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Palsson et al., (2013). The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models.

May 2016, model of the month by Corina Dueñas Roca
Original model: BIOMD0000000608.


Integrating multiple quantitative models on immune response in one reliable and reproducible model is a powerful tool to analyse the Immunological System. Fully-integrated Immune Response Model (FIRM) by Palsson et al., (2015) [1] is a simulator designed by integrating three immune response models that highlights different features of the immune system and the existing physiological knowledge. Furthermore, the examples presented in this study illustrates in detail how to develop and improve the quantitative immunological dynamic network in an iterative and interactive manner.

Blocking determined fluxes on this model results in disease conditions. Using the FIRM simulator immune response against four conditions (tuberculosis infection, blood borne pathogen infection, spontaneous tumour rejection and influence of Treg on tumour rejection) has been explored. Also new implementations on FIRM (addition of Treg or TGF-β dynamics) improve the quality of this model and show us the flexibility and interactive characteristics of this platform.


The primary goal of FIRM was to build an Immune response model, which after determined perturbations is able to reproduce the immune response cell behaviour. It is based on three existing immune response models [2,3,4], and physiological knowledge, including the relevant target tissues constituting the blood, the lymphoid tissues, the target organ (lung) , and sites of recognition and macrophages (Figure 1- FIRM simulator). The dynamic FIRM system itself was comprised of the T cells, B cells (antibodies), DC cells, Macrophages and relevant cytokines. Finally Treg and TGF-β mechanism where added into the system.

Normally in a bacterial pulmonary infection, humoral and cellular immune responses are released. An expansion of lymphoid cells, degradation of bacteria and a contraction of the lymphoid population are three fundamental steps. Mycobacterium tuberculosis bacteria follows an immunological model [2] where the migration of the bacteria from target tissue to the blood is blocked (blocking flux v87), and the pattern of the immune cell activation changes dramatically. (Figure 2.- Immune system response against common bacterial infection and tuberculosis infection). BIOMD0000000608 describes the immune response against tuberculosis (TB) infection.

Another example, blood borne pathogen (from [3]), is modelled in FIRM by blocking the migration of the pathogens from blood to target tissues (blocking fluxes v87 and v88).

On the other hand, tumour removal (from [4]) example is modelled by means of increasing the half-live of T helper cells and under the assumption that there is not antibody-mediated cell kill (blocking flux 88). At determined point of time the tumour is removed and the cells return to their basal state.

Figure 2

Figure 2Immune system response against common bacterial infection versus tuberculosis infection. Left column represent the behaviour of immune cells against tuberculosis infection, where flux 86 is blocked (no bacterial migration from tissue to the blood). The right column represent the immune cells behaviour in a common bacterial infection. IDC, immature Dendritic Cells; MD, mature Dendritic cells; MR, Macrophages resting; MA, Macrophages activated; MI, Macrophages inactivated; PE, extracellular pathogen; PI, Intracellular pathogen; T , naïve T cell; ThP, T helper precursor cell; TcP, T cytotoxic precursor cell. Figure taken from [1].

Figure 1

Figure 1FIRM simulator. This fully-integrated model is able to join mechanisms at different tissue levels, and also is flexible enough to add new mechanisms that can better reflect and characterise the immune systems response. Red circle point fluxes which are blocked in the examples to recreate the disease. Blue circles are used for the QC/AC analysis to demonstrate quantitative and accurate accounting of bacteria in the system. Figure taken from [1].

The FIRM is able to recreate the simulation results observed in the original models (for example, the simulation results of immune response after tuberculosis infection resemble the original tuberculosis model [2] from which FIRM is derived). However the authors declared that other experiments may be necessary to shed light upon some assumptions in the mechanism or provide more quantitative data over some other fluxes with the goal to improve the model in an iterative manner.


Initial simulations were carried out to examine the assumptions on immune system response in normal conditions. A QC/QA analysis was performed to demonstrate quantitative and accurate accounting of bacteria in the system (associated fluxes blocked v2 and v3 (bacteria) and activated v9 and v10 (macrophages)). Recent discoveries suggest a strong influence of Treg and TGF-β dynamics in cancer rejection. Thus these candidates have been implemented by the authors in the FIRM simulator. Once those mechanisms have been added, the model now successfully simulates the tumour growth and later rejection. In conclusion, adding into FIRM the Treg and TGF-β dynamics, an effective tumor-killing level is reached later than without those new components in the system (Figure 3.- Treg and TGF-β influence).

Figure 3

Figure 3Treg and TGF-β influence. Graphical simulation of immune response cells and tumour development taking account the Treg and TGF-β in the system (right column) or without their influence (left column). TcP, T cytotoxic precursor cell; Tc, T cytotoxic cell. Figure taken from [1].


By construction of a FIRM simulator, the authors provide a framework to expand and improve in an interactive and iterative way the representation of immune system features. To understand the immune response, more mechanisms can be added to FIRM, as NK cells dynamics or any other cytokines. The case studies described using the FIRM simulator suggests that there is a common and hybrid infrastructure for the immune system, and attacking selected edges are able to reproduce proved experiments related with different diseases. The authors remark that with experimental validation this simulator could reach a state of completion as genome-scale models of metabolism. FIRM is a starting point to achieve this objective.

Bibliographic references

  1. Palsson et al. The development of a fully-integrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models. BMC Syst Biol. 2013 Sep 28;7:95.
  2. Marino and Kirschner. The human immune response to Mycobacterium tuberculosis in lung and lymph node. J Theor Biol. 2004 Apr 21;227(4):463-86.
  3. Bell. Mathematical model of clonal selection and antibody production. J Theor Biol. 1970 Nov;29(2):191-232.
  4. De Boer et al. Macrophage T lymphocyte interactions in the anti-tumor immune response: a mathematical model. J Immunol. 1985 Apr;134(4):2748-58.