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<p>Ataxia-Telangiectasia (A-T) is a rare genetic disorder caused by ATM mutations, leading to impaired DNA repair, oxidative stress, and neurodegeneration. We developed a computational model of ATM-mediated signaling using ordinary differential equations in COPASI, capturing key processes including DNA damage sensing, cell cycle regulation, autophagy, and oxidative stress response. The model simulates physiological, ATM-deficient, and drug-treated conditions to explore repurposing strategies. We evaluated the effects of spermidine, omaveloxolone, and HDAC4 inhibition, revealing mechanisms by which these compounds modulate dysfunctional signaling. Sensitivity and stability analyses confirmed the model’s robustness, while enrichment analysis validated involvement of key pathways. Our results highlight the synergistic potential of combining autophagy activation and epigenetic modulation to partially restore homeostasis in ATM-deficient cells. This work introduces a generalizable modeling framework for simulating disease-specific signaling dysfunction and identifying therapeutic interventions, illustrating the value of computational systems biology in rare disease drug repurposing.</p>

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Computational modeling of ATM signaling: a predictive framework for drug repurposing in ataxia-telangiectasia

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月24日

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  • Aurora Eliana Merulla,
  • Valentina Di Salvatore,
  • Giorgia Serena Gullotta,
  • Avisa Maleki,
  • Giulia Russo,
  • Filippo Caraci,
  • Agata Copani,
  • Francesco Pappalardo

摘要

Ataxia-Telangiectasia (A-T) is a rare genetic disorder caused by ATM mutations, leading to impaired DNA repair, oxidative stress, and neurodegeneration. We developed a computational model of ATM-mediated signaling using ordinary differential equations in COPASI, capturing key processes including DNA damage sensing, cell cycle regulation, autophagy, and oxidative stress response. The model simulates physiological, ATM-deficient, and drug-treated conditions to explore repurposing strategies. We evaluated the effects of spermidine, omaveloxolone, and HDAC4 inhibition, revealing mechanisms by which these compounds modulate dysfunctional signaling. Sensitivity and stability analyses confirmed the model’s robustness, while enrichment analysis validated involvement of key pathways. Our results highlight the synergistic potential of combining autophagy activation and epigenetic modulation to partially restore homeostasis in ATM-deficient cells. This work introduces a generalizable modeling framework for simulating disease-specific signaling dysfunction and identifying therapeutic interventions, illustrating the value of computational systems biology in rare disease drug repurposing.