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