Connecting theory with data in host-parasite evolution

我的研究计划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，以了解生态群落和疾病爆发。我*建议探索寄生虫(即*致病生物体，如病毒、细菌或肠道蠕虫)及其宿主的短期进化。从长期来看，寄生虫通常进化到*中等攻击性水平(宿主利用)，*在未充分利用宿主(或被更具攻击性的寄生虫超越*)和太快杀死它们之间做出妥协；宿主*进化以通过攻击*寄生虫(抵抗力)或学会与它们共处(容忍)来减少寄生虫的破坏性影响。然而，在*短期内，例如在流行期间，*进化的方向往往令人惊讶；我将建立新的模型，并使用*现有数据来预测寄生虫利用和宿主防御*是如何共同进化的。我们必须将抗药性、耐受性和利用视为宿主-寄生虫关系的*相互关联的部分，而不是*单独进化的特征。虽然生物学家在测量寄主抗性和耐受性时越来越多地采用这样的整体观点，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，以预测流行病期间耐药性、耐受性和剥削方面的联合变化。由于数据的限制，*目前只有这些模型的简单版本可以在*现实世界中使用，但开发通用模型将帮助我们了解*未来我们需要收集哪些类型的数据才能*了解和预测疾病及其宿主的进化。*我最近的研究计划的另一个见解是，宿主和*疾病可以快速进化，即使流行病在*人群中传播：这些“生态进化”模型结合了生态学和*进化过程。我之前开发了简单的*生态进化模型，但我将继续构建更复杂、*现实的模型，这些模型可以应用于数据并进行测试。具体地说，我将为HIV(在人类)和*粘液瘤(在兔子)中的耐药性、耐受性和利用的进化*开发生态进化模型。虽然模型师已经开始研究这些疾病的生态进化动态，但他们的模型缺少重要的组成部分。我将回答以下问题：*伴侣关系持续时间或使用性工作者等*社会特征如何影响艾滋病毒和其他性传播疾病的进化速度？我们如何期望粘液瘤病在短的地理*尺度上或在一个季节的过程中发生变化？*这些问题的答案将建立我们对传染病的生态和进化的理解，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.**