Hahn Aria S, Konwar Kishori M, Louca Stilianos, Hanson Niels W, Hallam Steven J
Department of Microbiology & Immunology, University of British Columbia, Vancouver, BC, Canada; Koonkie, Inc., Menlo Park, CA, USA.
Koonkie, Inc., Menlo Park, CA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
Curr Opin Microbiol. 2016 Jun;31:209-216. doi: 10.1016/j.mib.2016.04.014. Epub 2016 May 13.
A revolution is unfolding in microbial ecology where petabytes of 'multi-omics' data are produced using next generation sequencing and mass spectrometry platforms. This cornucopia of biological information has enormous potential to reveal the hidden metabolic powers of microbial communities in natural and engineered ecosystems. However, to realize this potential, the development of new technologies and interpretative frameworks grounded in ecological design principles are needed to overcome computational and analytical bottlenecks. Here we explore the relationship between microbial ecology and information science in the era of cloud-based computation. We consider microorganisms as individual information processing units implementing a distributed metabolic algorithm and describe developments in ecoinformatics and ubiquitous computing with the potential to eliminate bottlenecks and empower knowledge creation and translation.
一场微生物生态学革命正在展开,通过下一代测序和质谱平台产生了拍字节级的“多组学”数据。这一生物信息的宝库具有巨大潜力,可揭示自然和工程生态系统中微生物群落隐藏的代谢能力。然而,要实现这一潜力,需要开发基于生态设计原则的新技术和解释框架,以克服计算和分析瓶颈。在此,我们探讨基于云计算时代微生物生态学与信息科学之间的关系。我们将微生物视为实施分布式代谢算法的个体信息处理单元,并描述生态信息学和普适计算的发展,这些发展有可能消除瓶颈并促进知识创造与转化。