., Freed, E. F. & Guarnieri, M. T. Phosphoketolase pathway engineering for carbon-efficient biocatalysis. Curr. Opin. Biotechnol. 36, 183–188 (2015).

    Article  CAS  PubMed  Google Scholar 

  111. Lin, P. P. et al. Construction and evolution of an Escherichia coli strain relying on nonoxidative glycolysis for sugar catabolism. Proc. Natl Acad. Sci. USA 115, 3538–3546 (2018). A very detailed and extensive demonstration of engineering non-oxidative glycolysis.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Bogorad, I. W., Lin, T.-S. & Liao, J. C. Synthetic non-oxidative glycolysis enables complete carbon conservation. Nature 502, 693–697 (2013).

    Article  CAS  PubMed  Google Scholar 

  113. Fleige, C., Kroll, J. & Steinbüchel, A. Establishment of an alternative phosphoketolase-dependent pathway for fructose catabolism in Ralstonia eutropha H16. Appl. Microbiol. Biotechnol. 91, 769–776 (2011).

    Article  CAS  PubMed  Google Scholar 

  114. Chubukov, V., Mukhopadhyay, A., Petzold, C. J., Keasling, J. D. & Martín, H. G. Synthetic and systems biology for microbial production of commodity chemicals. NPJ Syst. Biol. Appl. 2, 16009 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Tian, T., Kang, J. W., Kang, A. & Lee, T. S. Redirecting metabolic flux via combinatorial multiplex CRISPRi-mediated repression for isopentenol production in Escherichia coli. ACS Synth. Biol. 8, 391–402 (2019).

    Article  CAS  PubMed  Google Scholar 

  116. George, K. W. et al. Metabolic engineering for the high-yield production of isoprenoid-based C5 alcohols in E. coli. Sci. Rep. 5, 11128 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Strucko, T. et al. Laboratory evolution reveals regulatory and metabolic trade-offs of glycerol utilization in Saccharomyces cerevisiae. Metab. Eng. 47, 73–82 (2018).

    Article  CAS  PubMed  Google Scholar 

  118. Caspeta, L. et al. Biofuels. Altered sterol composition renders yeast thermotolerant. Science 346, 75–78 (2014).

    Article  CAS  PubMed  Google Scholar 

  119. Mohamed, E. T. et al. Generation of a platform strain for ionic liquid tolerance using adaptive laboratory evolution. Microb. Cell Fact. 16, 204 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  120. Lennen, R. M. et al. Adaptive laboratory evolution reveals general and specific chemical tolerance mechanisms and enhances biochemical production. Preprint at bioRxiv https://doi.org/10.1101/634105 (2019).

    Article  Google Scholar 

  121. Shepelin, D., Hansen, A. S. L., Lennen, R., Luo, H. & Herrgård, M. J. Selecting the best: evolutionary engineering of chemical production in microbes. Genes 9, 249 (2018). An excellent review on growth coupling.

    Article  PubMed Central  CAS  Google Scholar 

  122. Fong, S. S. et al. In silico design and adaptive evolution of Escherichia coli for production of lactic acid. Biotechnol. Bioeng. 91, 643–648 (2005).

    Article  CAS  PubMed  Google Scholar 

  123. Zhang, X., Jantama, K., Moore, J. C., Shanmugam, K. T. & Ingram, L. O. Production of L-alanine by metabolically engineered Escherichia coli. Appl. Microbiol. Biotechnol. 77, 355–366 (2007).

    Article  CAS  PubMed  Google Scholar 

  124. Shen, C. R. et al. Driving forces enable high-titer anaerobic 1-butanol synthesis in Escherichia coli. Appl. Environ. Microbiol. 77, 2905–2915 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Machado, H. B., Dekishima, Y., Luo, H., Lan, E. I. & Liao, J. C. A selection platform for carbon chain elongation using the CoA-dependent pathway to produce linear higher alcohols. Metab. Eng. 14, 504–511 (2012).

    Article  CAS  PubMed  Google Scholar 

  126. Reyes, L. H., Gomez, J. M. & Kao, K. C. Improving carotenoids production in yeast via adaptive laboratory evolution. Metab. Eng. 21, 26–33 (2014).

    Article  CAS  PubMed  Google Scholar 

  127. Tai, Y.-S. et al. Engineering nonphosphorylative metabolism to generate lignocellulose-derived products. Nat. Chem. Biol. 12, 247–253 (2016).

    Article  CAS  PubMed  Google Scholar 

  128. Hädicke, O. & Klamt, S. Computing complex metabolic intervention strategies using constrained minimal cut sets. Metab. Eng. 13, 204–213 (2011).

    Article  PubMed  CAS  Google Scholar 

  129. Harder, B.-J., Bettenbrock, K. & Klamt, S. Model-based metabolic engineering enables high yield itaconic acid production by Escherichia coli. Metab. Eng. 38, 29–37 (2016).

    Article  CAS  PubMed  Google Scholar 

  130. von Kamp, A. & Klamt, S. Growth-coupled overproduction is feasible for almost all metabolites in five major production organisms. Nat. Commun. 8, 15956 (2017).

    Article  CAS  Google Scholar 

  131. Banerjee, D. et al. Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale. Nat. Commun. 11, 5385 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. King, Z. A. et al. BiGG models: a platform for integrating, standardizing and sharing genome-scale models. Nucleic Acids Res. 44, D515–D522 (2016).

    Article  CAS  PubMed  Google Scholar 

  133. Landon, S., Rees-Garbutt, J., Marucci, L. & Grierson, C. Genome-driven cell engineering review: in vivo and in silico metabolic and genome engineering. Essays Biochem. 63, 267–284 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  134. Yim, H. et al. Metabolic engineering of Escherichia coli for direct production of 1,4-butanediol. Nat. Chem. Biol. 7, 445–452 (2011).

    Article  CAS  PubMed  Google Scholar 

  135. Ng, C. Y., Jung, M.-Y., Lee, J. & Oh, M.-K. Production of 2,3-butanediol in Saccharomyces cerevisiae by in silico aided metabolic engineering. Microb. Cell Fact. 11, 68 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Izallalen, M. et al. Geobacter sulfurreducens strain engineered for increased rates of respiration. Metab. Eng. 10, 267–275 (2008).

    Article  CAS  PubMed  Google Scholar 

  137. Fowler, Z. L., Gikandi, W. W. & Koffas, M. A. G. Increased malonyl coenzyme A biosynthesis by tuning the Escherichia coli metabolic network and its application to flavanone production. Appl. Environ. Microbiol. 75, 5831–5839 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Chemler, J. A., Fowler, Z. L., McHugh, K. P. & Koffas, M. A. G. Improving NADPH availability for natural product biosynthesis in Escherichia coli by metabolic engineering. Metab. Eng. 12, 96–104 (2010).

    Article  CAS  PubMed  Google Scholar 

  139. Asadollahi, M. A. et al. Enhancing sesquiterpene production in Saccharomyces cerevisiae through in silico driven metabolic engineering. Metab. Eng. 11, 328–334 (2009).

    Article  CAS  PubMed  Google Scholar 

  140. Brochado, A. R. et al. Improved vanillin production in baker’s yeast through in silico design. Microb. Cell Fact. 9, 84 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  141. Choi, H. S., Lee, S. Y., Kim, T. Y. & Woo, H. M. In silico identification of gene amplification targets for improvement of lycopene production. Appl. Environ. Microbiol. 76, 3097–3105 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  142. Becker, J., Zelder, O., Häfner, S., Schröder, H. & Wittmann, C. From zero to hero–design-based systems metabolic engineering of Corynebacterium glutamicum for L-lysine production. Metab. Eng. 13, 159–168 (2011).

    Article  CAS  PubMed  Google Scholar 

  143. Li, S., Huang, D., Li, Y., Wen, J. & Jia, X. Rational improvement of the engineered isobutanol-producing Bacillus subtilis by elementary mode analysis. Microb. Cell Fact. 11, 101 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Xu, P., Ranganathan, S., Fowler, Z. L., Maranas, C. D. & Koffas, M. A. G. Genome-scale metabolic network modeling results in minimal interventions that cooperatively force carbon flux towards malonyl-CoA. Metab. Eng. 13, 578–587 (2011).

    Article  CAS  PubMed  Google Scholar 

  145. Ranganathan, S. et al. An integrated computational and experimental study for overproducing fatty acids in Escherichia coli. Metab. Eng. 14, 687–704 (2012). An illustrative example of using GSMMs to guide bioengineering.

    Article  CAS  PubMed  Google Scholar 

  146. Otero, J. M. et al. Industrial systems biology of Saccharomyces cerevisiae enables novel succinic acid cell factory. PLoS ONE 8, e54144 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Ghosh, A. et al. 13C metabolic flux analysis for systematic metabolic engineering of S. cerevisiae for overproduction of fatty acids. Front. Bioeng. Biotechnol. 4, 76 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  148. d’Espaux, L. et al. Engineering high-level production of fatty alcohols by Saccharomyces cerevisiae from lignocellulosic feedstocks. Metab. Eng. 42, 115–125 (2017).

    Article  PubMed  CAS  Google Scholar 

  149. Lawson, C. E. et al. Machine learning for metabolic engineering: a review. Metab. Eng. 63, 34–60. A good introduction to machine learning for the metabolic engineer.

  150. Alonso-Gutierrez, J. et al. Principal component analysis of proteomics (PCAP) as a tool to direct metabolic engineering. Metab. Eng. 28, 123–133 (2015).

    Article  CAS  PubMed  Google Scholar 

  151. Ohtake, T. et al. Metabolomics-driven approach to solving a CoA imbalance for improved 1-butanol production in Escherichia coli. Metab. Eng. 41, 135–143 (2017).

    Article  CAS  PubMed  Google Scholar 

  152. Xu, P., Rizzoni, E. A., Sul, S.-Y. & Stephanopoulos, G. Improving metabolic pathway efficiency by statistical model-based multivariate regulatory metabolic engineering. ACS Synth. Biol. 6, 148–158 (2017).

    Article  CAS  PubMed  Google Scholar 

  153. Zhou, Y. et al. MiYA, an efficient machine-learning workflow in conjunction with the YeastFab assembly strategy for combinatorial optimization of heterologous metabolic pathways in Saccharomyces cerevisiae. Metab. Eng. 47, 294–302 (2018).

    Article  CAS  PubMed  Google Scholar 

  154. Costello, Z. & Martin, H. G. A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data. NPJ Syst. Biol. Appl. 4, 19 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  155. Opgenorth, P. et al. Lessons from two design-build-test-learn cycles of dodecanol production in Escherichia coli aided by machine learning. ACS Synth. Biol. 8, 1337–1351 (2019).

    Article  CAS  PubMed  Google Scholar 

  156. Jervis, A. J. et al. Machine learning of designed translational control allows predictive pathway optimization in Escherichia coli. ACS Synth. Biol. 8, 127–136 (2019). An excellent application of machine learning to transcriptional control.

    Article  CAS  PubMed  Google Scholar 

  157. HamediRad, M. et al. Towards a fully automated algorithm driven platform for biosystems design. Nat. Commun. 10, 5150 (2019). A fantastic example of the promise of combining machine learning, synthetic biology and automation.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  158. Zhang, J. et al. Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism. Nat. Commun. 11, 880 (2020).

    CAS  Google Scholar 

  159. Radivojević, T., Costello, Z., Workman, K. & Garcia Martin, H. A machine learning automated recommendation tool for synthetic biology. Nat. Commun. 11, 4879 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  160. Yadav, V. G., De Mey, M., Lim, C. G., Ajikumar, P. K. & Stephanopoulos, G. The future of metabolic engineering and synthetic biology: towards a systematic practice. Metab. Eng. 14, 233–241 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Carbonell, P., Radivojevic, T. & García Martín, H. Opportunities at the intersection of synthetic biology, machine learning, and automation. ACS Synth. Biol. 8, 1474–1477 (2019).

    Article  CAS  PubMed  Google Scholar 

  162. Dietrich, J. A., McKee, A. E. & Keasling, J. D. High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–590 (2010).

    Article  CAS  PubMed  Google Scholar 

  163. Crater, J. S. & Lievense, J. C. Scale-up of industrial microbial processes. FEMS Microbiol. Lett. 365, fny138 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  164. Davis, R. et al. Process design and economics for the conversion of lignocellulosic biomass to hydrocarbons: dilute-acid and enzymatic deconstruction of biomass to sugars and biological conversion of sugars to hydrocarbons (National Renewable Energy Laboratory, 2013).

  165. Cruz Bournazou, M. N. et al. Online optimal experimental re-design in robotic parallel fed-batch cultivation facilities. Biotechnol. Bioeng. 114, 610–619 (2017).

    Article  CAS  PubMed  Google Scholar 

  166. Tai, M., Ly, A., Leung, I. & Nayar, G. Efficient high-throughput biological process characterization: definitive screening design with the Ambr250 bioreactor system. Biotechnol. Prog. 31, 1388–1395 (2015).

    Article  CAS  PubMed  Google Scholar 

  167. Wong, B. G., Mancuso, C. P., Kiriakov, S., Bashor, C. J. & Khalil, A. S. Precise, automated control of conditions for high-throughput growth of yeast and bacteria with eVOLVER. Nat. Biotechnol. 36, 614–623 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Haringa, C. et al. Computational fluid dynamics simulation of an industrial P. chrysogenum fermentation with a coupled 9-pool metabolic model: towards rational scale-down and design optimization. Chem. Eng. Sci. 175, 12–24 (2017).

    Article  CAS  Google Scholar 

  169. Rugbjerg, P. & Sommer, M. O. A. Overcoming genetic heterogeneity in industrial fermentations. Nat. Biotechnol. 37, 869–876 (2019).

    Article  CAS  PubMed  Google Scholar 

  170. Wehrs, M. et al. Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation. Microb. Cell Fact. 19, 167 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  171. Wang, O. & Coates, J. D. Biotechnological applications of microbial (per)chlorate reduction. Microorganisms 5, 76 (2017).

    Article  CAS  PubMed Central  Google Scholar 

  172. Shaw, A. J. et al. Metabolic engineering of microbial competitive advantage for industrial fermentation processes. Science 353, 583–586 (2016).

    Article  CAS  PubMed  Google Scholar 

  173. Dahl, R. H. et al. Engineering dynamic pathway regulation using stress-response promoters. Nat. Biotechnol. 31, 1039–1046 (2013).

    Article  CAS  PubMed  Google Scholar 

  174. Dafoe, J. T. & Daugulis, A. J. In situ product removal in fermentation systems: improved process performance and rational extractant selection. Biotechnol. Lett. 36, 443–460 (2014).

    Article  CAS  PubMed  Google Scholar 

  175. Xue, C. et al. Integrated butanol recovery for an advanced biofuel: current state and prospects. Appl. Microbiol. Biotechnol. 98, 3463–3474 (2014).

    Article  CAS  PubMed  Google Scholar 

  176. Gaspar, D. Top ten blendstocks for turbocharged gasoline engines: bioblendstocks with potential to deliver the for highest engine efficiency (Pacific Northwest National Laboratory, 2019). A systematic analysis and down-election of petrol bioblendstock candidates based on fuel properties and engine performance.

  177. Monroe, E. et al. Discovery of novel octane hyperboosting phenomenon in prenol biofuel/gasoline blends. Fuel 239, 1143–1148 (2019).

    Article  CAS  Google Scholar 

  178. Ignea, C. et al. Synthesis of 11-carbon terpenoids in yeast using protein and metabolic engineering. Nat. Chem. Biol. 14, 1090–1098 (2018).

    Article  CAS  PubMed  Google Scholar 

  179. Huccetogullari, D., Luo, Z. W. & Lee, S. Y. Metabolic engineering of microorganisms for production of aromatic compounds. Microb. Cell Fact. 18, 41 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  180. Das, D. D., St. John, P. C., McEnally, C. S., Kim, S. & Pfefferle, L. D. Measuring and predicting sooting tendencies of oxygenates, alkanes, alkenes, cycloalkanes, and aromatics on a unified scale. Combust. Flame 190, 349–364 (2018).

    Article  CAS  Google Scholar 

  181. Huo, X. et al. Tailoring diesel bioblendstock from integrated catalytic upgrading of carboxylic acids: a “fuel property first” approach. Green Chem. 21, 5813–5827 (2019).

    Article  CAS  Google Scholar 

  182. Yang, M. et al. Accumulation of high-value bioproducts in planta can improve the economics of advanced biofuels. Proc. Natl Acad. Sci. USA 117, 8639–8648 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  183. Lin, C.-Y. & Eudes, A. Strategies for the production of biochemicals in bioenergy crops. Biotechnol. Biofuels 13, 71 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  184. Blombach, B. et al. Corynebacterium glutamicum tailored for efficient isobutanol production. Appl. Environ. Microbiol. 77, 3300–3310 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Higashide, W., Li, Y., Yang, Y. & Liao, J. C. Metabolic engineering of Clostridium cellulolyticum for production of isobutanol from cellulose. Appl. Environ. Microbiol. 77, 2727–2733 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  186. Sawyer, R. F. Trends in auto emissions and gasoline composition. Environ. Health Perspect. 101 (Suppl. 6), 5–12 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Ghosh, P., Hickey, K. J. & Jaffe, S. B. Development of a detailed gasoline composition-based octane model. Ind. Eng. Chem. Res. 45, 337–345 (2006).

    Article  CAS  Google Scholar 

  188. Ghosh, P. & Jaffe, S. B. Detailed composition-based model for predicting the cetane number of diesel fuels. Ind. Eng. Chem. Res. 45, 346–351 (2006).

    Article  CAS  Google Scholar 

  189. ASTM International. ASTM D1655 — 20e1: standard specification for aviation turbine fuels (ASTM, 2020).

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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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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 19, 701–715 (2021). https://doi.org/10.1038/s41579-021-00577-w

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