Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/171870
Title: Perspective: multiomics and machine learning help unleash the alternative food potential of microalgae
Authors: Helmy, Mohamed
Elhalis, Hosam
Liu, Yan
Chow, Yvonne
Selvarajoo, Kumar
Keywords: Science::Biological sciences
Issue Date: 2023
Source: Helmy, 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.2022.11.002
Project: W20W2D0017 
Journal: Advances in Nutrition 
Abstract: Food 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.
URI: https://hdl.handle.net/10356/171870
ISSN: 2156-5376
DOI: 10.1016/j.advnut.2022.11.002
Schools: School of Biological Sciences 
Organisations: Bioinformatics Institute, A*STAR 
Singapore Institute of Food and Biotechnology Innovation, A*STAR 
Synthetic Biology for Clinical and Technological Innovation, NUS 
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/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SBS Journal Articles

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