time course trajectories

07.09.2014 and 07.23.14

Predicting Dynamic Signaling Network Response under Unseen Perturbations

Fan Zhu 1 and Yuanfang Guan 1,2,3,*

1 Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA

2 Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA

3 Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI 48109, USA

* To whom correspondence should be addressed.


Motivation: Predicting trajectories of signaling networks under complex perturbations is one of the most valuable but challenging tasks in systems biology. Signaling networks are involved in most of the biological pathways and modeling their dynamics has wide applications including drug design and treatment outcome prediction.

Results: In this paper, we report a novel model for predicting the cell type-specific time course response of signaling proteins under unseen perturbations. This algorithm achieved the top performance in the 2013 8th Dialogue for Reverse Engineering Assessments and Methods (DREAM 8) sub challenge: time course prediction in breast cancer cell lines. We formulate the trajectory prediction problem into a standard regularization problem; the solution becomes solving this discrete ill-posed problem. This algorithm includes three steps: denoising, estimating regression coefficients and modeling trajectories under unseen perturbations. We further validated the accuracy of this method against simulation and experimental data. Furthermore, this method reduces computational time by magnitudes compared to state-of-the-art methods, allowing genome-wide modeling of signaling pathways and time course trajectories to be carried out in a practical time.

Availability and Implementation: Source code is available at http://guanlab.ccmb.med.umich.edu/DREAM/code.html and as supplementary file online. Contact: gyuanfan@umich.edu