Modelling the cellular cardiac neural axis in the control of excitability

心脏是一个非常高效和有效的机电泵，用于提供生命所必需的持续血液流动。心脏中调节收缩速率和力量的自主神经系统的破坏，对心脏细胞的机械和电特性产生许多威胁生命的变化。其功能的关键重要性表现为死亡率非常高，并与心脏自主神经疾病相关，在英国。西方世界。然而，尽管进行了广泛的实验研究，但对神经控制紊乱导致心率变化并导致危及生命的泵衰竭的复杂事件序列仍知之甚少。心率是由心脏中称为窦房结的专门区域的细胞控制的。这些细胞的电特性和随后的心脏起搏速率受到神经递质和其他小分子的严格调节，这些小分子在自动系统内相互作用。最近，新的实验技术为这些细胞的功能提供了令人兴奋的信息。调节带电化学离子进出细胞的单个蛋白质的特性现在可以常规测量。这些信息可以与荧光探针测量相结合，用于确定关键化学物质的浓度和控制心率的潜在细胞机制。最近已经开发了基因递送技术，其使得能够改变许多这些分子的浓度。这种类型的基因递送提供了一种以提供独特信息的方式干扰系统的方法，所述独特信息用于确定给定化合物如何调节健康和患病功能中的电兴奋性。尽管这些技术提供了丰富的信息来源，但决定心率的生化反应的基础系统的固有复杂性仍然使得这些实验数据难以直接解释。数学建模和计算的最新进展现在为这一目的提供了新的和强大的定量工具。通过使用数学方程表示每个单独的化学反应，可以定量地表征完整细胞网络的复杂性。这种方法已成功应用于许多其他心脏细胞类型，将测量与功能联系起来。然而，到目前为止，还没有模型的神经调节电兴奋性已被开发。在这个项目中，我们的目标是直接解决这个问题。我们将整合实验提供的新信息与起搏细胞耦合到神经细胞的计算模型。这样做，我们将能够确定将化学浓度变化与心率联系起来的亚细胞机制。该模型将提供一种隔离个体自主信号机制的方法，以准确了解自主神经紊乱期间心脏功能如何受损。该模型将用于解释实验数据，提出假设和优化实验方案。随着数据的收集，模型结构的参数化将得到完善，从而提供一种不断推进我们对系统理解的机制。使用这种方法，该研究将立即提供一种新的方法来研究和理解心脏自主控制的机制，并最终有助于心脏自主系统疾病的诊断，预防和新疗法的开发

The heart is a remarkably efficient and effective electro-mechanical pump for supplying the continuous flow of blood that is fundamental for life. Disruption of the autonomic nervous system in the heart, which regulates the rate and force for contraction, produces many life threatening changes to the mechanical and electrical properties of heart cells. The critical importance of its function is represented by the very high incidence of mortality and morbidly associated with cardiac autonomic disease both in the U.K. and western world. However, despite extensive experimental studies the complicated sequence of events from a disturbance in neural control which produces a change in heart rate that leads to life-threatening pump failure remains poorly understood. Heart rate is controlled by cells in the specialised region of the heart called the Sinoatrial Node. The electrical properties of these cells and the subsequent pacemaking rate of the heart is tightly regulated by neurotransmitters and other small molecules which interact within the automonic system. New experimental techniques have recently provided exciting information on how these cells function. The properties of individual proteins which regulate the flow of charged chemical ions in and out of the cell are now routinely measured. This information can be combined with fluorescent probe measurement used to determine the concentrations of the key chemicals and underlying cellular mechanisms which control heart rate. Most recently gene delivery techniques have been developed which makes available the ability to change the concentrations of many of these molecules. This type of gene delivery provides a method to perturb the system in ways which provides unique information for determining how a given compound regulates electrical excitability in both healthy and diseased function. Despite the rich sources of information these techniques provide, the inherent complexity of the underlying systems of biochemical reactions that determine heart rate still makes this experimental data difficult to interpret directly. Recent advances in mathematical modelling and computing now provide new and powerful quantitative tools for exactly this purpose. By representing each of the individual chemical reactions using mathematical equations the complexity of a full cellular network can be quantitatively characterised. This approach has been successfully applied to a number of other cardiac cell types to link measurement to function. However, to date no model of neural regulation of electrical excitability has been developed. In this project we aim to directly address this issue. We will integrate the new information provided experimentally with a computational model of a pacemaking cell coupled to a neural cell. In doing so we will be able identify the sub-cellular mechanisms which link changes in chemical concentrations to heart rate. The model will provide a way of isolating individual autonomic signalling mechanisms to understand exactly how cardiac function is impaired during an autonomic disturbance. The model will be used to interpret experimental data, suggest hypotheses and optimise experimental protocols. As data is collected the parameterisation of a structure of the model will be refined providing a mechanism of continuously advancing our understanding of the system. Using this approach the study will immediately provide a new method to investigate and understand the mechanisms of autonomic control in the heart and, ultimately, contribute to the improvement in the diagnosis, prevention and development of new therapies for diseases of the cardiac autonomic system