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. Challenges due to the lack of 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 an application to the DREAM data sets and discuss its implications to experiment design.
| Original language | English (US) |
|---|---|
| Article number | 7182286 |
| Pages (from-to) | 767-777 |
| Number of pages | 11 |
| Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
| Volume | 13 |
| Issue number | 4 |
| DOIs | |
| State | Published - Jul 1 2016 |
Fingerprint
Keywords
- gene regulatory network
- repairing
- System identification
ASJC Scopus subject areas
- Biotechnology
- Genetics
- Applied Mathematics
Cite this
Reconstruction of Gene Regulatory Networks Based on Repairing Sparse Low-Rank Matrices. / Chang, Young Hwan; Dobbe, Roel; Bhushan, Palak; Gray, Joe; Tomlin, Claire J.
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, Vol. 13, No. 4, 7182286, 01.07.2016, p. 767-777.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Reconstruction of Gene Regulatory Networks Based on Repairing Sparse Low-Rank Matrices
AU - Chang, Young Hwan
AU - Dobbe, Roel
AU - Bhushan, Palak
AU - Gray, Joe
AU - Tomlin, Claire J.
PY - 2016/7/1
Y1 - 2016/7/1
N2 - 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. Challenges due to the lack of 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 an application to the DREAM data sets and discuss its implications to experiment design.
AB - 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. Challenges due to the lack of 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 an application to the DREAM data sets and discuss its implications to experiment design.
KW - gene regulatory network
KW - repairing
KW - System identification
UR - http://www.scopus.com/inward/record.url?scp=84982085330&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84982085330&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2015.2465952
DO - 10.1109/TCBB.2015.2465952
M3 - Article
C2 - 27990101
AN - SCOPUS:84982085330
VL - 13
SP - 767
EP - 777
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
SN - 1545-5963
IS - 4
M1 - 7182286
ER -