Systems-based approaches to unravel multi-species microbial community functioning.
Systems-based approaches to unravel multi-species microbial community functioning.
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DOI：
10.1016/j.csbj.2014.11.009
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发表时间：
2015
影响因子：
6
通讯作者：
Abram F
中科院分区：
文献类型：
--
作者：
Abram F
一些最有希望解决本世纪重大社会挑战的最具变革性的发现很可能来自环境科学，特别是环境微生物学和生物技术。了解微生物如何在原位相互作用，以及微生物群落如何对环境变化做出反应，仍然是科学面临的巨大挑战。系统生物学提供了一个强大的实验策略来解决破译微生物相互作用的令人兴奋的任务。在这个框架中，整个微生物群落被认为是元生物，每个层次的生物信息（DNA、RNA、蛋白质和代谢物）都与原位环境特征一起被研究。通过这种方式，系统生物学可以帮助解开生态系统不同部分之间的相互作用，最终负责其涌现特性。事实上，每一层次的生物信息都提供了不同层次的微生物群落特征。宏基因组学、亚转录组学、宏蛋白质组学、代谢组学和sip组学可以用来研究微生物群落的结构、潜力、功能、活性和相互作用。组学方法是由21世纪的高通量技术实现的，本综述将讨论它们的实施如何彻底改变了我们对微生物群落的理解。
Some of the most transformative discoveries promising to enable the resolution of this century's grand societal challenges will most likely arise from environmental science and particularly environmental microbiology and biotechnology. Understanding how microbes interact in situ, and how microbial communities respond to environmental changes remains an enormous challenge for science. Systems biology offers a powerful experimental strategy to tackle the exciting task of deciphering microbial interactions. In this framework, entire microbial communities are considered as metaorganisms and each level of biological information (DNA, RNA, proteins and metabolites) is investigated along with in situ environmental characteristics. In this way, systems biology can help unravel the interactions between the different parts of an ecosystem ultimately responsible for its emergent properties. Indeed each level of biological information provides a different level of characterisation of the microbial communities. Metagenomics, metatranscriptomics, metaproteomics, metabolomics and SIP-omics can be employed to investigate collectively microbial community structure, potential, function, activity and interactions. Omics approaches are enabled by high-throughput 21st century technologies and this review will discuss how their implementation has revolutionised our understanding of microbial communities.
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影响因子：
15.5
作者：
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通讯作者：
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影响因子：
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作者：
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影响因子：
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作者：
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作者：
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