Modelling the cellular cardiac neural axis in the control of excitability

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

The recent development of novel experimental technologies is currently generating detailed and varied experimental data assaying the key biochemical regulatory mechanisms in autonomic control. These data add significant complexity to our understanding of cell to cell communication between the neuron and the myocyte and reassembling this signalling information into an integrative framework represents a major challenge. Computational modelling provides a promising solution for the detailed examination of just such a system. Via tight integration between the modelling and experimental programs, we aim to develop and use a species consistent model of the sympathetic regulation of pacemaking in the heart. The techniques employed to extend and develop the cellular components will follow the successful approach previously employed in both our work and the cardiac modelling field in general. Systems of ordinary differential equations will be fitted to represent the channel gating and flux of ionic concentrations across the cellular and intracellular organelle membranes. Parameter values in these models will be set by transparently linking direct measurement and experimental conditions with model fitting and sensitivity analysis techniques. The resulting model will be used iteratively to interpret experimental data, suggest experiments and then refine the modelling framework with the data that results. The development of neural models coupling cyclic nucleotide via NO regulation will be a completely new element that will map directly into the wider cardiac Physiome project. The model will also enable further developments both within our group and the wider community focusing on the autonomic physiology, the ionotropic effects of NO-cyclic nucleotide and integration of the cellular response into anatomical models which include neural architecture.