Human immunodeficiency virus (HIV-1) is the pathogenic agent of HIV infection thatprecedes the total breakdown of cellular immunity, a condition known as acquiredimmunodeficiency syndrome (AIDS). The pandemic nature of the disease has promptedintense research into its biology. Already, much is known about HIV-1 infection, lifecycle,and progression to aids. Systems biology enables the combination of complex data fromthese studies into a framework where their effect on the various levels of cellularorganization (i.e. Pathways, cells, tissues, organs and the whole body) could be studied insilico. In this thesis, first, we reviewed our knowledge of the HIV-1 Human InteractionDatabase. We examined its contents and identified processes that HIV-1 was not previouslyknown to interact with. Then, we attempted an in silico dynamic model of HIV-1 interaction.We built a model of HIV-1 interaction with the CD4 T cell activation pathway comprised of137 nodes (16 HIV-1, 121 human) and 336 interactions. The model reproduced expectedpatterns of T cell activation. Using interaction graph properties, we identified 26 host cellfactors, including MAPK1&3, Ikkb-Ikky-Ikka and PKA, which contribute to the net activationor inhibition of viral proteins. By following a logical Boolean formalism, we identified 9 hostcell factors essential to the functions of viral proteins in the activation pathway. This wasthe first attempt to model dynamic viral-host interaction relationships.Then, we organize HIV-1 interacting host genes into modules to represent cellular processesneeded by the virus. We combined HIV-1 interactions with host gene GO annotations toclassify host genes according to these needed cellular processes. We obtained 201 modulesand found the same set of viral proteins do not interact with host genes having similarmodules suggesting intelligence in its co-ordination of host processes. This work is one of agrowing list that explores coordination of HIV-1 interactions. But more importantly, it would bebeneficial to functionally downsize the large dynamic HIV-1 interaction network.Finally, in our discussion, we discuss our results and suggest possible ways in which our workon dynamic models could be improved. This work is opening up a new field of systems virologythat studies the effect of viruses on the host in terms of its temporal and spatial aspects.