PostDoctoral Research Fellowship

本行动资助NSF 2009年度博士后研究奖学金。该奖学金支持Adina Chuang的一项名为“Miscanthus gigantus根际宏基因组研究”的研究和培训计划。这项研究的主办机构是密歇根州立大学，赞助科学家是James Tiedje。芒草最近作为一种高潜力的国内可再生生物能源出现，可以在减少我国对外国石油供应的依赖和最大限度地减少持续化石燃料排放的后果方面发挥重要作用。与柳枝稷（美国能源部模式能源作物）相比，它已被证明可以在最小的农业投入下实现三到六倍的可收获生物质年转换率。对芒草的研究可以帮助显著减少土地需求，以满足未来的能源目标。尽管芒草在满足美国生物能源目标方面具有很高的潜力，但对其农艺和遗传学的研究和优化却很少。本研究利用环境基因组技术帮助深入了解芒草的土壤-植物环境关系，包括鉴定和优化根际细菌与植物生产力之间的有益关系。此外，本研究有助于我们更好地了解如何管理芒草根际提供的自然效益，以帮助最大限度地减少其在边缘土地上生长的投入成本，特别是施肥成本。培训目标包括(1)使用生物信息学工具从复杂的环境样本中识别和评估环境基因组信息，(2)使用其他计算工具简化复杂数据集的分析和比较，以及(3)将研究经验扩展到以应用问题解决为中心的独立项目管理中，并整合来自多学科的工具和专业知识。

This action funds an NSF Postdoctoral Research Fellowship for FY 2009. The fellowship supports a research and training plan entitled "Metagenomic Investigations of the Rhizosphere of Miscanthus gigantus" for Adina Chuang. The host institution for this research is Michigan State University, and the sponsoring scientist is James Tiedje.Miscanthus gigantus has recently emerged as a high potential domestic, renewable bioenergy source that could play a large role in decreasing our nation's dependency on foreign petroleum supplies and minimizing the consequences of continued fossil fuel emissions. It has been shown to achieve three to six times more annual conversion of harvestable biomass as compared to switchgrass (the DOE model energy crop)with minimal agricultural inputs. Research efforts into Miscanthus could help to significantly decrease land requirements to meet the energy goals of the future. Despite the high potential of Miscanthus to meet U.S. bioenergy goals, there have been very few efforts to study and optimize its agronomy and genetics. This research uses environmental genomic techniques to help gain insight into the soil-plant environmental relationship of Miscanthus, including identifying and optimizing beneficial relationships between rhizosphere bacteria and plant productivity. Furthermore, this research helps us better understand how to manage the natural benefits offered in the rhizosphere of Miscanthus to help minimize input costs, especially fertilization, for its growth on marginal lands. Training goals include (1) using bioinformatics tools to identify and evaluate environmental genomic information from complex environmental samples, (2) using other computational tools to streamline analysis and comparison of complex datasets, and (3) extending research experience into the management of an independent project centered around applied problem-solving and the integration of tools and expertise from multiple disciplines.