PINT: Pathways INtegration Tool

Wang, Y.-T.; Huang, Yuxing; Chen, Y.-C.; Hsu, Chia‐Lang; Yang, Ueng Cheng

doi:10.1093/nar/gkq499

Cited by 9 publications

(5 citation statements)

References 24 publications

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“…However, FBA predicted this pair is highly function-related with funSim of 0.65. Merging these two pathways by the Pathway Integration Tool (PINT) [36] shows that the S1P4 pathway appears as the upstream of the RHOA signaling pathway (Figure 4). Sphingosine 1-phosphate (S1P) is a signaling lipid that plays a significant role in the regulation of cell growth, survival, and migration [27].…”

Section: Resultsmentioning

confidence: 99%

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Discovering pathway cross-talks based on functional relations between pathways

Hsu

Yang

2012

BMC Genomics

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Background In biological systems, pathways coordinate or interact with one another to achieve a complex biological process. Studying how they influence each other is essential for understanding the intricacies of a biological system. However, current methods rely on statistical tests to determine pathway relations, and may lose numerous biologically significant relations. Results This study proposes a method that identifies the pathway relations by measuring the functional relations between pathways based on the Gene Ontology (GO) annotations. This approach identified 4,661 pathway relations among 166 pathways from Pathway Interaction Database (PID). Using 143 pathway interactions from PID as testing data, the function-based approach (FBA) is able to identify 93% of pathway interactions, better than the existing methods based on the shared components and protein-protein interactions. Many well-known pathway cross-talks are only identified by FBA. In addition, the false positive rate of FBA is significantly lower than others via pathway co-expression analysis. Conclusions This function-based approach appears to be more sensitive and able to infer more biologically significant and explainable pathway relations.

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Section: Resultsmentioning

confidence: 99%

“…The pathways are integrated and visualized via the Pathway Integration Tool (PINT) [36]. The oval nodes denote the proteins, and the octagon nodes denote the protein complexes.…”

Section: Resultsmentioning

confidence: 99%

Discovering pathway cross-talks based on functional relations between pathways

Hsu

Yang

2012

BMC Genomics

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“…From the software tools surveyed, currently only Tools-4-Metatool (Xavier et al, 2011), Compare Subsystems (Oberhardt et al, 2011), SemanticSBML (Krause et al, 2010), COBRA (Becker et al, 2007), The FAME (Boele et al, 2012), MetRxn (Kumar et al, 2012), BudHat (Waltemath et al, 2013), PINT (Wang et al, 2010) and MEMOSys (Pabinger et al, 2011) provide functionality that is related to the comparison of models. Software tools mostly rely on internal or external identifiers, like KEGG ID and ChEBI ID and do not tolerate even small differences in metabolite names like brackets, quotes, apostrophes, spaces, upper/lower case letters and some more symbols which may be caused by the modelers style of defining metabolites.…”

Section: Current Approach Of Metabolic Model Comparisonmentioning

confidence: 99%

Comparative Analysis of Biochemical Network Reconstructions

Mednis¹

2016

BJMC

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Abstract. Reconstruction of genome-scale metabolic network is a result of assembling various information sources about all biochemical reactions expected in the metabolic network of interest. Despite the efforts of leading bio-models databases to make comparison of biochemical networks by elements names obsolete, interest from researchers in using string similarity metrics in comparison of metabolites names has been growing. Multiple challenges in comparison of reconstructions are discussed in this article and an insight into current approach of metabolic model comparison has been given. The discussion of challenges and attempts to solve them are followed by the author's proposed algorithm for models comparison that can be particularly useful in case of reconstructions. Special attention is given to the use of metabolites names and chemical formulas. The author's proposed algorithm has been implemented in a software tool ModeRator. The article is concluded with use cases of the comparison algorithm and the software tool.

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“…The most sophisticated tool which supports a semi-automatic merging of network models of two quantitative models is semanticSBML [ 9 ]. Besides semanticSBML other software tools support structural model integration, for example, the Model Composition Tool [ 7 ] and the software PInt [ 23 ]. As in semanticSBML elements are matched based on annotations.…”

Section: Introductionmentioning

confidence: 99%

Challenges in horizontal model integration

Kolczyk

Conradi

2016

BMC Syst Biol

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BackgroundSystems Biology has motivated dynamic models of important intracellular processes at the pathway level, for example, in signal transduction and cell cycle control. To answer important biomedical questions, however, one has to go beyond the study of isolated pathways towards the joint study of interacting signaling pathways or the joint study of signal transduction and cell cycle control. Thereby the reuse of established models is preferable, as it will generally reduce the modeling effort and increase the acceptance of the combined model in the field.ResultsObtaining a combined model can be challenging, especially if the submodels are large and/or come from different working groups (as is generally the case, when models stored in established repositories are used). To support this task, we describe a semi-automatic workflow based on established software tools. In particular, two frequent challenges are described: identification of the overlap and subsequent (re)parameterization of the integrated model.ConclusionsThe reparameterization step is crucial, if the goal is to obtain a model that can reproduce the data explained by the individual models. For demonstration purposes we apply our workflow to integrate two signaling pathways (EGF and NGF) from the BioModels Database.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0266-3) contains supplementary material, which is available to authorized users.

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PINT: Pathways INtegration Tool

Cited by 9 publications

References 24 publications

Discovering pathway cross-talks based on functional relations between pathways

Discovering pathway cross-talks based on functional relations between pathways

Comparative Analysis of Biochemical Network Reconstructions

Challenges in horizontal model integration

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