Cellular Automata Model of Drug Therapy for HIV Infection - Cellular Automata - page 287

[Rule 3] Infected-A2 cells become dead cells.
- The depletion of the infected cells by the immune response.
[Rule 4]
(a) Dead cells can be replaced by healthy cells with probability p repl in the
next step ( p repl = 99% ) or remain dead with probability 1 - p repl .
- The replenishment of the depleted cells mimics the high ability of the
immune system to recover from the immuno-suppression generated by
infection. As a consequence, it will also mimic some diffusion of the cells
in the tissue.
(b) Each new healthy cell introduced, may be replaced by an infected-A1
cell with probability p infec ( p infec =10 − 5 ).
- The introduction of new infected cells in the system, either coming from
other compartments of the immune system or from the activation of the
latent infected cells.
2.3 The DTHI Model
Based on the model summarized above, we incorporate the drug therapy process
into the CAmodel for drug therapy. All approved anti-HIV, or anti-retroviral,
drugs attempt to block viral replication within cells by inhibiting either reverse
transcriptase or the HIV protease. In addition to the 'delayed' infection modelled
in Rule 1b and the latent infection in Rule 4b, the main source of HIV infection
in the HI model is Rule 1a. We limit the range of HIV infection (infected A1
cells) by giving a rank level N (0 ≤ N ≤ 7). This mimics the principle that
the drug prevents the virus from replication, resulting in less e A cient infection.
N represents the effectiveness of each drug. The bigger N , the less e A cient the
drug. Different drug therapies are modelled by different response functions P resp
over the time. P resp represents the response function for each drug therapy,
which have effects on the infected A1 cells after the starting of a drug therapy.
This models the fact that the drug therapy will not immediately influence all of
infected A1, but rather it will affect part of them at each time step. Over time
these effects of drug therapy can (and will) decay. At the same time, this also
mimics the concept of drug resistant virus strains.
[Modified Rule 1 (a)] Update of a healthy cell:
If there is one A1 neighbour during the time of drug therapy, N (0 ≤ N ≤
7) neighbour healthy cells become infected-A1 in the next time steps with
probability p resp .Otherwise,allofeightneighboursbecomeinfected-A1cells.
N isrelatedtoeffectivenessofeachdrug.Non-uniformCArulesisusedwhen
the therapy starts. At the time step t c during the therapy, p resp ( t c ) infected-
A1 cells have rank N and 1 − p resp ( t c ) infected-A1 cells have the max rank
eight.
[Modified Rule 3] We propose to adapt Rule 3 by adding 'In The Next Time
Step'. This mimics the fact that infected-A2 cell will also be present in the
lymph-node for a short time but with less infection ability, compared to
infected-A1 cells.
[The rest of rules] don't change.