Methods for quantifying selection in evolving populations

项目摘要 理解复杂进化群体中的选择是生物医学科学的一个共同主题。 例子包括表征导致癌症的驱动突变，病原体进化以逃避 人类免疫反应和抗药性细菌的生长。最近的实验进展 大大增加了时间遗传数据的可用性，可用于检测选择， 更高的准确性和精确度。然而，从时间遗传数据推断选择仍然是技术上的， 挑战性我研究的中心目标是开发和应用有效的计算和统计 定量描述进化动力学的方法，包括选择在进化中的作用。绘图 在从统计物理学衍生出来的新方法上，我们将开发出稳健的、可扩展的和可解释的方法 从时间遗传数据中推断突变的适应性效应，考虑到诸如遗传 连锁、上位性和时变选择。这些方法将被集成到一个软件包中， 使它们更广泛地为社区所用。我们将重点介绍两个具体应用：1） 研究人类免疫缺陷病毒（HIV）-1逃避适应性免疫反应的进化， 快速和复杂进化的典型例子，2）解释蛋白质的大规模平行测定 功能我们的研究计划将创造新的工具来理解复杂的进化群体，并应用于 他们阐明宿主-病原体协同进化动力学，并提高广泛使用的高通量 实验技术

PROJECT SUMMARY Understanding selection in complex evolving populations is a common theme across the biomedical sciences. Examples include the characterization of driver mutations that lead to cancer, pathogen evolution to escape human immune responses, and the growth of antibiotic-resistant bacteria. Recent experimental advances have substantially increased the availability of temporal genetic data, which could be exploited to detect selection with greater accuracy and precision. However, inferring selection from temporal genetic data remains technically challenging. The central goal of my research is to develop and apply efficient computational and statistical methods to quantitatively describe evolutionary dynamics, including the role of selection in evolution. Drawing on novel approaches derived from statistical physics, we will develop robust, scalable, and interpretable methods to infer the fitness effects of mutations from temporal genetic data, accounting for features such as genetic linkage, epistasis, and time-varying selection. These methods will be integrated into a software package in order to make them more widely accessible to the community. We will focus on two specific applications: 1) investigating the evolution of human immunodeficiency virus (HIV)-1 to evade adaptive immune responses, a prototypical example of rapid and complex evolution, and 2) interpreting massively parallel assays of protein function. Our research program will create new tools for understanding complex evolving populations and apply them to elucidate host-pathogen coevolutionary dynamics and to improve widely used high-throughput experimental techniques.