基于稳态数据和Goldbeter-Koshland动力学的网络推理
Network Inference Using Steady State Data and Goldbeter-Koshland Kinetics
摘要：
网络推理方法被广泛用于阐明基因和蛋白质等分子参与者之间的相互作用。感兴趣的网络（例如基因调节或蛋白质信号网络）的生化过程通常是非线性的。在许多情况下，有关化学动力学的知识是可用的。然而，现有的连续、稳态数据的网络推理方法通常植根于统计公式，其不利用化学动力学来指导推理。在这里，我们提出了一种方法，网络推理的稳态数据，是植根于非线性描述的生化机制。我们使用化学动力学的平衡分析，以获得功能的形式，反过来又用来推断网络使用稳态数据。我们提出的方法是直接适用于传统的稳态基因表达或蛋白质组数据，不需要任何网络拓扑结构或任何动力学参数的知识。我们说明了蛋白质磷酸化网络的背景下，使用从最近的机械模型和癌细胞系的蛋白质组学数据模拟的数据的方法。在前者中，真实的网络是已知的并用于评估，而在后者中，结果与已知的生物化学进行比较。我们发现，所提出的方法在估计网络拓扑结构方面比基于线性模型的方法更有效。
英文摘要：
Network inference approaches are widely used to shed light on regulatory interplay between molecular players such as genes and proteins. Biochemical processes underlying networks of interest (e.g. gene regulatory or protein signalling networks) are generally nonlinear. In many settings, knowledge is available concerning relevant chemical kinetics. However, existing network inference methods for continuous, steady-state data are typically rooted in statistical formulations, which do not exploit chemical kinetics to guide inference. Herein, we present an approach to network inference for steady-state data that is rooted in non-linear descriptions of biochemical mechanism. We use equilibrium analysis of chemical kinetics to obtain functional forms that are in turn used to infer networks using steady-state data. The approach we propose is directly applicable to conventional steady-state gene expression or proteomic data and does not require knowledge of either network topology or any kinetic parameters. We illustrate the approach in the context of protein phosphorylation networks, using data simulated from a recent mechanistic model and proteomic data from cancer cell lines. In the former, the true network is known and used for assessment, whereas in the latter, results are compared against known biochemistry. We find that the proposed methodology is more effective at estimating network topology than methods based on linear models.
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