NSF Postdoctoral Fellowship in Biology FY 2012

在进化生态学中使用基因网络模型：基因复制在自然环境下开花时间控制中的作用基因复制是进化新奇的丰富来源，并被认为促进物种形成和适应。然而，理解为什么特定的复制在进化中被保留下来是困难的，因为相关基因的功能往往会重叠。拟南芥开花时间途径是研究生态因子在维持遗传冗余中的作用的理想系统，这是由于在该物种花控制方面的详细分子遗传学、建模和进化生态学工作的长期历史。该奖学金支持将联合收割机数学建模相结合的研究，以探索基因网络和基因表达谱中的冗余作用，以确定重复基因之间的功能差异。这些数据将用于验证拟南芥开花时间通路中看似冗余的基因在现实环境下调节开花时间的不同作用的假设。培训目标包括熟练拟合实际和现实的生物系统模型，并进行有效的田间和实验室实验来验证这些模型的预测。更广泛的影响包括将进化模拟纳入怀俄明州正在进行的高中教师遗传学和进化暑期培训计划。此外，拟南芥开花时间基因网络在许多重要的作物物种中具有类似物。可以预测这种模式物种在复杂的自然环境下开花的模型可能会转化为对农业有用的作物模型的改进。

Using gene network models in evolutionary ecology: Gene duplication roles in flowering time control under natural environmentsGene duplications are a rich source for evolutionary novelty and are thought to promote both speciation and adaptation. However, understanding why particular duplications are preserved by evolution is difficult because the functions of related genes tend to overlap. The Arabidopsis flowering time pathway is an ideal system to investigate the role of ecological factors in maintaining genetic redundancy due to a long history of detailed molecular genetic, modeling and evolutionary ecological work on floral control in this species. This fellowships supports research that will combine mathematical modeling to explore the role of redundancy within gene networks and gene expression profiling in field trials to identify functional differences among duplicate genes. These data will be used to test the hypothesis that seemingly redundant genes in the Arabidopsis flowering time pathway have different roles in regulating the timing of flowering under real-world environments.Training objectives include becoming proficient in fitting practical and realistic models of biological systems, and carrying out effective field and lab experiments to test predictions of these models. The broader impacts include incorporating evolutionary simulations into an ongoing summer training programs on genetics and evolution for high school teachers in Wyoming. Also, the Arabidopsis flowering time gene network has analogs in many important crop species. Models that can predict flowering under complex, natural environments in this model species may translate into improvements in crop models useful for agriculture.