Channelpedia

PubMed 21586295


Referenced in Channelpedia wiki pages of: none

Automatically associated channels: Kir6.2



Title: A model of the physiological basis of a multivariate phenotype that is mediated by Ca(2+) signaling and controlled by ryanodine receptor composition.

Authors: Cortland K Griswold

Journal, date & volume: J. Theor. Biol., 2011 Aug 7 , 282, 14-22

PubMed link: http://www.ncbi.nlm.nih.gov/pubmed/21586295


Abstract
Calcium-signals occur in a wide variety of tissue types - from skeletal, smooth and cardiac muscle to pancreatic and brain tissues. Ca(2+) signals regulate diverse processes including muscle contraction, hormone secretion, neural communication and gene expression. Together these different tissues and processes form the basis of a multivariate trait. Calcium signals are characterized by Ca(2+) transients, which are sharp increases in Ca(2+) concentration over a short period of time. In this paper we derive and analyze a model of Ca(2+) transients for skeletal muscle, neurons and cardiac tissue based on underlying biophysical principles. Tissue differentiation in our model and in nature comes about by varying the ryanodine receptor (RyR) channel composition of tissues. In vertebrates, there are typically three types of RyR channels (labeled RyR1, RyR2 and RyR3 in mammals and α-RyR, cardiac-RyR and β-RyR in birds, amphibians and fish). Different compositions of these three RyR channels generate different Ca(2+) transient properties. There are four Ca(2+) transient properties that we measure: maximum amplitude, duration, half duration (D(50)) and integrated concentration. In agreement with experimental work, our results find that the addition of RyR3 amplifies Ca(2+) transients in skeletal muscle. An important consequence of shared molecular components between tissue types in a multivariate setting is that the shared components cause individual traits of a multivariate trait to be correlated in function. Here we show how correlations in Ca(2+) transient properties between tissues can be predicted using an underlying biophysical model.