Abstract
A key goal of systems biology is to elucidate molecularmechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
| Original language | English (US) |
|---|---|
| Journal | Interface Focus |
| Volume | 3 |
| Issue number | 4 |
| DOIs | |
| State | Published - Aug 6 2013 |
| Externally published | Yes |
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Keywords
- ARACNe
- Higher-order data processing inequality
- Information theory
- Reverse engineering
- Transcriptional regulatory network
ASJC Scopus subject areas
- Biophysics
- Biotechnology
- Biochemistry
- Bioengineering
- Biomedical Engineering
- Biomaterials
Cite this
hARACNe : Improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests. / Jang, Sock; Margolin, Adam; Califano, Andrea.
In: Interface Focus, Vol. 3, No. 4, 06.08.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - hARACNe
T2 - Improving the accuracy of regulatory model reverse engineering via higher-order data processing inequality tests
AU - Jang, Sock
AU - Margolin, Adam
AU - Califano, Andrea
PY - 2013/8/6
Y1 - 2013/8/6
N2 - A key goal of systems biology is to elucidate molecularmechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
AB - A key goal of systems biology is to elucidate molecularmechanisms associated with physiologic and pathologic phenotypes based on the systematic and genome-wide understanding of cell context-specific molecular interaction models. To this end, reverse engineering approaches have been used to systematically dissect regulatory interactions in a specific tissue, based on the availability of large molecular profile datasets, thus improving our mechanistic understanding of complex diseases, such as cancer. In this paper, we introduce high-order Algorithm for the Reconstruction of Accurate Cellular Network (hARACNe), an extension of the ARACNe algorithm for the dissection of transcriptional regulatory networks. ARACNe uses the data processing inequality (DPI), from information theory, to detect and prune indirect interactions that are unlikely to be mediated by an actual physical interaction. Whereas ARACNe considers only first-order indirect interactions, i.e. those mediated by only one extra regulator, hARACNe considers a generalized form of indirect interactions via two, three or more other regulators. We show that use of higher-order DPI resulted in significantly improved performance, based on transcription factor (TF)-specific ChIP-chip data, as well as on gene expression profile following RNAi-mediated TF silencing.
KW - ARACNe
KW - Higher-order data processing inequality
KW - Information theory
KW - Reverse engineering
KW - Transcriptional regulatory network
UR - http://www.scopus.com/inward/record.url?scp=84879435511&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879435511&partnerID=8YFLogxK
U2 - 10.1098/rsfs.2013.0019
DO - 10.1098/rsfs.2013.0019
M3 - Article
AN - SCOPUS:84879435511
VL - 3
JO - Interface Focus
JF - Interface Focus
SN - 2042-8898
IS - 4
ER -