iVUN: Interactive visualization of uncertain biochemical reaction networks

BMC Bioinformatics

Volumn 14, Issue , 2013, Pages

  Vehlow, Corinna ^a   Hasenauer, Jan ^b,c   Kramer, Andrei ^a   Raue, Andreas ^b,d   Hug, Sabine ^b,c   Timmer, Jens ^d   Radde, Nicole ^a   Theis, Fabian J ^b,c   Weiskopf, Daniel ^a  

^a UNIVERSITY OF STUTTGART   (Germany)

^b INSTITUTE OF EPIDEMIOLOGY   (Germany)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^d UNIVERSITY OF FREIBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DIFFERENTIAL EQUATIONS; DYNAMICS; ESTIMATION; ORDINARY DIFFERENTIAL EQUATIONS; PARAMETER ESTIMATION; VISUALIZATION;

ATTRIBUTE UNCERTAINTY; BIOCHEMICAL REACTION NETWORK; BIOLOGICAL PROCESS; CORRELATION MATRIX; DYNAMIC ATTRIBUTES; INTERACTIVE VISUALIZATIONS; NODE-LINK DIAGRAMS; VISUAL ANALYTICS SYSTEMS;

UNCERTAINTY ANALYSIS;

ARTICLE; BIOLOGICAL MODEL; BIOLOGY; CHEMICAL MODEL; COMPUTER GRAPHICS; METABOLISM; METHODOLOGY; SIGNAL TRANSDUCTION; UNCERTAINTY;

COMPUTATIONAL BIOLOGY; COMPUTER GRAPHICS; METABOLIC NETWORKS AND PATHWAYS; MODELS, BIOLOGICAL; MODELS, CHEMICAL; SIGNAL TRANSDUCTION; UNCERTAINTY;

EID: 84899975097     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-14-S19-S2     Document Type: Article

References (41)

1. Klipp E, Herwig R, Kowald A, Wierling C, Lehrach H: Systems Biology in Practice Wiley-VCH, Weinheim; 2005
2. Schöberl B, et al: Therapeutically targeting ErbB3: a key node in ligandinduced activation of the ErbB receptor-PI3K axis. Science Signaling 2009, 2:ra31
3. Brännmark C, Palmer R, Glad ST, Cedersund G, Strålfors P: Mass and information feedbacks through receptor endocytosis govern insulin signaling as revealed using a parameter-free modeling framework. Journal of Biological Chemistry 2010, 285:20171-20179
4. Kramer A, Hasenauer J, Allgöwer F, Radde N: Computation of the posterior entropy in a Bayesian framework for parameter estimation in biological networks. In Proceedings of IEEE Multi-Conference on Systems and Control (MCS). IEEE 2010, 493-498
5. Wilkinson DJ: Parameter inference for stochastic kinetic models of bacterial gene regulation: a Bayesian approach to systems biology. In Proceedings of 9th Valencia International Meeting on Bayesian Statistics. Oxford University Press 2010, 679-705
6. Vehlow C, Hasenauer J, Kramer A, Heinrich J, Radde N, Allgöwer F, Weiskopf D: Uncertainty-aware visual analysis of biochemical reaction networks. In Proceedings of IEEE Symposium on Biological Data Visualization (Biovis). IEEE 2012, 91-98
7. McNaught A, Wilkinson A: IUPAC Compendium of Chemical Terminology. 2 edition. Blackwell Science; 1997
8. Wilkinson DJ: Bayesian methods in bioinformatics and computational systems biology. Briefings in Bioinformatics 2007, 8:109-116
9. MacKay DJC: Information Theory, Inference, and Learning Algorithms. 7.2 edition. Cambridge: Cambridge University Press; 2005
10. Thomas JJ, Cook KA: Illuminating the Path: The Research and Development Agenda for Visual Analytics IEEE Computer Society Press; 2005
11. Huang ML: Graph visualization of web data with domain-specific attributes. In Proceedings of the 16th International Parallel and Distributed Processing Symposium. IPDPS '02, IEEE Computer Society 2002, 168-174
12. Beck F, Burch M, Diehl S: Towards an aesthetic dimensions framework for dynamic graph visualisations. In Proceedings of 13th International Conference on Information Visualization, IV. IEEE 2009, 592-597
13. Tufte ER: Envisioning Information Cheshire, CT: Graphics Press; 1990
14. Barsky A, Munzner T, Gardy J, Kincaid R: Cerebral: visualizing multiple experimental conditions on a graph with biological context. IEEE Transactions on Visualization and Computer Graphics 2008, 14:1253-1260
15. Meyer M, Wong B, Styczynski M, Munzner T, Pfister H: Pathline: a tool for comparative functional genomics. Computer Graphics Forum 2010, 29:1043-1052
16. Westenberg MA, van Hijum SAFT, Kuipers OP, Roerdink JBTM: Visualizing genome expression and regulatory network dynamics in genomic and metabolic context. Computer Graphics Forum 2008, 27:887-894
17. Junker B, Klukas C, Schreiber F: VANTED: a system for advanced data analysis and visualization in the context of biological networks. BMC Bioinformatics 2006, 7:109-121
18. Karp PD, Paley S, Romero P: The Pathway Tools software. Bioinformatics 2002, 18(Suppl 1):225-232
19. Hoops S, Sahle S, Gauges R, Lee C, Pahle J, Simus N, Singhal M, Xu L, Mendes P, Kummer U: COPASI-a COmplex PAthway SImulator. Bioinformatics 2006, 22:3067-3074
20. Funahashi A, Matsuoka Y, Jouraku A, Morohashi M, Kikuchi N, Kitano H: CellDesigner 3.5: a versatile modeling tool for biochemical networks. Proceedings of the IEEE 2008, 96:1254-1265
21. Johnson CR: Top scientific visualization research problems. IEEE Computer Graphics and Applications: Visualization Viewpoints 2004, 24:13-17
22. Griethe H, Schumann H: The visualization of uncertain data: methods and problems. In Proceedings of SimVis '06. SCS Publishing House e.V 2006, 143-156
23. Johnson CR, Sanderson AR: A next step: visualizing errors and uncertainty. IEEE Computer Graphics and Applications 2003, 23:6-10
24. Cesario N, Pang A, Singh L: Visualizing node attribute uncertainty in graphs. Proceedings of the SPIE (Visualization and Data Analysis 2011) 2011
25. Lee B, Robertson GG, Czerwinski M, Sims Parr C: CandidTree: visualizing structural uncertainty in similar hierarchies. Information Visualization 2007, 6:233-246
26. Pang AT, Wittenbrink CM, Lodha SK: Approaches to uncertainty visualization. The Visual Computer 1997, 13:370-390
27. The Systems Biology Markup Language. 2012 [http://sbml.org/ Main_Page]
28. Roberts JC: Encouraging coupled views for visualization exploration. Proceedings of SPIE, Visual Data Exploration and Analysis VI 1999, 3643:14-24
29. ColorBrewer. 2011 [http://colorbrewer2.org/]
30. Tversky B, Bauer Morrison J, Betrancourt M: Animation: can it facilitate? International Journal of Human-Computer Studies 2002, 57:247-262
31. von Landesberger T, Kuijper A, Schreck T, Kohlhammer J, van Wijk J, Fekete JD, Fellner D: Visual analysis of large graphs: state-of-the-art and future research challenges. Computer Graphics Forum 2011, 30:1719-1749
32. Becker V, Schilling M, Bachmann J, Baumann U, Raue A, Maiwald T, Timmer J, Klingmüller U: Covering a broad dynamic range: information processing at the erythropoietin receptor. Science 2010, 328:1404-1408
33. Bachmann J, Raue A, Schilling M, Böhm ME, Kreutz C, Kaschek D, Busch H, Gretz N, Lehmann WD, Timmer J, Klingmüller U: Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range. Molecular Systems Biology 2011, 7(516):1-15
34. Hug S, Raue A, Hasenauer J, Bachmann J, Klingmüller U, Timmer J, Theis FJ: High-dimensional Bayesian parameter estimation: case study for a model of JAK2/STAT5 signaling. Mathematical Biosciences 2013 [http://dx.doi.org/ 10.1016/j.mbs.2013.04.002]
35. Tarantola A: Inverse Problem Theory and Methods for Model Parameter Estimation SIAM; 2005
36. Gillespie DT: The chemical Langevin equation. Journal of Chemical Physics 2000, 113:297-306
37. Gillespie DT: A rigorous derivation of the chemical master equation. Physica A 1992, 188:404-425
38. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, Timmer J: Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics 2009, 25:1923-1929
39. Kreutz C, Raue A, Timmer J: Likelihood based observability analysis and confidence intervals for predictions of dynamic models. BMC Systems Biology 2012, 6:1-9
40. Java universal network/graph framework. 2008 [http://jung.sourceforge. net/]
41. JMathPlot: interactive 2D and 3D plots. 2009 [http://jmathtools.berlios.de/]