论文标题
细胞系扰动实验中的因果模型，预测和外推
Causal Models, Prediction, and Extrapolation in Cell Line Perturbation Experiments
论文作者
论文摘要
在细胞系扰动实验中，细胞的集合与外部药物（例如药物）和测量的蛋白质表达等反应扰动。由于成本限制，只能在体外测试所有可能的扰动的一小部分。这导致了计算模型的开发，这些模型可以预测细胞对扰动的反应。具有临床上有趣的预测反应的扰动可以优先考虑体外测试。在这项工作中，我们比较了黑色素瘤癌细胞系中扰动反应预测的因果和非因果回归模型。该数据集上的当前最佳性能方法是Cellbox，它使用普通微分方程（ODE）系统建模蛋白质如何相互影响。我们在线性情况下为ODES的Cellbox系统得出了封闭的形式解决方案。这些分析结果有助于将细胞箱与回归方法的比较。我们表明，诸如Cellbox之类的因果模型，同时需要更多的假设，以非毒物回归模型无法进行的方式推断。例如，因果模型可以预测未经测试药物的反应。我们说明了模拟中的这些优势和劣势。在对黑色素瘤细胞系数据的应用中，我们发现回归模型的表现优于Cellbox因果模型。
In cell line perturbation experiments, a collection of cells is perturbed with external agents (e.g. drugs) and responses such as protein expression measured. Due to cost constraints, only a small fraction of all possible perturbations can be tested in vitro. This has led to the development of computational (in silico) models which can predict cellular responses to perturbations. Perturbations with clinically interesting predicted responses can be prioritized for in vitro testing. In this work, we compare causal and non-causal regression models for perturbation response prediction in a Melanoma cancer cell line. The current best performing method on this data set is Cellbox which models how proteins causally effect each other using a system of ordinary differential equations (ODEs). We derive a closed form solution to the Cellbox system of ODEs in the linear case. These analytic results facilitate comparison of Cellbox to regression approaches. We show that causal models such as Cellbox, while requiring more assumptions, enable extrapolation in ways that non-causal regression models cannot. For example, causal models can predict responses for never before tested drugs. We illustrate these strengths and weaknesses in simulations. In an application to the Melanoma cell line data, we find that regression models outperform the Cellbox causal model.
