Evolution of Disease: Current patterns and future trends

描述(由申请人提供)：该项目的主要目标是从进化力量的角度理解当前的疾病行为，从而使用复杂的计算机模拟模型，并由实验室系统的详细实验观察支持，从而预测未来可能发生的变化。人类和动物疾病都应该接近进化稳定的策略，这样它们的生活史特征(例如传播率、潜伏期和感染期)不会被其他相互竞争的毒株入侵。我们试图确定潜在的过程，从而了解观察到的疾病行为的范围。然后，这种基本理解可以扩展到两个应用方向。第一个是确定当前疾病可能的演变，以回应社会(和性)混合模式的变化。第二个是检查药物或抗生素耐药性的演变以及控制其传播的方法。因此，这项工作具有与健康相关的重要后果，预测哪些疾病可能在未来几十年演变为主要的公共卫生问题，以及如何最好地保存数量不断减少的有效抗生素，因为没有耐药疾病。这项研究的基本工具将是复杂的计算机模拟，用于疾病在由潜在传播路线定义的网络上的传播和演变。在可能的情况下，这些模拟将通过更一般的数学模型来加强，例如成对或集合种群方程。到目前为止，所有进化模型的一个严重局限性是它们依赖于各种生活史要素之间的弱支持权衡；一个常见的假设是毒力随着传播率的增加而增加。这个项目将通过检查细菌与噬菌体相互作用中存在的权衡来克服这种数据的匮乏，这些相互作用可以在实验室中仔细控制。细菌-噬菌体系统是自然界中最常见的宿主-疾病相互作用的例子，因此在许多生态环境中都有深远的影响。细菌可以在不同的环境中培养(以模拟不同的人类传播网络)，细菌和噬菌体的生命周期都足够快，可以很容易地观察到巨大的进化变化。然后，在实验系统中看到的权衡和限制的类型可以用来制定更具生物学机械性的、更准确的高等生物体疾病进化的计算机模型。

DESCRIPTION (provided by applicant): The main aims of the project are to understand current disease behavior in terms of evolutionary forces, and consequently predict plausible future changes, using complex computer simulation models supported by detailed experimental observations from a laboratory system. Human and animal diseases should all be close to an evolutionary stable strategy, such that their live-history characteristics (e.g. transmission rate, incubation and infectious period) cannot be invaded by other competing strains. We seek to determine the underlying processes and hence understand the range of disease behaviors observed. This basic understanding can then be extended in two applied directions. The first is to determine the likely evolution of current diseases in response to changes in social (and sexual) mixing patterns. The second is to examine the evolution of drug or antibiotic resistance and methods to control its spread. This work therefore has important health related consequences, predicting which diseases are likely to evolve into a major public health concern in the coming decades and how to best conserve the dwindling number of effective antibiotics for which there are no resistant diseases. The basic tool for this research will be sophisticated computer simulation for disease spread and evolution on a network defined by potential transmission routes. Where possible these simulations will be strengthened by more generic mathematical models, such as pair-wise or metapopulation equations. One severe limitation with all evolutionary model to date is their dependence on weakly supported trade-offs between the various life-history elements; a common assumption is that virulence increases with transmission rate. This project will over-come this scarcity of data by examining the trade-offs present in bacteria-phage interactions which can be carefully controlled in the laboratory. Bacteria-phage systems are the most common example of host-disease interactions in nature, and as such have a profound impact in many ecological settings. Bacteria can be cultured in a variety of environments (to simulation different human transmission networks), and the life-cycle of both bacteria and phage are sufficiently rapid that large evolutionary changes can be readily observed. The types of trade-offs and constraints seen in the experimental systems can then be used to formulated more biologically mechanistic and more accurate computer models of disease evolution in higher organisms.