Institute for Medical Engineering & Science and Department of Biological Engineering, MIT, Cambridge, MA, USA.
Infectious Disease and Microbiome Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Nat Rev Microbiol. 2020 Sep;18(9):507-520. doi: 10.1038/s41579-020-0372-5. Epub 2020 May 29.
Predictive biology is the next great chapter in synthetic and systems biology, particularly for microorganisms. Tasks that once seemed infeasible are increasingly being realized such as designing and implementing intricate synthetic gene circuits that perform complex sensing and actuation functions, and assembling multi-species bacterial communities with specific, predefined compositions. These achievements have been made possible by the integration of diverse expertise across biology, physics and engineering, resulting in an emerging, quantitative understanding of biological design. As ever-expanding multi-omic data sets become available, their potential utility in transforming theory into practice remains firmly rooted in the underlying quantitative principles that govern biological systems. In this Review, we discuss key areas of predictive biology that are of growing interest to microbiology, the challenges associated with the innate complexity of microorganisms and the value of quantitative methods in making microbiology more predictable.
预测生物学是合成生物学和系统生物学的下一个重要篇章,特别是对于微生物而言。曾经看似不可行的任务,如设计和实现执行复杂传感和执行功能的复杂合成基因回路,以及组装具有特定、预定义组成的多物种细菌群落,现在越来越多地成为现实。这些成就的取得得益于生物学、物理学和工程学的跨学科专业知识的融合,从而对生物设计有了新兴的、定量的理解。随着越来越多的多组学数据集的出现,它们将理论转化为实践的潜在用途仍然牢牢扎根于控制生物系统的基本定量原则。在这篇综述中,我们讨论了预测生物学中越来越受到微生物学关注的关键领域、与微生物固有复杂性相关的挑战,以及定量方法在提高微生物学可预测性方面的价值。