Impact of network-structured populations on evolution

人口的结构通常是这样的，即个人只与其近邻互动，而不是所有个人。在这些结构中，进化动态发生。这些动态支撑了许多领域，包括我们物种的进化、抗菌素耐药性的发展和季节性流感以及思想、语言和社会的出现。表示这种结构的一个关键方法是通过联系网络，而进化图论(EGT)已经被用来描述和理解这些过程。这种结构在几个方面都是理想化的，在这里，我们的主要目标是使其更直接地适用于理解和描述进化动力学，特别是病原体进化动力学。进化图理论假定进化动力学和驱动其产生和死亡的基础生态过程之间存在明显的区别。这导致生物过程缺乏现实性，并限制了其解决实际问题的效用。通过解决这个问题，我们将获得一个新的、更适用的框架。我们将确定进化图论定理的稳健性，以及它们在多大程度上转化为更现实的场景。关键是，该模型将第一次与实验室条件下对结构化种群进行的真实抗菌素耐药性进化实验的经验数据相结合。这将证明新的数学框架的适用性，为其在描述对健康至关重要的结构化人群中发生的病原体进化的真实世界系统提供支持，例如医院环境中的抗菌素耐药性、航线上的流感以及地理限制。

Populations are often structured in the sense that individuals are only interacting with their immediate neighbours rather than all individuals. Within these structures, evolutionary dynamics occur. These dynamics underpin many areas including the evolution of our species, the development of antimicrobial resistance and seasonal influenza as well as the emergence of ideas, language and society. A key way of representing this type of structure is by networks of contacts, and Evolutionary Graph Theory (EGT) has been developed to describe and understand these processes. This structure is idealised in several ways and here, our primary objective is to make this more directly applicable to understanding and describing evolutionary dynamics and especially pathogen evolutionary dynamics. Evolutionary graph theory assumes a clear distinction between evolutionary dynamics and the underpinning ecological processes of birth and death that drive it. This leads to a lack of realism in the biological processes and limits its utility for addressing real problems. By resolving this, we will obtain a new, more applicable framework. We shall determine the robustness of the theorems of evolutionary graph theory and the extent to which they translate to more realistic scenarios.Crucially, this model will be the first to be coupled with empirical data from real antimicrobial resistance evolution experiments performed on structured populations in laboratory conditions. This will demonstrate the applicability of the new mathematical framework, providing support for its application in describing real-world systems of pathogen evolution which occurs in structured populations critical for health such as antimicrobial resistance in hospital environments, influenza over airline routes as well as geographic constraints.