CellNOptR: a flexible toolkit to train protein signaling networks to data using multiple logic formalisms

BMC Systems Biology

Julio Saez-Rodriguez²  Douglas A Lauffenburger¹  Martijn van Iersel²  Melody K Morris¹  Emanuel Goncalves²  Aidan MacNamara²  David Henriques²  Thomas Cokelaer²  Camille Terfve² 

[1] Biological Engineering Department, Massachusetts Institute of Technology, Cambridge, MA, USA;European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Cambridge CB10 1SD, UK

关键词: Perturbation data;    Logic modeling;    Phosphoproteomics;    Systems biology;    Signaling networks;   

Others  :  1143548
DOI  :  10.1186/1752-0509-6-133

 received in 2012-05-29, accepted in 2012-09-19,  发布年份 2012

【 摘 要 】

Background

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.

Results

Here we present CellNOptR, 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. CellNOptR 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 (CytoCopteR) to facilitate use and integration with Cytoscape network-based capabilities.

Conclusions

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 CellNOptR 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.

【 授权许可】

   
2012 Terfve et al; licensee BioMed Central Ltd.

【 图 表 】

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【 参考文献 】

• [1]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.
• [2]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.
• [3]Jorgensen C, Linding R: Simplistic pathways or complex networks? Curr Opin Genet Dev 2010, 20:15-22.
• [4]Khatri P, Sirota M, Butte A: Ten years of pathway analysis : current approaches and outstanding challenges. Plos Comp Bio 2012, 8:15-22.
• [5]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.
• [6]Joshi-Tope G, Gillespie M, Vastrik I, D’Eustachio 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.
• [7]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.
• [8]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.
• [9]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.
• [10]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.
• [11]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.
• [12]Aldridge B, Burke J, Lauffenburger D, Sorger P: Physicochemical modelling of cell signalling pathways. Nat Cell Biol 2006, 8:1195-1203.
• [13]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.
• [14]Terfve C, Saez-Rodriguez J: Modeling signaling networks using high-throughput phospho-proteomics. Adv Exp Med Biol 2012, 736:19-57.
• [15]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.
• [16]Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D: How to infer gene networks from expression profiles. Mol Syst Biol 2007, 3:78.
• [17]Watterson S, Marshall S, Ghazal P: Logic models of pathway biology. Drug discovery today 2008, 13:447-456.
• [18]Kauffman J: Metabolic stability and epigenesis in randomly constructed genetic nets. J Theor Biol 1969, 22:437-467.
• [19]Morris M, Saez-Rodriguez J, Sorger P, Lauffenburger D: Logic-based models for the analysis of cell signaling networks. Biochemistry 2010, 49:3216-3224.
• [20]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.
• [21]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.
• [22]Schlatter R, Schmich K, Avalos VI, Scheurich P, Sauter T, Borner C, Ederer M, Merfort I, Sawodny O: ON/OFF and beyond–a boolean model of apoptosis. PLoS Comput Biol 2009, 5:e1000595.
• [23]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.
• [24]Klamt S, Saez-Rodriguez J, Gilles E: Structural and functional analysis of cellular networks with CellNetAnalyzer. BMC Syst Biol 2007, 8:1.
• [25]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.
• [26]Albert I, Thakar J, Li S, Zhang R, Albert R: Boolean network simulations for life scientists. Source Code Biol Medl 2008, 14:16.
• [27]Mussel C, Hopfensitz M, Kestler H: BoolNet–an R package for generation, reconstruction and analysis of Boolean networks. Bioinformatics 2010, 26:1378-1380.
• [28]Helikar T, Rogers J: ChemChains: a platform for simulation and analysis of biochemical networks aimed to laboratory scientists. BMC Syst Biol 2009, 6:58.
• [29]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.
• [30]Di Cara A, Garg A, De Micheli G, Xenarios I, Mendoza L: Dynamic simulation of regulatory networks using SQUAD. BMC Bioinformatics 2007, 8:462.
• [31]Krumsiek J, Polsterl S, Wittmann D, Theis F: Odefy - From discrete to continuous models. BMC Bioinformatics 2010, 11:233.
• [32]de Jong H, Geiselmann J, Hernandez C, Page M: Genetic Network Analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 2003, 19:336-344.
• [33]The Bioconductor project [http://www.bioconductor.org webcite]
• [34]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.
• [35]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.
• [36]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.
• [37]genalg: R Based Genetic Algorithm, E Willighagen http://cran.r-project.org/web/packages/genalg/ webcite
• [38]Egea J, Marti R, Banga J: An evolutionary method for complex-process optimization. Computers & Operations Research 2010, 37:315-324.
• [39]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.
• [40]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.
• [41]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.
• [42]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.
• [43]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.
• [44]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.
• [45]Joughin B, Cheung E, Krishna R, Saez-Rodriguez J, Lauffenburger D, Liu E, Murthy Karuturi: Cellular Regulatory Networks. In Systems Biomedicine: Concepts and Perspectives. Edited by Lauffenburger DA, Liu ET. Academic Press, San Diego; 2009:57-108.
• [46]Markowetz F, Spang R: Inferring cellular networks - a review. BMC Bioinf 2007, 8:S5.
• [47]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.
• [48]Sharan R, Karp RM: Reconstructing Boolean Models of Signaling. In Lecture Notes in Computer Science. Edited by Chor B. Springer, Berlin, Heidelberg; 2012:261-271.
• [49]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, Springerin press
• [50]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.