Competition, diversity and success: understanding ecology with genomic data

随着时间的推移，细菌的基因组数据正在被大量和连续地收集。虽然大型基因组研究主要是描述性的，但这些数据首次为建立和测试进化和生态学的高分辨率定量模型提供了机会。这是这个探索基金研究项目的长期愿景：我计划将基因组研究从仅限于描述的领域转移出来，发展一系列研究，其中基因组数据可以帮助建立对生态现象的定量描述，并用于完善和测试假说。在接下来的五年里，我将把重点放在一个不确定的关键领域，即细菌如何构建它们的种群以及如何创造和保持多样性。我的目标是弥合模型和基因组数据之间的差距，将系统基因组数据与建模和统计联系起来，以了解三个不同的细菌模型系统中多样性的创建和维持。未来五年有三个中心目标：(1)确定一系列规模的种群结构，并直接根据系统基因组数据定义有意义的亚组；(2)描述组内和组内竞争的特征并量化多样性，建立与基因组数据匹配的可解释模型；以及(3)使用模型进行短期预测，从而用序列抽样数据检验模型。我将使用来自三个细菌系统的数据，包括系统发生数据(通过沿着系统发生树的进化而进化)和“非树状”数据(不是这样的数据，包括水平转移的基因、汇聚的位置、表型数据和元数据)。这些在这里被用来作为自然系统的模型，在存在选择的情况下，自然系统在可观察的时间尺度上进化。这些系统的数据之所以可以获得，部分原因是与人类健康的相关性，但这里的重点是基本现象。这种方法有四个关键的优点：(1)全基因组序列(WGS)数据的使用将使我们能够在不同的分辨率水平上工作，从小的细粒度尺度到更广泛的种群；(2)我们可以检测和模拟惯性和适应性模式，因为我们有关于选择的信息；(3)在这三个不同的系统中，我们有更多和更少的“树状”数据，所以我们可以用非树状数据来补充系统发育方法；(4)随着时间的推移，现在正在收集数据，这为通过时间对生态过程进行建模、将模型与系统发育数据相匹配并检验模型预测提供了机会。

Genomic data for bacteria are now being gathered in large quantities and serially through time. While large genomic studies have been primarily descriptive in nature, these data present the opportunity to build and test quantitative models in evolution and ecology, at a high level of resolution, for the first time. This is the long-term vision of this Discovery Grant research program: I plan to move genomic studies out of the description-only realm, developing a body of research in which genomic data can help to build quantitative descriptions of ecological phenomena and are used to refine and test hypotheses. In the next five years, I will focus on a key area of uncertainty, namely how bacteria structure their populations and create and maintain diversity. I aim to bridge the gap between models and genomic data, connecting phylogenomic data with modelling and statistics to understand the creation and maintenance of diversity in three distinct bacterial model systems. There are three central objectives over the next five years: (1) to identify population structure at a range of scales and define meaningful subgroups directly from phylogenomic data; (2) to characterize inter- and intra-group competition and quantify diversity, building interpretable models that match genomic data, and (3) to use the models to make short-term predictions and thereby test models with serially sampled data. I will use data from three bacterial systems, comprising both phylogenetic data (that evolves by descent along a phylogenetic tree) and "non tree-like" data (data that do not, including genes that are transferred horizontally, convergent loci, phenotypic data and metadata). These are used here as models of natural systems that are evolving on observable time scales in the presence of selection. Data for these systems are available in part due to relevance for human health, but the focus here is on fundamental phenomena. There are four key advantages to this approach: (1) the use of whole-genome sequence (WGS) data will enable us to work at different levels of resolution, from small fine-grained scales to broader populations; (2) we can detect and model both inertial and adaptive patterns, as we have information about selection; (3) we have both more and less "tree-like" data in these three distinct systems so we can complement phylogenetic methods with non-tree-like data; and (4) data are now being gathered over time, affording the opportunity to model ecological processes through time, fit models to phylogenomic data and test model predictions.