  Multiple mechanisms in the HIV lifecycle play a role in its ability to evade
therapy and become a chronic, difficult-to-treat infection. Within its major
cellular target, the activated T cell, many steps occur between viral entry and
viral burst, including reverse transcription of viral RNA, integration of the
viral DNA in the host genome, viral transcription, splicing, translation, host
and viral regulation, and viral packaging. These steps exploit complex networks
of macromolecular interactions that exhibit various forms of stochastic
behavior. While each of the steps of HIV infection have been individually
studied extensively, the combinatorial contribution of rare events in each of
the steps, and how series of these rare events lead to different infection
phenotypes, are not well understood. The complexity of these processes render
experimental study challenging. Therefore, we have built a comprehensive
computational model of this large system, by collating the community's
knowledge of the infection process. It is a stochastic model where rates of
different events in the system are represented as probabilities of the event
occurring in a timestep of the simulation. This model enables an understanding
of the noise and variation in the system. The model also facilitates a
dissected understanding of each small part of the large complex system, and its
impact on the overall system dynamics.
