Mock Thomas, Daines Stuart J, Geider Richard, Collins Sinead, Metodiev Metodi, Millar Andrew J, Moulton Vincent, Lenton Timothy M
School of Environmental Sciences, University of East Anglia, Norwich Research Park, NR4 7TJ, Norwich, UK.
College of Life and Environmental Sciences, University of Exeter, EX4 4QE, Exeter, UK.
Glob Chang Biol. 2016 Jan;22(1):61-75. doi: 10.1111/gcb.12983. Epub 2015 Jul 28.
The advent of genomic-, transcriptomic- and proteomic-based approaches has revolutionized our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New interdisciplinary approaches are needed to move from this descriptive level to improved quantitative, process-level understanding of the roles of marine microbes in biogeochemical cycles and of the impact of environmental change on the marine microbial ecosystem. Linking studies at levels from the genome to the organism, to ecological strategies and organism and ecosystem response, requires new modelling approaches. Key to this will be a fundamental shift in modelling scale that represents micro-organisms from the level of their macromolecular components. This will enable contact with omics data sets and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximization and evolutionary constraints. This way forward will build on ecological approaches that identify key organism traits and systems biology approaches that integrate traditional physiological measurements with new insights from omics. It will rely on developing an improved understanding of ecophysiology to understand quantitatively environmental controls on microbial growth strategies. It will also incorporate results from experimental evolution studies in the representation of adaptation. The resulting ecosystem-level models can then evaluate our level of understanding of controls on ecosystem structure and function, highlight major gaps in understanding and help prioritize areas for future research programs. Ultimately, this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers.
基于基因组学、转录组学和蛋白质组学的方法的出现，彻底改变了我们描述海洋微生物群落的能力，包括生物地理学、代谢潜力与多样性、适应机制以及系统发育和进化历史。需要新的跨学科方法，从这种描述性层面转向对海洋微生物在生物地球化学循环中的作用以及环境变化对海洋微生物生态系统的影响进行更完善的定量、过程层面的理解。将从基因组到生物体、再到生态策略以及生物体和生态系统响应等层面的研究联系起来，需要新的建模方法。关键在于建模尺度的根本转变，即从微生物的大分子组成层面来表征微生物。这将能够与组学数据集建立联系，并允许在表型水平（即性状）模拟适应和适应性反应，将其作为适应性最大化和进化限制的组合。这条前进道路将建立在识别关键生物体性状的生态学方法以及将传统生理学测量与组学新见解相结合 的系统生物学方法之上。它将依赖于对生态生理学的深入理解，以定量理解环境对微生物生长策略的控制。它还将在适应的表征中纳入实验进化研究的结果。由此产生的生态系统层面的模型，然后可以评估我们对生态系统结构和功能控制的理解水平，突出理解上的主要差距，并帮助确定未来研究计划的优先领域。最终，这种宏大的综合应提高生态系统对多种环境驱动因素响应的预测能力。
