Fault tolerance of information processing in gene networks

Physica A: Statistical Mechanics and its Applications

Volumn 388, Issue 4, 2009, Pages 541-548

  Emmert Streib, Frank ^a,b   Dehmer, Matthias ^c  

^a Center for Cancer Research and Cell Biology   (United Kingdom)

^b UNIVERSITY OF WASHINGTON   (United States)

^c VIENNA UNIVERSITY OF TECHNOLOGY   (Austria)

Author keywords

Information processing; Markov chain; Robustness; Scale free network

Indexed keywords

COMPLEX NETWORKS; DATA PROCESSING; FAULT TOLERANCE; MARKOV CHAINS; ROBUSTNESS (CONTROL SYSTEMS); TESTING;

GLOBAL EFFECTS; METABOLIC NETWORK; NETWORK TOPOLOGY; SINGLE-GENE KNOCKOUT; SPREAD OF INFORMATIONS; STATIONARY DISTRIBUTION; STATISTICAL HYPOTHESIS TEST; TRANSCRIPTIONAL REGULATORY NETWORKS;

GENES;

EID: 57349185507     PISSN: 03784371     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.physa.2008.10.032     Document Type: Article

References (32)

1. Albert R., and Barabasi A. Statistical mechanics of complex networks. Rev. Modern Phys. 74 (2002) 47
2. Albert R., Jeong H., and Barabasi A.L. Diameter of the world wide web. Nature 401 (1999) 130-131
3. Albert R., Jeong H., and Barabasi A.-L. Error and attack tolerance of complex networks. Nature 406 (2000) 378-482
4. Baldi P., Frasconi P., and Smyth P. Modeling the Internet and the Web: Probabilistic Methods and Algorithms (2003), Wiley
5. In: Bornholdt S., and Schuster H. (Eds). Handbook of Graphs and Networks: From the Genome to the Internet (2003), Wiley-VCH
6. Ciliberti S., Martin O., and Wagner A. Robustness can evolve gradually in complex regulatory networks with varying topology. PLoS Comput. Biology 3 2 (2007) e15
7. de Visser J., et al. Evolution and detection of genetic robustness. Evolution Int. J. Org. Evolution 57 9 (2003) 1959-1972
8. M. Dehmer, A novel method for measuring the structural information content of networks, Cybernet. Syst. (2007) (in press)
9. Dijkstra E. A note on two problems in connection with graphs. Numer. Math. 1 (1959) 269-271
10. Dorogovtesev S., and Mendes J. Evolution of Networks: From Biological Nets to the Internet and WWW (2003), Oxford University Press
11. Emmert-Streib F., and Dehmer M. Information theoretic measures of UHG graphs with low computational complexity. Appl. Math. Comput. 190 2 (2007) 1783-1794
12. Emmert-Streib. In: Dehmer F. (Ed). Analysis of Microarray Data: A Network Based Approach (2008), Wiley VCH
13. Erdös P., and Rényi A. On random graphs. Publ. Math. 6 (1959) 290-297
14. Erdös P., and Rényi A. On random graphs. Publ. Math. Inst. Hung. Acad. Sci. 5 (1960) 17
15. Feller W. An Introduction to Probability Theory and Its Applications, Vol. 1 (1968), John Wiley & Sons
16. Iguchi K., Kinoshita S., and Yamada H. Boolean dynamics of kauffman models with a scale-free network. J. Theoret. Biol. 247 1 (2007) 138-151
17. Jeong H., Tombor B., Albert Z.N., Olivai R., and Barabasi A.L. The large-scale organization of metabolic networks. Nature 407 (2000) 651-654
18. Kimura H., Okano H., and Tanaka R.J. Stochastic approach to molecular interactions and computational theory of metabolic and genetic regulations. J. Theoret. Biol. 248 4 (2007) 590-607
19. Kullback S. Information Theory and Statistics (1959), Wiley
20. Kullback S., and Leibler R. On information and sufficiency. Ann. Math. Stat. (1951)
21. Newman M.E.J. The structure and function of complex networks. SIAM Rev. 45 (2003) 167-256
22. Norris J. Markov Chains (1998), Cambridge University Press
23. L. Page, S. Brin, R. Motwani, T. Winograd, The pagerank citation ranking: Bringing order to the web, Technical report, Stanford University, 1998
24. Ravasz E., Somera A.L., Mongru D.A., Oltvai Z.N., and Barabasi A.L. Hierarchical organization of modularity in metabolic networks. Science 297 (2002) 1551-1555
25. Schwikowski B., Uetz P., and Fields S. A network of protein-protein interactions in yeast. Nat. Biotechnol. 18 (2000) 1257-1261
26. Sheskin D.J. Handbook of Parametric and Nonparametric Statistical Procedures. 3rd edition (2004), RC Press, Boca Raton, FL
27. Soinov L.A., Krestyaninova M.A., and Brazma A. Towards reconstruction of gene networks from expression data by supervised learning. Genome Biology (2003)
28. van Noort V., Snel B., and Huymen M.A. The yeast coexpression network has a small-world, scale-free architecture and can be explained by a simple model. EMBO Rep. 5 3 (2004) 280-284
29. Wagner A. Robustness, neutrality, and evolvability. FEBS Lett. 579 (2005) 1772-1778
30. Wagner A. Robustness and Evolvability in Living Systems (2007), Princeton University Press
31. Watts D. Small Worlds: The Dynamics of Networks Between Order and Randomness (1999), Princeton University Press
32. Watts D., and Strogatz S. Collective dynamics of 'small-world' networks. Nature 393 (1998) 440-442