Resistance to blood flow in peripheral vascular beds strongly influences cardiovascular function and tissue perfusion. The primary determinants of flow resistance are the geometrical structure of the vasculature and the flow properties of the blood. The vascular system is capable of dynamic structural change during growth and maturation, and in response to varying conditions occurring in health, injury and disease. Growth of new vessels, structural adaptation (remodeling) of vessels, and vessel regression lead to redistribution of blood flow. Abnormal structural adaptation and angiogenesis occur in hypertension and other diseases.
Specific Aim 1 is to develop theoretical models for the dynamics of structural adaptation. The models will be used to predict the time-course of diameters and wall thicknesses in vascular networks following alterations in hemodynamic or metabolic conditions. The relationship between vascular tone and structural adaptation will be explored. Predictions will be compared with observations in mouse hind-limb and skin-fold preparations.
Specific Aim 2 is to develop theoretical models for growth and regression of vascular networks. Sprouting and growth of new segments from existing segments, connection of segments to form new flow pathways, structural adaptation of flowing pathways, and loss of redundant segments will be simulated. Effects of the spatial distributions of oxygen and growth factor concentrations will be included. Resulting network structures will be compared with experimental data obtained from rat mesentery and mouse skin-fold preparations. The flow properties of blood strongly affect perfusion and flow resistance in the microcirculation. Two key factors are radial migration of red blood cells, causing a cell-depleted layer near vessel walls, and the presence of a relatively thick endothelial surface layer lining the walls.
Specific Aim 3 is to develop multi-cell models for blood flow in microvessels, including effects of the endothelial surface layer. Motion of red blood cells will be simulated and the width of the cell-depleted layer will be predicted. Results will be compared with experimental observations in the rat mesentery. In all these studies, emphasis will be placed on examining the physiological implications of the results in normal and abnormal states including hypertension. This will be facilitated by well-established and active collaborations with experimental physiologists.
|Rasmussen, Peter M; Smith, Amy F; Sakadži?, Sava et al. (2017) Model-based inference from microvascular measurements: Combining experimental measurements and model predictions using a Bayesian probabilistic approach. Microcirculation 24:|
|Reglin, Bettina; Secomb, Timothy W; Pries, Axel R (2017) Structural Control of Microvessel Diameters: Origins of Metabolic Signals. Front Physiol 8:813|
|Smith, Amy F; Nitzsche, Bianca; Maibier, Martin et al. (2016) Microvascular hemodynamics in the chick chorioallantoic membrane. Microcirculation 23:512-522|
|Secomb, Timothy W (2016) Hemodynamics. Compr Physiol 6:975-1003|
|Secomb, Timothy W; Pries, Axel R (2016) Microvascular Plasticity: Angiogenesis in Health and Disease--Preface. Microcirculation 23:93-4|
|Secomb, Timothy W (2016) A Green's function method for simulation of time-dependent solute transport and reaction in realistic microvascular geometries. Math Med Biol 33:475-494|
|Hariprasad, Daniel S; Secomb, Timothy W (2015) Prediction of noninertial focusing of red blood cells in Poiseuille flow. Phys Rev E Stat Nonlin Soft Matter Phys 92:033008|
|Pries, Axel R; Secomb, Timothy W (2014) Making microvascular networks work: angiogenesis, remodeling, and pruning. Physiology (Bethesda) 29:446-55|
|Hariprasad, Daniel S; Secomb, Timothy W (2014) Two-dimensional simulation of red blood cell motion near a wall under a lateral force. Phys Rev E Stat Nonlin Soft Matter Phys 90:053014|
|Secomb, Timothy W; Pries, Axel R (2013) Blood viscosity in microvessels: experiment and theory. C R Phys 14:470-478|
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