Mathematical Models for Immunology: Current State of the Art and Future Research Directions

Eftimie, Raluca; Gillard, Joseph; Cantrell, Doreen A.

doi:10.1007/s11538-016-0214-9

Cited by 151 publications

(130 citation statements)

References 254 publications

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“…4d). The resulting capacity to design more effective protective and therapeutic vaccines has the potential to optimise and potentially individualise vaccines for cancer and chronic infection and regulate autoimmune diseases [38]. Through the development of mechanistic models, virtual clinical trials akin to that described for tumour modelling [2] could be developed and applied to applied to immunological modelling.…”

Section: Application Of Models To Quantify or Predict T Cell Activatimentioning

confidence: 99%

“…Through the development of mechanistic models, virtual clinical trials akin to that described for tumour modelling [2] could be developed and applied to applied to immunological modelling. The resulting capacity to design more effective protective and therapeutic vaccines has the potential to optimise and potentially individualise vaccines for cancer and chronic infection and regulate autoimmune diseases [38].…”

Section: Application Of Models To Quantify or Predict T Cell Activatimentioning

confidence: 99%

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

Brown

Gaffney

Wagg

et al. 2018

Clinical and Experimental Immunology

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The application of in silico modelling is beginning to emerge as a key methodology to advance our understanding of mechanisms of disease pathophysiology and related drug action, and in the design of experimental medicine and clinical studies. From this perspective, we will present a non-technical discussion of a small number of recent and historical applications of mathematical, statistical and computational modelling to clinical and experimental immunology. We focus specifically upon mechanistic questions relating to human viral infection, tumour growth and metastasis and T cell activation. These exemplar applications highlight the potential of this approach to impact upon human immunology informed by ever-expanding experimental, clinical and 'omics' data. Despite the capacity of mechanistic modelling to accelerate therapeutic discovery and development and to de-risk clinical trial design, it is not widely utilised across the field. We outline ongoing challenges facing the integration of mechanistic modelling with experimental and clinical immunology, and suggest how these may be overcome. Advances in key technologies, including multiscale modelling, machine learning and the wealth of 'omics' data sets, coupled with advancements in computational capacity, are providing the basis for mechanistic modelling to impact on immunotherapeutic discovery and development during the next decade.

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Section: Application Of Models To Quantify or Predict T Cell Activatimentioning

confidence: 99%

Section: Application Of Models To Quantify or Predict T Cell Activatimentioning

confidence: 99%

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

Brown

Gaffney

Wagg

et al. 2018

Clinical and Experimental Immunology

View full text Add to dashboard Cite

show abstract

“…Several works (see [20] and the references therein), focused on modeling molecular mechanisms of the immune response coupled to cell population dynamics. Most of these works involve agent-based models.…”

Section: Introductionmentioning

confidence: 99%

Model-based assessment of the Role of Uneven Partitioning of Molecular Content on Heterogeneity and Regulation of Differentiation in CD8 T-cell Immune Responses

Girel

Arpin

Marvel

et al. 2018

Preprint

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Activation of naive CD8 T-cells can lead to the generation of multiple effector and memory subsets. Multiple parameters associated with activation conditions are involved in generating this diversity that is associated with heterogeneous molecular contents of activated cells. Naive cell polarisation upon antigenic stimulation and the asymmetric division that results are known to be a major source of heterogeneity and cell fate regulation. The consequences of stochastic uneven partitioning of molecular content upon subsequent divisions remain unclear. Here we aim at studying the impact of uneven partitioning on molecular-content heterogeneity and then on the immune response dynamics at the cellular level. To do so, we introduce a multiscale mathematical model of the CD8 T-cell immune response in the lymph node. In the model, cells are described as agents evolving and interacting in a 2D environment while a set of differential equations, embedded in each cell, models the regulation of intra and extracellular proteins involved in cell differentiation. Based on the analysis of in silico data at the single cell level, we show that immune response dynamics can be explained by the molecular-content heterogeneity generated by

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“…To this end, the phenotypic modeling approach is well-characterized and recognized as "simple enough to be studied mathematically, but not oversimplified to the point of losing contact with the experimental data" as insightfully argued by Gunawardena (2014). A the same time, largescale mechanistic or semi-mechanistic models construction is usually hampered by the general issue of sparse experimental measurements (De Boer and Perelson, 2013;Eftimie et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In our modeling studies, we also follow Eftimie et al (2016) who distinguish two general purposes for mathematical models: (i) to offer some general theoretical understanding for a biological problem, which does not require any formal model validation, and (ii) to help make predictions, which should already depend on a formal model validation.…”

Section: Introductionmentioning

confidence: 99%

Immunobiochemical reconstruction of influenza lung infection - Melanoma skin cancer interactions

Nikolaev

Zloza

Sontag

2018

Preprint

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Our recent experimental results that combine a mouse model of influenza A virus (IAV) infection (A/H1N1/PR8) and a highly aggressive model of infection-unrelated cancer, B16-F10 skin melanoma, showed that acute influenza infection of the lung promotes distal melanoma growth in the dermis of the flank and leads to decreased host survival. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates the T cell receptor signaling pathway, various transcription factors, and a gene regulatory network (GRN).A core component of our model is a biochemical motif, which we call a Triple Incoherent FeedForward Loop (TIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6.The different activity levels of the TIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes.Specifically, both the TIFFL reconstruction and quantitative estimates obtained from the model allowed us to formulate a hypothesis that it is the loss of the fundamental TIFFL-induced adaptation of the expression of PD-1 receptors on anti-melanoma CD8+ T cells that constitutes the essence of the previously unrecognized immunologic factor that promotes the experimentally observed distal tumor growth in the presence of acute non-ocogenic infection. We therefore hope that this work can further highlight the importance of adaptive mechanisms by which immune functions contribute to the balance between self and non-self immune tolerance, adaptive resistance, and the strength of TCR-induced activation, thus contributing to the understanding of a broader complexity of fundamental interactions between pathogens and tumors.2 All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

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Mathematical Models for Immunology: Current State of the Art and Future Research Directions

Cited by 151 publications

References 254 publications

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

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

Model-based assessment of the Role of Uneven Partitioning of Molecular Content on Heterogeneity and Regulation of Differentiation in CD8 T-cell Immune Responses

Immunobiochemical reconstruction of influenza lung infection - Melanoma skin cancer interactions

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