De Landtsheer Sébastien, Lucarelli Philippe, Sauter Thomas
Systems Biology Group, Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg.
Front Physiol. 2018 May 22;9:550. doi: 10.3389/fphys.2018.00550. eCollection 2018.
Understanding the functional properties of cells of different origins is a long-standing challenge of personalized medicine. Especially in cancer, the high heterogeneity observed in patients slows down the development of effective cures. The molecular differences between cell types or between healthy and diseased cellular states are usually determined by the wiring of regulatory networks. Understanding these molecular and cellular differences at the systems level would improve patient stratification and facilitate the design of rational intervention strategies. Models of cellular regulatory networks frequently make weak assumptions about the distribution of model parameters across cell types or patients. These assumptions are usually expressed in the form of regularization of the objective function of the optimization problem. We propose a new method of regularization for network models of signaling pathways based on the local density of the inferred parameter values within the parameter space. Our method reduces the complexity of models by creating groups of cell line-specific parameters which can then be optimized together. We demonstrate the use of our method by recovering the correct topology and inferring accurate values of the parameters of a small synthetic model. To show the value of our method in a realistic setting, we re-analyze a recently published phosphoproteomic dataset from a panel of 14 colon cancer cell lines. We conclude that our method efficiently reduces model complexity and helps recovering context-specific regulatory information.
了解不同来源细胞的功能特性是个性化医疗长期面临的挑战。尤其是在癌症领域，患者中观察到的高度异质性减缓了有效治疗方法的开发。细胞类型之间或健康与患病细胞状态之间的分子差异通常由调控网络的连接方式决定。在系统层面理解这些分子和细胞差异将改善患者分层，并有助于设计合理的干预策略。细胞调控网络模型通常对模型参数在不同细胞类型或患者中的分布做出较弱的假设。这些假设通常以优化问题目标函数正则化的形式表达。我们基于参数空间内推断参数值的局部密度，提出了一种用于信号通路网络模型的新正则化方法。我们的方法通过创建特定细胞系参数组来降低模型的复杂性，然后可以一起对这些参数进行优化。我们通过恢复正确的拓扑结构并推断一个小型合成模型的参数准确值，展示了我们方法的应用。为了在实际环境中展示我们方法的价值，我们重新分析了最近发表的来自14种结肠癌细胞系的磷酸化蛋白质组数据集。我们得出结论，我们的方法有效地降低了模型复杂性，并有助于恢复特定背景下的调控信息。
