Systems biology approaches for understanding cellular mechanisms of immunity in lymph nodes during infection

Abstract

Adaptive immunity is initiated in secondary lymphoid tissues when naive T cells recognize foreign antigen presented as MHC-bound peptide on the surface of dendritic cells. Only a small fraction of T cells in the naive repertoire will express T cell receptors specific for a given epitope, but antigen recognition triggers T cell activation and proliferation, thus greatly expanding antigen-specific clones. Expanded T cells can serve a helper function for B cell responses or traffic to sites of infection to secrete cytokines or kill infected cells. Over the past decade, two- photon microscopy of lymphoid tissues has shed important light on T cell development, antigen recognition, cell trafficking and effector functions. These data have enabled the development of sophisticated quantitative and computational models that, in turn, have been used to test hypotheses in silico that would otherwise be impossible or difficult to explore experimentally. Here, we review these models and their principal findings and highlight remaining questions where modeling approaches are poised to advance our understanding of complex immunological systems.

Multiscale Modelling of Lymphatic Drainage

“…We will discuss both very briefly below. In addition there are models that deal with the immune response within the lymph nodes [2,17,32], but since these do not usually have any fluid dynamics included in them we will not discuss them in this review.…”

Modeling Lymph Flow and Fluid Exchange with Blood Vessels in Lymph Nodes

“…Mathematical and computational modeling has been widely used to improve understanding of the physiology and immunology of biological tissues. [34][35][36][37] These methods can be utilized to identify key parameters of a system, suggest new hypotheses, and estimate parameters that cannot be measured. 35,38 The design of the model should take into account the important characteristics of the system being modeled, as well as the nature of the questions to be answered.…”

“…[34][35][36][37] These methods can be utilized to identify key parameters of a system, suggest new hypotheses, and estimate parameters that cannot be measured. 35,38 The design of the model should take into account the important characteristics of the system being modeled, as well as the nature of the questions to be answered. 36 Bocharov et al used reaction-diffusion models to simulate the distribution of interferon-a in the 3D geometry of the node, but lacked advective transport in the node.…”

“…39 Several other models of LNs have been developed using agent-based modeling, and cellular Potts model techniques, and have been able to simulate cellular motions, cell-cell interactions, and cell influx and efflux. [40][41][42] All the techniques described so far are useful in studying cellular interactions, but they all fail to account for the transport of lymph and the fluid exchange in the node. A computational fluid dynamics model based on the experimental data can investigate the lymph flow in the mouse LNs, and further expand our knowledge of antigen and chemokine transport in the node.…”

Computational Approach to 3D Modeling of the Lymph Node Geometry

“…Some studies have been performed in which the architecture of the LN is modeled as either 2D-or 3D regular orthogonal lattice [9][10][11][12][13][14] or a paradigmatic 3D view composed of a simplified set of basic structures [15]. The broad availability of various imaging techniques has enabled the characterization of the LN structures with a high degree of spatial resolution.…”