Limits...
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

Pseudo code of learning BN structure with MCMC algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4216050&req=5

f2-cin-suppl.1-2014-059: Pseudo code of learning BN structure with MCMC algorithm.

Mentions: We developed an algorithm integrating Gibbs-sampling-based belief propagation and a Monte Carlo approach to simultaneously address the items 2 and 3 in the previous paragraph (Fig. 2). Assuming that prior probability for any structure was uniformly distributed, we concentrated on computing an approximated marginal likelihood using samples obtained from Gibbs sampling and calculated the integration in Equation (4) via a Monte Carlo approach. Given a BN structure, we started N sampling chains, with each chain independently sampling the states of latent variables for all cases, updating model parameters and calculating the chain-specific likelihood of data P(D/S, θn). Then, the marginal likelihood can be approximated as follows:1N∑n=1Np(D/S,θn )p(θ)→N→∞∫θp(D/S,θ)p(θ)dθ(5)


Modeling signal transduction from protein phosphorylation to gene expression.

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

Pseudo code of learning BN structure with MCMC algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2-cin-suppl.1-2014-059: Pseudo code of learning BN structure with MCMC algorithm.
Mentions: We developed an algorithm integrating Gibbs-sampling-based belief propagation and a Monte Carlo approach to simultaneously address the items 2 and 3 in the previous paragraph (Fig. 2). Assuming that prior probability for any structure was uniformly distributed, we concentrated on computing an approximated marginal likelihood using samples obtained from Gibbs sampling and calculated the integration in Equation (4) via a Monte Carlo approach. Given a BN structure, we started N sampling chains, with each chain independently sampling the states of latent variables for all cases, updating model parameters and calculating the chain-specific likelihood of data P(D/S, θn). Then, the marginal likelihood can be approximated as follows:1N∑n=1Np(D/S,θn )p(θ)→N→∞∫θp(D/S,θ)p(θ)dθ(5)

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