Adapting Biosystems

我研究的一个主要目标是了解在谱系数量上产生多样性的过程。这是宏观进化领域的一个主要挑战，我在那里开始了我的研究；我们才刚刚开始能够解释为什么生命树的一些分支比其他分支更加多样化。我之前已经确定了在不同的有机体群体和不同的时间尺度上塑造生物多样性的过程中的一些基本规律；在我的研究小组中，我们希望了解可能解释这些规律的进化和生态机制。我还确定了科学家仅从分子数据就能了解多少生物多样性的基本限制，并表明在某些情况下，研究人员可能一直在挑战这些限制。在我的下一阶段研究中，我的目标是扩展我们之前的洞察力，并量化我们可以从分子数据集中提取关于研究人员广泛感兴趣的其他几种进化现象的信息的硬界限。这项工作将帮助我们准确地确定我们希望了解什么(以及我们到底不能了解什么)，为最大限度地利用现有数据开发新的战略，并确定需要额外类型数据的推断。这些结果也与疾病动力学的研究有关。对于快速进化的病原体，如SARS-CoV2，遗传多样性在流行病的时间尺度上积累，这意味着我们有可能利用分子系统发生来估计流行病学参数，如R0。由于用于研究病毒谱系多样性的工具与用于研究生物多样性的工具非常相似，我正在扩展我们的宏观进化工作，以量化病毒系统发育的信息含量。这将更好地衡量我们对流行病学推断的信心。在这项工作的同时，我正在开发新的方法，以便更好地将分子数据与更传统的病例计数数据相结合，以便这些不同类型的信息可以相互告知。我还计划使用宏观进化工具来重建B细胞(抗体)序列的多样化。与免疫学家合作，我正在开发新的统计技术，用于衡量个人B细胞谱系的进化，并准确定义我们可以和不能从纯粹基于序列的方法中学到什么。

A major goal of my research is to understand the processes that generate diversity in the number of lineages. This is a primary challenge in the field of macroevolution, where I got my start in research; we are only beginning to be able to explain why some branches of the Tree of Life are so much more diverse than others. I have previously identified some fundamental regularities in the processes that shape biodiversity across disparate groups of organisms and across disparate time scales; in my research group, we are looking to understand the evolutionary and ecological mechanisms that might explain these regularities. I have also identified some fundamental limits to how much scientists can learn about the processes that generate biodiversity from molecular data alone and have shown that in some cases, researchers may have been pushing these limits. In the next phase of my research, I aim to extend our previous insights and quantify hard bounds on what information we can extract from molecular datasets regarding several other evolutionary phenomena that are of broad interest to researchers. This work will help us determine exactly what we can hope to learn (and exactly what we cannot), to develop novel strategies for making the most of available data, and to identify inferences for which will require additional types of data. These results are also relevant to the study of disease dynamics. The fact that for rapidly evolving pathogens, such SARS-CoV2, genetic diversity accumulates on the time-scale of epidemics, means we can potentially estimate epidemiological parameters such as R0 using molecular phylogenies. As the tools used to study viral lineage diversity are very similar to those used to study organismal diversity, I am extending our macroevolutionary work to quantify the information content of viral phylogenies. This will provide a better measure of our confidence in epidemiological inferences. Alongside this work, I am developing novel approaches for better integrating molecular data with more conventional case count data so that these different types of information can reciprocally inform one another. I also plan to use macroevolutionary tools to reconstruct the diversification of B-cell (antibody) sequences. Working with immunologists, I am developing new statistical techniques for measuring the evolution of an individuals B-cell repertoire as well as precisely defining what we can and cannot learn from purely sequence-based approaches.