A general framework for modelling functional divergence at the molecular level, and investigating relationships to phenotype.

蛋白质参与各种各样的化学反应和物理任务，它们在几乎所有的生物过程中都起着至关重要的作用。由于这个原因，许多研究学科都有兴趣了解蛋白质功能的起源，并了解蛋白质编码DNA的变化如何与生物过程和结构的差异相关。后一个问题有时被称为“基因型-表型关系”，理解这种关系需要了解复杂的遗传和表型相互作用。最近的研究表明，多达80%的人类非编码DNA（以前被认为是“垃圾”DNA）也可能与功能有关，这只会增加问题的复杂性。核心问题是，根据我们对基因和分子的了解，我们能在多大程度上理解并最终预测给定的生物表型。通过对DNA序列变化的建模，研究人员已经开始探索许多遗传系统的复杂性。对蛋白质编码DNA进行建模已被证明是研究蛋白质如何随时间变化以及这种变化如何与功能相关的有力方法。然而，现有的蛋白质编码DNA模型有局限性，为了朝着我们的中心目标取得更好的进展，我们必须继续改进它们。更重要的是，我们需要开发新的框架来评估功能分歧，这些框架可以推广到不同类型的DNA数据（不仅仅是蛋白质编码）和不同的功能分歧模式（episodic“>”）。该研究计划将开发几个新的模型家族，用于检测DNA序列数据的功能分歧。这些新模型都有一个共同的目标，即检测DNA中随着时间的推移，通过自然选择强度的变化（或“开关”）进行进化的位点。这种现象是功能约束变化的标志，我建议将其建模为马尔可夫调制的马尔可夫过程。有了这些新模型，我就可以建立一个创新的框架来共同模拟表型变化和DNA序列进化。这些模型将允许我们测试DNA位点的亚群，其进化与表型变化相关。首先，我们将重点关注与单一蛋白质相关的表型。然而，通过模型开发的长期计划，并应用于实际数据，我设想分析更高层次的表型，如基因表达模式，或病原体表型的进化（对加拿大具有战略意义的研究领域）。我还计划将其扩展到宏基因组设置。

Proteins participate in an extraordinarily diverse set of chemical reactions and physical tasks, and they have an essential role to play in virtually all biological processes. For this reason there is interest within many research disciplines to understand the origin of protein functions, and to learn how a change in protein-coding DNA relates to differences in biological processes and structures. The latter question is sometimes referred to as "the genotype-phenotype relationship", and understanding this relationship requires knowledge of complex genetic and phenotypic interactions. Recent suggestions that as much as 80% of human non-coding DNA (previously thought to represent "junk" DNA) may also be related to function, only adds to the complexity of the problem. The central question is to what extent can we understand and eventually predict a given biological phenotype from our knowledge of genes and molecules. Through modelling of DNA sequence change, researchers have begun to explore the complexities of many genetic systems. Modelling protein-coding DNA has proved to be a powerful way to investigate how proteins change over time, and how this change might be related to function. However, existing models for protein-coding DNA have limitations, and to make better progress towards our central goal we must continue to improve them. Even more importantly, we need to develop new frameworks for assessing functional divergence that can be generalized across different types of DNA data (not just protein-coding) and different modes of functional divergence (episodic "> This research program will develop several new families of models for detecting functional divergence from DNA sequence data. The new models are united by the common goal of detecting sites within DNA where evolution proceeds via changes (or "switches") in the intensity of natural selection over time. This phenomenon is a hallmark of change in functional constraint, and I propose to model it as a Markov-modulated Markovian process. With the new models in place I can then build an innovative framework for jointly modelling both phenotypic change and DNA sequence evolution. These models will allow us to test for subsets of DNA sites whose evolution is correlated with changes in phenotype. To start, we will focus on a phenotype associated with a single protein. However, through a long-term program of model development, and application to real data, I envision the analyses of higher-level phenotypes such as the pattern of gene expression, or the evolution of pathogen phenotypes (research fields of strategic importance to Canada). I also plan to extend it to the metagenomic setting.