Numerical simulation of deformed red blood cell by utilizing neural network approach and finite element analysis.

In order to have research on the deformation characteristics and mechanical properties of human red blood cells (RBCs), finite element models of RBC optical tweezers stretching and atomic force microscope (AFM) indentation were established. Non-linear elasticity of cell membrane was determined by using the neo-Hookean hyperelastic material model, and the deformation of RBC during stretching and indentation had been researched in ABAQUS, respectively. Considering the application of machine learning (ML) in material parameters identification, ML algorithm was combined with finite element (FE) method to identify the constitutive parameters. The material parameters were estimated according to the deformation characteristics of RBC obtained from the change of cell diameter with stretching force when RBC was stretched. The non-linear relationship between material parameter and RBC deformation was established by building a FE-model. The FE simulation of RBC stretching was used to construct the training set and the neural network trained by a large number of samples was used to predict the material parameter. With the predicted parameter, FE simulation of RBC under AFM indentation to explore the local deformation mechanism was completed.