Deformation characteristics of red blood cells (RBCs) are closely linked to disease progression and hold promise as a tool for disease diagnosis. Recent studies have been conducted to measure these deformation characteristics using optical tweezers. Models of RBCs are developed to reproduce some of these measurements and to gain insights into the mechanical behaviour of RBCs. In this paper, a new inverse analysis approach, SelfSim (Self-Learning Simulations), is introduced to extract RBC material stress–strain behaviour from complementary boundary measurements of forces and displacements obtained by optical tweezers. SelfSim inverse analysis approach allows the user to discover new material stress–strain relationships to best reproduce the global force-displacement measurements. SelfSim is verified using simulated RBC experiments and then applied to physical measurements on healthy RBC. SelfSim reveals that in order to capture the interrelationship between measured axial and transverse deformations, the stress–strain relationship for healthy RBCs has to be anisotropic and thus differs from commonly assumed isotropic hyperelastic response.
- artificial neural network material models
- deformation characteristics
- inverse analysis
- red blood cell
ASJC Scopus subject areas
- Computer Science Applications
- Applied Mathematics