Department of Information and Computer Science, Aalto University, FI-00076 AALTO, Finland.
Bioinformatics. 2013 May 15;29(10):1283-91. doi: 10.1093/bioinformatics/btt130. Epub 2013 Mar 16.
Signaling networks mediate responses to different stimuli using a multitude of feed-forward, feedback and cross-talk mechanisms, and malfunctions in these mechanisms have an important role in various diseases. To understand a disease and to help discover novel therapeutic approaches, we have to reveal the molecular mechanisms underlying signal transduction and use that information to design targeted perturbations.
We have pursued this direction by developing an efficient computational approach, Sorad, which can estimate the structure of signal transduction networks and the associated continuous signaling dynamics from phosphoprotein time-course measurements. Further, Sorad can identify experimental conditions that modulate the signaling toward a desired response. We have analyzed comprehensive phosphoprotein time-course data from a human hepatocellular liver carcinoma cell line and demonstrate here that Sorad provides more accurate predictions of phosphoprotein responses to given stimuli than previously presented methods and, importantly, that Sorad can estimate experimental conditions to achieve a desired signaling response. Because Sorad is data driven, it has a high potential to generate novel hypotheses for further research. Our analysis of the hepatocellular liver carcinoma data predict a regulatory connection where AKT activity is dependent on IKK in TGFα stimulated cells, which is supported by the original data but not included in the original model.
An implementation of the proposed computational methods will be available at http://research.ics.aalto.fi/csb/software/.
tarmo.aijo@aalto.fi or harri.lahdesmaki@aalto.fi
Supplementary data are available at Bioinformatics online.
信号网络通过多种前馈、反馈和串扰机制对不同刺激做出反应，这些机制的功能障碍在各种疾病中起着重要作用。为了理解一种疾病并帮助发现新的治疗方法，我们必须揭示信号转导的分子机制，并利用这些信息来设计靶向干扰。
我们通过开发一种有效的计算方法 Sorad 来追求这一方向，该方法可以从磷酸化蛋白时间过程测量中估计信号转导网络的结构和相关的连续信号动力学。此外，Sorad 可以识别调节信号向期望响应的实验条件。我们分析了来自人类肝癌细胞系的综合磷酸化蛋白时间过程数据，结果表明，与之前提出的方法相比，Sorad 可以更准确地预测给定刺激下的磷酸化蛋白反应，并且重要的是，Sorad 可以估计实验条件以实现期望的信号响应。由于 Sorad 是数据驱动的，它具有生成新假说进行进一步研究的巨大潜力。我们对肝癌数据的分析预测了 AKT 活性在 TGFα 刺激的细胞中依赖于 IKK 的调节连接，这得到了原始数据的支持，但在原始模型中没有包含。
拟议的计算方法的实现将在 http://research.ics.aalto.fi/csb/software/ 上提供。
tarmo.aijo@aalto.fi 或 harri.lahdesmaki@aalto.fi
补充数据可在生物信息学在线获得。
