Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171870
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dc.contributor.authorHelmy, Mohameden_US
dc.contributor.authorElhalis, Hosamen_US
dc.contributor.authorLiu, Yanen_US
dc.contributor.authorChow, Yvonneen_US
dc.contributor.authorSelvarajoo, Kumaren_US
dc.date.accessioned2023-11-14T01:38:15Z-
dc.date.available2023-11-14T01:38:15Z-
dc.date.issued2023-
dc.identifier.citationHelmy, M., Elhalis, H., Liu, Y., Chow, Y. & Selvarajoo, K. (2023). Perspective: multiomics and machine learning help unleash the alternative food potential of microalgae. Advances in Nutrition, 14(1), 1-11. https://dx.doi.org/10.1016/j.advnut.2022.11.002en_US
dc.identifier.issn2156-5376en_US
dc.identifier.urihttps://hdl.handle.net/10356/171870-
dc.description.abstractFood security has become a pressing issue in the modern world. The ever-increasing world population, ongoing COVID-19 pandemic, and political conflicts together with climate change issues make the problem very challenging. Therefore, fundamental changes to the current food system and new sources of alternative food are required. Recently, the exploration of alternative food sources has been supported by numerous governmental and research organizations, as well as by small and large commercial ventures. Microalgae are gaining momentum as an effective source of alternative laboratory-based nutritional proteins as they are easy to grow under variable environmental conditions, with the added advantage of absorbing carbon dioxide. Despite their attractiveness, the utilization of microalgae faces several practical limitations. Here, we discuss both the potential and challenges of microalgae in food sustainability and their possible long-term contribution to the circular economy of converting food waste into feed via modern methods. We also argue that systems biology and artificial intelligence can play a role in overcoming some of the challenges and limitations; through data-guided metabolic flux optimization, and by systematically increasing the growth of the microalgae strains without negative outcomes, such as toxicity. This requires microalgae databases rich in omics data and further developments on its mining and analytics methods.en_US
dc.description.sponsorshipAgency for Science, Technology and Research (A*STAR)en_US
dc.language.isoenen_US
dc.relationW20W2D0017en_US
dc.relation.ispartofAdvances in Nutritionen_US
dc.rights© 2022 The Author(s). Published by Elsevier Inc. on behalf of American Society for Nutrition. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en_US
dc.subjectScience::Biological sciencesen_US
dc.titlePerspective: multiomics and machine learning help unleash the alternative food potential of microalgaeen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Biological Sciencesen_US
dc.contributor.organizationBioinformatics Institute, A*STARen_US
dc.contributor.organizationSingapore Institute of Food and Biotechnology Innovation, A*STARen_US
dc.contributor.organizationSynthetic Biology for Clinical and Technological Innovation, NUSen_US
dc.identifier.doi10.1016/j.advnut.2022.11.002-
dc.description.versionPublished versionen_US
dc.identifier.pmid36811582-
dc.identifier.scopus2-s2.0-85148549538-
dc.identifier.issue1en_US
dc.identifier.volume14en_US
dc.identifier.spage1en_US
dc.identifier.epage11en_US
dc.subject.keywordsMicroalgaeen_US
dc.subject.keywordsMachine Learningen_US
dc.description.acknowledgementThis project was supported by the Agency for Science, Technology and Research under the Singapore Food Story R&D Programme (Theme 2 – 1st Alternative Protein Seed Challenge; W20W2D0017).en_US
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