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Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices

Young Hwan Chang, Roel Dobbe, Palak Bhushan, Joe W. Gray, Claire J. Tomlin
doi: https://doi.org/10.1101/012534
Young Hwan Chang
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA
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Roel Dobbe
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA
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Palak Bhushan
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA
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Joe W. Gray
2Department of Biomedical Engineering and the Center for Spatial Systems Biomedicine, Oregon Health and Science University, Portland, OR 97239, USA
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Claire J. Tomlin
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720 USA
3Faculty Scientist, Life Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
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Abstract

With the growth of high-throughput proteomic data, in particular time series gene expression data from various perturbations, a general question that has arisen is how to organize inherently heterogenous data into meaningful structures. Since biological systems such as breast cancer tumors respond differently to various treatments, little is known about exactly how these gene regulatory networks (GRNs) operate under different stimuli. For example, when we apply a drug-induced perturbation to a target protein, we often only know that the dynamic response of the specific protein may be affected. We do not know by how much, how long and even whether this perturbation affects other proteins or not. Challenges due to the lack of such knowledge not only occur in modeling the dynamics of a GRN but also cause bias or uncertainties in identifying parameters or inferring the GRN structure. This paper describes a new algorithm which enables us to estimate bias error due to the effect of perturbations and correctly identify the common graph structure among biased inferred graph structures. To do this, we retrieve common dynamics of the GRN subject to various perturbations. We refer to the task as “repairing” inspired by “image repairing” in computer vision. The method can automatically correctly repair the common graph structure across perturbed GRNs, even without precise information about the effect of the perturbations. We evaluate the method on synthetic data sets and demonstrate advantages over C-regularized graph inference by advancing our understanding of how these networks respond across different targeted therapies. Also, we demonstrate an application to the DREAM data sets and discuss its implications to experiment design.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted December 11, 2014.
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Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices
Young Hwan Chang, Roel Dobbe, Palak Bhushan, Joe W. Gray, Claire J. Tomlin
bioRxiv 012534; doi: https://doi.org/10.1101/012534
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Reconstruction of Gene Regulatory Networks based on Repairing Sparse Low-rank Matrices
Young Hwan Chang, Roel Dobbe, Palak Bhushan, Joe W. Gray, Claire J. Tomlin
bioRxiv 012534; doi: https://doi.org/10.1101/012534

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