Impact of network-structured populations on evolution

网络结构种群对进化的影响

• 批准号: EP/T031727/1
• 负责人: Kieran Sharkey
• 金额: $ 58.9万
• 依托单位: University of Liverpool
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT031727%2F1
• 关键词: Impact; network; structured; populations; evolution

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.

群体的结构通常是这样的：个体只与他们的近邻而不是与所有个体进行交互。在这些结构中，发生了进化动力学。这些动态支撑着许多领域，包括我们物种的进化、抗菌素耐药性和季节性流感的发展以及思想、语言和社会的出现。表示此类结构的关键方法是通过联系网络，并且进化图论（EGT）已被开发来描述和理解这些过程。这种结构以多种方式理想化，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。进化图论假设进化动力学与驱动进化的出生和死亡的基础生态过程之间有明显的区别。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转化为更现实场景的程度。至关重要的是，该模型将是第一个与实验室条件下对结构化群体进行的真实抗菌素耐药性进化实验的经验数据相结合的模型。这将证明新数学框架的适用性，为其在描述现实世界的病原体进化系统中的应用提供支持，这些系统发生在对健康至关重要的结构化人群中，例如医院环境中的抗菌素耐药性、航空公司航线上的流感以及地理限制。

项目成果

Additional file 1 of Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2

BNT162b2 (Pfizer-BioNTech) 和 ChAdOx1 nCoV-19 (Oxford-AstraZeneca) 疫苗降低 SARS-CoV-2 Delta 变体 (B.1.617.2) 感染易感性的有效性的附加文件 1

• DOI: 10.6084/m9.figshare.19388922
• 发表时间: 2022
• 作者: Pattni K

Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2.

• DOI: 10.1186/s12879-022-07239-z
• 发表时间: 2022-03-20
• 期刊: BMC infectious diseases
• 影响因子: 3.7
• 作者: Pattni K;Hungerford D;Adams S;Buchan I;Cheyne CP;García-Fiñana M;Hall I;Hughes DM;Overton CE;Zhang X;Sharkey KJ
• 通讯作者: Sharkey KJ

Effectiveness of the BNT162b2 (Pfizer-BioNTech) and the ChAdOx1 nCoV-19 (Oxford-AstraZeneca) vaccines for reducing susceptibility to infection with the Delta variant (B.1.617.2) of SARS-CoV-2

BNT162b2（辉瑞-BioNTech）和 ChAdOx1 nCoV-19（牛津-阿斯利康）疫苗降低 SARS-CoV-2 Delta 变体（B.1.617.2）感染易感性的有效性

• DOI: 10.1101/2021.10.12.21264840
• 发表时间: 2021
• 作者: Pattni K