T-Cell Activation: A Queuing Theory Analysis at Low Agonist Density

Wedagedera, J. R.; Burroughs, Nigel John

doi:10.1529/biophysj.105.066001

Cited by 17 publications

(26 citation statements)

References 59 publications

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“…In effect, the successive ligation of TCRs serves as a gain, turning each pMHC into many activated receptors. The development of mathematical models has helped to explain how T cells have adaptedtoTCRbiochemical propertiesto achieve both fidelity and sensitivity (McKeithan 1995;Coombs et al 2002;Wedagedera and Burroughs 2006).…”

Section: Triggeringmentioning

confidence: 99%

Mediation of T-Cell Activation by Actin Meshworks

Beemiller¹,

Krummel²

2010

Cold Spring Harbor Perspectives in Biology

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Although the actin cytoskeleton and T-cell receptor (TCR) signaling complexes are seemingly distinct molecular structures, they are tightly integrated in T cells. The signaling pathways initiated by TCRs binding to peptide MHC complexes are extensively influenced by the actin cytoskeletal activities of the motile phase before TCR signaling, the signalosome scaffolding function of the cytoskeleton, and the translocation of signaling clusters that precedes the termination of signaling at these complexes. As these three successive phases constitute essentially all the steps consequent to immune synapse formation, it has become clear that the substantial physical forces and signaling interactions generated by the actin cytoskeleton dominate the signaling life cycle of TCR signalosomes. We discuss the contributions of the actin cytoskeleton to TCR signaling phases and model some remaining questions about how specific cytoskeletal factors regulate TCR signaling outcomes.

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Section: Triggeringmentioning

confidence: 99%

Mediation of T-Cell Activation by Actin Meshworks

Beemiller¹,

Krummel²

2010

Cold Spring Harbor Perspectives in Biology

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“…,q n ), whose marginals are independent and identically distributed is [25] D JS (p,q) = nD JS (p i ,q i ). (13) By assuming independence between the signaling units, it follows from Eq. (13) that the JS divergence increases linearly with the number of clusters.…”

Section: Dependence On the Number Of Receptors Within A Signaling mentioning

confidence: 99%

“…[11]. The method exploits ideas outlined for T-cell receptors (TCRs) [13], where each compartment of the kinetic proofreading scheme is described as a queue. By combining time thresholds and signal integration, the model in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Network-of-queues approach to B-cell-receptor affinity discrimination

Felizzi

Comoglio

2012

Phys. Rev. E

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The immune system is one of the most complex signal processing machineries in biology. The adaptive immune system, consisting of B and T lymphocytes, is activated in response to a large spectrum of pathogen antigens. B cells recognize and bind the antigen through B-cell receptors (BCRs) and this is fundamental for B-cell activation. However, the system response is dependent on BCR-antigen affinity values that span several orders of magnitude. Moreover, the ability of the BCR to discriminate between affinities at the high end (e.g., 10^{9}M^{-1}-10^{10}M^{-1}) challenges the formulation of a mathematical model able to robustly separate these affinity-dependent responses. Queuing theory enables the analysis of many related processes, such as those resulting from the stochasticity of protein binding and unbinding events. Here we define a network of queues, consisting of BCR early signaling states and transition rates related to the propensity of molecular aggregates to form or disassemble. By considering the family of marginal distributions of BCRs in a given signaling state, we report a significant separation (measured as Jensen-Shannon divergence) that arises from a broad spectrum of antigen affinities.

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“…The sensitivity–specificity balance was investigated using queuing theory models (12). This formalism fully accounts for stochasticity in the detection process.…”

Section: Limits Of Stochastic Signaling At Low Agonist Densitymentioning

confidence: 99%

Stochasticity and spatial heterogeneity in T‐cell activation

Burroughs

Merwe

2007

Immunological Reviews

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Stochastic and spatial aspects are becoming increasingly recognized as an important factor in T-cell activation. Activation occurs in an intrinsically noisy environment, requiring only a handful of agonist peptide-major histocompatibility complex molecules, thus making consideration of signal to noise of prime importance in understanding sensitivity and specificity. Furthermore, it is widely established that surface-bound ligands are more effective at activation than soluble forms, while surface patternation has highlighted the role of spatial relocation in activation. Here we consider the results of a number of models of T-cell activation, from a realistic model of kinetic segregation-induced T-cell receptor (TCR) triggering through to simple queuing theory models. These studies highlight the constraints on cell activation by a surface receptor that recruits kinases. Our analysis shows that TCR triggering based on trapping of bound TCRs in regions of close proximity that exclude large ectodomain-containing molecules, such as the phosphatases CD45 and CD148, can effectively reproduce known signaling characteristics and is a viable 'signal transduction' mechanism distinct from oligomerization and conformation-based mechanisms. A queuing theory analysis shows the interrelation between sensitivity and specificity, emphasizing that these are properties of individual cell functions and need not be, nor are likely to be, uniform across different functions. In fact, threshold-based mechanisms of detection are shown to be poor at ligand discrimination because, although they can be highly specific, that specificity is limited to a small range of peptide densities. Time integration mechanisms however are able to control noise effectively, while kinetic proofreading mechanisms endow them with good specificity properties. Thus, threshold mechanisms are likely to be important for rapidly detecting minimal signaling requirements, thus achieving efficient scanning of antigen-presenting cells. However, for good specificity, time integration on a scale of hours is required.

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T-Cell Activation: A Queuing Theory Analysis at Low Agonist Density

Cited by 17 publications

References 59 publications

Mediation of T-Cell Activation by Actin Meshworks

Mediation of T-Cell Activation by Actin Meshworks

Network-of-queues approach to B-cell-receptor affinity discrimination

Stochasticity and spatial heterogeneity in T‐cell activation

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