A user ready, well documented software package PyOIF contains an implementation of a robust validated computational model for cell flow modelling. The software is capable of simulating processes involving biological cells immersed in a fluid. The examples of such processes are flows in microfluidic channels with numerous applications such as cell sorting, rare cell isolation or flow fractionation. Besides the typical usage of such computational model in the design process of microfluidic devices, PyOIF has been used in the computer-aided discovery involving mechanical properties of cell membranes. With this software, single cell, many cell, as well as dense cell suspensions can be simulated. Many cell simulations include cell-cell interactions and analyse their effect on the cells. PyOIF can be used to test the influence of mechanical properties of the membrane in flows and in membrane-membrane interactions. Dense suspensions may be used to study the effect of cell volume fraction on macroscopic phenomena such as cell-free layer, apparent suspension viscosity or cell degradation. The PyOIF module is based on the official ESPResSo distribution with few modifications and is available under the terms of the GNU General Public Licence. PyOIF is based on Python objects representing the cells and on the C++ computational core for fluid and interaction dynamics. The source code is freely available at GitHub repository, runs natively under Linux and MacOS and can be used in Windows Subsystem for Linux. The communication among PyOIF users and developers is maintained using active mailing lists. This work provides a basic background to the underlying computational models and to the implementation of interactions within this framework. We provide the prospective PyOIF users with a practical example of simulation script with reference to our publicly available User Guide.

The inner viscosity of a biological red blood cell is about five times larger than the viscosity of the blood plasma. In this work, we use dissipative particles to enable the proper viscosity contrast in a mesh-based red blood cell model. Each soft particle represents a coarse-grained virtual cluster of hemoglobin proteins contained in the cytosol of the red blood cell. The particle interactions are governed by conservative and dissipative forces. The conservative forces have purely repulsive character, whereas the dissipative forces depend on the relative velocity between the particles. We design two computational experiments that mimic the classical viscometers. With these experiments we study the effects of particle suspension parameters on the inner cell viscosity and provide parameter sets that result in the correct viscosity contrast. The results are validated with both static and dynamic biological experiment, showing an improvement in the accuracy of the original model without major increase in computational complexity.

The studies that compare the metastatic potential of tumor cell clusters in microcirculation to that of single tumor cells show that the clusters contribute significantly to metastasizing. The metastatic potential is conditioned by the presence of the cancer cells near vessel walls. Detailed understanding of dynamical behavior of clusters near the vessel walls can thus elucidate the process of adhesion. We have developed a biomechanical model of cell clusters capable of simulating both strong and weak adhesion among the cells in the cluster in various spatial configurations. We have validated the model on data from cell separation experiments. The developed model has been used to study near-wall dynamics in shear flow with focus on cluster–wall contact. To quantify the presence of cells near walls, we have evaluated metrics involving time of contact and contact area of clusters tumbling and rolling near the wall. The computational results suggest two trends: First, more elastic clusters and clusters of weakly adhesive cells have decreased cluster–wall contact to the walls than rigid clusters or clusters composed of strongly adhesive cells. Second, more spherical cluster shapes tend to drift away from the walls, thus decreasing the wall contact time.

One of the most important characteristics of red blood cells is their elasticity and ability to return to original biconcave shape after external forces stop acting on them. There are several conservation laws that together govern this return and one of them is the area conservation. In this paper, we focus on local area conservation and introduce a new way of modeling it using a spring network model. We take into account the current shape of the network triangles and find the proportional allocation of area conservation forces, which would for individual triangles preserve their shapes. Since the force contributions in each node are combined from all adjacent triangles, the final resulting shape might not be the same, but overall effect on the triangulations is positive in the sense that this approach tends to regularize them.