Agent-Based Modeling of T Cell Receptor Cooperativity

Siokis, Anastasios; Robert, Philippe A.; Meyer‐Hermann, Michael

doi:10.3390/ijms21186473

Cited by 3 publications

(1 citation statement)

References 51 publications

(114 reference statements)

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“…The description of the pandemic with a finer resolution of the pandemic is possible with stochastic agent-based models but this is not considered and discussed in this paper. Some interesting results in this can be found in [ 27 , 34 , 43 ].…”

Section: Introductionmentioning

confidence: 95%

Modeling of COVID-19 propagation with compartment models

Bärwolff

2021

Math Semesterber

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The current pandemic is a great challenge for several research areas. In addition to virology research, mathematical models and simulations can be a valuable contribution to the understanding of the dynamics of the pandemic and can give recommendations to both physicians and politicians. In this paper we give an overview about mathematical models to describe the pandemic by differential equations. As a matter of principle the historic origin of the epidemic growth models will be remembered. Moreover we discuss models for the actual pandemic of 2020/2021. This will be done based on actual data of people infected with COVID-19 from the European Centre for Disease Prevention and Control (ECDC), input parameters of mathematical models will be determined and applied. These parameters will be estimated for the UK, Italy, Spain, and Germany and used in a SIR-type model. As a basis for the model’s calibration, the initial exponential growth phase of the COVID-19 pandemic in the named countries is used. Strategies for the commencing and ending of social and economic shutdown measures are discussed. To respect heterogeneity of the people density in the different federal states of Germany diffusion effects are considered.

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Section: Introductionmentioning

confidence: 95%

Modeling of COVID-19 propagation with compartment models

Bärwolff

2021

Math Semesterber

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Computational systems biology of cellular processes in the human lymph node

Scharf,

Ackermann,

Wurzel

et al. 2024

Current Opinion in Systems Biology

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Bayesian metamodeling of early T-cell antigen receptor signaling accounts for its nanoscale activation patterns

Neve-Oz,

Sherman,

Raveh

2024

Front. Immunol.

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T cells respond swiftly, specifically, sensitively, and robustly to cognate antigens presented on the surface of antigen presenting cells. Existing microscopic models capture various aspects of early T-cell antigen receptor (TCR) signaling at the molecular level. However, none of these models account for the totality of the data, impeding our understanding of early T-cell activation. Here, we study early TCR signaling using Bayesian metamodeling, an approach for systematically integrating multiple partial models into a metamodel of a complex system. We inform the partial models using multiple published super-resolution microscopy datasets. Collectively, these datasets describe the spatiotemporal organization, activity, interactions, and dynamics of TCR, CD45 and Lck signaling molecules in the early-forming immune synapse, and the concurrent membrane alterations. The resulting metamodel accounts for a distinct nanoscale dynamic pattern that could not be accounted for by any of the partial models on their own: a ring of phosphorylated TCR molecules, enriched at the periphery of early T cell contacts and confined by a proximal ring of CD45 molecules. The metamodel suggests this pattern results from limited activity range for the Lck molecules, acting as signaling messengers between kinetically-segregated TCR and CD45 molecules. We assessed the potential effect of Lck activity range on TCR phosphorylation and robust T cell activation for various pMHC:TCR association strengths, in the specific setting of an initial contact. We also inspected the impact of localized Lck inhibition via Csk recruitment to pTCRs, and that of splicing isoforms of CD45 on kinetic segregation. Due to the inherent scalability and adaptability of integrating independent partial models via Bayesian metamodeling, this approach can elucidate additional aspects of cell signaling and decision making.

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Agent-Based Modeling of T Cell Receptor Cooperativity

Cited by 3 publications

References 51 publications

Modeling of COVID-19 propagation with compartment models

Modeling of COVID-19 propagation with compartment models

Computational systems biology of cellular processes in the human lymph node

Bayesian metamodeling of early T-cell antigen receptor signaling accounts for its nanoscale activation patterns

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