PostDoctoral Research Fellowship

这项行动为2009财政年度的NSF博士后研究奖学金提供资金。该奖学金支持Adina Chuang的一项名为“Miscanthus arctus的生殖球宏基因组研究”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，赞助科学家是詹姆斯Tiedje。芒最近已经成为一个高潜力的国内，可再生生物能源，可以发挥很大的作用，减少我们国家对外国石油供应的依赖，并尽量减少化石燃料排放的后果。 与柳枝稷（能源部模式能源作物）相比，它每年可实现的可收获生物量转化率高出三至六倍，而农业投入最少。 对芒属植物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。 尽管芒草具有很高的潜力，以满足美国的生物能源目标，但很少有人努力研究和优化其农艺学和遗传学。本研究使用环境基因组技术来帮助深入了解芒草的土壤-植物环境关系，包括识别和优化根际细菌与植物生产力之间的有益关系。 此外，这项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投入成本，特别是施肥，使其在边缘土地上生长。 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的管理中，并整合来自多个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.