Toward a multiscale model of antigen presentation in immunity

Kirschner, Denise E.; Chang, Stewart T.; Riggs, Thomas W.; Perry, Nicolás; Linderman, Jennifer J.

doi:10.1111/j.1600-065x.2007.00490.x

Cited by 69 publications

(71 citation statements)

References 172 publications

(199 reference statements)

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“…We strongly believe that the ideas and results presented here will form an important interpretive framework with a wide array of applications in experimental settings, diagnostic tests [35], and perhaps in a more integrated model of cell dynamics [46,50].…”

Section: Discussionmentioning

confidence: 99%

A Division-Dependent Compartmental Model for Computing Cell Numbers in CFSE-based Lymphocyte Proliferation Assays

Banks¹,

Thompson²,

Peligero³

et al. 2012

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Some key features of a mathematical description of an immune response are an estimate of the number of responding cells and the manner in which those cells divide, differentiate, and die. The intracellular dye CFSE is a powerful experimental tool for the analysis of a population of dividing cells, and numerous mathematical treatments have been aimed at using CFSE data to describe an immune response [30,31,32,37,38,41,47,48]. Recently, partial differential equation structured population models, with measured CFSE fluorescence intensity as the structure variable, have been shown to accurately fit histogram data obtained from CFSE flow cytometry experiments [18,19,51,53]. In this report, the population of cells is mathematically organized into compartments, with all cells in a single compartment having undergone the same number of divisions. A system of structured partial differential equations is derived which can be fit directly to CFSE histogram data. From such a model, cell counts (in terms of the number of divisions undergone) can be directly computed and thus key biological parameters such as population doubling time and precursor viability can be determined. Mathematical aspects of this compartmental model are discussed, and the model is fit to a data set. As in [18,19], we find temporal and division dependence in the rates of proliferation and death to be essential features of a structured population model for CFSE data. Variability in cellular autofluorescence is found to play a significant role in the data, as well. Finally, the compartmental model is compared to previous work, and statistical aspects of the experimental data are discussed.Key words: Cell proliferation, cell division number, CFSE, label structured population dynamics, partial differential equations, inverse problems. 1 Report Documentation Page

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Section: Discussionmentioning

confidence: 99%

A Division-Dependent Compartmental Model for Computing Cell Numbers in CFSE-based Lymphocyte Proliferation Assays

Banks¹,

Thompson²,

Peligero³

et al. 2012

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“…The analysis can be performed either looking at specific inter-and intra-compartmental effects in which we are interested, or all the significant indexes can be listed and then classified as inter-or intra-compartmental effects. An example of multiscale sensitivity analysis can be found in (Chang et al, to appear in Infection and Immunity;Kirschner et al, 2007).…”

Section: Standard Versus Explorative Us Analysismentioning

confidence: 99%

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Marino

Hogue

Ray

et al. 2008

Journal of Theoretical Biology

Self Cite

2,123

1,892

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Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, both in deterministic and stochastic settings, and propose novel techniques to handle problems encountered during this type of analyses.

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“…It has become increasingly clear, however, that events at different length and time scales all simultaneously affect the overall outcome of biological processes including host-pathogen interactions. For example, the affinity of particular peptides for MHCII may play a role in the efficacy of antigen presentation, affecting the number of T cells generated which will dictate success or failure of the immune response in a given situation (Kirschner et al, 2007). Experiments that simultaneously track molecular, cellular, tissue, organism and population scale events over time are rare and difficult to perform with accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…However, multi-scale models offer a distinct advantage in being able to integrate information across scales. Multi-scale modelling requires building and validating models at individual scales and linking them together in a method that is computationally feasible (Alarcon et al, 2004;Kirschner et al, 2007).…”

Section: Discussionmentioning

confidence: 99%

Mathematical and computational approaches can complement experimental studies of host-pathogen interactions

Kirschner

Linderman

2008

Cellular Microbiology

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SummaryIn addition to traditional and novel experimental approaches to study host-pathogen interactions, mathematical and computer modelling have recently been applied to address open questions in this area. These modelling tools not only offer an additional avenue for exploring disease dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. In this review, we outline four examples where modelling has complemented current experimental techniques in a way that can or has already pushed our knowledge of host-pathogen dynamics forward. Two of the modelling approaches presented go hand in hand with articles in this issue exploring fluorescence resonance energy transfer and two-photon intravital microscopy. Two others explore virtual or 'in silico' deletion and depletion as well as a new method to understand and guide studies in genetic epidemiology. In each of these examples, the complementary nature of modelling and experiment is discussed. We further note that multi-scale modelling may allow us to integrate information across length (molecular, cellular, tissue, organism, population) and time (e.g. seconds to lifetimes). In sum, when combined, these compatible approaches offer new opportunities for understanding host-pathogen interactions.

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Toward a multiscale model of antigen presentation in immunity

Cited by 69 publications

References 172 publications

A Division-Dependent Compartmental Model for Computing Cell Numbers in CFSE-based Lymphocyte Proliferation Assays

A Division-Dependent Compartmental Model for Computing Cell Numbers in CFSE-based Lymphocyte Proliferation Assays

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Mathematical and computational approaches can complement experimental studies of host-pathogen interactions

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