Optimal design of gene knockout experiments for gene regulatory network inference

Bioinformatics

Volumn 32, Issue 6, 2016, Pages 875-883

  Ud Dean, S M Minhaz ^a,b   Gunawan, Rudiyanto ^a,b  

^a ETH ZURICH   (Switzerland)

^b SWISS INSTITUTE OF BIOINFORMATICS   (Switzerland)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ESCHERICHIA COLI; GENE EXPRESSION; GENE INACTIVATION; GENE REGULATORY NETWORK;

ALGORITHMS; ESCHERICHIA COLI; GENE EXPRESSION; GENE KNOCKOUT TECHNIQUES; GENE REGULATORY NETWORKS;

EID: 84962173551     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btv672     Document Type: Article

References (22)

1. Aho,A.V. et al. (1972) The transitive reduction of a directed graph. SIAM J. Comput., 1, 131-137.
2. Albert,R. and Barabasi,A.-L. (2002) Statistical mechanics of complex networks. Rev. Mod. Phys., 74, 47-97.
3. Birget,J.C. et al. (2012) A theoretical approach to gene network identification. In: Information Theory Workshop (ITW), IEEE, New York, NY, pp. 432-436.
4. Bjorklund,A. et al. (2004) Approximating longest directed paths and cycles. In: Lecture Notes in Computer Science, Automata, Languages and Programming, Springer, Heidelberg, Germany. Vol 3142, pp. 222-233.
5. Consortium,T.F. (2014) A promoter-level mammalian expression atlas. Nature, 507, 462-470.
6. Gawand,P. et al. (2015) Sub-optimal phenotypes of double-knockout mutants of Escherichia coli depend on the order of gene deletions. Integr. Biol., 7, 930-939.
7. Holland,J.H. (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis With Applications to Biology, Control, and Artificial Intelligence. MIT press, Cambridge, MA.
8. Ideker,T.E. et al. (2000) Discovery of regulatory interactions through perturbation: inference and experimental design. Pac. Symp. Biocomput., 5, 302-313.
9. Kalisch,M. and Bühlmann,P. (2007) Estimating high-dimensional directed acyclic graphs with the PC-algorithm. J. Mach. Learn. Res., 8, 613-636.
10. Kato,J.I., and Hashimoto,M. (2007) Construction of consecutive deletions of the Escherichia coli chromosome. Mol. Syst. Biol., 3, 132.
11. Lang,M. et al. (2014) Cutting the wires: modularization of cellular networks for experimental design. Biophys. J., 106, 321-333.
12. Levandowsky,M. and Winter,D. (1971) Distance between sets. Nature, 234, 34-35.
13. Maathuis,M.H. et al. (2010) Predicting causal effects in large-scale systems from observational data. Nat. Methods, 7, 247-248.
14. Marbach,D. et al. (2012) Wisdom of crowds for robust gene network inference. Nat. Methods, 9, 796-804.
15. Prill,R.J. et al. (2011) Crowdsourcing network inference: The DREAMpredictive signaling network challenge. Sci. Signal., 4, mr7.
16. Schaffter,T. et al. (2011) GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics, 27, 2263-2270.
17. Spieth,C. et al. (2004) Iteratively inferring gene regulatory networks with virtual knockout experiments. In: Applications of Evolutionary Computing. Springer, Heidelberg, Germany, pp. 104-112.
18. Steinke,F. et al. (2007) Experimental design for efficient identification of gene regulatory networks using sparse Bayesianmodels.BMC Syst. Biol., 1, 51.
19. Stolovitzky,G. et al. (2005) Statistical analysis of MPSS measurements: application to the study of LPS-activated macrophage gene expression. Proc. Natl. Acad. Sci. USA, 102, 1402-1407.
20. Szederkényi,G. et al. (2011) Inference of complex biological networks: distinguishability issues and optimization-based solutions. BMC Syst. Biol. 5, 177.
21. Tegné r,J. et al. (2003) Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling. Proc. Natl. Acad. Sci. USA, 100, 5944-5949.
22. Ud-Dean,S.M.M. and Gunawan,R. (2014) Ensemble inference and inferability of gene regulatory networks. PLoS One, 9, e103812.