2010
DOI: 10.1016/j.bpj.2009.09.043
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Modeling Multivalent Ligand-Receptor Interactions with Steric Constraints on Configurations of Cell-Surface Receptor Aggregates

Abstract: We use flow cytometry to characterize equilibrium binding of a fluorophore-labeled trivalent model antigen to bivalent IgE-FcepsilonRI complexes on RBL cells. We find that flow cytometric measurements are consistent with an equilibrium model for ligand-receptor binding in which binding sites are assumed to be equivalent and ligand-induced receptor aggregates are assumed to be acyclic. However, this model predicts extensive receptor aggregation at antigen concentrations that yield strong cellular secretory resp… Show more

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Cited by 44 publications
(74 citation statements)
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“…At concentrations above 10 µM poly(NBFuc) 100 , highly cooperative self-antagonism is observed. This inhibition is characteristic of a receptor that requires multivalent engagement of receptor [3437]. The inhibition arm may result from competition of the multivalent interaction with monovalent binding (K 1 vs K 2 , Figure 3B).…”
Section: Discussionmentioning
confidence: 99%
“…At concentrations above 10 µM poly(NBFuc) 100 , highly cooperative self-antagonism is observed. This inhibition is characteristic of a receptor that requires multivalent engagement of receptor [3437]. The inhibition arm may result from competition of the multivalent interaction with monovalent binding (K 1 vs K 2 , Figure 3B).…”
Section: Discussionmentioning
confidence: 99%
“…The demonstrations each require NFsim. The 'example3' demonstration concerns a rule-based model for interaction of a trivalent ligand with a bivalent cell-surface receptor and parameter estimation on the basis of equilibrium data collected via flow cytometry (Monine et al, 2010). The 'example4' demonstration concerns a rule-based model for early events in T-cell receptor signaling and parameter estimation on the basis of temporal phosphoproteomic data collected via mass spectrometry-based proteomics (Chylek et al, 2014b).…”
Section: Resultsmentioning
confidence: 99%
“…This reduces run time from dependence on the size of the network to dependence only on the number of species and reactions present at any instant in time. While steric constraints are a challenge for rule-base models, that challenge has been overcome for some systems, e.g., in modeling multivalent ligand-receptor interactions [123]. Further improvements to queuing methods for discrete event implementations of SSA [89, 40] made it possible to accelerate run time by eliminating quadratic time/memory dependencies in the standard algorithms.…”
Section: Self-assembly Modeling and Simulationmentioning
confidence: 99%