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Concentration sensing in crowded environments.
Stroberg, Wylie; Schnell, Santiago.
Afiliação
  • Stroberg W; Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan; Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada. Electronic address: stroberg@ualberta.ca.
  • Schnell S; Department of Molecular & Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan; Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan.
Biophys J ; 120(9): 1718-1731, 2021 05 04.
Article em En | MEDLINE | ID: mdl-33675760
ABSTRACT
Signal transduction within crowded cellular compartments is essential for the physiological function of cells. Although the accuracy with which receptors can probe the concentration of ligands has been thoroughly investigated in dilute systems, the effect of macromolecular crowding on the inference of concentration remains unclear. In this work, we develop an algorithm to simulate reversible reactions between reacting Brownian particles. Our algorithm facilitates the calculation of reaction rates and correlation times for ligand-receptor systems in the presence of macromolecular crowding. Using this method, we show that it is possible for crowding to increase the accuracy of estimated ligand concentration based on receptor occupancy. In particular, we find that crowding can enhance the effective association rates between small ligands and receptors to a degree sufficient to overcome the increased chance of rebinding due to caging by crowding molecules. For larger ligands, crowding decreases the accuracy of the receptor's estimate primarily by decreasing the microscopic association and dissociation rates.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transdução de Sinais Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Transdução de Sinais Idioma: En Ano de publicação: 2021 Tipo de documento: Article