Solving Immunology?

Abstract

Emergent responses of the immune system result from integration of molecular and cellular networks over time and across multiple organs. High-content and high-throughput analysis technologies, concomitantly with data-driven and mechanistic modeling, hold promise for systematic interrogation of these complex pathways. However, connecting genetic variation and molecular mechanisms to individual phenotypes and health outcomes has proven elusive. Gaps remain in data, and disagreements persist about the value of mechanistic modeling for immunology. Here, we present the perspectives that emerged from the NIAID workshop “Complex Systems Science, Modeling and Immunity” and subsequent discussions regarding the potential synergy of high-throughput data acquisition, data-driven modeling and mechanistic modeling to define new mechanisms of immunological disease and to accelerate the translation of these insights into therapies.

A Dynamic Model of Immune Responses to Antigen Presentation Predicts Different Regions of Tumor or Pathogen Elimination

“…Two influential contributions were the 1980 paper by Stepanova (Stepanova 1979) in which a set of two ODE’s was used to represent tumor and immune system cells, and the 1994 paper by Kuznetsov, Makalkin, Taylor, and Perelson (Kuznetsov et al 1994) in which a similarly simple model was used to provide an explanation for the sneaking-through phenomenon, though with escape of small tumors and with no mechanism for detection of rates of change of the immune challenge. It is impossible here to review the literature in this very active area of research; some reviews and textbooks are (Bell, Perelson, and (editors) 1978; Callard and Yates 2005; Andrew, Baker, and Bocharov 2007; Eftimie, Bramson, and Earn 2011; Wodarz and Komarova 2014; Pillis and Radunskaya 2014; Vodovotz et al 2017). …”

Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model

“…Akin to the current use of HPC resources in physics and meteorology, the approach demonstrated in this paper, the use of extremely large-scale simulation and simulated data, provides the only demonstrated effective scientific strategy to prospectively identify the boundaries of fruitful investigation. This concept is complementary to the growing recognition of the benefits of integrating experimental biology, data science and mechanistic modeling in the study of inflammation/immunity (see [54] for a recent review of the use of multi-scale modeling of immunology and inflammation, and existing control theory approaches in this arena), and for biomedicine in general. We propose that in addition to these approaches the use of large-scale simulation based science to establish epistemic boundaries of investigation represents an important piece of a potential path towards the full-scale application of engineering and control principles to the care of individuals in terms of personalized/precision medicine and truly rational design of effective therapeutics.…”

Applications of mechanistic modelling to clinical and experimental immunology: an emerging technology to accelerate immunotherapeutic discovery and development

“…4. both the technical process and more in-depth applications to non-clinical immunology and cancer biology have been previously reviewed in detail [39][40][41][42]. Each panel shows a model that, (a) found that chemokines cannot attract T-cells to antigen-bearing dendritic cells in an optimum search strategy [31]; (b) showed that the dynamics of T-cell movement can be explained entirely by interactions with their environment (as opposed to e.g.…”

“…chemokines) [32]; (c) investigated how T-cells can integrate many low-affinity interactions with dendritic cells to activate [33]; (d) considered the minimum number of dendritic cells required for T-cell response [36]. both the technical process and more in-depth applications to non-clinical immunology and cancer biology have been previously reviewed in detail [39][40][41][42]. Despite the emerging successes of mechanism-driven systems immunology, most models receive little attention and very few clinicians would use models that make clinical predictions in decision-making.…”