The model incorporates both an immune response , which is believed to be a major factor limiting viral expansion by killing of infected cells, and the formation of long lived latently infected cells, which are believed to play an important role in limiting the longterm effects of antiviral therapy.

In this paper we develop a new mathematical model which incorporates the basic principles of previous host-centric models including a virus-dependent immune response [8] , viral latency and a progressive increase in cell activation [26, 27].

Values for five parameters (Q0, 80, NM, K and D) describing the characteristics of the immune response were chosen for each patient to minimise the error of the predicted quasi-stable level of T cell counts (N5) and viral load (V5), and the time of progression to AIDS (tA).

And we use the term K % to model the death of infected T cells by the cellular immune response .

K is initially 0 and changes to a higher value in Table 1 when the cellular immune response kicks in D (default value: 30) days after the initial infection.

The term captures the relationship between the strength of the immune response and the density of infected CD4+ T cells [8].

When either route is abolished, infection is blocked completely; T cell level returns to normal and virus is cleared after the cellular immune response kicks in.

In the context of the model, the transition from phase 1 (acute) to phase 2 (stable chronic) is driven by a balance between several processes, including viral spreading through two parallel modes, and the cellular immune response , i.e.

when activation rate is fixed), HIV infection would not progress to AIDS after the onset of the cellular immune response .

Immune responses are regulated by diffusible mediators, the cytokines, which act at sub-nanomolar concentrations.

The discovery that immune responses are regulated by small diffusible proteins — the cytokines — has been surprising because cytokine diffusion to ‘bystander’ cells might compromise specificity.

Recent experimental observations suggest that also paracrine IL-2 signals towards other Th cells are important for regulation of immune responses , while true au-tocrine IL-2 signals are suppressed by the intracellular signal transduction pathway [5,37].

However, our simulations show that the need for paracrine cytokine signals provides several checkpoints for the induction of immune responses downstream of the T cell receptor.

Adaptive immune responses must be rapid and effective in the case of strong infection, but also carefully controlled to avoid autoimmune diseases.

The physiological cytokine milieu regulates critical processes like the type and strength of the immune response .

It is not known how they diffuse under such conditions and, in turn, regulate immune responses .

To further analyze the observed differences in immune response between cases resulting in tumor death vs. survival, we defined a statistical metric for assessing the relative prevalence of M2 cells within the overall macrophage population.

We next evaluated whether the early immune response , as measured by the MPI, remains highly predictive of tumor survival over a range of model parameterizations.

Thus, a general feature of our simulations was that tumor survival or death is a nearly deterministic consequence of the early immune response long before growth of the tumor is impacted.

Conversely, the immune surveillance theory of cancer predicts that some neoplasms and metastases are controlled by the immune response [12] , and moreover, it is widely hypothesized that the initial response to cancer is inflammatory and immunostimulatory [13,14].

In this study, we used a computational approach to elucidate general principles by which heterogeneities in the spatial structure of the TME and in immune cell phenotype may drive the dynamic evolution of the collective immune response to a nascent metastatic tumor.