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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

(A) Rat and human specific signaling edges in the predicted network. (B) Rat and human specific transcriptional edges in the predicted network.
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f5-cin-suppl.1-2014-059: (A) Rat and human specific signaling edges in the predicted network. (B) Rat and human specific transcriptional edges in the predicted network.

Mentions: The result also shows that the interactions among phospho-proteins in signaling pathways tend to be more conserved with fewer divergent points, whereas TF–gene interactions tend to be more divergent between rat and human. Figure 5 illustrates the rat/human specific signaling edges and transcriptional edges. Notably, there are no significant difference between rat and human at protein–protein signaling transduction level. Moreover, as shown in Figure 5A, most of the species specific edges were localized to only one or two interactions within the same signal cascade. However, gene expression might be more mediated in a species-specific fashion, where the same TF is responsible for regulating the expression of different sets of genes from rat to human as illustrated in Figure 5B.


Modeling signal transduction from protein phosphorylation to gene expression.

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

(A) Rat and human specific signaling edges in the predicted network. (B) Rat and human specific transcriptional edges in the predicted network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f5-cin-suppl.1-2014-059: (A) Rat and human specific signaling edges in the predicted network. (B) Rat and human specific transcriptional edges in the predicted network.
Mentions: The result also shows that the interactions among phospho-proteins in signaling pathways tend to be more conserved with fewer divergent points, whereas TF–gene interactions tend to be more divergent between rat and human. Figure 5 illustrates the rat/human specific signaling edges and transcriptional edges. Notably, there are no significant difference between rat and human at protein–protein signaling transduction level. Moreover, as shown in Figure 5A, most of the species specific edges were localized to only one or two interactions within the same signal cascade. However, gene expression might be more mediated in a species-specific fashion, where the same TF is responsible for regulating the expression of different sets of genes from rat to human as illustrated in Figure 5B.

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