A Deterministic Model Predicts the Properties of Stochastic Calcium Oscillations in Airway Smooth Muscle Cells
Author(s) -
Pengxing Cao,
Xiahui Tan,
Graham M. Donovan,
Michael J. Sanderson,
James Sneyd
Publication year - 2014
Publication title -
plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1003783
Subject(s) - calcium , physics , function (biology) , statistical physics , stochastic modelling , oscillation (cell signaling) , inositol , mathematics , chemistry , biology , receptor , statistics , biochemistry , organic chemistry , evolutionary biology
The inositol trisphosphate receptor ( ) is one of the most important cellular components responsible for oscillations in the cytoplasmic calcium concentration. Over the past decade, two major questions about thehave arisen. Firstly, how best should thebe modeled? In other words, what fundamental properties of theallow it to perform its function, and what are their quantitative properties? Secondly, although calcium oscillations are caused by the stochastic opening and closing of small numbers of, is it possible for a deterministic model to be a reliable predictor of calcium behavior? Here, we answer these two questions, using airway smooth muscle cells (ASMC) as a specific example. Firstly, we show that periodic calcium waves in ASMC, as well as the statistics of calcium puffs in other cell types, can be quantitatively reproduced by a two-state model of the, and thus the behavior of theis essentially determined by its modal structure. The structure within each mode is irrelevant for function. Secondly, we show that, although calcium waves in ASMC are generated by a stochastic mechanism,stochasticity is not essential for a qualitative prediction of how oscillation frequency depends on model parameters, and thus deterministicmodels demonstrate the same level of predictive capability as do stochastic models. We conclude that, firstly, calcium dynamics can be accurately modeled using simplifiedmodels, and, secondly, to obtain qualitative predictions of how oscillation frequency depends on parameters it is sufficient to use a deterministic model.
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