NSF Postdoctoral Fellowship in Biology FY 2010

这一行动为2010财年的NSF博士后研究奖学金提供资金。该奖学金支持一项研究和培训计划，题为“确定驱动生物体复杂性演变的环境属性”，为杰夫·克卢恩。这项研究的主办机构是康奈尔大学，赞助科学家是Lipson Hod。生物学中的一个主要开放问题是进化如何产生自然形式中的复杂性，例如身体和大脑的结构组织（规则性，模块性和层次结构）。驱动这种结构组织进化的环境压力很难理解，主要是因为它们很难在自然系统中进行实验研究。然而，理解结构组织是重要的，因为它是生物系统的基本属性，因为它影响生物体对新环境的反应，因为它增加了进化适应的速度，这为改进生物工程提供了可能性。该研究员正在利用表现出进化动力学的计算系统来确定驱动结构组织进化的环境属性。具体来说，该研究（1）开发了新的统计算法来量化结构组织，（2）使用计算机中的进化实验来测试八种不同的环境属性是否驱动结构组织的进化，（3）量化生物表型中的结构组织，（4）测试计算机进化实验中结构组织的环境驱动因素是否（在2中确定）也驱动生物表型中的结构组织（使用来自3的数据）。培训目标包括提高数学和机器学习技能，更好地理解大脑的计算模型。更广泛的影响包括开发一个网站，允许访问者参与演变结构组织的表型，并发布量化结构组织的开源软件工具。

This action funds an NSF Postdoctoral Research Fellowship for FY 2010. The fellowship supports a research and training plan entitled "Identifying the Environmental Attributes that Drive the Evolution of Complexity in Organisms" for Jeff Clune. The host institution for this research is Cornell University, and the sponsoring scientist is Lipson Hod.One of the major open questions in biology is how evolution produces the complexity seen in natural forms, such as the structural organization (regularity, modularity, and hierarchy) of bodies and brains. The environmental pressures that drive the evolution of such structural organization are poorly understood largely because they are difficult to study experimentally in natural systems. Understanding structural organization is important, however, because it is a fundamental property of biological systems, because it affects how organisms will respond to novel environments, and because it increases the speed of evolutionary adaptation, which opens possibilities for improving bioengineering. The Fellow is taking advantage of computational systems that exhibit evolutionary dynamics to identify the environmental attributes that drive the evolution of structural organization. Specifically, the research (1) develops novel statistical algorithms to quantify structural organization, (2) uses evolution experiments in computers to test whether eight different environmental attributes drive the evolution of structural organization, (3) quantifies structural organization in biological phenotypes, (4) tests if the environmental drivers of structural organization in computer evolution experiments (identified in 2) also drive structural organization in biological phenotypes (using data from 3). The training goals include enhancing mathematical and machine learning skills and better understanding computational models of brains. The broader impacts include development of a website that allows visitor participation in evolving structurally-organized phenotypes, and publishing open-source software tools for quantifying structural organization.