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Physical Module Networks: an integrative approach for reconstructing transcription regulation.

Novershtern N, Regev A, Friedman N - Bioinformatics (2011)

Bottom Line: While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation.Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Hebrew University, Jerusalem 91904, Israel.

ABSTRACT

Motivation: Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.

Results: Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation. Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators. Finally, we show the power of Physical Module Networks to reconstruct meaningful regulatory pathways in the genetically perturbed yeast and during the yeast cell cycle, as well as during the response of primary epithelial human cells to infection with H1N1 influenza.

Availability: The PMN software is available, free for academic use at http://www.compbio.cs.huji.ac.il/PMN/.

Contact: aregev@broad.mit.edu; nirf@cs.huji.ac.il.

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Related in: MedlinePlus

Performance on synthetic data. (a) Log likelihood of test samples, achieved by PMN (solid line) and MN (dashed line) as a function of module number. Plots show average over 10-fold cross validation experiments; error bars show 2 STD. (b) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of number of modules. Plots as in (a). (c) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of smoothness of the expression data. Plots as in (a). (d) Reconstructed pathways as a function of noise in the protein–DNA data. Plots show average precision and recall over 10-fold cross validation; error bars indicate 2 STD.
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Figure 3: Performance on synthetic data. (a) Log likelihood of test samples, achieved by PMN (solid line) and MN (dashed line) as a function of module number. Plots show average over 10-fold cross validation experiments; error bars show 2 STD. (b) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of number of modules. Plots as in (a). (c) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of smoothness of the expression data. Plots as in (a). (d) Reconstructed pathways as a function of noise in the protein–DNA data. Plots show average precision and recall over 10-fold cross validation; error bars indicate 2 STD.

Mentions: We find that the likelihood of a test set given the learned PMN is almost identical to the likelihood given the MN (Fig. 3a), indicating that both models have similar predictive power. Thus, the additional constraints in a PMN (by consistency requirements) do not compromise its predictive power.Fig. 3.


Physical Module Networks: an integrative approach for reconstructing transcription regulation.

Novershtern N, Regev A, Friedman N - Bioinformatics (2011)

Performance on synthetic data. (a) Log likelihood of test samples, achieved by PMN (solid line) and MN (dashed line) as a function of module number. Plots show average over 10-fold cross validation experiments; error bars show 2 STD. (b) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of number of modules. Plots as in (a). (c) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of smoothness of the expression data. Plots as in (a). (d) Reconstructed pathways as a function of noise in the protein–DNA data. Plots show average precision and recall over 10-fold cross validation; error bars indicate 2 STD.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3117354&req=5

Figure 3: Performance on synthetic data. (a) Log likelihood of test samples, achieved by PMN (solid line) and MN (dashed line) as a function of module number. Plots show average over 10-fold cross validation experiments; error bars show 2 STD. (b) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of number of modules. Plots as in (a). (c) Precision rate of reconstructing regulator-target pairs, achieved by PMN and MN, as a function of smoothness of the expression data. Plots as in (a). (d) Reconstructed pathways as a function of noise in the protein–DNA data. Plots show average precision and recall over 10-fold cross validation; error bars indicate 2 STD.
Mentions: We find that the likelihood of a test set given the learned PMN is almost identical to the likelihood given the MN (Fig. 3a), indicating that both models have similar predictive power. Thus, the additional constraints in a PMN (by consistency requirements) do not compromise its predictive power.Fig. 3.

Bottom Line: While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation.Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, Hebrew University, Jerusalem 91904, Israel.

ABSTRACT

Motivation: Deciphering the complex mechanisms by which regulatory networks control gene expression remains a major challenge. While some studies infer regulation from dependencies between the expression levels of putative regulators and their targets, others focus on measured physical interactions.

Results: Here, we present Physical Module Networks, a unified framework that combines a Bayesian model describing modules of co-expressed genes and their shared regulation programs, and a physical interaction graph, describing the protein-protein interactions and protein-DNA binding events that coherently underlie this regulation. Using synthetic data, we demonstrate that a Physical Module Network model has similar recall and improved precision compared to a simple Module Network, as it omits many false positive regulators. Finally, we show the power of Physical Module Networks to reconstruct meaningful regulatory pathways in the genetically perturbed yeast and during the yeast cell cycle, as well as during the response of primary epithelial human cells to infection with H1N1 influenza.

Availability: The PMN software is available, free for academic use at http://www.compbio.cs.huji.ac.il/PMN/.

Contact: aregev@broad.mit.edu; nirf@cs.huji.ac.il.

Show MeSH
Related in: MedlinePlus