Edinburgh Research Explorer

Spatial computational model of calcium signalling in the dendritic spines and its relation to Alzheimer's Disease


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PublisherEdinburgh DataShare
Date made available2 Aug 2018


Memory in its simplest forms involves plastic changes in synaptic strength. Synaptic long-term potentiation (LTP) and long-term depression (LTD) exist in a delicate balance, which is disturbed in a number of diseases. In Alzheimer's Disease, the balance is skewed towards LTD, which results in cognitive impairments.
Long-term synaptic plasticity relies on Calcium signalling in the postsynaptic neuron and the activation of calmodulin and, subsequently, of CaMKII or calcineurin, leading to the induction of LTP or LTD, respectively. In Alzheimer's disease, basal Calcium levels are elevated because of changes in Ca release from intracellular stores, and in transmembrane receptor function. This results in increased activation of calcineurin and hence, increased LTD.
We develop a spatial particle-based stochastic model of Ca signalling in the dendritic spine in early LTP and LTD. The model includes Calcium influx through, activation of calmodulin, activation of calmodulin-dependent kinases and phosphatases and their effect on AMPA receptor and NMDA receptor function. This model helps us understand downstream effects of Calcium dysregulation in Alzheimer's disease. We tested the impact of different calcium levels on the molecular pathways of long term plasticity.
The healthy version of the model shows how the calcium signalling works in the dendritic spine head. While disease model have impaired calcium pumps, which leads to a build up of calcium within the spine head. The dynamics of the calcium influx can be changed to test the reactivity of pathways in response to calcium signal. This can done by changing the quantity to release and release pattern in MCell.

Data Citation

Arora, Sagar; Stefan, Melanie. (2018). Spatial computational model of calcium signalling in the dendritic spines and its relation to Alzheimer's Disease, [dataset]. University of Edinburgh. Deanery of Biomedical Sciences. https://doi.org/10.7488/ds/2400.

ID: 76891681