Erratum: Corrigendum: The BioPAX community standard for pathway data sharing

Demir, Emek; Cary, Michael P.; Paley, Suzanne M.; Fukuda, Ken; Lemer, Christian; Västrik, Imre; Wu, Guanming; D'Eustachio, P; Schaefer, Carl F.; Luciano, Joanne S.; Schacherer, Frank; Martínez-Flores, Irma; Hu, Zhenjun; Jiménez-Jacinto, Verónica; Geeta, Joshi-Tope; Kandasamy, Kumaran; López-Fuentes, Alejandra; Mi, Huaiyu; Pichler, Elgar; Rodchenkov, Igor; Splendiani, Andrea; Tkachev, Sasha; Zucker, Jeremy; Gopinath, Gopal; Rajasimha, Harsha; Ramakrishnan, Ranjani; Shah, Imran; Syed, Mustafa; Anwar, Nadia; Babur, Özgün; Blinov, Michael L.; Brauner, Erik; Corwin, Dan; Donaldson, Sylva L.; Gibbons, Frank; Goldberg, Robert N.; Hornbeck, Peter; Luna, Augustin; Murray‐Rust, Peter; Neumann, Eric; Reubenacker, Oliver; Samwald, Matthias; Iersel, Martijn P. van; Wimalaratne, Sarala; Allen, K.W.; Braun, Burk; Whirl‐Carrillo, Michelle; Cheung, Kei Hoi; Dahlquist, Kam D; Finney, Andrew; Gillespie, Marc; Glass, Elizabeth M.; Gong, Li; Haw, Robin; Honig, Michael; Hubaut, Olivier; Kane, David W.; Krupa, Shiva; Kutmon, Martina; Leonard, Julie Mosty; Marks, Debbie; Merberg, David; Petri, Victoria; Pico, Alexander R.; Ravenscroft, Dean; Ren, Liya; Shah, Nigam H.; Sunshine, Margot; Tang, Rebecca; Whaley, Ryan; Letovksy, Stan; Buetow, Kenneth H.; Rzhetsky, Andrey; Schächter, Vincent; Sobral, Bruno W. S.; Doğrusöz, Uğur; McWeeney, Shannon K.; Aladjem, Mirit I.; Birney, Ewan; Collado‐Vides, Julio; Goto, Susumu; Hucka, Michael; Novère, Nicolas Le; Maltsev, Natalia; Pandey, Akhilesh; Thomas, Paul; Wingender, Edgar; Karp, Peter D.; Sander, Chris; Bader, Gary D.

doi:10.1038/nbt0412-365c

Cited by 4 publications

(8 citation statements)

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“…The TWEAK data in NetPath are available freely and can be used by the scientific community. The data are represented in various standard exchange formats that include Biological PAthway eXchange (BioPAX) [ 73 ], Systems Biology Markup Language (SBML) [ 74 ] and Proteomics Standards Initiative Molecular Interaction (PSI-MI) [ 75 ] language formats. The PSI-MI is a community standard language for molecular interaction data used for data comparison and exchange.…”

Section: Resultsmentioning

confidence: 99%

“…Importantly, the “NetPath” repository is dynamic and will allow a progressive update of relevant data, as more published literature is introduced. In addition to the direct usage of the data stored in the “NetPath” database, data can also be exported to other databases, enabling comparison and sharing amongst multiple databases, especially those which have compatible language formats, such as BioPAX [ 73 ]. Despite the minimal amount of data, ours can also be used in the overlay of various high-throughput data enabling pathway analysis and can be accessed by any pathway resource to generate a customized pathway.…”

Section: Discussionmentioning

confidence: 99%

“…Despite the minimal amount of data, ours can also be used in the overlay of various high-throughput data enabling pathway analysis and can be accessed by any pathway resource to generate a customized pathway. We are currently working on the features in “NetPath”, which are incompatible with BioPAX, especially the ontology hierarchy that has been proposed by the BioPAX group [ 73 ]. To our knowledge, our compilation of data in “NetPath” will allow, for the first time for any available scientific repository, a comprehensive study of the TWEAK pathway and its potential biomedical applications.…”

Section: Discussionmentioning

confidence: 99%

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A Bioinformatics Resource for TWEAK-Fn14 Signaling Pathway

Bhattacharjee

Raju

Radhakrishnan

et al. 2012

Journal of Signal Transduction

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TNF-related weak inducer of apoptosis (TWEAK) is a new member of the TNF superfamily. It signals through TNFRSF12A, commonly known as Fn14. The TWEAK-Fn14 interaction regulates cellular activities including proliferation, migration, differentiation, apoptosis, angiogenesis, tissue remodeling and inflammation. Although TWEAK has been reported to be associated with autoimmune diseases, cancers, stroke, and kidney-related disorders, the downstream molecular events of TWEAK-Fn14 signaling are yet not available in any signaling pathway repository. In this paper, we manually compiled from the literature, in particular those reported in human systems, the downstream reactions stimulated by TWEAK-Fn14 interactions. Our manual amassment of the TWEAK-Fn14 pathway has resulted in cataloging of 46 proteins involved in various biochemical reactions and TWEAK-Fn14 induced expression of 28 genes. We have enabled the availability of data in various standard exchange formats from NetPath, a repository for signaling pathways. We believe that this composite molecular interaction pathway will enable identification of new signaling components in TWEAK signaling pathway. This in turn may lead to the identification of potential therapeutic targets in TWEAK-associated disorders.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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A Bioinformatics Resource for TWEAK-Fn14 Signaling Pathway

Bhattacharjee

Raju

Radhakrishnan

et al. 2012

Journal of Signal Transduction

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“…Without a common language to describe models, they cannot be simulated across different platforms or freely shared. For this reason, systems biology model representation has become standardized using XML-based languages SBML [10,13], CellML [14], and BioPAX [15]. NeuroML [16], similar to SBML and CellML, is used to represent neuronal models, but is beyond the scope of this review.…”

Section: Model Representationmentioning

confidence: 99%

“…BioPAX (Biological Pathway Exchange) is an ontology, a formal systems of describing knowledge that structures biological pathway data making it more easily processed by computer software [15]. It describes the biological semantics of metabolic, signaling, molecular, gene-regulatory, and genetic interaction networks [15].…”

Section: Biopaxmentioning

confidence: 99%

Adapting modeling and simulation credibility standards to computational systems biology

Tatka¹,

Smith²,

Hellerstein³

et al. 2023

J Transl Med

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Computational models are increasingly used in high-impact decision making in science, engineering, and medicine. The National Aeronautics and Space Administration (NASA) uses computational models to perform complex experiments that are otherwise prohibitively expensive or require a microgravity environment. Similarly, the Food and Drug Administration (FDA) and European Medicines Agency (EMA) have began accepting models and simulations as forms of evidence for pharmaceutical and medical device approval. It is crucial that computational models meet a standard of credibility when using them in high-stakes decision making. For this reason, institutes including NASA, the FDA, and the EMA have developed standards to promote and assess the credibility of computational models and simulations. However, due to the breadth of models these institutes assess, these credibility standards are mostly qualitative and avoid making specific recommendations. On the other hand, modeling and simulation in systems biology is a narrower domain and several standards are already in place. As systems biology models increase in complexity and influence, the development of a credibility assessment system is crucial. Here we review existing standards in systems biology, credibility standards in other science, engineering, and medical fields, and propose the development of a credibility standard for systems biology models.

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A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers

et al. 2016

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Protein expression and post-translational modification levels are tightly regulated in neoplastic cells to maintain cellular processes known as ‘cancer hallmarks’. The first Pan-Cancer initiative of The Cancer Genome Atlas (TCGA) Research Network has aggregated protein expression profiles for 3,467 patient samples from 11 tumor types using the antibody based reverse phase protein array (RPPA) technology. The resultant proteomic data can be utilized to computationally infer protein-protein interaction (PPI) networks and to study the commonalities and differences across tumor types. In this study, we compare the performance of 13 established network inference methods in their capacity to retrieve the curated Pathway Commons interactions from RPPA data. We observe that no single method has the best performance in all tumor types, but a group of six methods, including diverse techniques such as correlation, mutual information, and regression, consistently rank highly among the tested methods. We utilize the high performing methods to obtain a consensus network; and identify four robust and densely connected modules that reveal biological processes as well as suggest antibody–related technical biases. Mapping the consensus network interactions to Reactome gene lists confirms the pan-cancer importance of signal transduction pathways, innate and adaptive immune signaling, cell cycle, metabolism, and DNA repair; and also suggests several biological processes that may be specific to a subset of tumor types. Our results illustrate the utility of the RPPA platform as a tool to study proteomic networks in cancer.

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Erratum: Corrigendum: The BioPAX community standard for pathway data sharing

Cited by 4 publications

References 1 publication

A Bioinformatics Resource for TWEAK-Fn14 Signaling Pathway

A Bioinformatics Resource for TWEAK-Fn14 Signaling Pathway

Adapting modeling and simulation credibility standards to computational systems biology

A Multi-Method Approach for Proteomic Network Inference in 11 Human Cancers

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