Experimental design schemes for learning Boolean network models

Bioinformatics

Volumn 30, Issue 17, 2014, Pages

  Atias, Nir ^a   Gershenzon, Michal ^a   Labazin, Katia ^a   Sharan, Roded ^a  

^a TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

EPIDERMAL GROWTH FACTOR RECEPTOR; INTERLEUKIN 1;

ALGORITHM; BIOLOGICAL MODEL; HUMAN; METABOLISM; METHODOLOGY; SIGNAL TRANSDUCTION;

ALGORITHMS; HUMANS; INTERLEUKIN-1; MODELS, BIOLOGICAL; RECEPTOR, EPIDERMAL GROWTH FACTOR; RESEARCH DESIGN; SIGNAL TRANSDUCTION;

EID: 84907024690     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btu451     Document Type: Conference Paper

References (22)

1. Apgar, J.F. et al. (2008) Stimulus design for model selection and validation in cell signaling. PLoS Comput. Biol., 4, e30.
2. Balsa-Canto, E. et al. (2008) Computational procedures for optimal experimental design in biological systems. IET Syst. Biol., 2, 163-172.
3. Bandara, S. et al. (2009) Optimal experimental design for parameter estimation of a cell signaling model. PLoS Comput. Biol., 5, e1000558.
4. Barrett, C.L. and Palsson, B.O. (2006) Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach. PLoS Comput. Biol., 2, e52.
5. Gutenkunst, R.N. et al. (2007) Universally sloppy parameter sensitivities in systems biology models. PLoS Comput. Biol., 3, 1871-1878.
6. Harrington, H.A. et al. (2012) Parameter-free model discrimination criterion based on steady-state coplanarity. Proc. Natl Acad. Sci. USA, 109, 15746-15751.
7. Huang, S.-S. and Fraenkel, E. (2009) Integrating proteomic, transcriptional, and interactome data reveals hidden components of signaling and regulatory networks. Sci. Signal., 2, ra40.
8. Hughey, J.J. et al. (2010) Computational modeling of mammalian signaling networks. Wiley Interdiscip. Rev. Syst. Biol. Med., 2, 194-209.
9. Ideker, T.E. et al. (2000) Discovery of regulatory interactions through perturbation: inference and experimental design. Pac. Symp. Biocomput., 5, 305-316.
10. Karlebach, G. and Shamir, R. (2008) Modelling and analysis of gene regulatory networks. Nat. Rev. Mol. Cell Biol., 9, 770-780.
11. Kremling, A. et al. (2004) A benchmark for methods in reverse engineering and model discrimination: problem formulation and solutions. Genome Res., 14, 1773-1785.
12. Kreutz, C. and Timmer, J. (2009) Systems biology: experimental design. FEBS J., 276, 923-942.
13. Maritz, J.S. and Jarrett, R.G. (1978) A note on estimating the variance of the sample median. J. Am. Stat. Assoc., 73, 194-196.
14. Mélyḱuti B. et al 2010) Discriminating between rival biochemical network models: three approaches to optimal experiment design. BMC Syst. Biol 4 38.
15. Mitsos, A. et al. (2009) Identifying drug effects via pathway alterations using an integer linear programming optimization formulation on phosphoproteomic data. PLoS Comput. Biol., 5, e1000591.
16. Ryll, A. et al. (2011) Large-scale network models of IL-1 and IL-6 signalling and their hepatocellular specification. Mol. Biosyst., 7, 3253-3270.
17. Saez-Rodriguez, J. et al. (2009) Discrete logic modelling as a means to link protein signalling networks with functional analysis of mammalian signal transduction Mol. Syst. Biol., 5, 331.
18. Samaga, R. et al. (2009) The logic of EGFR/ErbB signaling: theoretical properties and analysis of high-throughput data. PLoS Comput. Biol., 5, e1000438.
19. Sharan, R. and Karp, R.M. (2012) Reconstructing boolean models of signaling. In: Proceedings of the 16th Annual international conference on Research in Computational Molecular Biology. RECOMB12. pp. 261-271. Springer- Verlag, Berlin, Heidelberg.
20. Szczurek, E. et al. (2009) Elucidating regulatory mechanisms downstream of a signaling pathway using informative experiments. Mol. Syst. Biol., 5, 287.
21. Yeger-Lotem, E. et al. (2009) Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat. Genet., 41, 316-323.
22. Yosef, N. et al. (2009) Toward accurate reconstruction of functional protein networks. Mol. Syst. Biol., 5, 248