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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

Overview of the network inference problem. The task is to predict two separate signaling networks for rat and human by adding and trimming edges from reference network by applying BN learning method to gene expression and reverse phase protein array data.
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f1-cin-suppl.1-2014-059: Overview of the network inference problem. The task is to predict two separate signaling networks for rat and human by adding and trimming edges from reference network by applying BN learning method to gene expression and reverse phase protein array data.

Mentions: To assess the current methods in learning cell signaling network and also try to understand the differences between rat and human cell signaling networks in response to common stimuli, SBV IMPROVER organized a Species Specific Network Inference challenge (https://www.sbvimprover.com/challenge-2/sub-challenge-4-species-network-inference) based on rat and human genomic and proteomic data.22 In this challenge, a literature curated reference network with 220 nodes and 501 edges was provided as prior knowledge, from which the participants could add or remove edges. Figure 1 shows the overview of the network inference problem.


Modeling signal transduction from protein phosphorylation to gene expression.

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

Overview of the network inference problem. The task is to predict two separate signaling networks for rat and human by adding and trimming edges from reference network by applying BN learning method to gene expression and reverse phase protein array data.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f1-cin-suppl.1-2014-059: Overview of the network inference problem. The task is to predict two separate signaling networks for rat and human by adding and trimming edges from reference network by applying BN learning method to gene expression and reverse phase protein array data.
Mentions: To assess the current methods in learning cell signaling network and also try to understand the differences between rat and human cell signaling networks in response to common stimuli, SBV IMPROVER organized a Species Specific Network Inference challenge (https://www.sbvimprover.com/challenge-2/sub-challenge-4-species-network-inference) based on rat and human genomic and proteomic data.22 In this challenge, a literature curated reference network with 220 nodes and 501 edges was provided as prior knowledge, from which the participants could add or remove edges. Figure 1 shows the overview of the network inference problem.

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