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Contributions of myogenic and shear responses to vascular regulation analyzed using a representative‐segment vascular network model
Author(s) -
Carlson Brian Ernest,
Secomb Timothy W.
Publication year - 2006
Publication title -
the faseb journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.709
H-Index - 277
eISSN - 1530-6860
pISSN - 0892-6638
DOI - 10.1096/fasebj.20.4.a278-b
Subject(s) - autoregulation , myogenic contraction , shear stress , blood flow , anatomy , medicine , biology , cardiology , chemistry , blood pressure , endocrinology , mechanics , physics , smooth muscle
The contributions of myogenic and shear‐dependent vascular responses to autoregulation and metabolic regulation of blood flow were analyzed using a theoretical representative‐segment model, in which the vasculature is divided into several vascular levels, each represented by a set of identical vessel segments connected in parallel. Responses of arteriolar segments to changes in pressure (myogenic response) and wall shear stress were characterized using a previously published model. To simulate autoregulation, mean arterial pressure was varied from 60 to 150mmHg. With both responses present, the predicted flow increase over this range was reduced by 80% compared to the increase without these responses, indicating strong autoregulation. To simulate metabolic regulation, the conductance of small arteriolar segments was assumed to increase by a factor of 16 in response to local metabolic signals. With myogenic and shear responses in the upstream arteriolar segments, the predicted increase in flow was almost three‐fold higher than when the myogenic and shear responses were suppressed. The results show that the myogenic and shear responses contribute significantly to autoregulation and to metabolic regulation amplification. However, the predicted responses were not as strong as seen experimentally, suggesting that other responses, particularly the upstream conducted response, must be significantly involved. Supported by NIH Grant HL070657.