{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T23:49:33Z","timestamp":1768002573215,"version":"3.49.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Syst Biol"],"published-print":{"date-parts":[[2012,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:sec>\n            <jats:title>Background<\/jats:title>\n            <jats:p>Cells process signals using complex and dynamic networks. Studying how this is performed in a context and cell type specific way is essential to understand signaling both in physiological and diseased situations. Context-specific medium\/high throughput proteomic data measured upon perturbation is now relatively easy to obtain but formalisms that can take advantage of these features to build models of signaling are still comparatively scarce.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Here we present <jats:italic>CellNOptR<\/jats:italic>, an open-source R software package for building predictive logic models of signaling networks by training networks derived from prior knowledge to signaling (typically phosphoproteomic) data. <jats:italic>CellNOptR<\/jats:italic> features different logic formalisms, from Boolean models to differential equations, in a common framework. These different logic model representations accommodate state and time values with increasing levels of detail. We provide in addition an interface via Cytoscape (<jats:italic>CytoCopteR<\/jats:italic>) to facilitate use and integration with Cytoscape network-based capabilities.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>Models generated with this pipeline have two key features. First, they are constrained by prior knowledge about the network but trained to data. They are therefore context and cell line specific, which results in enhanced predictive and mechanistic insights. Second, they can be built using different logic formalisms depending on the richness of the available data. Models built with <jats:italic>CellNOptR<\/jats:italic> are useful tools to understand how signals are processed by cells and how this is altered in disease. They can be used to predict the effect of perturbations (individual or in combinations), and potentially to engineer therapies that have differential effects\/side effects depending on the cell type or context.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1752-0509-6-133","type":"journal-article","created":{"date-parts":[[2012,10,18]],"date-time":"2012-10-18T22:16:01Z","timestamp":1350598561000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":188,"title":["CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms"],"prefix":"10.1186","volume":"6","author":[{"given":"Camille","family":"Terfve","sequence":"first","affiliation":[]},{"given":"Thomas","family":"Cokelaer","sequence":"additional","affiliation":[]},{"given":"David","family":"Henriques","sequence":"additional","affiliation":[]},{"given":"Aidan","family":"MacNamara","sequence":"additional","affiliation":[]},{"given":"Emanuel","family":"Goncalves","sequence":"additional","affiliation":[]},{"given":"Melody K","family":"Morris","sequence":"additional","affiliation":[]},{"given":"Martijn van","family":"Iersel","sequence":"additional","affiliation":[]},{"given":"Douglas A","family":"Lauffenburger","sequence":"additional","affiliation":[]},{"given":"Julio","family":"Saez-Rodriguez","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2012,10,18]]},"reference":[{"key":"1057_CR1","doi-asserted-by":"publisher","first-page":"1849","DOI":"10.1074\/mcp.M110.000406","volume":"9","author":"LG Alexopoulos","year":"2010","unstructured":"Alexopoulos LG, Saez-Rodriguez J, Cosgrove BD, Lauffenburger DA, Sorger PK: Networks inferred from biochemical data reveal profound differences in toll-like receptor and inflammatory signaling between normal and transformed hepatocytes. Molecular & Cellular Proteomics: MCP. 2010, 9: 1849-1865.","journal-title":"Molecular & Cellular Proteomics: MCP"},{"key":"1057_CR2","doi-asserted-by":"publisher","first-page":"12867","DOI":"10.1073\/pnas.0705158104","volume":"104","author":"PH Huang","year":"2007","unstructured":"Huang PH, Mukasa A, Bonavia R, Flynn RA, Brewer ZE, Cavenee WK, Furnari FB, White FM: Quantitative analysis of EGFRvIII cellular signaling networks reveals a combinatorial therapeutic strategy for glioblastoma. Proc Nat Acad Sci USA. 2007, 104: 12867-12872.","journal-title":"Proc Nat Acad Sci USA"},{"key":"1057_CR3","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.gde.2009.12.003","volume":"20","author":"C Jorgensen","year":"2010","unstructured":"Jorgensen C, Linding R: Simplistic pathways or complex networks?. Curr Opin Genet Dev. 2010, 20: 15-22.","journal-title":"Curr Opin Genet Dev"},{"key":"1057_CR4","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1371\/journal.pcbi.1002375","volume":"8","author":"P Khatri","year":"2012","unstructured":"Khatri P, Sirota M, Butte A: Ten years of pathway analysis : current approaches and outstanding challenges. Plos Comp Bio. 2012, 8: 15-22.","journal-title":"Plos Comp Bio"},{"key":"1057_CR5","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1038\/msb.2009.47","volume":"5","author":"A Bauer-Mehren","year":"2009","unstructured":"Bauer-Mehren A, Furlong L, Sanz F: Pathway databases and tools for their exploitation: benefits, current limitations and challenges. Mol Syst Biol. 2009, 5: 290-","journal-title":"Mol Syst Biol"},{"key":"1057_CR6","first-page":"428","volume":"1","author":"G Joshi-Tope","year":"2005","unstructured":"Joshi-Tope G, Gillespie M, Vastrik I, D\u2019Eustachio P, Schmidt E, de Bono B, Jassal B, Gopinath G, Wu G, Matthews L, Lewis S, Birney E, Stein L: Reactome: a knowledgebase of biological pathways. Nucl Acids Res. 2005, 1: 428-32.","journal-title":"Nucl Acids Res"},{"key":"1057_CR7","doi-asserted-by":"publisher","first-page":"2042","DOI":"10.1093\/bioinformatics\/btq310","volume":"26","author":"T Korcsmaros","year":"2010","unstructured":"Korcsmaros T, Farkas IJ, Szalay MS, Rovo P, Fazekas D, Spiro Z, Bode C, Lenti K, Vellai T, Csermely P: Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery. Bioinformatics. 2010, 26: 2042-2050.","journal-title":"Bioinformatics"},{"key":"1057_CR8","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1093\/nar\/gkg115","volume":"31","author":"P Thomas","year":"2003","unstructured":"Thomas P, Kejariwal A, Campbell M, Mi H, Diemer K, Guo N, Ladunga I, Ulitsky-Lazareva B, Muruganujan A, Rabkin S, Vandergriff J, Doremieux O: PANTHER: a browsable database of gene products organized by biological function, using curated protein family and subfamily classification. Nucl Acids Res. 2003, 31: 334-341.","journal-title":"Nucl Acids Res"},{"key":"1057_CR9","doi-asserted-by":"publisher","first-page":"685","DOI":"10.1093\/nar\/gkq1039","volume":"39","author":"E Cerami","year":"2010","unstructured":"Cerami E, Gross B, Demir E, Rodchenkov I, Babur O, Anwar N, Schultz N, Bader G, Sander C: Pathway Commons, a web resource for biological pathway data. Nucl Acids Res. 2010, 39: 685-690.","journal-title":"Nucl Acids Res"},{"key":"1057_CR10","doi-asserted-by":"publisher","first-page":"674","DOI":"10.1093\/nar\/gkn653","volume":"37","author":"C Schaefer","year":"2009","unstructured":"Schaefer C, Anthony K, Krupa S, Buchoff J, Day M, Hannay T, Buetow K: PID: The Pathway Interaction Database. Nucl Acids Res. 2009, 37: 674-679.","journal-title":"Nucl Acids Res"},{"key":"1057_CR11","doi-asserted-by":"publisher","first-page":"331","DOI":"10.1038\/msb.2009.87","volume":"5","author":"J Saez-Rodriguez","year":"2009","unstructured":"Saez-Rodriguez J, Alexopoulos L, Epperlein J, Samaga R, Lauffenburger D, Klamt S, Sorger P: Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction. Mol Syst Biol. 2009, 5: 331-","journal-title":"Mol Syst Biol"},{"key":"1057_CR12","doi-asserted-by":"publisher","first-page":"1195","DOI":"10.1038\/ncb1497","volume":"8","author":"B Aldridge","year":"2006","unstructured":"Aldridge B, Burke J, Lauffenburger D, Sorger P: Physicochemical modelling of cell signalling pathways. Nat Cell Biol. 2006, 8: 1195-1203.","journal-title":"Nat Cell Biol"},{"key":"1057_CR13","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1186\/1752-0509-6-29","volume":"6","author":"D Kirouac","year":"2012","unstructured":"Kirouac D, Saez-Rodriguez J, Swantek J, Burke J, Lauffenburger D, Sorger P: Creating and analyzing pathway and protein interaction compendia for modelling signal transduction networks. BMC Syst Biol. 2012, 6: 29-","journal-title":"BMC Syst Biol"},{"key":"1057_CR14","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-1-4419-7210-1_2","volume":"736","author":"C Terfve","year":"2012","unstructured":"Terfve C, Saez-Rodriguez J: Modeling signaling networks using high-throughput phospho-proteomics. Adv Exp Med Biol. 2012, 736: 19-57.","journal-title":"Adv Exp Med Biol"},{"key":"1057_CR15","first-page":"189","volume":"30","author":"R Prill","year":"2011","unstructured":"Prill R, Saez-Rodriguez J, Alexopoulos L, Sorger P, Stolovitzky G: Crowdsourcing network inference: the DREAM predictive signaling network challenge science signaling 2011. Sci Signal. 2011, 30: 189-","journal-title":"Sci Signal"},{"key":"1057_CR16","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1038\/msb4100120","volume":"3","author":"M Bansal","year":"2007","unstructured":"Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D: How to infer gene networks from expression profiles. Mol Syst Biol. 2007, 3: 78-","journal-title":"Mol Syst Biol"},{"key":"1057_CR17","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1016\/j.drudis.2008.03.019","volume":"13","author":"S Watterson","year":"2008","unstructured":"Watterson S, Marshall S, Ghazal P: Logic models of pathway biology. Drug discovery today. 2008, 13: 447-456.","journal-title":"Drug discovery today"},{"key":"1057_CR18","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1016\/0022-5193(69)90015-0","volume":"22","author":"J Kauffman","year":"1969","unstructured":"Kauffman J: Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol. 1969, 22: 437-467.","journal-title":"J Theor Biol"},{"key":"1057_CR19","doi-asserted-by":"publisher","first-page":"3216","DOI":"10.1021\/bi902202q","volume":"49","author":"M Morris","year":"2010","unstructured":"Morris M, Saez-Rodriguez J, Sorger P, Lauffenburger D: Logic-based models for the analysis of cell signaling networks. Biochemistry. 2010, 49: 3216-3224.","journal-title":"Biochemistry"},{"key":"1057_CR20","doi-asserted-by":"publisher","first-page":"173","DOI":"10.1038\/msb.2008.7","volume":"4","author":"L Calzone","year":"2008","unstructured":"Calzone L, Gelay A, Zinovyev A, Radvanyi F, Barillot E: A comprehensive modular map of molecular interactions in RB\/E2F pathway. Mol Syst Biol. 2008, 4: 173-","journal-title":"Mol Syst Biol"},{"key":"1057_CR21","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1093\/bioinformatics\/btn266","volume":"24","author":"A Gonzalez","year":"2008","unstructured":"Gonzalez A, Chaouiya C, Thieffry D: Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc. Bioinformatics. 2008, 24: 234-240.","journal-title":"Bioinformatics"},{"key":"1057_CR22","doi-asserted-by":"publisher","first-page":"e1000595","DOI":"10.1371\/journal.pcbi.1000595","volume":"5","author":"R Schlatter","year":"2009","unstructured":"Schlatter R, Schmich K, Avalos VI, Scheurich P, Sauter T, Borner C, Ederer M, Merfort I, Sawodny O: ON\/OFF and beyond\u2013a boolean model of apoptosis. PLoS Comput Biol. 2009, 5: e1000595-","journal-title":"PLoS Comput Biol"},{"key":"1057_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1752-0509-3-1","volume":"3","author":"O Sahin","year":"2009","unstructured":"Sahin O, Frohlich H, Lobke C, Korf U, Burmester S, Majety M, Mattern J, Schupp I, Chaouiya C, Thieffry D, Poustka A, Wiemann S, Beissbarth T, Arlt D: Modeling ERBB receptor-regulated G1\/S transition to find novel targets for de novo trastuzumab resistance. BMC Syst Biol. 2009, 3: 1-","journal-title":"BMC Syst Biol"},{"key":"1057_CR24","first-page":"1","volume":"8","author":"S Klamt","year":"2007","unstructured":"Klamt S, Saez-Rodriguez J, Gilles E: Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol. 2007, 8: 1-","journal-title":"BMC Syst Biol"},{"key":"1057_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/gb-2008-9-1-r1","volume":"9","author":"I Ulitsky","year":"2008","unstructured":"Ulitsky I, Gat-Viks I, Shamir R: MetaReg: a platform for modeling, analysis and visualization of biological systems using large-scale experimental data. Genome Biol. 2008, 9: 1-","journal-title":"Genome Biol"},{"key":"1057_CR26","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1186\/1751-0473-3-16","volume":"14","author":"I Albert","year":"2008","unstructured":"Albert I, Thakar J, Li S, Zhang R, Albert R: Boolean network simulations for life scientists. Source Code Biol Medl. 2008, 14: 16-","journal-title":"Source Code Biol Medl"},{"key":"1057_CR27","doi-asserted-by":"publisher","first-page":"1378","DOI":"10.1093\/bioinformatics\/btq124","volume":"26","author":"C Mussel","year":"2010","unstructured":"Mussel C, Hopfensitz M, Kestler H: BoolNet\u2013an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics. 2010, 26: 1378-1380.","journal-title":"Bioinformatics"},{"key":"1057_CR28","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1186\/1752-0509-3-58","volume":"6","author":"T Helikar","year":"2009","unstructured":"Helikar T, Rogers J: ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists. BMC Syst Biol. 2009, 6: 58-","journal-title":"BMC Syst Biol"},{"key":"1057_CR29","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.biosystems.2005.10.003","volume":"84","author":"A Gonzalez","year":"2006","unstructured":"Gonzalez A, Naldi A, Sanchez L, Thieffry D, Chaouiya C: GINsim: a software suite for the qualitative modelling, simulation and analysis of regulatory networks. Biosystems. 2006, 84: 91-100.","journal-title":"Biosystems"},{"key":"1057_CR30","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1186\/1471-2105-8-462","volume":"8","author":"A Di Cara","year":"2007","unstructured":"Di Cara A, Garg A, De Micheli G, Xenarios I, Mendoza L: Dynamic simulation of regulatory networks using SQUAD. BMC Bioinformatics. 2007, 8: 462-","journal-title":"BMC Bioinformatics"},{"key":"1057_CR31","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1186\/1471-2105-11-233","volume":"11","author":"J Krumsiek","year":"2010","unstructured":"Krumsiek J, Polsterl S, Wittmann D, Theis F: Odefy - From discrete to continuous models. BMC Bioinformatics. 2010, 11: 233-","journal-title":"BMC Bioinformatics"},{"key":"1057_CR32","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1093\/bioinformatics\/btf851","volume":"19","author":"H de Jong","year":"2003","unstructured":"de Jong H, Geiselmann J, Hernandez C, Page M: Genetic Network Analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics. 2003, 19: 336-344.","journal-title":"Bioinformatics"},{"key":"1057_CR33","unstructured":"The Bioconductor project. [http:\/\/www.bioconductor.org]"},{"key":"1057_CR34","doi-asserted-by":"publisher","first-page":"840","DOI":"10.1093\/bioinformatics\/btn018","volume":"24","author":"J Saez-Rodriguez","year":"2008","unstructured":"Saez-Rodriguez J, Goldsipe A, Muhlich J, Alexopoulos L, Millard B, Lauffenburger D, Sorger P: Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics. 2008, 24: 840-847.","journal-title":"Bioinformatics"},{"key":"1057_CR35","doi-asserted-by":"publisher","first-page":"e1001099","DOI":"10.1371\/journal.pcbi.1001099","volume":"7","author":"M Morris","year":"2011","unstructured":"Morris M, Saez-Rodriguez J, Clarke D, Sorger P, Lauffenburger D: Training signaling pathway maps to biochemical data with constrained fuzzy logic: quantitative analysis of liver cell responses to inflammatory stimuli. PLoS Comput Biol. 2011, 7: e1001099-","journal-title":"PLoS Comput Biol"},{"key":"1057_CR36","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1186\/1752-0509-3-98","volume":"28","author":"D Wittmann","year":"2009","unstructured":"Wittmann D, Krumsiek J, Saez-Rodriguez J, Lauffenburger D, Klamt S, Theis F: Transforming Boolean models to continuous models: methodology and application to T-cell receptor signaling. BMC Syst Biol. 2009, 28: 98-","journal-title":"BMC Syst Biol"},{"key":"1057_CR37","unstructured":"genalg: R Based Genetic Algorithm, E Willighagen.http:\/\/cran.r-project.org\/web\/packages\/genalg\/,"},{"key":"1057_CR38","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.cor.2009.05.003","volume":"37","author":"J Egea","year":"2010","unstructured":"Egea J, Marti R, Banga J: An evolutionary method for complex-process optimization. Computers & Operations Research. 2010, 37: 315-324.","journal-title":"Computers & Operations Research"},{"key":"1057_CR39","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1093\/bioinformatics\/btq675","volume":"27","author":"M Smoot","year":"2011","unstructured":"Smoot M, Ono K, Ruscheinski J, Wang P, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011, 27: 431-432.","journal-title":"Bioinformatics"},{"key":"1057_CR40","doi-asserted-by":"publisher","first-page":"045003","DOI":"10.1088\/1478-3975\/9\/4\/045003","volume":"9","author":"A MacNamara","year":"2012","unstructured":"MacNamara A, Terfve C, Henriques D, Penalver-Bernave B, Saez-Rodriguez J: State-time spectrum of signal transduction logic model. Physical Biology. 2012, 9: 045003-","journal-title":"Physical Biology"},{"key":"1057_CR41","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/1471-2105-7-56","volume":"7","author":"S Klamt","year":"2006","unstructured":"Klamt S, Saez-Rodriguez J, Lindquist J, Simeoni L, Gilles E: A methodology for the structural and functional analysis of signaling and regulatory networks. BMC Bioinf. 2006, 7: 56-","journal-title":"BMC Bioinf"},{"key":"1057_CR42","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1523\/JNEUROSCI.4288-04.2005","volume":"25","author":"B Lee","year":"2005","unstructured":"Lee B, Butcher G, Hoyt K, Impey S, Obrietan K: Activity-dependent neuroprotection and cAMP response element-binding protein (CREB): kinase coupling, stimulus intensity, and temporal regulation of CREB phosphorylation at serine 133. J Neurosci. 2005, 25: 1137-1148.","journal-title":"J Neurosci"},{"key":"1057_CR43","doi-asserted-by":"publisher","first-page":"2583","DOI":"10.1158\/1078-0432.CCR-08-1137","volume":"15","author":"K Sakamoto","year":"2009","unstructured":"Sakamoto K, Frank D: CREB in the pathophysiology of cancer: implications for targeting transcription factors for cancer therapy. Clin Cancer Res. 2009, 15: 2583-2587.","journal-title":"Clin Cancer Res"},{"key":"1057_CR44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1158\/0008-5472.CAN-10-4453","volume":"71","author":"J Saez-Rodriguez","year":"2011","unstructured":"Saez-Rodriguez J, Alexopoulos LG, Zhang M, Morris MK, Lauffenburger DA, SP K: Comparing signaling networks between normal and transformed hepatocytes using discrete logical models. Cancer Res. 2011, 71: 1-12.","journal-title":"Cancer Res"},{"key":"1057_CR45","first-page":"57","volume-title":"Systems Biomedicine: Concepts and Perspectives","author":"B Joughin","year":"2009","unstructured":"Joughin B, Cheung E, Krishna R, Saez-Rodriguez J, Lauffenburger D, Liu E, Murthy Karuturi: Cellular Regulatory Networks. Systems Biomedicine: Concepts and Perspectives. Edited by: Lauffenburger DA, Liu ET. 2009, Academic Press, San Diego, 57-108."},{"key":"1057_CR46","doi-asserted-by":"publisher","first-page":"S5","DOI":"10.1186\/1471-2105-8-S6-S5","volume":"8","author":"F Markowetz","year":"2007","unstructured":"Markowetz F, Spang R: Inferring cellular networks - a review. BMC Bioinf. 2007, 8: S5-","journal-title":"BMC Bioinf"},{"issue":"12","key":"1057_CR47","doi-asserted-by":"publisher","first-page":"e1000591","DOI":"10.1371\/journal.pcbi.1000591","volume":"5","author":"A Mitsos","year":"2009","unstructured":"Mitsos A, Melas IN, Siminelakis P, Chairakaki A, Saez-Rodriguez J, Alexopoulos LG: Identifying drug effects via pathway alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Datas. PLoS Comp Biol. 2009, 5 (12): e1000591-","journal-title":"PLoS Comp Biol"},{"key":"1057_CR48","first-page":"261","volume-title":"Lecture Notes in Computer Science","author":"R Sharan","year":"2012","unstructured":"Sharan R, Karp RM: Reconstructing Boolean Models of Signaling. Lecture Notes in Computer Science. Edited by: Chor B. 2012, Springer, Berlin, Heidelberg, 261-271."},{"key":"1057_CR49","unstructured":"Videla S, Guziolowski C, Eduati F, Thiele S, Grabe N, Saez-Rodriguez J, Siegel A: Revisiting the training of logic models of protein signaling networks with a formal approach based on answer set programming. Lecture Notes in Computer Science, Springer. in press"},{"issue":"18","key":"1057_CR50","doi-asserted-by":"publisher","first-page":"2311","DOI":"10.1093\/bioinformatics\/bts363","volume":"28","author":"F Eduati","year":"2012","unstructured":"Eduati F, De Las Rivas J, Di Camillo B, Toffolo G, Saez-Rodriguez J: Integrating literature-constrained and data-driven inference of signalling networks. Bioinformatics. 2012, 28 (18): 2311-2317.","journal-title":"Bioinformatics"}],"container-title":["BMC Systems Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1752-0509-6-133.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T22:38:37Z","timestamp":1630535917000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcsystbiol.biomedcentral.com\/articles\/10.1186\/1752-0509-6-133"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012,10,18]]},"references-count":50,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2012,12]]}},"alternative-id":["1057"],"URL":"https:\/\/doi.org\/10.1186\/1752-0509-6-133","relation":{},"ISSN":["1752-0509"],"issn-type":[{"value":"1752-0509","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,10,18]]},"assertion":[{"value":"29 May 2012","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 September 2012","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 October 2012","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"133"}}