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

0534018 - ÚVGZ 2021 RIV US eng J - Journal Article
Teng, S. H. - Yew, G. Y. - Sukačová, Kateřina - Show, P. L. - Máša, V. - Chang, J. - S.
Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products.
Biotechnology Advances. Roč. 44, NOV 15 (2020), č. článku 107631. ISSN 0734-9750. E-ISSN 1873-1899
R&D Projects: GA MŠMT(CZ) EF16_026/0008413
Institutional support: RVO:86652079
Keywords : neural-network * chlamydomonas-reinhardtii * strain selection * genome * algae * optimization * biodiesel * dna * cultivation * generation * Microalgae * Artificial intelligence * Genetic engineering * Process optimization * System design * Process integration
OECD category: Plant sciences, botany
Impact factor: 14.227, year: 2020
Method of publishing: Limited access
https://www.sciencedirect.com/science/article/pii/S0734975020301336?via%3Dihub

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.
Permanent Link: http://hdl.handle.net/11104/0312238