HemoCell

HemoCell is a parallel computing framework for simulation of dense deformable capsule suspensions, with special emphasis on blood flows and blood related vesicles (cells). The library implements validated mechanical models for Red Blood Cells (RBCs) and is capable of reproducing emergent transport characteristics of such complex cellular systems [1]. HemoCell is capable of handling large simulation domain sizes and high shear-rate flows providing a virtual environment to evaluate a wide palette of microfluidic scenarios [2, 3, 4].

For the simulation of dense flows, HemoCell employs the Immersed Boundary Method (IBM) to couple the immersed vesicles, e.g. RBCs, platelets (PLTs), leukocytes, or other custom cell types to the fluid (e.g., plasma). The particles are tracked using a Lagrangian discrete element approach, while the flow field is implemented using the lattice Boltzmann method (LBM). The current implementation is based on the Palabos library. HemoCell manages all required data structures, such as materials and cell models (particles), their interactions within the flow field, load-balancing, and communication among the processors.

The library provides validated and highly optimised mechanical models for the simulation of red blood cells [5, 6]. Furthermore, the library is extensible and allows to implement different mechanical (cell) models [7, 8] and cell-binding techniques [9] to study numerous application-specific behaviour.

The code is implemented in C/C++ with parallelism achieved through MPI, although only for advanced use-cases the users are required to interact with the parallelism, which is otherwise hidden from the user. The system is build with CMake and runs on a variety of systems and HPC clusters (see getting started).

Multiple examples are provided to illustrate typical use-cases of HemoCell:

• Developing mechanical models for different cell types and their interaction. These are typically quick running simulations on single processors (order of seconds to minutes) that aim to investigate/validate different formulations of mechanical models for the immersed cells, e.g. shearing, stretching, or “parachuting” of a single cell. Additionally, one might want to study the interaction between colliding particles, e.g. Colliding cells with interior viscosity.

  

• Studying large simulation domains with large number of immersed particles. These simulations are typically derived from straight channel flow conditions, where the domain size, number of immersed particles, and flow conditions are varied. These simulations can vary from quick running simulations on small hardware (desktop/workstation) to long lasting simulations on large HPC compute clusters with thousands of cores. Examples of smaller pipe flow cases are presented in Pipe flow and Pipe flow with periodic inflow.

  

When using HemoCell please cite the corresponding HemoCell paper(s) [1].

User Guide

• HemoCell Getting Started
  • Setting up HemoCell from source
  • Compiling HemoCell from source
  • Generating initial positions for cells
  • Running a HemoCell case
  • Case output folder
  • Parsing the output of a HemoCell case
  • Resuming from a checkpoint
• Example cases
  • One shearing cell
  • One stretching cell
  • A parachuting cell
  • Colliding cells with interior viscosity
  • Pipe flow
  • Pipe flow with periodic inflow
  • Capillary flow
• Creating your own HemoCell Case
  • Editing your newCase.cpp
  • Including external code in your example
  • Creating a bare config.xml
  • Creating the initial positions for the Cells
  • Running our newly created case
  • Compiling examples with special features
• Configuration Files
  • Config.xml
  • CELL.xml and CELL.pos
  • config/constant_defaults.h
• Helper scripts
  • hemocell/patch/patchPLB.sh
  • hemocell/scripts/batchPostProcess.sh
  • hemocell/scripts/cartesius[_intel]_env.sh
  • hemocell/scripts/CellInfoMergeCSV.sh
  • convert_xmf_to_x3d.py
• Advanced use-cases
  • Custom cells and reading cells from STL
  • Running a pure fluid flow
  • Repulsive forces
  • Saving only the CSV output
• Visualization and post-processing
  • Paraview
  • Blender
  • pos_to_vtk
• Utility tools
  • Viewing of cell packings
• Common mistakes
  • Numerical instabilities
  • Cell deletions

• Downloads

• Frequently Asked Questions

• HemoCell Doxygen Documentation

Acknowledgments

HemoCell is developed and maintained by the following persons, where any questions can be directed towards: info@hemocell.eu.

Gábor Závodszky	Developer and Co-PI	G.Zavodszky at uva.nl
Alfons Hoekstra	Co-PI
Ben Czaja	Developer	B.E.Czaja at uva.nl
Christian Spieker	Developer	C.J.Spieker at uva.nl
Mark Wijzenbroek	Package maintainer
Max van der Kolk	Former developer
Lampros Mountrakis	Former developer
Victor Azizi	Former developer
Britt van Rooij	Former developer
Saad Allowayyed	Former developer
Daan van Ingen	Former contributor
Hendrik Cornelisse	Former contributor
Mike de Haan	Former contributor
Kevin de Vries	Former contributor
Jonathan de Bouter	Former contributor
Roland Joo-Kovacs	Former contributor

Citing HemoCell

When using HemoCell please cite the HemoCell paper:

@article{Zavodszky:2017,
  author={Závodszky, Gábor and van Rooij, Britt and Azizi, Victor and Hoekstra, Alfons},
  title={Cellular Level In-silico Modeling of Blood Rheology with An Improved Material Model for Red Blood Cells},
  journal={Frontiers in Physiology},
  volume={8},
  pages={563},
  year={2017},
  url={https://www.frontiersin.org/article/10.3389/fphys.2017.00563},
  doi={10.3389/fphys.2017.00563},
  issn={1664-042X},
}

HemoCell related publications

1(1,2)

Gábor Závodszky, Britt van Rooij, Victor Azizi, and Alfons Hoekstra. Cellular level in-silico modeling of blood rheology with an improved material model for red blood cells. Frontiers in Physiology, 8:563, 2017. URL: https://www.frontiersin.org/article/10.3389/fphys.2017.00563, doi:10.3389/fphys.2017.00563.

2

Gábor Závodszky, Britt van Rooij, Ben Czaja, Victor Azizi, David de Kanter, and Alfons G Hoekstra. Red blood cell and platelet diffusivity and margination in the presence of cross-stream gradients in blood flows. Physics of Fluids, 31(3):031903, 2019.

3

B Czaja, G Závodszky, V Azizi Tarksalooyeh, and AG Hoekstra. Cell-resolved blood flow simulations of saccular aneurysms: effects of pulsatility and aspect ratio. Journal of The Royal Society Interface, 15(146):20180485, 2018.

4

BJM van Rooij, G Závodszky, AG Hoekstra, and DN Ku. Haemodynamic flow conditions at the initiation of high-shear platelet aggregation: a combined in vitro and cellular in silico study. Interface Focus, 11(1):20190126, 2020.

5

Victor Azizi Tarksalooyeh, Gábor Závodszky, and Alfons G Hoekstra. Optimizing parallel performance of the cell based blood flow simulation software hemocell. In International Conference on Computational Science, 537–547. Springer, 2019.

6

S. Alowayyed, G. Závodszky, V. Azizi, and A.G. Hoekstra. Load balancing of parallel cell-based blood flow simulations. Journal of Computational Science, 24:1 – 7, 2018. URL: http://www.sciencedirect.com/science/article/pii/S1877750317302788, doi:https://doi.org/10.1016/j.jocs.2017.11.008.

7

Benjamin Czaja, Mario Gutierrez, Gábor Závodszky, David de Kanter, Alfons Hoekstra, and Omolola Eniola-Adefeso. The influence of red blood cell deformability on hematocrit profiles and platelet margination. PLOS Computational Biology, 16(3):e1007716, 2020.

8

Mike Haan, Gábor Závodszky, Victor Azizi, and Alfons Hoekstra. Numerical investigation of the effects of red blood cell cytoplasmic viscosity contrasts on single cell and bulk transport behaviour. Applied Sciences, 8:1616, 09 2018. doi:10.3390/app8091616.

9

BJM van Rooij, Gábor Závodszky, VW Azizi Tarksalooyeh, and Alfons Georgius Hoekstra. Identifying the start of a platelet aggregate by the shear rate and the cell-depleted layer. Journal of the Royal Society Interface, 16(159):20190148, 2019.

10

Benjamin Czaja, Gábor Závodszky, and Alfons Hoekstra. A heterogeneous multi-scale model for blood flow. In International Conference on Computational Science, 403–409. Springer, 2020.

11

Kevin de Vries, Anna Nikishova, Benjamin Czaja, Gábor Závodszky, and Alfons G Hoekstra. Inverse uncertainty quantification of a cell model using a gaussian process metamodel. International Journal for Uncertainty Quantification, 2020.