Toward Quantitative Simulation of Germinal Center Dynamics: Biological and Modeling Insights from Experimental Validation

Kleinstein, Steven H.; Singh, Jaswinder Pal

doi:10.1006/jtbi.2001.2344

Cited by 34 publications

(38 citation statements)

References 41 publications

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“…Rapid takeover of GCs was first described by Radmacher et al (41), who observed that within a GC, usually all or none of the BCRs carry the key mutation. This all-or-none behavior of GCs is assumed to rely on a winner-takes-all selection mechanism, where, once found, B cells bearing high-affinity key mutations rapidly take over the entire population of a GC (32). Notably, sudden collapses, as predicted by our model, have the intrinsic property of generating a rapid takeover and oligoclonality.…”

Section: Discussionmentioning

confidence: 78%

“…According to the model of Kleinstein and Singh (32), many GCs remain small if the survival of GC B cells depends on the affinity of their BCRs, if the probability of generating high-affinity mutants is low, and when selection is driven by escape from apoptosis. However, a strong, but experimentally unvalidated, prediction of their model is that iGC size is positively correlated with the level of affinity maturation (32).…”

Section: Discussionmentioning

confidence: 99%

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Broad Volume Distributions Indicate Nonsynchronized Growth and Suggest Sudden Collapses of Germinal Center B Cell Populations

Wittenbrink

Weber²,

Klein³

et al. 2010

The Journal of Immunology

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Section: Discussionmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Broad Volume Distributions Indicate Nonsynchronized Growth and Suggest Sudden Collapses of Germinal Center B Cell Populations

Wittenbrink

Weber²,

Klein³

et al. 2010

The Journal of Immunology

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“…Agent-based modeling is increasingly being recognized as a viable way to simulate biological processes [24][25][26][27]. It has the following advantages:…”

Section: It Is Very Much An Open Question As To What Sort Of Mathematmentioning

confidence: 99%

A virtual look at Epstein–Barr virus infection: Simulation mechanism

Shapiro

Duca

Lee

et al. 2008

Journal of Theoretical Biology

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Epstein-Barr virus (EBV) is an important human pathogen that establishes a lifelong persistent infection and for which no precise animal model exists. In this paper we describe in detail an agentbased model and computer simulation of EBV infection. Agents representing EBV and sets of B and T lymphocytes move and interact on a three-dimensional grid approximating Waldeyer's ring, together with abstract compartments for lymph and blood. The simulation allows us to explore the development and resolution of virtual infections in a manner not possible in actual human experiments. Specifically, we identify parameters capable of inducing clearance, persistent infection, or death.

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“…Abstract models of affinity maturation have been used to gauge the rate of response to antigens [12]. These models have accurately predicted qualitative details of the immune response, and have hinted at the importance of secondary mechanisms for achieving quantitative agreement with experimental observations [13,14].…”

Section: Introductionmentioning

confidence: 99%

Short-time evolution in the adaptive immune system

Guttenberg

Tabei²,

Dinner³

2011

Phys. Rev. E

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We exploit a simple model to numerically and analytically investigate the effect of enforcing a time constraint for achieving a system-wide goal during an evolutionary dynamics. This situation is relevant to finding antibody specificities in the adaptive immune response as well as to artificial situations in which an evolutionary dynamics is used to generate a desired capability in a limited number of generations. When the likelihood of finding the target phenotype is low, we find that the optimal mutation rate can exceed the error threshold, in contrast to conventional evolutionary dynamics. We also show how a logarithmic correction to the usual inverse scaling of population size with mutation rate arises. Implications for natural and artificial evolutionary situations are discussed.

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Toward Quantitative Simulation of Germinal Center Dynamics: Biological and Modeling Insights from Experimental Validation

Cited by 34 publications

References 41 publications

Broad Volume Distributions Indicate Nonsynchronized Growth and Suggest Sudden Collapses of Germinal Center B Cell Populations

Broad Volume Distributions Indicate Nonsynchronized Growth and Suggest Sudden Collapses of Germinal Center B Cell Populations

A virtual look at Epstein–Barr virus infection: Simulation mechanism

Short-time evolution in the adaptive immune system

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