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Modeling the Cluster Size Distribution of Vascular Endothelial Growth Factor (VEGF) Receptors.
Güven, Emine; Wester, Michael J; Edwards, Jeremy S; Halász, Ádám M.
Afiliação
  • Güven E; Department of Biomedical Engineering, Düzce University, Düzce, Turkey.
  • Wester MJ; Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA.
  • Edwards JS; Department of Physics and Astronomy, University of New Mexico, Albuquerque, NM, USA.
  • Halász ÁM; Department of Chemistry and Chemical Biology University of New Mexico, Albuquerque, NM, USA.
Bioinform Biol Insights ; 16: 11779322221085078, 2022.
Article em En | MEDLINE | ID: mdl-35356495
We previously developed a method of defining receptor clusters in the membrane based on mutual distance and applied it to a set of transmission microscopy images of vascular endothelial growth factor receptors. An optimal length parameter was identified, resulting in cluster identification and a procedure that assigned a geometric shape to each cluster. We showed that the observed particle distribution results were consistent with the random placement of receptors within the clusters and, to a lesser extent, the random placement of the clusters on the cell membrane. Here, we develop and validate a stochastic model of clustering, based on a hypothesis of preexisting domains that have a high affinity for receptors. The proximate objective is to clarify the mechanism behind cluster formation and to estimate the effect on signaling. Receptor-enriched domains may significantly impact signaling pathways that rely on ligand-induced dimerization of receptors. We define a simple statistical model, based on the preexisting domain hypothesis, to predict the probability distribution of cluster sizes. The process yielded sets of parameter values that can readily be used in dynamical calculations as the estimates of the quantitative characteristics of the clustering domains.
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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article