Connecting theory with data in host-parasite evolution

我的研究项目旨在通过建立数学和统计模型来分析数据并将其与理论联系起来，从而了解生态社区和疾病爆发。我 * 建议探索寄生虫的短期进化（即，*致病生物，如病毒、细菌或肠道蠕虫）及其宿主。从长远来看，寄生虫通常会进化到中等水平的攻击性（宿主剥削），在不充分利用宿主（或被更具侵略性的寄生虫击败）和过快杀死它们之间进行妥协;宿主通过攻击它们（抵抗）或学习与它们共存（耐受）来减少寄生虫的破坏性影响。然而，在短期内，例如在流行病期间，* 进化的方向往往令人惊讶;我将建立新的模型，并使用 * 现有的数据来预测寄生虫利用和宿主防御如何共同进化。我们必须把抗性、耐受性和利用看作是宿主-寄生虫关系中相互关联的部分，而不是各自独立进化的特征。虽然生物学家在测量宿主抗性和耐受性时越来越多地采用这种整体观点，但我们还没有一个完整的理论框架来解释这三个特征的共同进化。我将构建数学和计算模型，以预测在流行病期间抵抗力、耐受性和利用率的联合变化。由于数据的限制，* 目前只有这些模型的简单版本可以在 * 真实的世界中使用，但开发通用模型将帮助我们了解 * 未来我们需要收集什么样的数据，以便 * 了解和预测疾病及其宿主的演变。我的研究计划的另一个最新见解是，宿主和 * 疾病可以迅速进化，即使流行病在 * 人群中传播：这些“生态进化”模型结合了联合收割机生态和 * 进化过程。我以前开发过简单的生态进化模型，但我将继续构建更复杂、更现实的模型，这些模型可以应用于数据并进行测试。 特别是，我将为艾滋病毒（人类）和多发性粘液瘤（兔子）的抗性、耐受性和利用的进化开发生态进化模型。虽然建模者已经开始解决这些疾病的生态进化动力学问题，但他们的模型缺少重要的组成部分。我来回答问题：* 社会特征，如伴侣关系的持续时间或性工作者的使用 * 如何影响艾滋病毒和其他性传播 * 疾病的演变速度？我们如何期望粘液瘤病在短的地理范围内或在一个季节内发生变化？这些问题的答案将建立我们对传染病的生态学和进化的理解，帮助我们预测和管理野生动物、家养动植物和人类的传染病。

My research programme aims to understand ecological***communities and disease outbreaks by building mathematical and***statistical models to analyze data and connect it with theory. I***propose to explore the short-term evolution of parasites (i.e.,***disease-causing organisms such as viruses, bacteria, or intestinal***worms) and their hosts. Over the long term, parasites often evolve to***intermediate levels of aggressiveness (host exploitation),***compromising between underexploiting their hosts (or being outcompeted***by more aggressive parasites) and killing them too quickly; hosts***evolve to reduce the damaging effects of parasites either by attacking***them (resistance) or learning to live with them (tolerance). Over the***short term, however, e.g. during an epidemic, the direction of***evolution is often surprising; I will make new models, and use***existing data, to predict how parasite exploitation and host defences***co-evolve.******We must consider resistance, tolerance, and exploitation as***interlocking parts of the host-parasite relationship, rather than as***separately evolving traits. While biologists have increasingly adopted***such a holistic view when measuring host resistance and tolerance, we***do not yet have an integrated theoretical framework for the***coevolution of these three traits. I will construct mathematical and***computational models to predict joint changes in resistance,***tolerance, and exploitation during epidemics. Due to data limitations,***at present only simple versions of these models can be used in the***real world, but developing the general models will help us understand***what kinds of data we need to collect in the future in order to***understand and predict the evolution of diseases and their hosts.******Another recent insight of my research programme is that hosts and***diseases can evolve quickly, even as an epidemic spreads in the***population: these “eco-evolutionary” models combine ecological and***evolutionary processes. I have previously developed simple***eco-evolutionary models, but will move forward to build more complex,***realistic models that can be applied and tested against data. In***particular, I will develop eco-evolutionary models for the evolution***of resistance, tolerance and exploitation in HIV (in humans) and in***myxomatosis (in rabbits). While modellers have already begun to tackle***the eco-evolutionary dynamics of these diseases, their models are***missing important components. I will answer the questions: how do***social characteristics like partnership duration or use of sex workers***affect the rate of evolution of HIV and other sexually transmitted***diseases? How do we expect myxomatosis to change over short geographic***scales or over the course of a season?***The answers to these questions will build our understanding of the ecology and evolution of infectious disease, helping us to predict and manage infectious diseases of wildlife, domestic plants and animals, and humans.**