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Our methodology integrates mathematical logic modeling with experimental data to identify disease drivers, mechanisms, and potential therapeutic targets. We validated the model by comparing the model\u2019s readout with experimental data from different biological levels, such as single-cell RNA sequencing analyses and a CLL\/RS patient formalin-fixed paraffin-embedded (FFPE) tissue cohort. Our analyses identified BMI1 proto-oncogene and TP53 loss as key RS progression regulators. In addition, we performed an in silico target screening to identify promising target combinations in a personalized fashion. 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