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

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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.