Lawson Christopher E
Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada.
mSystems. 2021 Aug 31:e0092521. doi: 10.1128/mSystems.00925-21.
Microbial communities (microbiomes) have been harnessed in biotechnology applications such as wastewater treatment and bioremediation for over a century. Traditionally, engineering approaches have focused on shaping the environment to steer microbiome function versus direct manipulation of the microbiome's metabolic network. While these selection-based approaches have proven to be invaluable for guiding bioprocess engineering, they do not enable the precise manipulation and control of microbiomes required for unlocking their full potential. Over the past 2 decades, systems biology has revolutionized our understanding of the metabolic networks driving microbiome processes, and more recently genetic engineering tools have started to emerge for nonmodel microorganisms and microbiomes. In this commentary, I discuss how systems biology approaches are being used to generate actionable understanding of microbiome functions in engineered ecosystems. I also highlight how integrating synthetic biology, automation, and machine learning can accelerate microbiome engineering to meet the sustainability challenges of the future.
一个多世纪以来，微生物群落（微生物组）已被应用于生物技术领域，如废水处理和生物修复。传统上，工程方法侧重于塑造环境以引导微生物组功能，而非直接操纵微生物组的代谢网络。虽然这些基于选择的方法已被证明对指导生物过程工程非常宝贵，但它们无法实现充分发挥微生物组潜力所需的精确操纵和控制。在过去20年中，系统生物学彻底改变了我们对驱动微生物组过程的代谢网络的理解，最近，针对非模式微生物和微生物组的基因工程工具也开始出现。在这篇评论中，我将讨论系统生物学方法如何用于对工程生态系统中的微生物组功能产生可操作的理解。我还将强调整合合成生物学、自动化和机器学习如何能够加速微生物组工程，以应对未来的可持续发展挑战。
