Mathematical modeling of immunological reactions

Morel, Penelope A.

doi:10.2741/a278

Cited by 21 publications

(11 citation statements)

References 88 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Q uantitative models of cellular immune responses have permitted investigators to create experimentally testable hypotheses, which have often been counterintuitive, concerning fundamental lymphocyte processes (1). Such models have been used to predict B cell and TCR gene rearrangements (2), T cell population kinetics (3)(4)(5), the kinetics of HIV infection in CD4 ϩ T cell populations (6), and to compare hypotheses regarding B cell Ag receptor complex allelic exclusion (7).…”

mentioning

confidence: 99%

Discrete Event Modeling of CD4+ Memory T Cell Generation

Zand

Briggs

Bose

et al. 2004

The Journal of Immunology

View full text Add to dashboard Cite

Studies of memory T cell differentiation are hampered by a lack of quantitative models to test hypotheses in silico before in vivo experimentation. We created a stochastic computer model of CD4+ memory T cell generation that can simulate and track 101–108 individual lymphocytes over time. Parameters for the model were derived from experimental data using naive human CD4+ T cells stimulated in vitro. Using discrete event computer simulation, we identified two key variables that heavily influence effector burst size and the persistent memory pool size: the cell cycle dependent probability of apoptosis, and the postactivation mitosis at which memory T cells emerge. Multiple simulations were performed and varying critical parameters permitted estimates of how sensitive the model was to changes in all of the model parameters. We then compared two hypotheses of CD4+ memory T cell generation: maturation from activated naive to effector to memory cells (model I) vs direct progression from activated naive to memory cells (model II). We find that direct progression of naive to memory T cells does not explain published measurements of the memory cell mass unless postactivation expansion of the memory cell cohort occurs. We conclude that current models suggesting direct progression of activated naive cells to the persistent memory phenotype (model II) do not account for the experimentally measured size of the postactivation CD4+, Ag-specific, memory T cell cohort.

show abstract

mentioning

confidence: 99%

Discrete Event Modeling of CD4+ Memory T Cell Generation

Zand

Briggs

Bose

et al. 2004

The Journal of Immunology

View full text Add to dashboard Cite

show abstract

“…Throughout history, the language of mathematics has proven well suited for integrating diverse empirical findings into a holistic quantitative framework (Morel, 1998;Nowak and May, 2000;Perelson, 2002). Mathematical models have the benefit over empirical studies that they are free from physical constraints, thus enabling to test a wide spectrum of scenarios that may be difficult to test experimentally.…”

Section: The Need For Mathematical Host-pathogen Interaction Modelsmentioning

confidence: 99%

“…In immunology, mathematical models have adopted the role of providing the (quantitative) theoretical framework for interpreting the wealth of available immunological data. In addition, the value of mathematical models in advancing the understanding of the immune system and in the development and in silico testing of treatment strategies has been convincingly described by models of human immunodeficiency virus (HIV) and some other infections in humans that emerged in the 1990s (see reviews by Morel, 1998;Perelson, 2002;Davenport et al, 2007). The success story of the early models has sparked much collaboration between mathematicians and immunologists since.…”

Section: Prrsv (Virus) Infection (See Example 1)mentioning

confidence: 99%

“…Detailed information about various types of models of immune system dynamics published in the immunological and mathematical literature is beyond the scope of this review, but can be found in several excellent reviews (e.g. Morel, 1998;Nowak and May, 2000;Yates et al, 2001;Perelson, 2002;Louzoun, 2007;Kirschner and Linderman, 2009). …”

Section: Prrsv (Virus) Infection (See Example 1)mentioning

confidence: 99%

“…' Another critical factor for choosing an appropriate model concerns model validation. Generally, 'the more assumptions that have to be put into a model, the harder it is to be confident about the conclusions' (Morel, 1998). A model that contains many parameters with unknown values is difficult to validate, as a comprehensive sensitivity analysis testing the outcome for all possible parameter combinations is virtually impossible.…”

Section: What Is a Good Mathematical Model?mentioning

confidence: 99%

See 2 more Smart Citations

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

Doeschl‐Wilson

2011

Animal

View full text Add to dashboard Cite

Compared with the application of mathematical models to study human diseases, models that describe animal responses to pathogen challenges are relatively rare. The aim of this review is to explain and show the role of mathematical host-pathogen interaction models in providing underpinning knowledge for improving animal health and sustaining livestock production. Existing host-pathogen interaction models can be assigned to one of three categories: (i) models of the infection and immune system dynamics, (ii) models that describe the impact of pathogen challenge on health, survival and production and (iii) models that consider the co-evolution of host and pathogen. State-of-the-art approaches are presented and discussed for models belonging to the first two categories only, as they concentrate on the host-pathogen dynamics within individuals. Models of the third category fall more into the class of epidemiological models, which deserve a review by themselves. An extensive review of published models reveals a rich spectrum of methodologies and approaches adopted in different modelling studies, and a strong discrepancy between models concerning diseases in animals and models aimed at tackling diseases in humans (most of which belong to the first category), with the latter being generally more sophisticated. The importance of accounting for the impact of infection not only on health but also on production poses a considerable challenge to the study of host-pathogen interactions in livestock. This has led to relatively simplistic representations of host-pathogen interaction in existing models for livestock diseases. Although these have proven appropriate for investigating hypotheses concerning the relationships between health and production traits, they do not provide predictions of an animal's response to pathogen challenge of sufficient accuracy that would be required for the design of appropriate disease control strategies. A synthesis between the modelling methodologies adopted in categories 1 and 2 would therefore be desirable. The progress achieved in mathematical modelling to study immunological processes relevant to human diseases, together with the current advances in the generation and analysis of biological data related to animal diseases, offers a great opportunity to develop a new generation of host-pathogen interaction models that take on a fundamental role in the study and control of disease in livestock.

show abstract

A Massively Multi-agent System for Discovering HIV-Immune Interaction Dynamics

Liu

2005

Massively Multi-Agent Systems I

View full text Add to dashboard Cite

Mathematical modeling of immunological reactions

Cited by 21 publications

References 88 publications

Discrete Event Modeling of CD4+ Memory T Cell Generation

Discrete Event Modeling of CD4+ Memory T Cell Generation

The role of mathematical models of host–pathogen interactions for livestock health and production – a review

A Massively Multi-agent System for Discovering HIV-Immune Interaction Dynamics

Contact Info

Product

Resources

About