Models, Methods, and Criteria for Phylogeny Construction

这个探索性研究（SGER）的小补助金，由计算理论（TOC）计划，CCR，计算生物学活动，BIR和系统生物学计划，DEB共同资助，解决了遗传学或进化树构建中的问题。 这一问题的制定和有效解决需要生物学家、统计学家和计算机科学家的多学科协作努力。 解决这一问题的理想方法包括以下步骤：（a）观察关于现存物种的数据;（B）确定一种生物模型（如Jukes-Cantor模型或Kimura双参数模型）;（c）基于来自步骤（B）的模型，设计目标函数和用于该函数的有效优化方法，使得优化该目标函数的树是最佳拟合的树该模型不幸的是，这一理想的计划是不可能实现的，因为在每一步的障碍：（i）数据是受实验误差和错误，由于其解释和使用的遗传学建设方法;（ii）似乎很难确定一个精确的生物模型的进化;（iii）给定随机进化模型，最优树的一个候选者是“最可能树”。 考虑到什么是真正的“最佳”模型的不确定性，一个模型下最有可能的树仍然应该是一个稍微不同的模型下非常有可能的树。 证明这一点已被证明是一个非常困难的问题。 本SGER提案的目标包括：（1）修改理想的方法，以便消除困难（i）-（iii）;（2）使用由分子生物学家提供的r-RNA和tufA序列数据测试各种模型;（3）使用实验结果，设计推断遗传学的通用方法;（4）将这些新模型与现有模型进行比较。 此外，该SGER奖的目标之一是在宾夕法尼亚大学的生物学家，统计学家和计算机科学家之间发起多学科的努力。

This Small Grant for Exploratory Research (SGER), jointly funded by the Theory of Computing (TOC) Program, CCR, by Computational Biology Activity, BIR and by the Systemic Biology Program, DEB, addresses problems in the construction of phylogenies or evolutionary trees. The formulation and effective solution of this problem requires a collaborative multidisciplinary effort from biologists, statisticians and computer scientists. The ideal methodology for solving this problem would include the following steps:(a) Observe data on the species that exist today; (b) Identify a biological model (such as the Jukes-Cantor model or Kimura two parameter model); (c) Based on the model from Step (b), design an objective function and efficient optimization methods for this function so that the tree that optimizes this objective function is the tree that best fits the model. Unfortunately, this ideal program is impossible to realize, because of roadblocks at every step: (i) Data is subject to experimental error and to errors due to its interpretation and use in phylogeny construction methods;(ii) It seems difficult to identify a precise biological model for evolution; (iii) Given the stochastic model of evolution, one candidate for the optimizing tree is the ``most likely tree''. Given the uncertainty of what is actually the ``best'' model, a most likely tree under one model should still be a very likely tree under a slightly different model. Demonstrating this has proven to be a very difficult problem. The goals of this SGER proposal include:(1) Modification of the ideal methodology so that Difficulties(i) -- (iii) are removed; (2) Testing of various models using r-RNA and tufA sequence data supplied by a molecular biologist; (3) Using the experimental results, design of general methods for inferring phylogeny;(4) Comparison of these new models with existing models for the data. In addition, one of the goals of this SGER award is to initiate a multidisciplinary effort between the biologists, statisticians, and computer scientists at the University of Pennsylvania.***