Local identifiability of a HIV-1 infection model using a sensitivity approach
The dynamic modeling of the Human Immunodeficiency Virus 1 (HIV-1) infection is still one of the great challenges in systems biology. The high prevalence of Acquired Immune Deficiency Syndrome (AIDS), known to be caused by HIV, and the fact that no cure has yet been discovered, confers relevancy to this area of study. In this paper, a dynamic model for the HIV-1 infection is analyzed. The sensitivity and identifiability issues are addressed with the purpose of optimizing the time points at which patients' blood samples should be drawn. This paper shows that there are time periods far more informative than others, thus improving parameter identifiability and estimability in the reverse engineering step.