ITR: Evaluating Phylogeny Reconstruction Algorithms with Digital Organisms

EIA-0219229 Torng，Eric KMichigan State University ITR：使用数字组织评估系统发育重建算法研究人员研究仅使用现有生物的知识来确定物种之间历史关系的方法，这种技术被称为“系统发育树重建”。许多树重建算法是已知的，但很难对它们进行适当的测试，因为这些算法很有用--原始树丢失在历史中。提出的研究利用了一种基于人工进化系统Avida的新评估方法。在Avida，数字有机体(自我复制的计算机程序)种群在争夺有限的资源时经历自然选择，并将进化成新物种--通常带有全新的基因。这样一个系统的历史可以监测，因此从最终状态重建可以测量其准确性。拟议的活动对社会有几个更广泛的影响。这是密歇根州立大学生物建模中心的核心活动，该中心是一个新的跨学科研究和教育中心。包括未被充分代表的少数族裔在内的本科生将通过学习小而独立的问题来参与进来。最后，加强对系统发育重建算法的理解将提高我们解释基因序列的能力，有助于药物设计和帮助重建进化的“生命树”。

EIA-0219229Torng, Eric KMichigan State UniversityITR: Evaluating Phylogeny Reconstruction Algorithms with Digital OrganismsThe investigators study methods of determining the historic relationshipbetween species using only knowledge of currently existing organisms, a technique called "phylogenetic tree reconstruction". Many tree reconstruction algorithms are known, but it is difficult to properly test them for the veryreason that the algorithms are useful -- the original trees are lost tohistory.The studies proposed make use of a new evaluation methodology based on anartificial evolving system called Avida. In Avida, populations of digitalorganisms (self-replicating computer programs) experience natural selectionas they compete for limited resources, and will evolve into new speciesoften with entirely new genes. The history of such a system can bemonitored, and hence a reconstruction from the final state can have itsaccuracy measured.The proposed activity has several broader impacts on society. It is a coreactivity in the Center for Biological Modeling, a new interdisciplinaryresearch and education center at Michigan State University. Undergraduatestudents including underrepresented minorities will be involved by studyingsmall, self-contained questions. Finally, enhanced understanding ofphylogeny reconstruction algorithms will improve our ability to interpretthesequences of genes, aiding in drug design and helping efforts to reconstructan evolutionary "tree of life".