Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

Bulashevska, Svetlana; Bulashevska, Alla; Eils, Roland

doi:10.1186/1471-2105-11-46

Cited by 16 publications

(18 citation statements)

References 60 publications

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“…These probabilistic models can be used to evaluate the probability that an edge is present in the network. They can also be used to quantify topological properties of networks by summarizing them in a parametric form and associating sufficient statistics with those parameters [ 19 , 24 ]. In this study, we use a special class of exponential family distributions known as ERGM (Exponential Random Graph Models), also known as the p 1 -model, which was introduced by Holland and Leinhardt [ 24 ].…”

Section: Methodsmentioning

confidence: 99%

“…Let the binary outcome u ij = 1 if g e n e i interacts with g e n e j , or u ij = 0 otherwise. Then u is a binary data matrix [ 19 ]. Let P r ( u ) be the probability function on G given by …”

Section: Methodsmentioning

confidence: 99%

“…We set a 0 = 0.001 and b 0 = 0.001 to make the prior for θ noninformative , making its standard deviation wide to express large uncertainty [ 19 ]. For attractiveness/ expansiveness parameters α i and α j , we followed the approach used by Adams et al [ 30 ].…”

Section: Methodsmentioning

confidence: 99%

“…network edges) may not always be present or detected, whereas some other typical relationships may be absent. The stochastic nature of biological systems can be used to predict edge probabilities by formalizing them into a probabilistic model with other network properties [ 19 ]. Hill et al reported a data-driven approach that exploits a Dynamic Bayesian Network (DBN) model to infer probabilistic relationships between node-pairs in a context-specific signaling network [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling

2015

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BackgroundInitial success of inhibitors targeting oncogenes is often followed by tumor relapse due to acquired resistance. In addition to mutations in targeted oncogenes, signaling cross-talks among pathways play a vital role in such drug inefficacy. These include activation of compensatory pathways and altered activities of key effectors in other cell survival and growth-associated pathways.ResultsWe propose a computational framework using Bayesian modeling to systematically characterize potential cross-talks among breast cancer signaling pathways. We employed a fully Bayesian approach known as the p1-model to infer posterior probabilities of gene-pairs in networks derived from the gene expression datasets of ErbB2-positive breast cancer cell-lines (parental, lapatinib-sensitive cell-line SKBR3 and the lapatinib-resistant cell-line SKBR3-R, derived from SKBR3). Using this computational framework, we searched for cross-talks between EGFR/ErbB and other signaling pathways from Reactome, KEGG and WikiPathway databases that contribute to lapatinib resistance. We identified 104, 188 and 299 gene-pairs as putative drug-resistant cross-talks, respectively, each comprised of a gene in the EGFR/ErbB signaling pathway and a gene from another signaling pathway, that appear to be interacting in resistant cells but not in parental cells. In 168 of these (distinct) gene-pairs, both of the interacting partners are up-regulated in resistant conditions relative to parental conditions. These gene-pairs are prime candidates for novel cross-talks contributing to lapatinib resistance. They associate EGFR/ErbB signaling with six other signaling pathways: Notch, Wnt, GPCR, hedgehog, insulin receptor/IGF1R and TGF- β receptor signaling. We conducted a literature survey to validate these cross-talks, and found evidence supporting a role for many of them in contributing to drug resistance. We also analyzed an independent study of lapatinib resistance in the BT474 breast cancer cell-line and found the same signaling pathways making cross-talks with the EGFR/ErbB signaling pathway as in the primary dataset.ConclusionsOur results indicate that the activation of compensatory pathways can potentially cause up-regulation of EGFR/ErbB pathway genes (counteracting the inhibiting effect of lapatinib) via signaling cross-talk. Thus, the up-regulated members of these compensatory pathways along with the members of the EGFR/ErbB signaling pathway are interesting as potential targets for designing novel anti-cancer therapeutics.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-014-0135-x) contains supplementary material, which is available to authorized users.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling

2015

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“…Time‐course gene expression data are collected at a series of time points during a biological process of interest and thus reflect the dynamic activity of genes during this process. At present, some methods use a threshold to determine whether genes are significantly expressed. In this study, the 3‐sigma principle was used to compute the active threshold as proposed in ref.…”

Section: Methodsmentioning

confidence: 99%

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Xiao

Luo

et al. 2019

Proteomics

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Cellular functions are always performed by protein complexes. At present, many approaches have been proposed to identify protein complexes from protein–protein interaction (PPI) networks. Some approaches focus on detecting local dense subgraphs in PPI networks which are regarded as protein‐complex cores, then identify protein complexes by including local neighbors. However, from gene expression profiles at different time points or tissues it is known that proteins are dynamic. Therefore, identifying dynamic protein complexes should become very important and meaningful. In this study, a novel core‐attachment–based method named CO‐DPC to detect dynamic protein complexes is presented. First, CO‐DPC selects active proteins according to gene expression profiles and the 3‐sigma principle, and constructs dynamic PPI networks based on the co‐expression principle and PPI networks. Second, CO‐DPC detects local dense subgraphs as the cores of protein complexes and then attach close neighbors of these cores to form protein complexes. In order to evaluate the method, the method and the existing algorithms are applied to yeast PPI networks. The experimental results show that CO‐DPC performs much better than the existing methods. In addition, the identified dynamic protein complexes can match very well and thus become more meaningful for future biological study.

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Identifying Protein Complexes from Protein–Protein Interaction Networks

Pan¹,

Wang²,

Li³

2013

Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics

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Bayesian statistical modelling of human protein interaction network incorporating protein disorder information

Cited by 16 publications

References 60 publications

Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling

Prediction of signaling cross-talks contributing to acquired drug resistance in breast cancer cells by Bayesian statistical modeling

A Novel Core‐Attachment–Based Method to Identify Dynamic Protein Complexes Based on Gene Expression Profiles and PPI Networks

Identifying Protein Complexes from Protein–Protein Interaction Networks

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