Microfluidic technology is a useful tool to help answer unsolved problems in multidisciplinary fields, including molecular biology, clinical pathology and the pharmaceutical industry.Current microfluidic based devices with diverse structures have been constructed via extensively used soft lithography orphotolithography fabrication methods. A layer-by-layer stacking of 2D planar microchannel arrays can achieve limited degrees of three dimensionality. However, assembly of large-scale multi-tiered structures is tedious, and the inherently planar nature of the individual layers restricts the network’s topological complexity. In order to overcome the limitations of existing microfabrication methodswe demonstrate several novel methods that enable microvasculature networks: electrostatic discharge,global channel deformation and enzymatic sculpting to fabricate complex surface topologies. These methods enable construction of networks of branched microchannels arranged in a tree-like architecture with diameters ranging from approximately 10 μm to 1 mm. Interconnected networks with multiple fluidic access points can be straightforwardly constructed, and quantification of their branching characteristics reveals remarkable similarity to naturally occurring vasculature. In addition, by harnessing enzymatic micromachining we are able to construct nanochannels, microchannels containing embedded features templated by the substrate’s crystalline morphology, and an irregular cross section of microchannel capable of performing isolation and enrichment of cells from whole blood with throughput 1 – 2 orders of magnitude faster than currently possible. These techniques can play a key role in developing an organ-sized engineered tissue scaffolds and high-throughput continuous flow separations.

Particle adhesion to the vasculature depends critically upon particle/cell properties (size, receptors), scale/geometric features of vasculature (diameter, bifurcation, etc.) and local hemodynamic factors (stress, torque, etc.) Current investigations using in vitro parallel-plate flow chambers suffer from several limitations including (a) idealized constructs, (b) lack of critical morphological features (bifurcations, network), (c) inability to distinguish between healthy vs. diseased vasculature, (d) large volumes and (e) non-disposability. To overcome these limitations, microvascular networks, obtained from digitization of in vivo topology were prototyped using soft-lithography techniques to generate Synthetic Microvascular Networks (SMN). CFD-ACE+, a finite volume based Computational Fluid Dynamics (CFD) software, was used to develop a computational model of the digitized networks. Dye perfusion patterns predicted by the simulations matched well with experimental observations indicating presence of well perfused as well as stagnant regions. Studies using functionalized microparticles showed non-uniform particle adhesion, with preferential adhesion at a distance of 2 vessel diameters or less from the nearest bifurcation which was validated with in vivo data. Bifurcation adhesion ratio (BAR) was found to be significantly higher for experiments (49% and 36%) and simulations (67% and 52%) compared to expected values of 24% and 21%. A single experimental run in SMN generated the entire shear adhesion map highlighting the benefits of the SMN assay. Green Fluorescent Protein (GFP) gene delivery studies with a nanopolymeric based gene delivery system showed preferential GFP expression in the vicinity of bends and bifurcation of the microvascular networks. The developed SMN based microfluidic device will have critical applications both in basic research, where it can be used to characterize and develop next generation delivery vehicles, and in drug discovery, where it can be used to study the efficacy of the drug in these realistic microvascular networks. / Mechanical Engineering

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The importance of coronary microcirculatory perfusion is highlighted by the severe impact of microvascular diseases such as diabetes and hypertension on heart function. Recently, highly-detailed three-dimensional (3D) data on ex vivo coronary microvascular structure have become available. However, hemodynamic information in individual myocardial capillaries cannot yet be obtained using current in vivo imaging techniques. In this thesis, a novel data-driven modelling framework is developed to predict tissue-scale flow properties from discrete anatomical data, which can in future be used to aid interpretation of coarse-scale perfusion imaging data in healthy and diseased states. Mathematical models are parametrised by the 3D anatomical data set of Lee (2009) from the rat myocardium, and tested using flow measurements in two-dimensional rat mesentery networks. Firstly, algorithmic and statistical tools are developed to separate branching arterioles and venules from mesh-like capillaries, and then to extract geometrical properties of the 3D capillary network. The multi-scale asymptotic homogenisation approach of Shipley and Chapman (2010) is adapted to derive a continuum model of coronary capillary fluid transport incorporating a non-Newtonian viscosity term. Tissue-scale flow is captured by Darcy's Law whose coefficient, the permeability tensor, transmits the volume-averaged capillary-scale flow variations to the tissue-scale equation. This anisotropic permeability tensor is explicitly calculated by solving the capillary-scale fluid mechanics problem on synthetic, stochastically-generated periodic networks parametrised by the geometrical data statistics, and a thorough sensitivity analysis is conducted. Permeability variations across the myocardium are computed by parametrising synthetic networks with transmurally-dependent data statistics, enabling the hypothesis that subendocardial permeability is much higher in diastole to compensate for severely-reduced systolic blood flow to be tested. The continuum Darcy flow model is parametrised by purely structural information to provide tissue-scale perfusion metrics, with the hypothesis that this model is less sensitive and more reliably parametrised than an alternative, estimated discrete network flow solution.