Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks
Contributors
- 1. Institue of Fluid Dynamics, ETH Zurich, Switzerland
- 2. Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
- 3. Department of Physics, University of California at San Diego, La Jolla, California, USA
Description
The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017.
For each network the following data are provided:
- Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs).
- RBC trajectories describing the motion of individual RBCs through the microvascular networks.
- The data is stored as a graph, i.e. vertices connected by edges.
- Details regarding the simulation parameters can be found in Schmid et al., 2017.
- Data format (pickle - files containing python dictonairies).
Microvascular networks:
edgesDict.pkl: dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um])
verticesDict.pkl: dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg])
Additional comments on dictionary keys:
- pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details)
- tuple: connectivity of graph, tuple of vertices
- httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned.
- nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown
- htt: tube hematocrit
- nRBC: number of red blood cells
- points: list of tortuous vessel coordinates per edge
- diameters: local diameter measurements associated to the 'points' key.
RBC trajectories:
RBC_trajectories.pkl: dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft
Additional comments on dictionary keys per RBC:
- RBCleft: bool to indicate that RBC left the computational domain
- edges: edge indices through which the RBC moves on its way through the vasculature
- pressure: pressure [mmHg] values at the nodes along the RBC trajectory
- times: time [ms] the RBC spends in the respective edge segment
- nkindsMod: nkind at the nodes along the RBC trajectory
- lengths: cummulative length travelled [um]
Files
Files
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Additional details
Related works
- Is cited by
- 10.1371/journal.pcbi.1005392 (DOI)
- References
- 10.1523/JNEUROSCI.3287-09.2009 (DOI)
References
- Blinder P, Tsai PS, Kaufhold JP, Knutsen PM, Suhl H, Kleinfeld D. The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow. Nature Neurosci. 2013;16(7):889–897
- Schmid F, Tsai PS, Kleinfeld D, Jenny P, Weber B. Depth-Dependent Flow and Pressure Characteristics in Cortical Microvascular Networks. PLOS Computational Biology. 2017. doi: 10.1371/journal.pcbi.1005392