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.

随着诸如RNAi，ZFN，TALEN和CRISPR-Cas9等新型基因编辑工具的最新进展，将微藻改变为各种用途的设计特性的可能性已成为现实。微藻基因组的改变可以修饰代谢途径，从而提高脂质，生物质和其他成分的产量。这种经过遗传优化的微藻的潜力可以在种植，加工，系统设计，工艺集成和革命性产品等方面进一步提供优化杠杆作用，从而产生“多米诺效应”。然而，由于微藻基因组序列又长又复杂，目前对各种微藻基因序列的功能信息的了解仍然是原始的，不足。从这个角度来看，这项工作建议使用人工智能（AI）来弥补微藻遗传信息和优化的生物产品之间的知识鸿沟。随着最近AI研究的加速，可以通过结合这两个领域的尖端技术来适当地分析来自微藻研究的大量复杂数据。在这项工作中，针对微藻应用的不同情况，讨论了最合适的一类AI算法（例如主动学习，半监督学习和元学习）。这项工作简明扼要地回顾了研究里程碑的当前状态，并重点介绍了已进行的一些最新技术研究，为未来的研究提供了有见地的途径。还讨论了AI算法在微藻培养，系统优化和供应链其他方面的应用。