Analytical and computational modelling techniques to characterize the
mechanical property of biological cells using micro-constriction
channels
Abstract
Mechanical property of biological cells can act as an indicator for the
health state of a human being. Mathematical modelling of cells help to
understand and predict the cell deformation patterns that might provide
insightful finding for cell mechanics. Many models have been developed
that tries to explain the cell mechanics at the cellular level. We
propose an analytical model that considers the poroelastic nature of
cells to understand their deformation behavior. The validity of the
model is tested by comparing the predicted cell deformations against the
experimental observations reported by Raj et al. (2017). Also, a
computational study is performed, where we employ an in house Python
code along with MS Excel GRG solver which incorporates the cell
deformation predictions from developed poroelastic model and predicts
the Young’s modulus value of the cells. The predictions using GRG based
approach showed a good match with the experimental results with a
maximum error of 12.09% in the case of MDA MB-231 cells. Further, we
present an artificial neural network model to predict the Young’s
modulus and viscosity of cells based on the experimentally measured
input parameters such as entry time, transit velocity, initial cell
diameter and extension ratio from the cell migration process through a
micro-constriction channel. It was found that the neural network with
architecture of 4-5-1 was best suited for the MCF-10A cells while the
4-8-1 architecture was giving better results for the MDA MB-231 cells.
The developed ANN model is further tested for Young’s Modulus prediction
of HeLa cells with completely new set of data. The predictions from ANN
based model for HeLa cells matched well the experimental prediction
within 4.5 % of error.