Perspective: multiomics and machine learning help unleash the alternative food potential of microalgae

• Main Authors: Helmy, Mohamed, Elhalis, Hosam, Liu, Yan, Chow, Yvonne, Selvarajoo, Kumar
• Other Authors: School of Biological Sciences
• Format: Article
• Language: English
• Published: 2023
• Subjects: Science::Biological sciences; Microalgae; Machine Learning
• Online Access: https://hdl.handle.net/10356/171870
• Institution: Nanyang Technological University

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.