Cancer is considered one of the deadliest diseases of our time. Countless researches are being conducted around the globe to find a cure for this disease. Apart from Doctors and Pharmacists, Engineers do have a part in tackling this disease as much as others. From making world-class high precision equipment for surgeries and diagnosis to building reliable and affordable devices, engineers have a huge role in the field of medicine.
Researchers from Canada were working on a simulation model which predicts the growth of cancer cell line H1975 (Lung Cancer). This is achieved by the use of a spring-based network model, which consists of fluid-solid interaction. In other words, this model replicates the interaction between cancer cells and plasma or blood.
Though the elastic parameters used for this spring network model are widely used in other models, the viscosity and friction between the cell and the wall are usually ignored in these models. This has a significant impact on the final result. Viscous and elastic parameters of the model are adapted by the spring-network model of a cell. The cell passage is also numerically modeled with a constriction of 6 μm in width, 15 μm in depth, and 50 μm in length with a 45° tapered entrance was used. Cells were kept in cell solution (RPMI 1640) at 37 °C and sent through the microchannel with a constant pressure drop that drove the flow in the fluidic microchannel. The flow rate of the blank media at the drop pressure of 1.5 psi was reported to be 38 μL/h. The measurements of the passage of H1975 were performed at two different drop pressures of 1.8 psi and 0.9 psi.
A model with a higher node count will always bring a more accurate result but demands a high computational cost. Algorithms used in this experiment can also be further enhanced in a way that the total run time will reduce providing computational efficiency. Also, a genetic algorithm has been used in this particular model. The algorithm is responsible to find the most accurate set of variable parameters that produce the largest possible deformation for the simulation. One of the interesting aspects of this research is that the authors proposed an enhanced genetic algorithm that has the capacity to find the set of parameters without compromising on overall runtime. Additional inclusion of error functions to eliminate unrealistic datasets and match the experimental results is also an important feature.
Fig1. Relaxed and stretched state comparison.
After enhancing this model the takeaways are as follows: the time taken to squeeze and completely enter the passage is highly dominated by the deformability characteristics of the cell. Whereas, its time of travel inside the passage is dependent on both surface friction and deformability. As the simulation is performed with two different flow rates, it is observed that the overall deformation and strain of the cell at a lower flow rate are slightly higher when compared to its counterpart. Ignoring the viscosity of the cell membrane and the friction between cell and wall will drastically change the results. Although the spring networks have been widely used in this field of research for applications such as modeling red blood cells, leukocytes, platelets, and cancer cells, the proposed method in this research is probably the first systematic method to find parameters of spring networks proving the novelty of this research. This model is also tested with red blood cells, to prove its accuracy. The simulated data is found to be in close agreement with that of the experimental data. This is because the model uses the approach of finding the parameters based on the experimental data related to the deformation of the entire cell. This proves the novelty and accuracy of the research.
Fig2. Effect of viscosity on cell membrane
 A systematic approach for developing mechanistic models for realistic simulation of cancer cell motion and deformation, Pouyan Keshavarz Motamed, Nima Maftoon. https://doi.org/10.1038/s41598-021-00905-3
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