Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

• Published in: Biotechnology advances Vol. 44; pp. 107631 - 17
• Main Authors: Teng, Sin Yong, Yew, Guo Yong, Sukačová, Kateřina, Show, Pau Loke, Máša, Vítězslav, Chang, Jo-Shu
• Format: Journal Article
• Language: English
• Published: England Elsevier Inc 15.11.2020; Elsevier Science Ltd
• Subjects: Algae; Algorithms; Artificial Intelligence; Biomass; Clustered Regularly Interspaced Short Palindromic Repeats; Cultivation; Gene Editing; Gene sequencing; Genetic engineering; Genetic modification; Genomes; Lipids; Machine learning; Microalgae; Microalgae - genetics; Optimization; Process integration; Process optimization; Supply chains; System design; Systems design; System design; Process optimization; Microalgae; Genetic engineering; Artificial intelligence; Process integration
• ISSN: 0734-9750; 1873-1899
• DOI: 10.1016/j.biotechadv.2020.107631

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a “domino effect” in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications. [Display omitted] •Recent gene editing tools and NGS platform allow for precise microalgae genetic engineering.•AI algorithms can provide analytics for understanding the genetic sequences in microalgae.•Advances in computer vision have allowed for real-time microalgae identification.•AI algorithms can be used to fine-tune microalgae conversion, system design and integration.•A commercial oriented “multi-omics” pipeline has been proposed for microalgae research.