Perspective: Multiomics and Machine Learning Help Unleash the Alternative Food Potential of Microalgae

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 intel-ligence 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 da-tabases rich in omics data and further developments on its mining and analytics methods.

Automated summary

Food security is a pressing issue due to population growth, the COVID-19 pandemic, political conflicts, and climate change. Fundamental changes to the current food system and exploring alternative food sources are necessary. Microalgae are gaining attention as a lab-based source of nutrition and their potential in the circular economy. Overcoming challenges and limitations, such as toxicity, can be achieved through systems biology and artificial intelligence, with the help of data-driven metabolic optimization and advanced analytics methods using microalgal databases.