Collaborative Research: Infection Multiplicity and Virus Evolution, from Experiments to Large Scale Multi-Population Stochastic Computations

要了解生物体是如何随时间进化的，需要发展对进化理论的预测性数学描述，并进行实验研究来检验这些预测。由于渐进和可遗传的基因变化发生在多个世代之间，进化论很难研究。病毒遵循与高等生物体相同的基本进化规则，但复制和进化要快得多。因此，它们为进化和选择理论的检验提供了一个近乎理想的模型系统。病毒还能够进行社会互动，从而改变它们的进化行为。社会互动是由一种称为联合感染的过程产生的，通过这种过程，多个病毒基因组在单个细胞中感染和复制，并可能导致许多鲜为人知的相互作用。研究小组将进行一系列综合的实验和数学分析，旨在为这些复杂的过程提供见解。这项研究对观察到不同病原体同时感染同一宿主并影响彼此传播能力的联合感染模式的其他流行病学情况也具有意义。因此，正在开发的新的数值技术在包括癌症研究在内的更一般的健康领域具有相关性和意义。RNA病毒的特点是突变率高，使它们能够迅速多样化并随时适应环境挑战。因此，它们提供了一个近乎理想的模型系统，用于测试进化和选择理论，而这些理论在更复杂的生物体中是难以接近的。通常，病毒基因组被认为是孤立的实体，然而，细胞的多重感染(合并感染)是常见的，并导致一系列鲜为人知的社会互动，这些互动具有塑造进化轨迹的能力。评估该模型的一个高度易处理的实验系统是在体外复制人类免疫缺陷病毒(HIV-1)。HIV-1的多重感染是通过细胞间的接触和病毒学突触的形成而促进的，在突触中，多个病毒同时从一个细胞转移到另一个细胞。相反，通过释放游离病毒颗粒传播会促进单一感染。突触和自由病毒传播的相对发生，从而感染的多样性，可以用创新的实验技术来优雅地操纵。将使用几种实验技术来生成不同感染倍数下的病毒生长和病毒进化的数据。在病毒传播过程中，传播途径(突触和游离病毒)将被操纵以改变感染的多样性。进化动力学将在经历各种社会互动的不同突变类型的背景下进行探索。将引入新的技术，以操纵无细胞和突触传递的相对重要性，从而控制感染的多样性。将开发新的计算算法，以充分了解多重感染和社会互动如何影响动态。数学模型将被测试、参数化，并被用来探索大种群规模下的进化动力学。这项研究将对影响生物体进化的机制的相对重要性提供更多的见解。

To understand how organisms evolve with time requires the development of predictive mathematical descriptions of evolutionary theories along with experimental studies to test these predictions. Because incremental and inheritable genetic changes happen over multiple generations evolutionary theory is difficult to study. Viruses obey the same basic evolutionary rules as higher organisms, but replicate and evolve much faster. Therefore they provide a near-ideal model system for the test of evolutionary and selection theories. Viruses are also capable of social interactions which can change their evolutionary behaviors. Social interactions arise by a process called co-infection, whereby multiple virus genomes infect and replicate in a single cell, and can result in a number of poorly understood interactions. The research team will perform a series of integrated experimental and mathematical analyses aimed at providing insights into these complex processes. This research also has significance to other epidemiological situations in which co-infection patterns are observed when different pathogen species simultaneously infect the same host and impact each others' ability to spread. Thus the novel numerical techniques being developed have relevance and significance in more general health areas including cancer studies. RNA viruses are characterized by a high mutation rate, allowing them to rapidly diversify and readily adapt to environmental challenges. They provide, therefore, a near-ideal model system for the testing of evolutionary and selection theories that are difficult to approach in more complex organisms. Typically, virus genomes are considered as isolated entities, however, multiple infection (coinfection) of cells is a common occurrence and results in a series of poorly understood social interactions that have the power to shape evolutionary trajectories. A highly tractable experimental system for assessing the model is replication of the human immunodeficiency virus (HIV-1) in vitro. HIV-1 multiple infection is promoted by cell-to-cell contact and the formation of virological synapses, in which multiple viruses are simultaneously transferred from one cell to another. In contrast, spread via the release of free virus particles promotes single infection. The relative occurrence of synaptic and free virus transmission, and hence infection multiplicity, can be elegantly manipulated with innovative experimental techniques. Several experimental techniques will be used to generate data on virus growth and virus evolution at different infection multiplicities. Transmission pathways (synaptic and free virus) will be manipulated to change infection multiplicity during virus spread. Evolutionary dynamics will be explored in the context of different mutant types that undergo a variety of social interactions. New techniques will be introduced in order to manipulate the relative importance of cell-free and synaptic transmission, and thus infection multiplicity. Novel computational algorithms will be developed in order to fully understand how multiple infection and social interactions impact the dynamics. Mathematical models will be tested, parameterized, and employed to explore the evolutionary dynamics at large population sizes. This research will provide greater insights on the relative significance of mechanisms that impact evolution of organisms.