Markov Chain-Based Stochastic Modelling of HIV-1 Life Cycle in a CD4 T Cell

Replication of Human Immunodeficiency Virus type 1 (HIV) in infected CD4+ T cells represents a key driver HIV infection. The life cycle is characterised by the heterogeneity with respect to multiplicity infection and variability viral progeny. This can result from phenotypic diversity as well random effects fluctuations kinetics biochemical reactions underlying virus replication cycle. To quantify contribution stochastic kinetics, we propose high-resolution mathematical model formulated Markov chain jump process. applied generate statistical characteristics (i) cell multiplicity, (ii) cooperative nature replication, (iii) secretion phenotypically identical cells. We show that fixed number viruses per leads some integrated proviral genomes. bottleneck factors production are identified, including Gag-Pol proteins. Sensitivity analysis enables ranking parameters strength their impact on size first three globally influential transport genomic mRNA membrane, tolerance transcription activation Tat-mediated regulation, degradation free mature virions. These be considered potential therapeutical targets.