Biomedical Engineering - Theses
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ItemNo Preview AvailableNovel machine-learning approaches to create a structurally accurate virtual model of the heart cell( 2023-06)Cardiomyocytes are densely packed with parallel columns of myofibrils and mitochondria. Research has shown a strong correlation between changes in ultrastructure and changes in the heart’s function. For example, heart pumping is often compromised in heart diseases such as cardiomyopathy, hypertension and diabetes. Hence, understanding the 3D architecture of cardiac cells will underpin breakthroughs in cardiovascular disease treatment and prevention. With the advent of high-throughput microscopy image datasets resulting from modalities such as serial block-face and focused ion-beam scanning electron microscopy, we can acquire large datasets of cardiac muscle cells in 3D. However, segmenting these datasets is challenging due to low contrast and high noise ratio. The community often relies on manual segmentation and image tracing, a laborious and cost-inefficient approach that hinders novel breakthroughs. This thesis proposes state-of-the-art deep neural networks to segment ultrastructures of cardiac cells in EM datasets and obtains 3D statistical architecture of the cardiomyocyte. EM-net and EM-stellar are cloud-based software proposed to segment EM image datasets and benchmark a wide range of segmentation performance measures. EM-net is a scalable convolutional neural network offering fast convergence during optimisation and can be trained with minimal ground-truth information due to its novel architecture. EM-stellar is hosted on Google Colab, and it can be used to benchmark the performance of state-of-the-art deep neural networks on a user-specified dataset. Together these pipelines offer the research community more efficient ways to segment and analyse cardiac muscle ultrastructure from electron microscopy datasets. Finally, we propose CardioVinci, a workflow utilising generative adversarial networks (GANs) to obtain a statistical 3D model of cardiomyocyte architecture. CardioVinci addresses a significant challenge with large EM datasets: the time taken to collect tissue samples, acquire the data, extract key characteristics and statistically analyse 3D changes in the ultrastructures. It encodes the 2D and 3D variations in the ultrastructures across the image volume into a generative model. As a result, the community will be able to statistically quantify the morphologies and spatial assembly of mitochondria, myofibrils, and Z-disks with minimal manual annotation.
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ItemA bond graph approach to integrative biophysical modelling( 2019)A major goal of systems biology is to develop comprehensive, multi-scale mathematical models of physiological systems that integrate biological knowledge from the scale of molecules to the scale of tissues and organs. Models on this scale hold great potential in advancing our knowledge of biology and medicine, but they have yet to be achieved in complex biological systems. It is widely acknowledged that constructing large-scale models requires the reuse and integration of existing models; however, model integration is currently challenging because many existing models violate the conservation laws of physics, especially conservation of energy. It is therefore highly desirable to express models in a framework that respects the laws of physics and thermodynamics. Bond graphs are an energy-based modelling framework, initially developed for use in multi-physics engineering systems to help derive equations consistent with the laws of physics. More recently, bond graphs have been applied to the field of biology where they have helped in making models physically and thermodynamically consistent. While bond graphs provide several advantages for large-scale modelling such as thermodynamic consistency and hierarchical modelling, they have yet to be applied to large-scale dynamic models of biological systems. This thesis aims to develop methods based on the bond graph framework to facilitate model reuse and integration. These methods are demonstrated by applying them to biomolecular systems within the cardiac cell. Firstly, bond graphs are applied to membrane transporters, demonstrating that bond graphs can be used to correct thermodynamic inconsistencies within existing models. Secondly, independently developed models of ion channels and transporters are coupled into a model of cardiac electrophysiology, showing that bond graphs can be used to systematically explain the issues of drift and non-unique steady states that affect many existing models. Finally, a generalised method for simplifying models of enzyme kinetics is developed and used to facilitate the development of simple, thermodynamically consistent models of enzymes that are easily incorporated into larger models.
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ItemRole of ultrastructural alterations in diabetic cardiomyopathy( 2019)Cardiomyocytes inside the heart are densely packed with parallel columns of myofibrils and mitochondria. Growing evidence indicates a strong correlation between alterations in this sub-cellular ultrastructure and the alterations in energy metabolism during various pathological conditions of heart. The central hypothesis explored in this thesis is that changes in cardiac sub-cellular architecture in pathological conditions can also affect cardiac bioenergetics by interfering with various mechanisms of intracellular energy transport. Type 1 diabetic cardiomyopathy is an ideal candidate for a model disease state to understand this hypothesized interplay between ultrastructure and metabolism. It exhibits many common conditions which accompany heart failure, such as increased mitochondrial reactive oxygen species production and decreased reserve of creatine phosphate. In a preliminary study, 2D electron microscope images collected from control and streptozotocin induced type I diabetic rat hearts were analysed. It was found that diabetic cardiomyopathy leads to an increased mitochondrial fission and formation of large mitochondrial clusters. Further analysis showed that effective surface-to-volume ratio of mitochondrial clusters increases by 22.5% in diabetic cells. Subsequently, a compartmental model of cardiac energy transfer was developed. This simple model predicted that this increase in the surface-to-volume ratio can have a moderate compensatory effect by elevating the availability of adenosine triphosphate (ATP) in the cytosol when ATP synthesis within the mitochondria is compromised. Next, 3D electron microscope images from control animals were investigated. The analysis revealed that cardiac mitochondria are arranged non-uniformly in parallel columns of varying sizes. Following this, the compartmental model was extended to a reaction diffusion based 2D finite element model incorporating a realistic description of the observed sub-cellular ultrastructure. The new model predicted that rapid diffusion of creatine and creatine phosphate acts to maintain homogenous ATP distribution and uniform force dynamics in the control cardiomyocytes, despite the heterogeneous mitochondrial organization. Subsequently, 3D electron microscope images of cardiomyocytes from streptozotocin (STZ) induced type I diabetic rats were compared with controls. The analysis revealed that mitochondrial distribution along the transverse sections was significantly more heterogeneous in type I diabetes compared to control cells. Moreover, mitochondrial area fraction in the studied type I diabetic cells was higher than the control cells. Finally, 2D models of cardiac energy metabolism were created based on the electron microscope images collected from the control and diabetics cells. The results indicated that an increased fraction of mitochondria in diabetic cells can compensate for the reduced ATP synthesising capacity of diabetic mitochondria. The models also predicted that lower activity of mitochondrial enzymes in type I diabetes, coupled with the observed non-uniform mitochondrial distribution, can lead to large spatial variation in concentration of ATP and adenosine diphosphate (ADP). The heterogeneous metabolic landscape in the diabetic cell cross sections was also reflected in large spatial gradients of myofibrillar ATP consumption rate. This finding is important since ATP consumption rate correlates with the speed of muscle shortening. Different parts of a diabetic cell might contract at different rates, which can decrease the energy efficiency of the cell and also damage the cell structure. Thus, this thesis, combining image analysis with computational modelling, provides new insights into how the ultrastructure regulates the metabolism of the cardiomyocytes in disease and health.