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Modeling signal transduction from protein phosphorylation to gene expression.

Cai C, Chen L, Jiang X, Lu X - Cancer Inform (2014)

Bottom Line: We were able to effectively identify sparse signaling networks that modeled the observed transcriptomic and proteomic data.Our methods were able to identify distinct signaling pathways for rat and human cells in a data-driven manner, based on the facts that rat and human cells exhibited distinct transcriptomic and proteomics responses to a common set of stimuli.Our model performed well in the SBV IMPROVER challenge in comparison to other models addressing the same task.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

ABSTRACT

Background: Signaling networks are of great importance for us to understand the cell's regulatory mechanism. The rise of large-scale genomic and proteomic data, and prior biological knowledge has paved the way for the reconstruction and discovery of novel signaling pathways in a data-driven manner. In this study, we investigate computational methods that integrate proteomics and transcriptomic data to identify signaling pathways transmitting signals in response to specific stimuli. Such methods can be applied to cancer genomic data to infer perturbed signaling pathways.

Method: We proposed a novel Bayesian Network (BN) framework to integrate transcriptomic data with proteomic data reflecting protein phosphorylation states for the purpose of identifying the pathways transmitting the signal of diverse stimuli in rat and human cells. We represented the proteins and genes as nodes in a BN in which edges reflect the regulatory relationship between signaling proteins. We designed an efficient inference algorithm that incorporated the prior knowledge of pathways and searched for a network structure in a data-driven manner.

Results: We applied our method to infer rat and human specific networks given gene expression and proteomic datasets. We were able to effectively identify sparse signaling networks that modeled the observed transcriptomic and proteomic data. Our methods were able to identify distinct signaling pathways for rat and human cells in a data-driven manner, based on the facts that rat and human cells exhibited distinct transcriptomic and proteomics responses to a common set of stimuli. Our model performed well in the SBV IMPROVER challenge in comparison to other models addressing the same task. The capability of inferring signaling pathways in a data-driven fashion may contribute to cancer research by identifying distinct aberrations in signaling pathways underlying heterogeneous cancers subtypes.

No MeSH data available.


Related in: MedlinePlus

The predicted rat and human signaling network.
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Related In: Results  -  Collection


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f4-cin-suppl.1-2014-059: The predicted rat and human signaling network.

Mentions: We applied our BN learning method to infer the rat and human specific signaling networks from experimental data, incorporating the augmented reference network which contains 220 nodes and 817 edges. The predicted networks are much sparser than the given the reference network, which only contains about half the edges in the reference network: the rat network is composed of 171 nodes and 366 edges, and the human network is composed of 171 nodes and 355 edges (Table 1 and Figure 4). Notably, most of the trimmed edges correspond to TF->gene interactions, ie, 38 out of 72 genes were deleted for having no incoming transcriptional signal. The result was evaluated and scored by the SBV IMPROVER Species Specific Network Inference challenge committee, and ranked as one of two best performing teams in the competition, ie, team PITT.DBMI.DREAM (https://www.sbvimprover.com/challenge-2/overview).


Modeling signal transduction from protein phosphorylation to gene expression.

Cai C, Chen L, Jiang X, Lu X - Cancer Inform (2014)

The predicted rat and human signaling network.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4216050&req=5

f4-cin-suppl.1-2014-059: The predicted rat and human signaling network.
Mentions: We applied our BN learning method to infer the rat and human specific signaling networks from experimental data, incorporating the augmented reference network which contains 220 nodes and 817 edges. The predicted networks are much sparser than the given the reference network, which only contains about half the edges in the reference network: the rat network is composed of 171 nodes and 366 edges, and the human network is composed of 171 nodes and 355 edges (Table 1 and Figure 4). Notably, most of the trimmed edges correspond to TF->gene interactions, ie, 38 out of 72 genes were deleted for having no incoming transcriptional signal. The result was evaluated and scored by the SBV IMPROVER Species Specific Network Inference challenge committee, and ranked as one of two best performing teams in the competition, ie, team PITT.DBMI.DREAM (https://www.sbvimprover.com/challenge-2/overview).

Bottom Line: We were able to effectively identify sparse signaling networks that modeled the observed transcriptomic and proteomic data.Our methods were able to identify distinct signaling pathways for rat and human cells in a data-driven manner, based on the facts that rat and human cells exhibited distinct transcriptomic and proteomics responses to a common set of stimuli.Our model performed well in the SBV IMPROVER challenge in comparison to other models addressing the same task.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

ABSTRACT

Background: Signaling networks are of great importance for us to understand the cell's regulatory mechanism. The rise of large-scale genomic and proteomic data, and prior biological knowledge has paved the way for the reconstruction and discovery of novel signaling pathways in a data-driven manner. In this study, we investigate computational methods that integrate proteomics and transcriptomic data to identify signaling pathways transmitting signals in response to specific stimuli. Such methods can be applied to cancer genomic data to infer perturbed signaling pathways.

Method: We proposed a novel Bayesian Network (BN) framework to integrate transcriptomic data with proteomic data reflecting protein phosphorylation states for the purpose of identifying the pathways transmitting the signal of diverse stimuli in rat and human cells. We represented the proteins and genes as nodes in a BN in which edges reflect the regulatory relationship between signaling proteins. We designed an efficient inference algorithm that incorporated the prior knowledge of pathways and searched for a network structure in a data-driven manner.

Results: We applied our method to infer rat and human specific networks given gene expression and proteomic datasets. We were able to effectively identify sparse signaling networks that modeled the observed transcriptomic and proteomic data. Our methods were able to identify distinct signaling pathways for rat and human cells in a data-driven manner, based on the facts that rat and human cells exhibited distinct transcriptomic and proteomics responses to a common set of stimuli. Our model performed well in the SBV IMPROVER challenge in comparison to other models addressing the same task. The capability of inferring signaling pathways in a data-driven fashion may contribute to cancer research by identifying distinct aberrations in signaling pathways underlying heterogeneous cancers subtypes.

No MeSH data available.


Related in: MedlinePlus