A computational model of urinary bladder smooth muscle syncytium: Validation and investigation of electrical properties
Research output: Contribution to journal › Article
- Computational NeuroPhysiology Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai - 400076, Maharashtra, India, email@example.com.
Certain smooth muscles, such as the detrusor of the urinary bladder, exhibit a variety of spikes that differ markedly in their amplitudes and time courses. The origin of this diversity is poorly understood but is often attributed to the syncytial nature of smooth muscle and its distributed innervation. In order to help clarify such issues, we present here a three-dimensional electrical model of syncytial smooth muscle developed using the compartmental modeling technique, with special reference to the bladder detrusor. Values of model parameters were sourced or derived from experimental data. The model was validated against various modes of stimulation employed experimentally and the results were found to accord with both theoretical predictions and experimental observations. Model outputs also satisfied criteria characteristic of electrical syncytia such as correlation between the spatial spread and temporal decay of electrotonic potentials as well as positively skewed amplitude frequency histogram for sub-threshold potentials, and lead to interesting conclusions. Based on analysis of syncytia of different sizes, it was found that a size of 21-cube may be considered the critical minimum size for an electrically infinite syncytium. Set against experimental results, we conjecture the existence of electrically sub-infinite bundles in the detrusor. Moreover, the absence of coincident activity between closely spaced cells potentially implies, counterintuitively, highly efficient electrical coupling between such cells. The model thus provides a heuristic platform for the interpretation of electrical activity in syncytial tissues.
|Number of pages||21|
|Journal||Journal of Computational Neuroscience|
|Early online date||8 Oct 2014|
|Publication status||Published - Feb 2015|