Dynamic adaptation of vascular networks induced by pathological conditions such as hypertension can alter both morphology and function of the microvasculature. Presumably, the result of this remodeling process includes the return to control of vascular network functions such as flow, capillary pressure, and oxygen delivery regulation. One of the most striking changes that occurs in some organs in hypertension is microvascular rarefaction, i.e., a substantial loss of arterioles and capillaries that is mediated via structural degeneration of the vessels. Recent studies by our group indicate that microvascular rarefaction may also occur in response to elevated salt intake. A widespread rarefaction of microvessels could contribute to the elevated peripheral vascular resistance in chronic hypertension, and may have important implications for tissue perfusion in key target organs such as the heart and brain. Furthermore, a permanent reduction in vessel density mediated by structural degenera tion of microvessels could have significant implications for the treatment of hypertension, since it may lead to a sustained elevation in vascular resistance and alterations in tissue perfusion which would be refractory to therapy with vasodilator agents. Individual adaptive mechanisms have been successfully studied in the laboratory; however, experimental evaluation of the interaction and influence of these factors on overall network adaptation is a difficult task. Mathematical models of vascular remodeling allow us to study and quantify the influence and interaction of remodeling mechanisms on the morphological and functional adaptation of networks exposed to physiological stress. For example, these models can be used to analyze capillary density variation observed in normotensive sham-operated control (SHAM) and hypertensive reduced renal mass (RRM) rats on high salt diets. In the present project, we propose to use mathematical modeling to investigate both the mechanisms and the con sequences of dynamic microvascular remodeling du to such physiological stresses.
| Bassingthwaighte, James B; Butterworth, Erik; Jardine, Bartholomew et al. (2012) Compartmental modeling in the analysis of biological systems. Methods Mol Biol 929:391-438 |
| Dash, Ranjan K; Bassingthwaighte, James B (2010) Erratum to: Blood HbO2 and HbCO2 dissociation curves at varied O2, CO2, pH, 2,3-DPG and temperature levels. Ann Biomed Eng 38:1683-701 |
| Bassingthwaighte, James B; Raymond, Gary M; Butterworth, Erik et al. (2010) Multiscale modeling of metabolism, flows, and exchanges in heterogeneous organs. Ann N Y Acad Sci 1188:111-20 |
| Dash, Ranjan K; Bassingthwaighte, James B (2006) Simultaneous blood-tissue exchange of oxygen, carbon dioxide, bicarbonate, and hydrogen ion. Ann Biomed Eng 34:1129-48 |
| Dash, Ranjan K; Bassingthwaighte, James B (2004) Blood HbO2 and HbCO2 dissociation curves at varied O2, CO2, pH, 2,3-DPG and temperature levels. Ann Biomed Eng 32:1676-93 |
| Kellen, Michael R; Bassingthwaighte, James B (2003) Transient transcapillary exchange of water driven by osmotic forces in the heart. Am J Physiol Heart Circ Physiol 285:H1317-31 |
| Kellen, Michael R; Bassingthwaighte, James B (2003) An integrative model of coupled water and solute exchange in the heart. Am J Physiol Heart Circ Physiol 285:H1303-16 |
| Wang, C Y; Bassingthwaighte, J B (2001) Capillary supply regions. Math Biosci 173:103-14 |
| Swanson, K R; True, L D; Lin, D W et al. (2001) A quantitative model for the dynamics of serum prostate-specific antigen as a marker for cancerous growth: an explanation for a medical anomaly. Am J Pathol 158:2195-9 |
| Swanson, K R; Alvord Jr, E C; Murray, J D (2000) A quantitative model for differential motility of gliomas in grey and white matter. Cell Prolif 33:317-29 |
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