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image of Green Energy Revolution: Production of Environmentally Friendly Sustainable Biofuels using Yeasts with the Help of Artificial Intelligence

Abstract

Artificial Intelligence (AI) has made significant advancements in recent years in the development and genetic editing of living organisms, especially yeasts, which play a key role in producing biofuels. This article examines how AI contributes to accelerating the growth of yeast strains for biofuel production and progress toward sustainable development. In this review, extensive searches were conducted using keywords such as artificial intelligence, yeast, biofuel, and fermentation to find articles relevant to the research objective. The results revealed that using AI-modified yeasts to create alcohol allows for higher yield production, heavy metal absorption and conversion, more efficient use of bioplastics, and lactic acid synthesis. This turns them into a reliable and environmentally friendly alternative to fossil fuels. Thus, Artificial Intelligence plays a significant role in advancing yeasts for biofuel production. These advancements lead to the development of yeast strains with higher biofuel production yields and a reduction in biological pollution.

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2025-02-04
2025-09-30

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References

  1. López-Sandin I. Zavala-García F. Levin L. Ruiz H.A. Hernández-Luna C.E. Gutiérrez-Soto G. Evaluation of bioethanol production from sweet sorghum variety roger under different tillage and fertilizer treatments. BioEnergy Res. 2021 14 4 1058 1069 10.1007/s12155‑020‑10215‑7
    [Google Scholar]
  2. Khaire K.C. Moholkar V.S. Goyal A. Bioconversion of sugarcane tops to bioethanol and other value added products: An overview. Mater. Sci. Energy Technol. 2021 4 54 68 10.1016/j.mset.2020.12.004
    [Google Scholar]
  3. Bessou C. Ferchaud F. Gabrielle B. Mary B. Biofuels, greenhouse gases and climate change. Sustain. Agric 2011 2 365 468
    [Google Scholar]
  4. Khanna M. Chen X. Economic, energy security, and greenhouse gas effects of biofuels: Implications for policy. Am. J. Agric. Econ. 2013 95 5 1325 1331 10.1093/ajae/aat037
    [Google Scholar]
  5. Malla F.A. Bandh S.A. Wani S.A. Hoang A.T. Sofi N.A. Biofuels: Potential alternatives to fossil fuels. Biofuels in circular economy. Springer 2023 1 15
    [Google Scholar]
  6. Liu Z. Moradi H. Shi S. Darvishi F. Yeasts as microbial cell factories for sustainable production of biofuels. Renew. Sustain. Energy Rev. 2021 143 110907 10.1016/j.rser.2021.110907
    [Google Scholar]
  7. Ko J.K. Lee J.H. Jung J.H. Lee S.M. Recent advances and future directions in plant and yeast engineering to improve lignocellulosic biofuel production. Renew. Sustain. Energy Rev. 2020 134 110390 10.1016/j.rser.2020.110390
    [Google Scholar]
  8. Ruchala J. Sibirny A.A. Pentose metabolism and conversion to biofuels and high-value chemicals in yeasts. FEMS Microbiol. Rev. 2021 45 4 fuaa069 10.1093/femsre/fuaa069 33316044
    [Google Scholar]
  9. Chatterjee S. Mohan S.V. Yeast fermentation towards biodiesel: Maximizing resource recovery by integrating with biohydrogen production in biorefinery framework. Biomass. Bioenerg 2020 142 105747 10.1016/j.biombioe.2020.105747
    [Google Scholar]
  10. Ruan R. Zhang Y. Chen P. Liu S. Fan L. Zhou N. Biofuels: Introduction. Biofuels: Alternative feedstocks and conversion processes for the production of liquid and gaseous biofuels. Elsevier 2019 3 43 10.1016/B978‑0‑12‑816856‑1.00001‑4
    [Google Scholar]
  11. Meena M. Shubham S. Paritosh K. Pareek N. Vivekanand V. Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling. Bioresour. Technol. 2021 340 125642 10.1016/j.biortech.2021.125642 34315128
    [Google Scholar]
  12. Mohanty S.K. Swain M.R. Bioethanol production from corn and wheat: Food, fuel, and future. Bioethanol production from food crops. Elsevier 2019 45 59 10.1016/B978‑0‑12‑813766‑6.00003‑5
    [Google Scholar]
  13. Pinzi S. Leiva D. López-García I. Redel-Macías M.D. Dorado M.P. Latest trends in feedstocks for biodiesel production. Biofuels Bioprod. Biorefin. 2014 8 1 126 143 10.1002/bbb.1435
    [Google Scholar]
  14. Huynh L-H. Kasim N.S. Ju Y-H. Biodiesel production from waste oils. Biofuel Elsevier 2011 375 396
    [Google Scholar]
  15. Chen Y. Nie X. Ye J. Wang Y. Chen J. Xu J. Biodiesel from microorganisms: A review. Energy Technol. (Weinheim) 2021 9 10 2001053 10.1002/ente.202001053
    [Google Scholar]
  16. Jacobus A.P. Gross J. Evans J.H. Ceccato-Antonini S.R. Gombert A.K. Saccharomyces cerevisiae strains used industrially for bioethanol production. Essays Biochem. 2021 65 2 147 161 10.1042/EBC20200160 34156078
    [Google Scholar]
  17. Vamvakas S.S. Kapolos J. Factors affecting yeast ethanol tolerance and fermentation efficiency. World J. Microbiol. Biotechnol. 2020 36 8 114 10.1007/s11274‑020‑02881‑8 32656576
    [Google Scholar]
  18. Wan Z. Hu H. Liu K. Qiao Y. Guo F. Wang C. Xin F. Zhang W. Jiang M. Engineering industrial yeast for improved tolerance and robustness. Crit. Rev. Biotechnol. 2024 1 17 10.1080/07388551.2024.2326677 38503543
    [Google Scholar]
  19. da Silva Fernandes F de Souza ÉS Carneiro LM Alves Silva JP de Souza JVB da Silva Batista J Current ethanol production requirements for the yeast Saccharomyces cerevisiae. Int. J. Microbiol 2022 222 7878830 10.1155/2022/7878830
    [Google Scholar]
  20. Wang W.Y. Wang B.P. Su H.S. Wei M.M. Wei Y.T. Niu F.X. Key role of K+ and Ca2+ in high-yield ethanol production by S. Cerevisiae from concentrated sugarcane molasses. Microb. Cell Fact. 2024 23 1 123 10.1186/s12934‑024‑02401‑5 38724968
    [Google Scholar]
  21. Nandy S.K. Srivastava R.K. A review on sustainable yeast biotechnological processes and applications. Microbiol. Res. 2018 207 83 90 10.1016/j.micres.2017.11.013 29458873
    [Google Scholar]
  22. Sharma J. Kumar V. Prasad R. Gaur N.A. Engineering of Saccharomyces cerevisiae as a consolidated bioprocessing host to produce cellulosic ethanol: Recent advancements and current challenges. Biotechnol. Adv. 2022 56 107925 10.1016/j.biotechadv.2022.107925 35151789
    [Google Scholar]
  23. Alperstein L. Gardner J.M. Sundstrom J.F. Sumby K.M. Jiranek V. Yeast bioprospecting versus synthetic biology—which is better for innovative beverage fermentation? Appl. Microbiol. Biotechnol 2020 104 5 1939 1953 10.1007/s00253‑020‑10364‑x 31953561
    [Google Scholar]
  24. Michou S. Tsouko E. Vastaroucha E.S. Diamantopoulou P. Papanikolaou S. Growth potential of selected yeast strains cultivated on xylose-based media mimicking lignocellulosic wastewater streams: High production of microbial lipids by Rhodosporidium Toruloides. Fermentation 2022 8 12 713 10.3390/fermentation8120713
    [Google Scholar]
  25. Palladino F. Rodrigues R.C.L.B. Cadete R.M. Barros K.O. Rosa C.A. Novel potential yeast strains for the biotechnological production of xylitol from sugarcane bagasse. Biofuels Bioprod. Biorefin. 2021 15 3 690 702 10.1002/bbb.2196
    [Google Scholar]
  26. Kwak S. Jo J.H. Yun E.J. Jin Y.S. Seo J.H. Production of biofuels and chemicals from xylose using native and engineered yeast strains. Biotechnol. Adv. 2019 37 2 271 283 10.1016/j.biotechadv.2018.12.003 30553928
    [Google Scholar]
  27. Naveed M.H. Khan M.N.A. Mukarram M. Naqvi S.R. Abdullah A. Haq Z.U. Ullah H. Mohamadi H.A. Cellulosic biomass fermentation for biofuel production: Review of artificial intelligence approaches. Renew. Sustain. Energy Rev. 2024 189 113906 10.1016/j.rser.2023.113906
    [Google Scholar]
  28. Pereira R.D. Badino A.C. Cruz A.J.G. Framework based on artificial intelligence to increase industrial bioethanol production. Energy Fuels 2020 34 4 4670 4677 10.1021/acs.energyfuels.0c00033
    [Google Scholar]
  29. Okolie J.A. Introduction of machine learning and artificial intelligence in biofuel technology. Curr. Opin. Green Sustain. Chem. 2024 47 100928 10.1016/j.cogsc.2024.100928
    [Google Scholar]
  30. Ahmad J. Awais M. Rashid U. Ngamcharussrivichai C. Raza Naqvi S. Ali I. A systematic and critical review on effective utilization of artificial intelligence for bio-diesel production techniques. Fuel 2023 338 127379 10.1016/j.fuel.2022.127379
    [Google Scholar]
  31. Bhardwaj A. Kishore S. Pandey D.K. Artificial intelligence in biological sciences. Life 2022 12 9 1430 10.3390/life12091430 36143468
    [Google Scholar]
  32. Tullio V. Yeast genomics and its applications in biotechnological processes: What is our present and near future? J. Fungi 2022 8 7 752 10.3390/jof8070752 35887507
    [Google Scholar]
  33. Itto-Nakama K. Watanabe S. Kondo N. Ohnuki S. Kikuchi R. Nakamura T. Ogasawara W. Kasahara K. Ohya Y. AI-based forecasting of ethanol fermentation using yeast morphological data. Biosci. Biotechnol. Biochem. 2021 86 1 125 134 10.1093/bbb/zbab188 34751736
    [Google Scholar]
  34. Tse T.J. Wiens D.J. Reaney M.J.T. Production of bioethanol—A review of factors affecting ethanol yield. Fermentation 2021 7 4 268 10.3390/fermentation7040268
    [Google Scholar]
  35. Krajang M. Malairuang K. Sukna J. Rattanapradit K. Chamsart S. Single-step ethanol production from raw cassava starch using a combination of raw starch hydrolysis and fermentation, scale-up from 5-L laboratory and 200-L pilot plant to 3000-L industrial fermenters. Biotechnol. Biofuels 2021 14 1 68 10.1186/s13068‑021‑01903‑3 33726825
    [Google Scholar]
  36. Owusu WA Marfo SA Artificial intelligence application in bioethanol production. Int. J. Energy Res 2023 2023 7844835 10.1155/2023/7844835
    [Google Scholar]
  37. Merdan O. Şişman A.S. Aksoy S.A. Kızıl S. Tüzemen N.Ü. Yılmaz E. Ener B. Investigation of the defective growth pattern and multidrug resistance in a clinical isolate of Candida glabrata using whole-genome sequencing and computational biology applications. Microbiol. Spectr. 2022 10 4 e00776-22 10.1128/spectrum.00776‑22 35867406
    [Google Scholar]
  38. D’Agaro E. Artificial intelligence used in genome analysis studies. EuroBiotech J. 2018 2 2 78 88 10.2478/ebtj‑2018‑0012
    [Google Scholar]
  39. van Aalst A.C.A. van der Meulen I.S. Jansen M.L.A. Mans R. Pronk J.T. Co-cultivation of Saccharomyces cerevisiae strains combines advantages of different metabolic engineering strategies for improved ethanol yield. Metab. Eng. 2023 80 151 162 10.1016/j.ymben.2023.09.010 37751790
    [Google Scholar]
  40. Adebami G.E. Kuila A. Ajunwa O.M. Fasiku S.A. Asemoloye M.D. Genetics and metabolic engineering of yeast strains for efficient ethanol production. J. Food Process Eng. 2022 45 7 e13798 10.1111/jfpe.13798
    [Google Scholar]
  41. Yuan B. Wang W.B. Wang Y.T. Zhao X.Q. Regulatory mechanisms underlying yeast chemical stress response and development of robust strains for bioproduction. Curr. Opin. Biotechnol. 2024 86 103072 10.1016/j.copbio.2024.103072 38330874
    [Google Scholar]
  42. Qiu X. Zhang J. Zhou J. Fang Z. Zhu Z. Li J. Du G. Stress tolerance phenotype of industrial yeast: Industrial cases, cellular changes, and improvement strategies. Appl. Microbiol. Biotechnol. 2019 103 16 6449 6462 10.1007/s00253‑019‑09993‑8 31256230
    [Google Scholar]
  43. Shen D. He X. Weng P. Liu Y. Wu Z. A review of yeast: High cell-density culture, molecular mechanisms of stress response and tolerance during fermentation. FEMS Yeast Res. 2022 22 1 foac050 10.1093/femsyr/foac050 36288242
    [Google Scholar]
  44. Patra P. B R D. Kundu P. Das M. Ghosh A. Recent advances in machine learning applications in metabolic engineering. Biotechnol. Adv. 2023 62 108069 10.1016/j.biotechadv.2022.108069 36442697
    [Google Scholar]
  45. de Jongh R.P.H. van Dijk A.D.J. Julsing M.K. Schaap P.J. de Ridder D. Designing eukaryotic gene expression regulation using machine learning. Trends Biotechnol. 2020 38 2 191 201 10.1016/j.tibtech.2019.07.007 31431299
    [Google Scholar]
  46. Helmy M. Smith D. Selvarajoo K. Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering. Metab. Eng. Commun. 2020 11 e00149 10.1016/j.mec.2020.e00149 33072513
    [Google Scholar]
  47. Fiamenghi M.B. Bueno J.G.R. Camargo A.P. Borelli G. Carazzolle M.F. Pereira G.A.G. dos Santos L.V. José J. Machine learning and comparative genomics approaches for the discovery of xylose transporters in yeast. Biotechnol. Biofuels Bioprod. 2022 15 1 57 10.1186/s13068‑022‑02153‑7 35596177
    [Google Scholar]
  48. Grinberg N.F. Orhobor O.I. King R.D. An evaluation of machine-learning for predicting phenotype: Studies in yeast, rice, and wheat. Mach. Learn. 2020 109 2 251 277 10.1007/s10994‑019‑05848‑5 32174648
    [Google Scholar]
  49. den Haan R. Kroukamp H. Mert M. Bloom M. Görgens J.F. van Zyl W.H. Engineering Saccharomyces cerevisiae for next generation ethanol production. J. Chem. Technol. Biotechnol. 2013 88 6 983 991 10.1002/jctb.4068
    [Google Scholar]
  50. Laluce C. Schenberg A.C.G. Gallardo J.C.M. Coradello L.F.C. Pombeiro-Sponchiado S.R. Advances and developments in strategies to improve strains of Saccharomyces cerevisiae and processes to obtain the lignocellulosic ethanol--a review. Appl. Biochem. Biotechnol. 2012 166 8 1908 1926 10.1007/s12010‑012‑9619‑6 22391693
    [Google Scholar]
  51. Ceccato-Antonini S.R. Microbiology of Ethanol Fermentation in Sugarcane Biofuels: Fundamentals. Springer 2022 10.1007/978‑3‑031‑12292‑7
    [Google Scholar]
  52. Franco-Duarte R. Umek L. Zupan B. Schuller D. Computational approaches for the genetic and phenotypic characterization of a Saccharomyces cerevisiae wine yeast collection. Yeast 2009 26 12 675 692 10.1002/yea.1728 19894212
    [Google Scholar]
  53. Khamwachirapithak P. Sae-Tang K. Mhuantong W. Tanapongpipat S. Zhao X.Q. Liu C.G. Wei D.Q. Champreda V. Runguphan W. Optimizing ethanol production in Saccharomyces cerevisiae at ambient and elevated temperatures through machine learning-guided combinatorial promoter modifications. ACS Synth. Biol. 2023 12 10 2897 2908 10.1021/acssynbio.3c00199 37681736
    [Google Scholar]
  54. Barbosa C. Ramalhosa E. Vasconcelos I. Reis M. Mendes-Ferreira A. Machine learning techniques disclose the combined effect of fermentation conditions on yeast mixed-culture dynamics and wine quality. Microorganisms 2022 10 1 107 10.3390/microorganisms10010107 35056556
    [Google Scholar]
  55. Kim G.B. Kim W.J. Kim H.U. Lee S.Y. Machine learning applications in systems metabolic engineering. Curr. Opin. Biotechnol. 2020 64 1 9 10.1016/j.copbio.2019.08.010 31580992
    [Google Scholar]
  56. Huang J. Li C.D. Zhao H. Yu M. Zhang A. Fang B. Artificial intelligence system for enhanced automated 1,3-propanediol green biosynthesis. Green Chem. 2023 25 22 9175 9186 10.1039/D3GC01586F
    [Google Scholar]
  57. Sewsynker-Sukai Y. Faloye F. Kana E.B.G. Artificial neural networks: An efficient tool for modelling and optimization of biofuel production (a mini review). Biotechnol. Biotechnol. Equip. 2017 31 2 221 235 10.1080/13102818.2016.1269616
    [Google Scholar]
  58. Xu Z. Theodoropoulos C. Pittman J.K. Optimization of a Chlorella–Saccharomyces co–culture system for enhanced metabolite productivity. Algal Res. 2024 79 103455 10.1016/j.algal.2024.103455
    [Google Scholar]
  59. Eswari J.S. Suryawanshi N. Optimization of Sustainable Enzymes Production: Artificial Intelligence and Machine Learning Techniques. CRC Press 2022 10.1201/9781003292333
    [Google Scholar]
  60. Dixit M. Chhabra D. Shukla P. Optimization of endoglucanase-lipase-amylase enzyme consortium from Thermomyces lanuginosus VAPS25 using Multi-Objective genetic algorithm and their bio-deinking applications. Bioresour. Technol. 2023 370 128467 10.1016/j.biortech.2022.128467 36509307
    [Google Scholar]
  61. Khanal S.K. Tarafdar A. You S. Artificial intelligence and machine learning for smart bioprocesses. Elsevier 2023 128826
    [Google Scholar]
  62. Wang K. Chen J. Martiniuk J. Ma X. Li Q. Measday V. Lu X. Species identification and strain discrimination of fermentation yeasts Saccharomyces cerevisiae and Saccharomyces uvarum using Raman spectroscopy and convolutional neural networks. Appl. Environ. Microbiol. 2023 89 12 e01673-23 10.1128/aem.01673‑23 38038459
    [Google Scholar]
  63. Sharmila V.G. Shanmugavel S.P. Banu J.R. A review on emerging technologies and machine learning approaches for sustainable production of biofuel from biomass waste. Biomass Bioenergy 2024 180 106997 10.1016/j.biombioe.2023.106997
    [Google Scholar]
  64. Khasim S. Ghosh H. Rahat I.S. Shaik K. Yesubabu M. Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements. EAI Endorsed Transactions 2024 10
    [Google Scholar]
  65. Saha R. Chauhan A. Rastogi Verma S. Machine learning: An advancement in biochemical engineering. Biotechnol. Lett. 2024 46 4 497 519 10.1007/s10529‑024‑03499‑8 38902585
    [Google Scholar]
  66. Scholes A.N. Stuecker T.N. Hood S.E. Locke C.J. Stacy C.L. Zhang Q. Lewis J.A. Natural variation in yeast reveals multiple paths for acquiring higher stress resistance. BMC Biol. 2024 22 1 149 10.1186/s12915‑024‑01945‑7 38965504
    [Google Scholar]
  67. Riles L. Fay J.C. Genetic basis of variation in heat and ethanol tolerance in Saccharomyces cerevisiae. G3: Genes, genomes. Genetics 2019 9 1 179 188 31754017
    [Google Scholar]
  68. Sreeharsha R.V. Venkata Mohan S. Genome mining and metabolic engineering of photosynthetic microbes for value addition. Microbial Photosynthesis Springer 2024 139 154
    [Google Scholar]
  69. Zafar I. Rafique A. Fazal J. Manzoor M. Ain Q.U. Rayan R.A. Genome and Gene editing by Artificial Intelligence programs. Advanced AI Techniques and Applications in Bioinformatics. CRC Press 2021 165 188
    [Google Scholar]
  70. Dixit S. Kumar A. Srinivasan K. Vincent P.M.D.R. Ramu Krishnan N. Advancing genome editing with artificial intelligence: Opportunities, challenges, and future directions. Front. Bioeng. Biotechnol. 2024 11 1335901 10.3389/fbioe.2023.1335901 38260726
    [Google Scholar]
  71. Güell Cargol M. New synthetic biological functions and their implications for the present and future of society. 2021 Available from :https://publicacions.iec.cat/repository/pdf/00000308/00000025.pdf
  72. Mohan M. Manohar M. Mothi R. Rahamathullah N. Ganesh P. Dhanalakshmi M. System-enabled microbial cell factories for the production of biomolecules. Whole-Cell Biocatalysis. Apple Academic Press 2024 173 198
    [Google Scholar]
  73. Cai G. Lin Z. Shi S. Development and expansion of the CRISPR/Cas9 toolboxes for powerful genome engineering in yeast. Enzyme Microb. Technol. 2022 159 110056 10.1016/j.enzmictec.2022.110056 35561628
    [Google Scholar]
  74. Wang L. Deng A. Zhang Y. Liu S. Liang Y. Bai H. Cui D. Qiu Q. Shang X. Yang Z. He X. Wen T. Efficient CRISPR–Cas9 mediated multiplex genome editing in yeasts. Biotechnol. Biofuels 2018 11 1 277 10.1186/s13068‑018‑1271‑0 30337956
    [Google Scholar]
  75. Jakopović Ž. Valinger D. Hanousek Čiča K. Mrvčić J. Domijan A.M. Čanak I. Kostelac D. Frece J. Markov K. A Predictive assessment of ochratoxin A’s effects on oxidative stress parameters and the fermentation ability of yeasts using neural networks. Foods 2024 13 3 408 10.3390/foods13030408 38338543
    [Google Scholar]
  76. Damian C.S. Devarajan Y. Thandavamoorthy R. Jayabal R. Harnessing artificial intelligence for enhanced bioethanol productions: A cutting-edge approach towards sustainable energy solution. Int. J. Chem. React. Eng. 2024 22 7 719 727 10.1515/ijcre‑2024‑0074
    [Google Scholar]
  77. Li H. Chen J. Li X. Gan J. Liu H. Jian Z. Xu S. Zhang A. Li G. Chen K. Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation. Bioresour. Technol. 2024 393 130151 10.1016/j.biortech.2023.130151 38049019
    [Google Scholar]
  78. Sonu Rani G.M. Pathania D. Abhimanyu Umapathi R. Rustagi S. Huh Y.S. Gupta V.K. Kaushik A. Chaudhary V. Agro-waste to sustainable energy: A green strategy of converting agricultural waste to nano-enabled energy applications. Sci. Total Environ. 2023 875 162667 10.1016/j.scitotenv.2023.162667 36894105
    [Google Scholar]
  79. Awad D. Younes S. Glemser M. Wagner F.M. Schenk G. Mehlmer N. Towards high-throughput optimization of microbial lipid production: From strain development to process monitoring. Sustain. Energy Fuels 2020 4 12 5958 5969 10.1039/D0SE00540A
    [Google Scholar]
  80. Goswami L. Kayalvizhi R. Dikshit P.K. Sherpa K.C. Roy S. Kushwaha A. Kim B.S. Banerjee R. Jacob S. Rajak R.C. A critical review on prospects of bio-refinery products from second and third generation biomasses. Chem. Eng. J. 2022 448 137677 10.1016/j.cej.2022.137677
    [Google Scholar]
  81. Sasikumar K. Sundar L. Nampoothiri K.M. Microbial production of sugar alcohols. Handbook of Biorefinery Research and Technology: Production of Biofuels and Biochemicals. Springer 2024 449 472 10.1007/978‑981‑97‑7586‑6_20
    [Google Scholar]
  82. Rodrigues AJ Physiological features of Saccharomyces cerevisiae and alternative wine yeast species in relation to alcohol level reduction in wine. 2019 Available from: file:///C:/Users/Khan%20Muhammad/Downloads/Dialnet-PhysiologicalFeaturesOfSaccharomycesCerevisiaeAndA-221181.pdf
  83. Ting T.Y. Li Y. Bunawan H. Ramzi A.B. Goh H.H. Current advancements in systems and synthetic biology studies of Saccharomyces cerevisiae. J. Biosci. Bioeng. 2023 135 4 259 265 10.1016/j.jbiosc.2023.01.010 36803862
    [Google Scholar]
  84. Wei Y. Ji B. Ledesma-Amaro R. Chen T. Ji X.J. Engineering yeast to produce plant natural products. Front. Bioeng. Biotechnol. 2021 9 798097 10.3389/fbioe.2021.798097 34926435
    [Google Scholar]
  85. Wegat V. Fabarius J.T. Sieber V. Synthetic methylotrophic yeasts for the sustainable fuel and chemical production. Biotechnol. Biofuels Bioprod. 2022 15 1 113 10.1186/s13068‑022‑02210‑1 36273178
    [Google Scholar]
  86. Rahmat E. Kang Y. Yeast metabolic engineering for the production of pharmaceutically important secondary metabolites. Appl. Microbiol. Biotechnol. 2020 104 11 4659 4674 10.1007/s00253‑020‑10587‑y 32270249
    [Google Scholar]
  87. Prabhu A.A. Thomas D.J. Ledesma-Amaro R. Leeke G.A. Medina A. Verheecke-Vaessen C. Coulon F. Agrawal D. Kumar V. Biovalorisation of crude glycerol and xylose into xylitol by oleaginous yeast Yarrowia lipolytica. Microb. Cell Fact. 2020 19 1 121 10.1186/s12934‑020‑01378‑1 32493445
    [Google Scholar]
  88. Naseri G. A roadmap to establish a comprehensive platform for sustainable manufacturing of natural products in yeast. Nat. Commun. 2023 14 1 1916 10.1038/s41467‑023‑37627‑1 37024483
    [Google Scholar]
  89. Thomas DJ Bioproduction of xylitol by oleaginous yeast yarrowia lipolytica. Microb. Cell. Fact 2020 121
    [Google Scholar]
  90. Beopoulos A. Cescut J. Haddouche R. Uribelarrea J.L. Molina-Jouve C. Nicaud J.M. Yarrowia lipolytica as a model for bio-oil production. Prog. Lipid Res. 2009 48 6 375 387 10.1016/j.plipres.2009.08.005 19720081
    [Google Scholar]
  91. Lacerda M.P. Oh E.J. Eckert C. The model system Saccharomyces cerevisiae versus emerging non-model yeasts for the production of biofuels. Life 2020 10 11 299 10.3390/life10110299 33233378
    [Google Scholar]
  92. Ha-Tran D.M. Nguyen T.T.M. Huang C.C. Kluyveromyces marxianus: Current state of omics studies, strain improvement strategy and potential industrial implementation. Fermentation 2020 6 4 124 10.3390/fermentation6040124
    [Google Scholar]
  93. Hosseini S.N. Javidanbardan A. Khatami M. Accurate and cost‐effective prediction of HBsAg titer in industrial scale fermentation process of recombinant Pichia pastoris by using neural network based soft sensor. Biotechnol. Appl. Biochem. 2019 66 4 681 689 10.1002/bab.1785 31169323
    [Google Scholar]
  94. Bastos M.L. Benevides C.A. Zanchettin C. Menezes F.D. Inácio C.P. de Lima Neto R.G. Filho J.G.A.T. Neves R.P. Almeida L.M. Breaking barriers in Candida spp. detection with electronic noses and artificial intelligence. Sci. Rep. 2024 14 1 956 10.1038/s41598‑023‑50332‑9 38200060
    [Google Scholar]
  95. Dil E.A. Ghaedi M. Ghezelbash G.R. Asfaram A. Ghaedi A.M. Mehrabi F. Modeling and optimization of Hg 2+ ion biosorption by live yeast Yarrowia lipolytica 70562 from aqueous solutions under artificial neural network-genetic algorithm and response surface methodology: kinetic and equilibrium study. RSC Advances 2016 6 59 54149 54161 10.1039/C6RA11292G
    [Google Scholar]
  96. Ahmad M.F. Haydar S. Bhatti A.A. Bari A.J. Application of artificial neural network for the prediction of biosorption capacity of immobilized Bacillus subtilis for the removal of cadmium ions from aqueous solution. Biochem. Eng. J. 2014 84 83 90 10.1016/j.bej.2014.01.004
    [Google Scholar]
  97. Shelare S.D. Belkhode P.N. Nikam K.C. Jathar L.D. Shahapurkar K. Soudagar M.E.M. Veza I. Khan T.M.Y. Kalam M.A. Nizami A-S. Rehan M. Biofuels for a sustainable future: Examining the role of nano-additives, economics, policy, internet of things, artificial intelligence and machine learning technology in biodiesel production. Energy 2023 282 128874 10.1016/j.energy.2023.128874
    [Google Scholar]
  98. Sun G.L. Reynolds E.E. Belcher A.M. Designing yeast as plant-like hyperaccumulators for heavy metals. Nat. Commun. 2019 10 1 5080 10.1038/s41467‑019‑13093‑6 31704944
    [Google Scholar]
  99. Moteshareie H. Hajikarimlou M. Mulet Indrayanti A. Burnside D. Paula Dias A. Lettl C. Ahmed D. Omidi K. Kazmirchuk T. Puchacz N. Zare N. Takallou S. Naing T. Hernández R.B. Willmore W.G. Babu M. McKay B. Samanfar B. Holcik M. Golshani A. Heavy metal sensitivities of gene deletion strains for ITT1 and RPS1A connect their activities to the expression of URE2, a key gene involved in metal detoxification in yeast. PLoS One 2018 13 9 e0198704 10.1371/journal.pone.0198704 30231023
    [Google Scholar]
  100. Kumar K. Shinde A. Aeron V. Verma A. Arif N.S. Genetic engineering of plants for phytoremediation: Advances and challenges. J. Plant Biochem. Biotechnol. 2023 32 1 12 30 10.1007/s13562‑022‑00776‑3
    [Google Scholar]
  101. Hoffmann S.A. Cai Y. Engineering stringent genetic biocontainment of yeast with a protein stability switch. Nat. Commun. 2024 15 1 1060 10.1038/s41467‑024‑44988‑8 38316765
    [Google Scholar]
  102. Zhang F.L. Zhang L. Zeng D.W. Liao S. Fan Y. Champreda V. Runguphan W. Zhao X.Q. Engineering yeast cell factories to produce biodegradable plastics and their monomers: Current status and prospects. Biotechnol. Adv. 2023 68 108222 10.1016/j.biotechadv.2023.108222 37516259
    [Google Scholar]
  103. Zhang Y. Guo X. Yang H. Shi S. The studies in constructing yeast cell factories for the production of fatty acid alkyl esters. Front. Bioeng. Biotechnol. 2022 9 799032 10.3389/fbioe.2021.799032 35087801
    [Google Scholar]
  104. Wilson A.N. St John P.C. Marin D.H. Hoyt C.B. Rognerud E.G. Nimlos M.R. Cywar R.M. Rorrer N.A. Shebek K.M. Broadbelt L.J. Beckham G.T. Crowley M.F. PolyID: Artificial intelligence for discovering performance-advantaged and sustainable polymers. Macromolecules 2023 56 21 8547 8557 10.1021/acs.macromol.3c00994 38024155
    [Google Scholar]
  105. Nduko J.M. Taguchi S. Microbial production of biodegradable lactate-based polymers and oligomeric building blocks from renewable and waste resources. Front. Bioeng. Biotechnol. 2021 8 618077 10.3389/fbioe.2020.618077 33614605
    [Google Scholar]
  106. Juturu V. Wu J.C. Microbial production of lactic acid: The latest development. Crit. Rev. Biotechnol. 2016 36 6 967 977 10.3109/07388551.2015.1066305
    [Google Scholar]
  107. Helmes R.J.K. López-Contreras A.M. Benoit M. Abreu H. Maguire J. Moejes F. Burg S.W.K. Environmental impacts of experimental production of lactic acid for bioplastics from Ulva spp. Sustainability 2018 10 7 2462 10.3390/su10072462
    [Google Scholar]
  108. Ahmad A. Banat F. Taher H. A review on the lactic acid fermentation from low-cost renewable materials: Recent developments and challenges. Environ. Technol. Innov. 2020 20 101138 10.1016/j.eti.2020.101138
    [Google Scholar]
  109. Das M. Santra S. Lactic Acid production from fungal machineries and mechanism of pla synthesis: Application of ai-based technology for improved productivity. Fungi and Fungal Products in Human Welfare and Biotechnology. Springer 2023 211 256
    [Google Scholar]
  110. Yamamoto Y. Yamada R. Matsumoto T. Ogino H. Construction of a machine-learning model to predict the optimal gene expression level for efficient production of d-lactic acid in yeast. World J. Microbiol. Biotechnol. 2023 39 3 69 10.1007/s11274‑022‑03515‑x 36607503
    [Google Scholar]
  111. Kumar R. Dhanarajan G. Sarkar D. Sen R. Multi-fold enhancement in sustainable production of biomass, lipids and biodiesel from oleaginous yeast: An artificial neural network-genetic algorithm approach. Sustain. Energy Fuels 2020 4 12 6075 6084 10.1039/D0SE00922A
    [Google Scholar]
  112. Vega-Ramon F. Zhu X. Savage T.R. Petsagkourakis P. Jing K. Zhang D. Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty. Biotechnol. Bioeng. 2021 118 12 4854 4866 10.1002/bit.27950 34612511
    [Google Scholar]
/content/journals/cgc/10.2174/0122133461336557241210051632
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Green Energy Revolution: Production of Environmentally Friendly Sustainable Biofuels using Yeasts with the Help of Artificial Intelligence
Bentham Science Publishers ; https://doi.org/10.2174/0122133461336557241210051632
/content/journals/cgc/10.2174/0122133461336557241210051632
/content/journals/cgc/10.2174/0122133461336557241210051632
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  • Article Type:     Review Article
Keywords: yeast ; Artificial intelligence ; genetic engineering ; fermentation ; heavy metal absorption ; biofuel production

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