Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection.

For precision medicine to be implemented through the lens of in silico technology, it is imperative that biophysical research workflows offer insight into treatments that are specific to a particular illness and to a particular subject. The boundaries of precision medicine can be extended using multiscale, biophysics-centred workflows that consider the fundamental underpinnings of the constituents of cells and tissues and their dynamic environments. Utilising numerical techniques that can capture the broad spectrum of biological flows within complex, deformable and permeable organs and tissues is of paramount importance when considering the core prerequisites of any state-of-the-art precision medicine pipeline. In this work, a succinct breakdown of two precision medicine pipelines developed within two Virtual Physiological Human (VPH) projects are given. The first workflow is targeted on the trajectory of Alzheimer's Disease, and caters for novel hypothesis testing through a multicompartmental poroelastic model which is integrated with a high throughput imaging workflow and subject-specific blood flow variability model. The second workflow gives rise to the patient specific exploration of Aortic Dissections via a multi-scale and compliant model, harnessing imaging, computational fluid-dynamics (CFD) and dynamic boundary conditions. Results relating to the first workflow include some core outputs of the multiporoelastic modelling framework, and the representation of peri-arterial swelling and peri-venous drainage solution fields. The latter solution fields were statistically analysed for a cohort of thirty-five subjects (stratified with respect to disease status, gender and activity level). The second workflow allowed for a better understanding of complex aortic dissection cases utilising both a rigid-wall model informed by minimal and clinically common datasets as well as a moving-wall model informed by rich datasets.

Sphingosine kinase 1 overexpression induces MFN2 fragmentation and alters mitochondrial matrix Ca2+ handling in HeLa cells.

Sphingosine kinase 1 (SK1) converts sphingosine to the bioactive lipid sphingosine 1-phosphate (S1P). S1P binds to G-protein-coupled receptors (S1PR1-5) to regulate cellular events, including Ca2+ signaling. The SK1/S1P axis and Ca2+ signaling both play important roles in health and disease. In this respect, Ca2+ microdomains at the mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) are of importance in oncogenesis. Mitofusin 2 (MFN2) modulates ER-mitochondria contacts, and dysregulation of MFN2 is associated with malignancies. We show that overexpression of SK1 augments agonist-induced Ca2+ release from the ER resulting in increased mitochondrial matrix Ca2+. Also, overexpression of SK1 induces MFN2 fragmentation, likely through increased calpain activity. Further, expressing putative calpain-cleaved MFN2 N- and C-terminal fragments increases mitochondrial matrix Ca2+ during agonist stimulation, mimicking the SK1 overexpression in cells. Moreover, SK1 overexpression enhances cellular respiration and cell migration. Thus, SK1 regulates MFN2 fragmentation resulting in increased mitochondrial Ca2+ and downstream cellular effects.

Improved hybrid/GPU algorithm for solving cardiac electrophysiology problems on Purkinje networks.

Cardiac Purkinje fibers provide an important pathway to the coordinated contraction of the heart. We present a numerical algorithm for the solution of electrophysiology problems across the Purkinje network that is efficient enough to be used in in silico studies on realistic Purkinje networks with physiologically detailed models of ion exchange at the cell membrane. The algorithm is on the basis of operator splitting and is provided with 3 different implementations: pure CPU, hybrid CPU/GPU, and pure GPU. Compared to our previous work, we modify the explicit gap junction term at network bifurcations to improve its mathematical consistency. Due to this improved consistency of the model, we are able to perform an empirical convergence study against analytical solutions. The study verified that all 3 implementations produce equivalent convergence rates, and shows that the algorithm produces equivalent result across different hardware platforms. Finally, we compare the efficiency of all 3 implementations on Purkinje networks of increasing spatial resolution using membrane models of increasing complexity. Both hybrid and pure GPU implementations outperform the pure CPU implementation, but their relative performance difference depends on the size of the Purkinje network and the complexity of the membrane model used.