System and method for predictive modeling of cellular systems

Abstract

A system and method for predictive modeling of cellular systems assesses compound effectiveness on cellular metabolic and inflammatory states. Cellular samples are exposed to metabolic MRX sensors detecting oxidative stress, reporting cytoskeletal organization, or monitoring mitochondrial morphology, dynamics, or compartment-specific redox activity. Images of MRX sensor-labeled samples are captured using an imaging device and analyzed by trained predictive models to determine compound impact on immunological activation, inflammatory status, oxidative stress, mitochondrial function, and bio-energetic state. The system supports multisensor integration and spectral unmixing to generate multidimensional metabolic or ROS related MRX fingerprints that characterize cellular phenotypes, drug responses, and enable comparison to reference states. Preprocessing modules perform image normalization, augmentation, segmentation, and feature extraction to enhance model performance. Predictive models classify treatment conditions, identify perturbation-specific signatures, and enable compound ranking or mechanism-of-action inference. The system provides an objective, scalable, and high-content platform for drug screening, toxicity assessment, and inflammation research.

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