Dissecting heart failure mechanisms by integrating in vivo and in vitro data within customised in silico models
Heart Failure (HF) is defined by the heart's reduced ability to pump blood due to a drop in left ventricular (LV) systolic and diastolic function. With the improved survival after a heart attack and the expansion of the U.K.'s elderly population, HF is rapidly becoming an epidemic accounting for a significant mortality and morbidity burden. Recently, the strength of new experimental techniques has contributed several new pieces to the mechanistic puzzle that underpins the clinical syndrome of HF. Despite these efforts, however, our knowledge of this important process remains fragmented, hampering the identification of robust targets for therapeutic intervention.
The discipline of computational cardiac physiology offers an exciting approach to address this issue by quantitatively describing the physiological behavior of the heart using mathematical and computational models. The development of such models presents the ability capture the complex and multi-factorial cause and effect relationships which link underlying patho-physiological mechanisms. Furthermore, the heart is arguable the most advanced example of an integrated model and as such represents exciting tool with which to focus, in the case of HF, on an important disease.
Combining this computational technology with state of the art in-vivo and in-vitro experimental techniques, we aim to assimilate multiple data sets to test our understanding of HF mechanisms within a consistent model. Our preliminary experimental measurements indicate that in HF individual cardiac cells may continue to contract normally, however, it is a change in the way they are connected in heart tissue which may be adversely affecting contraction. Applying our integrated experimental and computational approach, we will be able to test this hypothesis and in doing so indicate a potentially brand new direction for the development of new treatment strategies for this disease.