 Research
 Open Access
 Published:
Hybrid approach to model the spatial regulation of T cell responses
BMC Immunology volume 18, Article number: 29 (2017)
Abstract
Background
Moving from the molecular and cellular level to a multiscale systems understanding of immune responses requires the development of novel approaches to integrate knowledge and data from different biological levels into mechanismbased integrative mathematical models. The aim of our study is to present a methodology for a hybrid modelling of immunological processes in their spatial context.
Methods
A twolevel hybrid mathematical model of immune cell migration and interaction integrating cellular and organ levels of regulation for a 2D spatial consideration of idealized secondary lymphoid organs is developed. It considers the population dynamics of antigenpresenting cells, CD4 ^{+} and CD8 ^{+} T lymphocytes in naive, proliferation and differentiated states. Cell division is assumed to be asymmetric and regulated by the extracellular concentration of interleukin2 (IL2) and type I interferon (IFN), together controlling the balance between proliferation and differentiation. The cytokine dynamics is described by reactiondiffusion PDEs whereas the intracellular regulation is modelled with a system of ODEs.
Results
The mathematical model has been developed, calibrated and numerically implemented to study various scenarios in the regulation of T cell immune responses to infection, in particular the change in the diffusion coefficient of type I IFN as compared to IL2. We have shown that a hybrid modelling approach provides an efficient tool to describe and analyze the interplay between spatiotemporal processes in the emergence of abnormal immune response dynamics.
Discussion
Virus persistence in humans is often associated with an exhaustion of T lymphocytes. Many factors can contribute to the development of exhaustion. One of them is associated with a shift from a normal clonal expansion pathway to an altered one characterized by an early terminal differentiation of T cells. We propose that an altered T cell differentiation and proliferation sequence can naturally result from a spatial separation of the signaling events delivered via TCR, IL2 and type I IFN receptors. Indeed, the spatial overlap of the concentration fields of extracellular IL2 and IFN in lymph nodes changes dynamically due to different migration patterns of APCs and CD4 ^{+} T cells secreting them.
Conclusions
The proposed hybrid mathematical model of the immune response represents a novel analytical tool to examine challenging issues in the spatiotemporal regulation of cell growth and differentiation, in particular the effect of timing and location of activation signals.
Background
The immune system is regulated by multiple processes at various levels of biological organization including the genetic, cellular, tissue, organ and the whole organism levels. The resulting structural and functional complexity of the immune system called for a major shift towards informationrich, systemsbased approaches in immunological research. High throughput technologies generate vast amounts of data that facilitate dissection of the immunological processes at ever finer resolution. The need to embed immune processes into their spatial context both at the molecular and cellular level is a hallmark of the systems immunology approach. In fact, there are many examples of how the fate decisions in the immune system depend on the spatialtemporal dynamics of cytokines, e.g. the interleukin2 (IL2) [1] and type I interferon (IFN) [2, 3].
Moving from the molecular and cellular level to a multiscale model requires the development of novel modelling methodologies for an iterative integration of data from different biological levels into mechanismbased modular mathematical models [4–6]. So far, very few mathematical models have been proposed to describe the multiscale spatial regulation of immune responses in a genuine hybrid manner [7–10]. The major features of the developed models are summarized in Table 1.
The model developed in this work takes into account: 1) spatial aspects of the immune response in the lymph node by means of cell and concentration distributions, 2) regulation of T lymphocytes in the lymph node including their asymmetric division and their interaction with extracellular cytokine concentrations, 3) the intracellular regulation of T cells depending on IL2 and type I IFN, 4) the interaction of the tissue level and the whole organism through the infection level.
Conventional models of the immune response are based on ordinary differential equations, and they do not take into account spatial distributions of cells and concentrations in the lymph node. The multiscale models previously developed (see Table 1) use a similar agentbased cell description. However they do not take into account processes such as asymmetric cell division, their interaction with IFN or the interaction of the tissue level with the organism level which is one of the key features of our multiscale model.
One of the essential features of our modelling approach is the description of cells as individual objects which can move, divide, differentiate or die by apoptosis. Dead cells are removed from the computation domain. Cells are considered as soft spheres with their motion described by Newton’s second law for their centers. Cell fate is determined by the intracellular regulation. The spatial distribution of cytokines is described by reaction–diffusion equations, while the intracellular regulation and infection dynamics are described by ordinary differential equations conventionally accepted in mathematical immunology. A similar approach was developed and justified in modelling of other physiological systems such as blood diseases and hematopoiesis [11–17]. Here we develop it for an integrative modelling of immunological processes in their spatial context.
Methods
Biophysics of the immune response
To formulate the mathematical model, we consider a part of the lymph node, i.e., the T cell zone, which contains various cell types, mainly the antigen presenting cells (APCs) and subsets of T lymphocytes. Naive T cells and some APCs (such as plasmocytoid Dendritic Cells, pDCs) enter the node with blood flow via the High Endothelial Venules (HEVs) whereas effector and/or memory T cells, and mainly DCs and macrophages home to lymph nodes via afferent lymphatic vessels [18, 19]. Following activation with pathogens, APCs acquire a motile state that allows their translocation to the T cell zone of draining lymph node with the afferent lymph flow [20, 21]. Therefore, we assume that the influx of APCs is proportional to the level of infection in the organism. Differentiation of naive T cells into CD4 ^{+} and CD8 ^{+} T cells occurs in the thymus from progenitor T cells [22]. We suppose that they enter lymph nodes already differentiated and that there is a given influx of each cell type.
The APCs bearing foreign antigens activate the clonal expansion of naive T lymphocytes. The activation of T cell division and death is regulated by a set of signals coming from the interactions of the antigenspecific T cell receptors (TCRs) with the MHC class I or class II presented peptides and IL2 receptors binding IL2. Naive T cells undergo asymmetric division [23] (Fig. 1). Some of the daughter cells continue to proliferate and differentiate. Mature CD4 ^{+} T cells produce IL2 [22, 24, 25] which influences survival and differentiation of both CD4 ^{+} and CD8 ^{+} T cells. The proliferation of CD8 ^{+} T cells is stimulated by IL2 [24]. They can expand their number many thousandfold. In addition to IL2 enhancing the proliferation of T cells, APCs start to secrete type I IFN which has an antiviral and immunomodulatory function. In fact, the effect of IFN α depends on the temporal sequence of the signals obtained by naïve T cells [2]. It can change from a normal activation of T cells followed by their proliferation and differentiation to an already differentiated state followed by apoptosis as shown schematically in Fig. 2. Overall, the regulated death of T cells by apoptosis depends on the availability and the timing of TCR, IL2 and IFN signalling.
Mature CD8 ^{+} T cells (effector cells) leave the lymph node and kill infected cells. Therefore, there is a negative feedback between production of mature CD8 ^{+} T cells and the influx of APCs.
In the model, an asymmetric T cell division is considered as shown in Fig. 3. Naive T cell entering the draining lymph node is recruited into the immune response after the contact interaction via the T cell receptor (TCR) with APC presenting the foreign antigen. The activation and prolonged contact with APC can results in polarity of the lymphocyte. The position of the contact with the APC determines the direction of cell division and the difference between the daughter cells in terms of their differentiation state. According to [23], the proximal daughter cell will undergo clonal proliferation and differentiation resulting in the generation of terminally differentiated effector cells (mature CD8 ^{+} T cells) that leave the lymph node for peripheral tissues to search and kill infected cells. The distal daughter cell becomes a memory cell. The memory cells are capable of selfrenewal by slowly dividing symmetrically in the absence of recurrent infection.
Hybrid model of cell dynamics
In our model of cell dynamics, cells are considered as individual objects that can move, divide, differentiate and die. Their behavior is determined by the surrounding cells, by intracellular regulatory networks described by ordinary differential equations and by various substances in the extracellular matrix whose concentrations are described by partial differential equations. This approach was used to model hematopoiesis and blood diseases [11–17].
Cells and concentrations. Cells in the lymph node:

1.
n _{ APC }(x,t)  the density of APCs in T cell zone;

2.
n _{ CD4}(x,t)  the density of CD4 ^{+} T cells in T cell zone (with different levels of maturity);

3.
n _{ CD8}(x,t)  the density of CD8 ^{+} T cells in T cell zone (with different levels of maturity);
Extracellular variables:

4.
I _{ e }(x,t)  the concentration of IL2 in T cell zone;

5.
C _{ e }(x,t)  the concentration of type I IFN in T cell zone;
Intracellular variables:

6.
I _{ i }(t)  the intracellular concentration of IL2induced signalling molecules in the ith cell;

7.
C _{ i }(t)  the intracellular concentration of type I IFNinduced signalling molecules in the ith cell;
The state variables at the level of the whole organism:

8.
N _{ ef }(t)  the total number of effector CD8 ^{+}T cells in the body;

9.
N _{ inf }(t)  the total number of infected cells in the body;
Cell displacement. In the model, cells are represented by individual elastic spheres. There are two mechanisms of motion of cells in the lymph node. First of all they move in a random way. This motion allows naive T cells to meet APCs which is necessary for their activation, division and differentiation. Second, each two cells, when they meet, they push each other due to a direct mechanical interaction. We consider this interaction as an elastic force acting on cells and influencing their motion. Let us describe it in more detail.
The cells divide and can increase their number which involves pushing each other leading to their displacement in the lymph node. We describe cells displacement by the following model. Let us denote the center of two cells by x _{1} and x _{2} and their radii by r _{1} and r _{2} respectively. Then, if the distance h _{12} between the two cells is less than the sum of their radii (r _{1}+r _{2}), there will be a repulsive force f _{12} between them. This force should depend on the difference between (r _{1}+r _{2}) and h _{12}. Let us consider the case of one cell interacting with different cells in the lymph node. The total force applied to this cell will be \(F_{i} = \sum _{j\ne i} f_{ij}\). We describe the motion of the particles as the motion of their centers which can be found by the applying Newton’s second law:
where m is the mass of the particle, μ is the friction factor due to contact with the surrounding medium. The potential force between two cells is given explicitly by:
where h _{ ij } is the distance between the centers of the two cells i and j, h _{0} is the sum of their radii, K is a positive parameter and h _{1} is the sum of the incompressible part of each cell. The force between the particles tends to infinity if h _{ ij } decreases to h _{0}−h _{1}.
Cell division and differentiation. APC and naive T cells enter the computational domain with a given frequency if there is available space. Naive T cells move in the computational domain randomly. If they contact APC, they divide asymmetrically (Fig. 3). The distant daughter cell is similar to the mother cell, the proximal daughter cell becomes differentiated.
When the cell reaches the half of its life cycle, it will increase its size. When it divides, two daughter cells appear, the direction of the axis connecting their centers is chosen randomly from 0 to 2π. The duration of the cell cycle is 18 hours with a random perturbation of −3 to 3 hours.
We consider two levels of maturity of CD4 ^{+} T cells and three levels of CD8 ^{+} T cells. If a differentiated cell has enough IL2 (see the next paragraph), then it divides and gives two more mature cells. Finally differentiated cells leave the lymph node. In the simulations, this means that they are removed from the computational domain.
Intracellular regulation. The survival and differentiation of activated CD4 ^{+} and CD8 ^{+} T lymphocytes depends on the amount of signalling via the IL2 receptor and the type I IFN receptor. It is controlled primarily by the concentration of the above cytokines in the close proximity of the respective receptors. The signalling events lead to the upregulation of the genes responsible for cell proliferation, differentiation and death. One can use similar type of equation to model qualitatively the accumulation of the respective intracellular signalling molecules linked to IL2 and type I IFN receptors. The IL2 dependent regulatory signal dynamics in individual cells can be described by the following equation:
Here I _{ i } is the intracellular concentration of signalling molecules accumulated as a consequence of IL2 signals transmitted through transmembrane receptor IL2R downstream the signaling pathway to control the gene expression in the ith cell. The concentrations inside two different cells are in general different from each other. The first term in the righthand side of this equation shows the cumulative effect of IL2 signalling. The extracellular concentration I _{ e } is taken at the coordinate x _{ i } of the center of the cell. The second term describes the degradation of IL2induced signalling molecules inside the cell. Furthermore, n _{ T } is the number of molecules internalized by T cell receptors.
In a similar way, the IFNdependent regulatory signal dynamics in individual cells can be described by the following equation:
Here C _{ i } is the intracellular concentration of signalling molecules accumulated as a consequence of IFN signals transmitted through transmembrane receptor IFNR downstream the signaling pathway to control the gene expression in the ith cell. The concentrations inside two different cells are in general different from each other. The first term in the righthand side of this equation shows the cumulative effect of IFN signalling. The extracellular concentration C _{ e } is taken at the coordinate x _{ i } of the center of the cell. The second term describes the degradation of IFNinduced signalling molecules inside the cell.
To model the fate regulation of growth versus differentiation of the activated cells in relation to the timing of the IL2 and type I IFN signalling we implement the following decision mechanism.

C1
If the concentration of activation signals induced by type I IFN, C _{ i }, is greater than some critical level \(C_{i}^{*}\) at the beginning of the cell cycle and that of I _{ i }, is smaller than the critical level \(I_{i}^{*}\), then the cell will differentiate resulting in a mature cell.

C2
If the concentration of activation signals induced by IL2, I _{ i }, is greater than some critical level \(I_{i}^{*}\) at the end of the cell cycle, then the cell will divide producing two more mature cells.

C3
If \(C_{i}<C_{i}^{*}\) at the beginning of cell cycle and \(I_{i}<I_{i}^{*}\) at the end of cell cycle, then the cell will die by apoptosis and will be removed from the computational domain.
Stochastic aspects of the model.
As it is discussed above, mechanical interaction of cells results in their displacement described by equation (1) for their centers. In order to describe random motion of cells we add random variables to the cell velocity in the horizontal and vertical directions.
Duration of cell cycle is given as a random variable in the interval [ T−τ,T+τ].
Extracellular dynamics of cytokines.
Proliferation and differentiation of T cells in the lymph node depends on the concentration of IL2 and type I IFN. These cytokines are produced by mature CD4 ^{+} T cells and antigenpresenting cells, respectively. Their spatial distribution is described by a similar reactiondiffusion equation as follows
Here I _{ ex } is the extracellular concentration of IL2, D is the diffusion coefficient, W _{ IL } is the rate of its production by CD4 ^{+} T cells, and the last term in the righthand side of this equation describes its consumption and degradation. The production rate W _{ IL } is determined by mature CD4 ^{+} T cells. We consider each such cell as a source term with a constant production rate ρ _{ IL } at the area of the cell. Let us note that we do not take into account explicitly consumption of IL2 by immature cells in order not to introduce an additional parameter. Implicitly this consumption is taken into account in the degradation term.
For type I IFN, the equation and the terms in it have a similar interpretation:
Initial and boundary conditions for both concentrations IL2 and IFN are taken zero. As before, the production rate W _{ IFN } equals ρ _{ IFN } at the area filled by APC cells and zero otherwise.
Infection. Mature T cells leave the lymph node. The level of CD8 ^{+} T cells (effector cells) N _{ ef } in the body is determined by the equation
where T is their number in the lymph nodes. So the first term in the righthand side of this equation describes production of effector cells in the lymph nodes and the second term their death in the body.
Denote by N _{ inf } the number virusinfected cells. We will describe it by the equation
The first term in the righthand side of this equation describes growth of the number of infected cells and the second term their elimination by the effector cells. The function f will be considered in the form:
where a and h are some positive constants.
Finally, the influx of APC cells into the lymph nodes is proportional to the number of infected cells N _{ inf }.
This influx is limited by the place available in the lymph node. If there is a free place sufficient to put a cell, the new cells are added. Let us also note that the lymph nodes can increase due to infection in order to produce more effector cells.
Results
We illustrate the model performance by considering two scenarios, reflecting different spatial patterns of IL2 and type IFN concentration fields. In the first one, both cytokines have the same diffusion coefficient D _{ IL2}=D _{ IFN }, whereas in the second case the diffusion rate of IFN is 10fold faster. The details of the numerical implementation of the hybrid model and the parameter values used for the simulations are presented in Additional file 1: Appendix. Cell population densities and cytokine concentrations are scaled with respect to some reference values. These are determined by the cell density in the lymph node ∼10^{5}−10^{6} mm ^{−3}, the relative proportions of APCs, CD4 ^{+} T cells and CD8 ^{+} T cells [26–32] and the production rate of the cytokines (described in detail in the appendix, see Additional file 1). The considered cell numbers correspond to a computational domain in the T cell zone of about 100μ m×100μ m×100μ m.
The model presented above contains two compartments, lymph node where effector cells are produced and the body where infection develops. The lymph node is described with the hybrid model while infection development in the organism by ordinary differential equations for infected cells and for effector cells. These two compartments are coupled by means of flux of effector cells from the lymph node to the body and by the flux of APC cells to the lymph node.
The results of the simulations are shown in Figs. 4, 5, 6 and 7. Figure 4 represents a snapshot of the lymph node T cell zone with all cells participating in the simulations: APC cells, naive T cells, differentiated CD4 ^{+} T and CD8 ^{+} T cells. Naive T cells divide when they are close to APC cells. It is an asymmetric division where a proximal daughter cell differentiates while a distant cell remains undifferentiated. Differentiated cells continue their division and maturation in the presence of IL2 produced by mature CD4 ^{+} T cells [33]. If the level of IL2 is not sufficient, they die by apoptosis. Mature T cells leave the lymph node. One can see that the cytokine fields are nonuniform and their distribution patterns change essentially if the turnover parameters, e.g. the diffusion coefficient, are varied. Note that the cell distribution is more uniform in the case of large diffusion coefficient of IFN (Fig. 4, lower image) compared with the case of small diffusion coefficient (upper image).
The evolution of the total number of CD4 ^{+} and CD8 ^{+} T cells in the lymph node T cell zone is shown in Fig. 5. The dynamics of APC cells in the lymph node T cell zone and the effector cells in the body is shown in Fig. 6. The magnitude of the immune response is not sufficient to eradicate completely the infection. Indeed, the number of infected cells decreases but remains positive (Fig. 7). As virus infection is not cleared, the cell populations fluctuate around some constant values. Overall, the model reproduces the qualitative patterns of longterm persistent infection (experimental infections and in humans) dynamics (e.g., [34–38]). The primary clonal expansion takes about seven days and is followed by an enhanced longterm T cell response to the persistent infection. The increase in the spread of type I IFN changes the relative distributions pattern of IL2 and IFN, so that the resulting alteration in the cytokine signalling reduces the clonal expansion and increases the overall level of the virus infection.
The cumulative numbers of CD4 ^{+} and CD8 ^{+} T cells and virus infection load. As single simulation runs of the stochastic model are characterized by a fluctuating and overlapping dynamics, we quantified integrative characteristics of the model behavior. To describe the effect of the diffusion coefficient D _{ IFN } on the Tcells production, we compared the cumulative numbers of CD4 ^{+} and CD8 ^{+} T cells as well as the infection load over the overall time of the simulation for the two scenarios. We also show the cumulative numbers of effector Tcells in the body N _{ ef }. The results are shown in Table 2.
The net effect of the increase in the diffusion rate of type I IFN is a reduction in the clonal expansion of the T cells, in particular the effector T cells in the peripheral organs (by ∼10%) and a rise in the infection level (by ∼20%). The changes in the clonal T cell expansion are the consequence of the differences in the cytokine concentration fields, which in turn alter the timing and the sequence of the IL2 and type I IFN signalling.
The types and the relative densities of immune cells considered in the model essentially correspond to the clonal, APCinduced expansion of T cells activated by virus infections (see, e.g., [39]). The motility of T cells in the lymph node is determined by their random motion and mechanical cellcell interactions [40]. The spatial distribution of cytokines considered in the model (IL2 and type I IFN), though it requires more detailed investigation, corresponds to the actual understanding of the role of these cytokines. In order to reveal their impact on cell distribution, we show in Fig. 8 the spatial cell distributions and the concentrations of cytokines separately. We can clearly see the role of the cytokine diffusivity on their distributions and on the cell distributions.
Discussion
The activation of T cell division and death is regulated by a set of signals coming from the interactions of TCRs with the MHC class I and II presented peptides, IFN and IL2 receptors. However, the effect of type I interferon depends on the temporal sequence of the signals obtained by naïve T cells [2]. It can change from a normal activation of T cells followed by their proliferation and differentiation to an already differentiated state followed by apoptosis. We propose that the altered T cell differentiation and proliferation sequence can result from a spatial separation of the signaling events obtained by T cells, i.e. the TCR versus IFN receptor signaling, due to a generally different location of the APCs and the type I IFN concentration field. The hypothesis is formulated using the results of our study of the spatiotemporal dynamics of the T cell response to infection with the developed hybrid mathematical model integrating intracellular, and systemic levels of the immune response regulation.
Virus persistence in humans is often associated with an exhaustion of T lymphocytes. Many factors can contribute to the development of exhaustion. One of them is associated with a shift from a normal clonal expansion pathway to an altered one characterized by an early terminal differentiation of T cells. The proposed hybrid model allows us to investigate the integrative effects of numerous biophysical and biochemical parameters on the immune regulation that are beyond the scope of the existing experimental techniques.
The need for development of multiscale integrative models in mathematical immunology is well realized [7, 9, 10]. However, implementation of such models embedded into spatial context of immune responses remains a challenge [4, 41]. Our hybrid model has been developed in a modular form to describe a range of specific interactions and regulations of the immune response. This implies that the structure and composition of the model can be adaptively changed to meet the needs of any specific studies. The present study is based upon a simplified description of the virustarget cell interaction. This block of the hybrid model can be easily refined using the existing set of lumped mathematical models. We presented the results of single runs for two different scenarios. Obviously, multiple runs will be used to address the effect of local and global variations in the parameters on the immune response dynamics in future studies.
The presented methodology for developing a hybrid model of immune processes enables integration of data and knowledge across multiple scales. This is essential for understanding the control of the lower level processes by the properties of the higher level processes. We considered here an important example of the regulation of the T cell fate [2] by the spatial structure and material properties of lymphoid organs in which the transport and cell interaction take place [4].
Mathematical modelling of human immune system represents an important challenge. Whereas a macroscopic level population dynamics of the immune response can be followed experimentally and captured with mathematical models under the framework of mono or two/three compartmental modelling based on ODEs, the embedding of dynamics and interactions of various cells into the spatial organization of real lymphoid organs represents a fundamental challenge. The effects of spatial heterogeneity of the cytokine fields and cells localization in the lymphoid organs on quantitative and phenotypic features of the T cell responses are poorly understood as these types of parameters are difficult to examine experimentally. Indeed, to describe the cytokine and cellular interactions one need to integrate in a unified manner a range of processes including the lymph flow, constrained diffusion, chemotaxis and haptotaxis in soft tissues. A complementary problem is the assimilation of the data on intracellular regulation of immune cells’ states resulting from a parallel signaling via cell surface receptors to antigens, cytokines, chemokines, hormones, etc. The complexity of the issue is well exemplified in a recent review [42]. Following the presented modelling approach, we hope to be able to examine the sensitivity of the immune responses at the macroscopic level to parameters of the cell interaction at the microscopic level. This should assist in fine tuning of the offset dynamics of immune responses using a broad spectrum of modern immunomodulatory drugs.
Conclusions
The proposed hybrid mathematical model of the immune response represents a novel analytical tool to examine challenging issues in the spatiotemporal regulation of cell growth and differentiation, in particular the effect of timing and location of activation signals. It allows us to overcome the limitations of reductionist approach to a single factor analysis of the immune regulation and to proceed to the studies of the structurefunction relationships in a genuinely systemic manner. As W.E. Paul stated in [33] “…the behavior of immune cells is highly colored by the cellular/molecular environment in which they exist…It is to the quantitative prediction of the outcome of given perturbations in the immune system that we envisage our mathematical/ modeling colleagues will apply themselves.” In our view, hybrid modelling approach provides the means for a comprehensive analysis and interpretation of contentrich metadata obtained by a broad range of scalespecific data acquisition techniques, including imaging, flow cytometry, transcriptome sequencing in an anatomically correct and immunologically meaningful way.
The practical implementation of a hybrid approach to multiscale modelling presents a number of challenges ranging from the numerical accuracy and consistency of the different methods being used to compute the system components dynamics on one side to the risk of producing modelling artefacts because of the system complexity and parameter uncertainty on the other side. In fact, one needs to have clear computational methodologies for the development of various mathematical modelling tools including simple singlelevel resolution phenomenological models, largescale multicompartmental models and highresolution multiscale models. In the end, their appropriateness depends on the questions to study and available data.
References
Khan SH, Martin MD, StarbeckMiller GR, Xue HH, Harty JT, Badovinac VP. The Timing of Stimulation and IL2 Signaling Regulate Secondary CD8 ^{+} T Cell Responses. PLoS Pathog. 2015; 11(10):e1005199.
Welsh RM, Bahl K, Marshall HD, Urban SL. Type 1 Interferons and Antiviral CD8 ^{+} TCell Responses. PLoS Pathog. 2012; 8(1):e1002352.
HervasStubbs S, PerezGracia JL, Rouzaut A, Sanmamed MF, Le Bon A, Melero I. Direct Effects of Type I Interferons on Cells of the Immune System. Clin Cancer Res. 2011; 17:2619–27.
Ludewig B, Stein JV, Sharpe J, CervantesBarragan L, Thiel V, Bocharov G. A global “imaging” view on systems approaches in immunology. Eur J Immunol. 2012; 42:3116–25.
Palsson S, Hickling TP, BradshawPierce EL, Zager M, Jooss K, O’Brien PJ, Spilker ME, Palsson BO, Vicini P. The development of a fullyintegrated immune response model (FIRM) simulator of the immune response through integration of multiple subset models. BMC Syst Biol. 2013; 7:95. doi:10.1186/17520509795.
Cilfone NA, Kirschner DE, Linderman JJ. Strategies for efficient numerical implementation of hybrid multiscale agentbased models to describe biological systems. Cel Mol Bioeng. 2015; 8(1):119–36.
Baldazzi V, Paci P, Bernaschi M, Castiglione F. Modeling lymphocyte homing and encounters in lymph nodes. BMC Bioinforma. 2009; 10:387.
FallahiSichani M, ElKebir M, Marino S, Kirschner DE, Linderman JJ. Multiscale computational modeling reveals a critical role for TNF receptor 1 dynamics in tuberculosis granuloma formation. J Immunol. 2011; 186(6):3472–83.
Gong C, Mattila JT, Miller M, Flynn JL, Linderman JJ, Kirschner D. Predicting lymph node output efficiency using systems biology. J Theor Biol. 2013; 335:169–84.
Prokopiou SA, Barbarroux L, Bernard S, Mafille J, Leverrier Y, Arpin C, Marvel J, Gandrillon O, Crauste F. Multiscale Modeling of the Early CD8 ^{+} T TCell Immune Response in Lymph Nodes:An Integrative Study. Computation. 2014; 2:159–81.
Bessonov N, Eymard N, Kurbatova P, Volpert V. Mathematical modelling of erythropoiesis in vivo with multiple erythroblastic islands. Appl Math Lett. 2012; 25:1217–21.
Fischer S, Kurbatova P, Bessonov N, Gandrillon O, Volpert V, Crauste F. Modelling erythroblastic islands: using a hybrid model to assess the function of central macrophage. J Theoretical Biol. 2012; 298:92–106.
Kurbatova P, Bernard S, Bessonov N, Crauste F, Demin I, Dumontet C, Fischer S, Volpert V. Hybrid model of erythropoiesis and leukemia treatment with cytosine arabinoside. SIAM J Appl Math. 2011; 71(6):2246–68.
Volpert V, Bessonov N, Eymard N, Tosenberger A. Modèle multiéchelle de la dynamique cellulaire. In: Glade N, et d’Angelique Stephanou, editeurs. Le vivant discret et continu. Paris: Éditions Matériologiques: 2013. p. 91–111.
Kurbatova P, Eymard NV, V. Volpert V. Hybrid Model of Erythropoiesis. Acta Biotheoretica. 2013; 61:305–15.
Eymard N, Bessonov N, Gandrillon O, Koury MJ, Volpert V. The role of spatial organization of cells in erythropoiesis. J Math Biol. 2015; 70:71–97.
Stéphanou A, Volpert V. Hybrid modelling in biology: a classification review. Math Model Nat Phenom. 2016; 11(1):37–48.
Mueller SN, Germain RN. Stromal cell contributions to the homeostasis and functionality of the immune system. Nat Rev Immunol. 2009; 9(9):618–29.
Girard JP, Moussion C, Förster R. HEVs, lymphatics and homeostatic immune cell trafficking in lymph nodes. Nat Rev Immunol. 2012; 12(11):762–73.
Junt T, Scandella E, Ludewig B. Form follows function: lymphoid tissue microarchitecture in antimicrobial immune defence. Nat Rev Immunol. 2008; 8(10):764–75.
Förster R, Braun A, Worbs T. Lymph node homing of T cells and dendritic cells via afferent lymphatics. Trends Immunol. 2012; 33(6):271–80.
Goldsby RA, Kindt TJ, Osborne BA. Kuby Immunology. New York: W H Freeman & Co (Sd); 2000. Ch.10:222.
Chang JT, Reiner SL. Asymmetric Division and Stem Cell Renewal without a Permanent Niche: Lessons from Lymphocytes. Cold Spring Harb Symp Quant Biol. 2008; 73:73–9.
Broere F, Apasov SG, Sitkovsky MV, van Eden W. T cell subsets and T cellmediated immunity In: Nijkamp FP, Parnham MJ, editors. Principles of Immunopharmacology: 3rd revised and extended edition. Basel: Springer AG: 2011.
Nelson BH. IL2, Regulatory T Cells, and Tolerance. J Immunol. 2004; 172:3983–8.
Ganusov VV, De Boer RJ. Do most lymphocytes in humans really reside in the gut?Trends Immunol. 2007; 28(12):514–8.
Scandella E, Bolinger B, Lattmann E, Miller S, Favre S, Littman DR, Finke D, Luther SA, Junt T, Ludewig B. Restoration of lymphoid organ integrity through the interaction of lymphoid tissueinducer cells with stroma of the T cell zone. Nat Immunol. 2008; 9(6):667–75.
Kumar V, Scandella E, Danuser R, Onder L, Nitschké M, Fukui Y, Halin C, Ludewig B, Stein JV. Global lymphoid tissue remodeling during a viral infection is orchestrated by a B celllymphotoxindependent pathway. Blood. 2010; 115(23):4725–33.
Bocharov G, Züst R, CervantesBarragan L, Luzyanina T, Chiglintsev E, Chereshnev VA, et al. A Systems Immunology Approach to Plasmacytoid Dendritic Cell Function in Cytopathic Virus Infections. PLoS Pathog. 2010; 6(7):e1001017.
Bocharov G, Chereshnev V, Gainova I, Bazhan S, Bachmetyev B, Argilaguet J, Martinez J, Meyerhans A. Human Immunodeficiency Virus Infection: from Biological Observations to Mechanistic Mathematical Modelling. Math Model Nat Phenom. 2012; 7(5):78–104.
Cremasco V, Woodruff MC, Onder L, Cupovic J, NievesBonilla JM, Schildberg FA, Chang J, Cremasco F, Harvey CJ, Wucherpfennig K, Ludewig B, Carroll MC, Turley SJ. B cell homeostasis and follicle confines are governed by fibroblastic reticular cells. Nat Immunol. 2014; 15(10):973–81.
Giese C, Marx U. Human immunity in vitro  solving immunogenicity and more. Adv Drug Deliv Rev. 2014; 69–70:103–22.
Paul WE. The Immune System  Complexity Exemplified. Math Model Nat Phenom. 2012; 7(5):4–6.
Moskophidis D, Lechner F, Pircher H, Zinkernagel RM. Virus persistence in acutely infected immunocompetent mice by exhaustion of antiviral cytotoxic effector T cells. Nature. 1993; 362(6422):758–61.
Moskophidis D, Battegay M, van den Broek M, Laine E, HoffmannRohrer U. Zinkernagel R. Role of virus and host variables in virus persistence or immunopathological disease caused by a noncytolytic virus. J. Gen. Virol. 1995; 76(2):381–391.
McMichael AJ, Borrow P, Tomaras GD, Goonetilleke N, Haynes BF. The immune response during acute HIV1 infection: clues for vaccine development. Nat Rev Immunol. 2010; 10(1):11–23.
CervantesBarragan L, Lewis KL, Firner S, Thiel V, Hugues S, Reith W, Ludewig B, Reizis B. Plasmacytoid dendritic cells control Tcell response to chronic viral infection. Proc Natl Acad Sci USA. 2012; 109(8):3012–7.
Hansen SG, Piatak M, Ventura AB, Colette M, Hughes CM, Gilbride RM, Ford JC, Oswald K, Shoemaker R, Li Y, Lewis MS, Gilliam AN, Xu G, Whizin N, Burwitz BJ, Planer SL, Turner JM, Legasse AW, Axthelm MK, Nelson JA, Früh K, Sacha JB, Estes JD, Keele BF, Edlefsen PT, Lifson JD, Picker LJ. Immune clearance of highly pathogenic SIV infection. Nature. 2013; 502(7469):100–4.
Kaech SM, Wherry EJ. Heterogeneity and cellfate decisions in effector and memory CD8+ T cell differentiation during viral infection. Immunity. 2007; 27(3):393–405.
Fricke GM, Letendre KA, Moses ME, Cannon JL. Persistence and Adaptation in Immunity: T Cells Balance the Extent and Thoroughness of Search. PLoS Comput Biol. 2016; 12(3):e1004818.
Cappuccio A, Tieri P, Castiglione F. Multiscale modelling in immunology: a review. Brief Bioinform. 2015; pii:bbv012:1–11.
Grossman Z, Paul WE. Dynamic tuning of lymphocytes: physiological basis, mechanisms, and function. Annu Rev Immunol. 2015; 33:677–713.
Baker CTH, Bocharov GA, Paul CAH. Mathematical Modelling of the Interleukin2 TCell System: A Comparative Study of Approaches Based on Ordinary and Delay Differential Equation. J Theor Medicine. 1997; 1(2):117–28.
Acknowledgments
This study was supported by the grant of the Russian Science Foundation, project no. 151100029 (to GB, AM and VV).
Funding
The publication charge for this article was funded by the Russian Science Foundation, project no. 151100029. The funder had no role in study design, analysis, decision to publish, or preparation of the manuscript.
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Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
Authors’ contributions
All authors of this paper have contributed to the study design. AB and VV developed the hybrid model and conducted the numerical simulations. AB, GB, AM, VV conceived the study design and manuscript preparation. All authors read and approved the final manuscript.
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The authors declare that they have no competing interests.
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This article has been published as part of BMC Immunology Volume 18 Supplement 1, 2017. Systems Immunology & ImmunoInformatics. The full contents of the supplement are available online https://bmcimmunol.biomedcentral.com/articles/supplements/volume18supplement1.
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Additional file 1
Appendix. Numerical method; numerical implementation; values of parameters [43]. (PDF 38 kb)
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Bouchnita, A., Bocharov, G., Meyerhans, A. et al. Hybrid approach to model the spatial regulation of T cell responses. BMC Immunol 18 (Suppl 1), 29 (2017). https://doi.org/10.1186/s1286501702050
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DOI: https://doi.org/10.1186/s1286501702050
Keywords
 Immune system
 T cell
 Spatial dynamics
 Multiscale regulation
 Hybrid model