Stochastic modeling for studying the effects of BET inhibitors on the modulation of P-TEFb levels

Latent reservoir of HIV is the major obstacle in eradicating HIV from infected patients. Reversing this latency is an important goal for developing effective treatment strategy. Recent studies have shown that BET protein inhibitors can successfully reverse this latency by inhibiting the binding of BET proteins with cellular cofactor P-TEFb. Such inhibition leads to enhanced association of P-TEFb with viral Tat proteins which can lead to HIV transactivation. However, in cells of our immune system which are primarily infected by the virus, number of P-TEFb is very low and is considered as one of the factors in inducing viral latency. At such small numbers of P-TEFb, the internal fluctuations can have a decisive role in the cell fate decision and corresponding noise in the P-TEFb levels can switch the HIV to either a state of active replication or to a state of latency. Aimed at quantitative understanding of how BET inhibitors affect the statistics of PTEFb level, we develop a coarse-grained stochastic model. The interaction between P-TEFb and BET proteins makes the problem analytically challenging. Based on biologically relevant approximations, we derive analytical results for the mean and noise associated with P-TEFb levels in the steady state. The results derived will be helpful in estimating the model parameters as well in identifying the pathways that can intervened for effective HIV transactivation.