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Bridging the Gap between Omics and Earth System Science to better understand how Environmental Change impacts Marine Microbes

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Original languageEnglish
Pages (from-to)61–75
JournalGlobal Change Biology
Volume22
Issue number1
Early online date19 May 2015
DOIs
Publication statusPublished - 2015

Abstract

The advent of genomic, transcriptomic and proteomic-based approaches has revolutionised our ability to describe marine microbial communities, including biogeography, metabolic potential and diversity, mechanisms of adaptation, and phylogeny and evolutionary history. New inter-disciplinary 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. To link 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 microorganisms from the level of their macromolecular components. This will enable contact with omics datasets, and allow acclimation and adaptive response at the phenotype level (i.e. traits) to be simulated as a combination of fitness maximisation 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 prioritise areas for future research programs. Ultimately this grand synthesis should improve predictive capability of the ecosystem response to multiple environmental drivers. This article is protected by copyright. All rights reserved.

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