Hernandez-Hernandez Gonzalo, Tieu Mindy, Yang Pei-Chi, Santana L Fernando, Clancy Colleen E
Center for Precision Medicine and Data Science, University of California, Davis, California.
Department of Physiology and Membrane Biology, University of California, Davis, California.
bioRxiv. 2025 Jul 7:2025.07.03.663046. doi: 10.1101/2025.07.03.663046.
Lipidomics is emerging as a domain of opportunity for precision medicine, with phosphatidylinositol 4,5-bisphosphate (PI(4,5)P) functioning as a central regulator of membrane signaling and metabolic control. Its rapid turnover and involvement in diseases like cancer and neurodegeneration highlight its dual role as a biomarker and therapeutic target. However, the inherent complexity of lipid networks complicates traditional modeling approaches. We present a modular, extendable framework for constructing mechanistic models of lipid kinetics via a case study, modeling the synthesis and degradation of phosphatidylinositol 4,5-bisphosphate (PI(4,5)P), a second messenger central to receptor signaling and calcium regulation. Our method efficiently identifies rate constants governing enzymatic dynamics and can be extended to a range of signaling networks. Using experimental time-course data for PI(4)P, PI(4,5)P, and IP, we optimized five kinetic parameters to capture the lipid and second messenger dynamics. The resulting model achieved a strong correlation with experimental trends and reproduced dynamic behaviors relevant to cellular signaling. We then applied the model to simulate signaling perturbations linked to and loss-of-function, two lipid kinases associated with neurodevelopmental and neuromuscular disorders. This modeling framework provides a scalable foundation for predictive biochemical modeling and offers a path toward individualized applications in precision medicine.
脂质组学正成为精准医学领域的一个机遇领域，磷脂酰肌醇4,5-二磷酸（PI(4,5)P）作为膜信号传导和代谢控制的核心调节因子发挥作用。它的快速周转以及在癌症和神经退行性疾病等疾病中的参与突出了其作为生物标志物和治疗靶点的双重作用。然而，脂质网络固有的复杂性使传统建模方法变得复杂。我们通过一个案例研究，提出了一个用于构建脂质动力学机制模型的模块化、可扩展框架，对磷脂酰肌醇4,5-二磷酸（PI(4,5)P）的合成和降解进行建模，PI(4,5)P是受体信号传导和钙调节的核心第二信使。我们的方法有效地识别了控制酶动力学的速率常数，并且可以扩展到一系列信号网络。利用PI(4)P、PI(4,5)P和IP的实验时间进程数据，我们优化了五个动力学参数以捕捉脂质和第二信使的动态变化。所得模型与实验趋势具有很强的相关性，并再现了与细胞信号传导相关的动态行为。然后，我们应用该模型模拟与和功能丧失相关的信号扰动，和是两种与神经发育和神经肌肉疾病相关的脂质激酶。这个建模框架为预测性生化建模提供了一个可扩展的基础，并为精准医学中的个性化应用提供了一条途径。
