Descripción
Cellular systems behave as active matter, continuously consuming metabolic energy to generate forces and maintain dynamics far from thermodynamic equilibrium. Understanding how these active fluctuations modulate intracellular mechanical properties is essential for linking cell mechanics with cancer progression. Here we present a computational microscopy framework for the mechanical analysis of cancer cells based on high-resolution imaging, multiple particle tracking (MPT), and stochastic trajectory analysis. Time-resolved imaging sequences are processed using particle-tracking algorithms to reconstruct intracellular trajectories, which are subsequently analyzed to extract dynamical observables such as mean squared displacement, correlation functions, and probability propagators. Within the framework of stochastic thermodynamics, entropy production and Kullback–Leibler divergence between forward and backward trajectories are used to quantify nonequilibrium activity. Time series are also mapped into topological representations using Horizontal Visibility Graphs, enabling structural analysis of intracellular dynamics. In addition, machine-learning approaches based on tokenized trajectory representations and Self-Organizing Maps allow the identification of dynamic regimes associated with mechanical anomalies. Applications to leukemia and pancreatic cancer cells reveal measurable links between intracellular dynamics, cellular mechanics, and metabolic regulation.
| Institución | Universidad Francisco de Vitoria |
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