Document Type
Article
Department/Program
Applied Science
Journal Title
Computational and Mathematical Methods in Medicine
Pub Date
12-2012
Volume
2012
Issue
Cardiovascular System Modeling
Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.
Abstract
Cardiac myocyte calcium signaling is often modeled using deterministic ordinary differential equations (ODEs) and mass-action kinetics. However, spatially restricted “domains” associated with calcium influx are small enough (e.g., 10−17 liters) that local signaling may involve 1–100 calcium ions. Is it appropriate to model the dynamics of subspace calcium using deterministic ODEs or, alternatively, do we require stochastic descriptions that account for the fundamentally discrete nature of these local calcium signals? To address this question, we constructed a minimal Markov model of a calcium-regulated calcium channel and associated subspace. We compared the expected value of fluctuating subspace calcium concentration (a result that accounts for the small subspace volume) with the corresponding deterministic model (an approximation that assumes large system size). When subspace calcium did not regulate calcium influx, the deterministic and stochastic descriptions agreed. However, when calcium binding altered channel activity in the model, the continuous deterministic description often deviated significantly from the discrete stochastic model, unless the subspace volume is unrealistically large and/or the kinetics of the calcium binding are sufficiently fast. This principle was also demonstrated using a physiologically realistic model of calmodulin regulation of L-type calcium channels introduced by Yue and coworkers.
Recommended Citation
Weinberg, Seth H. and Smith, Gregory D., Discrete-State Stochastic Models of Calcium-Regulated Calcium Influx and Subspace Dynamics Are Not Well-Approximated by ODEs That Neglect Concentration Fluctuations (2012). Computational and Mathematical Methods in Medicine, 2012(Cardiovascular System Modeling).
https://doi.org/10.1155/2012/897371
DOI
https://doi.org/10.1155/2012/897371