Systems immunology: a survey of modeling formalisms, applications and simulation tools

Abstract

Immunological studies frequently analyze individual components (e.g., signaling pathways) of immune systems in a reductionist manner. In contrast, systems immunology aims to give a synthetic understanding of how these components function together as a whole. While immunological research involves in vivo and in vitro experiments, systems immunology research can also be conducted in silico. With an increasing interest in systems-level studies spawned by high-throughput technologies, many immunologists are looking forward to insights provided by computational modeling and simulation. However, modeling and simulation research has mainly been conducted in computational fields, and therefore, little material is available or accessible to immunologists today. This survey is an attempt at bridging the gap between immunologists and systems immunology modeling and simulation. Modeling and simulation refer to building and executing an in silico replica of an immune system. Models are specified within a mathematical or algorithmic framework called formalism and then implemented using software tools. A plethora of modeling formalisms and software tools are reported in the literature for systems immunology. However, it is difficult for a new entrant to the field to know which of these would be suitable for modeling an immunological application at hand. This paper covers three aspects. First, it introduces the field of system immunology emphasizing on the modeling and simulation components. Second, it gives an overview of the principal modeling formalisms, each of which is illustrated with salient applications in immunological research. This overview of formalisms and applications is conducted not only to illustrate their power but also to serve as a reference to assist immunologists in choosing the best formalism for the problem at hand. Third, it lists major software tools, which can be used to practically implement models in these formalisms. Combined, these aspects can help immunologists to start experimenting with in silico models. Finally, future research directions are discussed. Particularly, we identify integrative frameworks to facilitate the coupling of different modeling formalisms and modeling the adaptation properties through evolution of immune systems as the next key research efforts necessary to further develop the multidisciplinary field of systems immunology.

Modeling Leukocyte-Leukocyte Non-Contact Interactions in a Lymph Node

“…The modeling of a lymph node (LN), that is already a very complex system, or even the whole immune system, has been addressed in the literature with elegant methods [19]–[24]. This is not our aim here.…”

“…The outlined scenario is complex and the role played by non-contact interactions is not easy to be discerned. We are not looking here for a comprehensive model that describes such complex scenario and would require further developments that take advantage of the sophisticated models reported in the literature [19]–[24]. We address instead the extent of the effect of non-contact interactions on the observed kinematics of the leukocytes, primarily on the interaction duration.…”

A primer on recent developments in cancer immunotherapy, with a focus on neoantigen vaccines

“…The resulting descriptions tend to have a multi-scale (hierarchical) character in both space and time [229,230] . The wide variety of available modeling formalisms and applications has been surveyed by Narang et al [231] . Mathematical modeling has begun to impact the clinic through efforts to optimize dosage and timing ("schedule optimization"), which have gained a foothold in chemotherapy [232,233] and radiotherapy [234,235] .…”

Modeling the T cell immune response: a fascinating challenge

“…Different tools and approaches are often used to address similar questions and it can be difficult to compare results from these kinds of models. In addition, the field is in need of more developed systems immunology approaches [140,141]. These are models that incorporate the spatial and time scales of the whole immune response.…”