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Atomic force microscopy (AFM) image analysis of supported bilayers, such as tethered bilayer membranes (tBLMs) can reveal the nature of the membrane damage by pore-forming proteins and predict the electrochemical impedance spectroscopy (EIS) response of such objects. However, automated analysis involving pore detection in such images is often non-trivial and can require AI-based object detection techniques. The specific object-detection algorithm we used to determine the defect coordinates in real AFM images was a convolutional neural network (CNN). Defect coordinates allow to predict the EIS response of tBLMs populated by the pore-forming toxins using finite element analysis (FEA) modeling. We tested if the accuracy of the CNN algorithm affected the EIS spectral features sensitive to defect densities and other physical parameters of tBLMs. We found that the EIS spectra can be predicted sufficiently well, however, systematic errors of characteristic spectral points were observed and need to be taken into account. Importantly, the comparison of predicted EIS curves with experimental ones allowed to estimate important physical parameters of tBLMs such as the specific resistance of submembrane reservoir. This reservoir separates phospholipid bilayer from the solid support. We found that the specific resistance of the reservoir amounts to $$10^{4.25 \pm 0.10}$$ 10 4.25 ± 0.10 $$\Omega \cdot cm$$ Ω · c m which is approximately two orders of a magnitude higher compared to the specific resistance of the buffer bathing tBLMs studied in this work. We hypothesize that such effect may be related in part due to decreased concentration of ionic carriers in the submembrane due to decreased relative dielectric permittivity in this region.

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This study deals with computational modeling of defect clustering effects observed in bilayer phospholipid membranes. Two defect clustering models (algorithms) are presented and compared with the random defect distribution approach. Specific defect distribution instances are evaluated using a simple methodology based on Voronoi diagrams and statistical properties of their sector areas. Computational experiments are performed by using the models to generate synthetic defect distributions with different parameter combinations. The proposed methodology is also validated against atomic force microscopy images of real membranes with defects.