Real-Time Identification of Cell Mechanical Properties

In the last years, the exploitation of nanotechnology to the study of biological systems opened new avenues towards innovative clinical approaches based on single cell mechanical characterization. Nevertheless, due to the lack of throughput typical of high resolution tools, a complete translation of research findings to real life applications has not been accomplished yet. One of the aspects hindering this process is associated to the analytical estimation of physical parameters belonging to single cells. The typical approach consists in fitting a mathematical model to the experimental signals or, in other words, in optimizing the parameters of a chosen functional. This strategy is fast and effective for deterministic models, slightly affected by environmental noise, but a paradigm shift is required when dealing with nanoscale systems, intrinsically described by a thermal-driven statistical distribution. The method proposed in this work consists in extracting meaningful mechanical parameters from experimental data based on solving the associated estimation problem. An approach to cell mechanics investigation based on Sequential Monte Carlo algorithms have been implemented. Starting from a generalized state space representation, the experiment and its uncertainties and noise have been modelled and the characterizing parameters have been identified through the exploitation of estimation algorithms. The analysis has been applied in a simulation environment, allowing to check the algorithm inference capability and to define the optimal experimental protocol. Single cell mechanical estimation experiments have been designed to match the selected procedure, implementing the estimation strategy in an embedded platform based on a single core dsPIC device. The effectiveness of the proposed approach has been tested on a cell line before and after treatment with cytoskeleton-disrupting drugs. Preliminary results show that modern filtering theory can be effectively applied to extract single cell mechanical parameters in real-time, also providing a valuable guide to identify the most efficient experimental design.