It Does Not Do to Dwell on Single Components and Forget the Importance of Complete Networks: Optimizing an Integrated Hemodynamic Model Derived from Experimental Data

In our previous study, we demonstrated the important role of capillary resistance and venular network geometry on arteriolar blood flow and red blood cell (RBC) distribution in terminal arterioles (TAs). Using arteriolar and corresponding venular networks reconstructed from intravital videomicroscopy (IVVM) data obtained in rat gluteus maximus preparations, as well as mathematical modelling and simulation, we showed that for RBC flow, adding the same resistance to all TAs to represent downstream capillary beds significantly decreased the coefficient of variation of TA RBC flow (CV TA (RBC), standard deviation/mean, n = 8 networks) by 37%, whereas, adding the venular network did not further significantly change CV TA (RBC). However, we found that adding constant resistive elements to the TAs did not significantly decrease the coefficient of variation of TA tube hematocrit, whereas, adding the venular network significantly decreased CV TA (H T ) by 20%. Given the need for this network‐oriented approach, we are optimizing our arteriolar network analysis to include as much detail as possible through experimental acquisition and data reconstruction of arterioles and venules, as well as estimation of missing data using theoretical methods. Our goal is to develop an analysis technique that accounts for the interconnectivity of microvascular systems, and that can be applied to networks in a wide range of situations. In our current methods, we reconstructed corresponding arteriolar and venular networks from experimental data, and estimated and applied total capillary resistance for each network, based on the arteriolar network resistance and the relative pressure drop between the arteriolar and capillary sections of the network. The capillary resistance is now distributed to each TA segment according to its diameter, and therefore, variable. We acquired fluorescent streaks for experimental blood flow data in an arteriolar network, which we used to validate flow values predicted by our model. Using the experimental flow data, we also calculated a Murray's law exponent of approximately a=2.8, to which we compared predicted values. For 3 networks, we found a=2.78±0.30 using variable capillary resistance vs. a=1.95±0.17 using constant capillary resistance. Our results, using improved theoretical methods and newly acquired flow data, show that our network‐oriented approach is moving towards more accurately predicting hemodynamic properties of arteriolar networks under normal baseline conditions. We are currently working to extend this approach and apply it to networks under different experimental conditions. Support or Funding Information This work was supported by Natural Sciences and Engineering Research Council of Canada (NSERC) Grants R4081A03 (DG) and R4218A03 (DNJ). This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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