The processes that keep the heart beating healthily involve chemical signals, i.e. the movement of calcium, which are controlled by specific proteins. Precise modelling of these variable processes has been limited in the past, making it difficult to tailor drug treatments. This project aims to develop new ways of obtaining a more complete statistical description of calcium handling in the heart, in order to better predict the effects of drugs, improve treatments, and understand the conditions that lead to heart disease.
One of six British Heart Foundation funded projects.
Explaining the science
With every heart-beat, electricity travels across the heart and activates muscle cells to contract. The chemical signal involved in this process is calcium, which is normally kept in stores inside cells and is released upon the arrival of electricity. This calcium activates contractile fibres. When the heart relaxes to complete the heart-beat cycle, calcium is taken-up into storage.
Movement of calcium to and fro between stores and contractile fibres is controlled by five different types of proteins. These processes will differ between cells due to naturally-occurring biological variation. Moreover, some processes may vary in a coordinated manner. A healthy heartbeat requires that these processes work within a certain range; otherwise, the heart may succumb to disease.
Differences between patients can also cause differing responses to drugs. Drug companies are already using computers to understand how the heart works, but these are not able to describe variations, making it difficult to tailor medicine to individuals.
To develop new ways of obtaining a more complete statistical description of the heart's calcium handling (the coordination of chemical signals that keep the heart beating healthily).
By combining data science methods, medical imaging, and electrophysiology (studying the electrical properties of cells), information will be obtained about the variability of the proteins that control these chemical signals.
Analysing this experimental data in terms of statistical distributions will provide a better understanding of the effects drugs may have on the chemical processes in the heart and the conditions that lead to heart disease, thereby improving clinical treatments.
By obtaining statistical descriptions of the variability in mathematical models of cardiac cells, it's possible to generate virtual cell populations that represent the variability present in real hearts.
By applying virtual drugs to the cell models, it's then possible to identify if extreme cell types are at risk of an adverse response to a particular drug. This virtual drug evaluation allows for high throughput screening of novel molecules early in the drug development process.