Jain Siddhartha, Arrais Joel, Venkatachari Narasimhan J, Ayyavoo Velpandi, Bar-Joseph Ziv
Computer Science Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Department of Computer Science, University of Coimbra, Coimbra, Portugal.
Bioinformatics. 2016 Jun 15;32(12):i253-i261. doi: 10.1093/bioinformatics/btw254.
Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages.
We present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus. TimePath uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights. We experimentally validated several of TimePaths' predictions highlighting the usefulness of temporal models.
Data, Supplementary text and the TimePath software are available from http://sb.cs.cmu.edu/timepath
Supplementary data are available at Bioinformatics online.
大多数从高通量数据重建反应网络的方法生成的是静态模型，无法区分早期和晚期反应阶段。
我们提出了TimePath，这是一种整合时间序列和静态数据集以重建宿主对刺激反应的动态模型的新方法。TimePath使用整数规划公式来选择一组共同解释观察到的动态反应的通路子集。将TimePath应用于研究人类对HIV-1的反应，准确重建了几条已知的调控和信号通路，并获得了新的机制见解。我们通过实验验证了TimePath的几个预测，突出了时间模型的实用性。
数据、补充文本和TimePath软件可从http://sb.cs.cmu.edu/timepath获取。
补充数据可在《生物信息学》在线获取。
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