An investigation into the population abundance distribution of mRNAs, proteins, and metabolites in biological systems

Bioinformatics

Volumn 25, Issue 16, 2009, Pages 2020-2027

  Lu, Chuan ^a   King, Ross D ^a  

^a ABERYSTWYTH UNIVERSITY   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

MESSENGER RNA;

ARTICLE; BIOINFORMATICS; CONTROLLED STUDY; INTERMETHOD COMPARISON; METABOLITE; METABOLOMICS; NORMAL DISTRIBUTION; POPULATION ABUNDANCE; POSITIVE FEEDBACK; PRIORITY JOURNAL; PROTEOMICS; STATISTICAL MODEL; STOCHASTIC MODEL; TRANSCRIPTOMICS;

COMPUTER SIMULATION; METABOLOME; MODELS, BIOLOGICAL; PROTEINS; RNA, MESSENGER;

EID: 68549085470     PISSN: 13674803     EISSN: 14602059     Source Type: Journal    
DOI: 10.1093/bioinformatics/btp360     Document Type: Article

References (54)

1. Arita,M. (2005) Scale-freeness and biological networks. J. Biochem. 138, 1-4.
2. Bantscheff,M. and Schirle,M. (2007) Quantitative mass spectrometry in proteomics: A critical review. Anal. Bioanal. Chem., 389, 1017-1031.
3. Barabási,A.L. and Albert,R. (1999) Emergence of scaling in random networks. Science, 286, 509-512.
4. Bertone,P. et al. (2004) Global identification of human transcribed sequences with genome tiling arrays. Science, 306 2242-2246.
5. Canales,R. et al. (2006) The microarray quality control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol., 24, 1151-1161.
6. Catchpole,G.S. et al. (2005) Hierarchical metabolomics demonstrates substantial coompositional similarity between genetically modified and conventional potato crops. Proc. Natl Acad. Sci.USA, 102, 14458-14462.
7. Chua,Alvin, L. et al. (2006) Pareto-gamma statistic reveals global rescaling in transcriptomes of low and high aggressive breast cancer phenotypes. In Pattern Recognition in Bioinformatics, International workshop, PRIB 2006 Vol. 4146 of Lecture Notes in Computer Science (LNCS), Springer, Berlin/Heidelberg, pp. 49-59.
8. Cravatt, Benjamin,F. et al. (2007) The biological impact of mass-spectrometry-based proteomics. Nature, 450, 991-1000.
9. Elf,J. et al. (2003) Near-critical phenomena in intracellular metabolite pools. Biophys. J., 84, 154-170.
10. Fiehn,O. (2002) Metabolomics - the link between genotypes and phenotypes. Plant Mol. Biol., 48, 155-171.
11. Friedman,N. et al. (2006) Linking stochastic dynamics to population distribution: An analytical framework of gene expression. Phys. Rev. Lett., 97, 16830.
12. Furusawa,C. and Kaneko,K. (2003) Zipf's law in gene expression. Phys. Rev. Lett., 90, 088102.
13. Hornung,G. and Barkai,N. (2008) Noise propagation and signaling sensitivity in biological networks: A role for positive feedback. PLoS Comput. Biol., 4, e8.
14. Hoyle,D.C. et al. (2002) Making sense of microarray data distributions. Bioinformatics, 18, 576-584.
15. Ishihama,Y. et al. (2008) Protein abundance profiling of the Escherichia coli cytosol. BMC Genomics, 9, 102, http://www.biomedcentral.com/1471-2164/9/102.
16. Jaynes,E. (2005) Probability Theory: The Logic of Science. Cambridge University Press, Cambridge.
17. Jeong,H. et al. (2000) The large-scale organization of metabolic networks. Nature, 407, 651-654.
18. Jeong,H. et al. (2001) Lethality and centrality in protein networks. Nature, 411, 41-42.
19. Kell,D.B. (2004) Metabolomics and systems biology: Making sense of the soup. Curr. Opin Microbiol., 7, 296-307.
20. Kenneth,J.R. and Sander,G. (eds) (1998) Modern Epidemiology. 2nd edn. Lippincott Williams & Wilkins, Philadelphia.
21. Khanin,R. and Wit,E. (2006) How scale-free are biological networks. J. Computat. Biol., 13, 810-818.
22. Koltai,H. and Weingarten-Baror,C. (2008) Specificity of DNA microarray hybridization: Characterization, effectors and approaches for data correction. Nucleic Acids Res., 36, 2395-2405.
23. Konishi,T. (2004) Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment. BMC Bioinformatics., 5, 1471-2105.
24. Kuznetsov,V.A. et al. (2002) General statistics of stochastic process of gene expression in eukaryotic cells. Genetics, 161 1321-1332.
25. Limpert,E. et al. (2001) Log-normal distributions across the sciences: Keys and clues. Bioscience, 51, 341-352.
26. Lockhart,D.J. and Winzeler,E.A. (2000) Genomics, gene expression and DNA arrays. Nature, 405, 827-836.
27. Marcelino,L.A. et al. (2006) Accurately quantifying low-abundant targets amid similar sequences by revealing hidden correlations in oligonucleotide microarray data. Proc. Natl Acad. Sci. USA, 103 13629-13634.
28. Milo,R. et al. (2002) Network motifs: Simple building blocks of complex networks. Science, 298, 824-827.
29. Mitzenmacher,M. (2004) A brief history of generative models for power law and lognormal distributions. Internet Math., 1, 226-251.
30. Monod,J. (1971) Chance and Necessity. Alfred A. Knopf, New York.
31. Nagalakshmi,U. et al. (2008) The transcriptional landscape of the yeast genome defined by RNA sequencing. Science, 320, 1344-1349.
32. Newman,J.R.S. et al. (2006) Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature, 441, 840-846.
33. Ochiai,T. et al. (2004) A constructive approach to gene expression dynamics. Phys. Lett. A, 330, 313-321.
34. Paulsson,J. (2004) Summing up the noise in gene networks. Nature, 427, 415-418.
35. Paulsson,J. (2005) Models of stochastic gene expression. Phys. Life Rev., 2, 157-175.
36. Rasmussen,S. et al. (2004) Transitions from nonliving to living matter. Science, 303, 963-965.
37. Reed,W.J. (2003) The Pareto law of incomes - an explaination and an extention. Physica A, 319, 469-486.
38. Reed,W.J. and Jorgensen,M.J. (2004) The double Pareto-lognormal distribution - a new parametric model for size distributions. Com. Stats Theory Methods, 33, 1733-1753.
39. Schulze,A. and Downward,J. (2001) Navigating gene expression using microarrays a technology review. Nat. Cell Biol., 3, E190-E195.
40. Sinnott,E.W. (1937) The relation of gene to character in quantitative inheritance. Proc. Natl Acad. Sci. USA, 23, 224-227.
41. Stelling,J. et al. (2004) Robustness of cellular functions. Cell, 118, 675-685.
42. Stephens,M.A. (1974) EDF statistics for goodness of fit and some comparisons. J. Am. Stat. Assoc., 69, 730-737.
43. Stumpf,M.P.H. et al. (2005a) Statistical model selection applied to biological network data. Proc. Computat. Syst. Biology, 3 65-73.
44. Stumpf,M.P.H. et al. (2005b) Subnets of scale-free networks are not scale-free: Sampling properties of the networks. Proc. Natl Acad. Sci. USA, 102, 4221-4224.
45. Tokita,K. (2006) Statistical mechanics of relative species abundance. Ecol. Inform., 1, 315-324.
46. Tong,A.H. et al (2004) Global mapping of the yeast genetic interaction network. Science, 303, 808-813.
47. The Gene Ontology Consortium (2000) Gene ontology: Tool for the unification of biology. Nat. Genet., 25, 25-29.
48. Tyers,M. and Mann,M. (2003) From genomics to proteomics. Nature, 422, 193-197.
49. Ueda,H.R. et al. (2004) Universality and flexibility in gene expression from bacteria to human. Proc. Natl Acad. Sci. USA, 101, 3765-3769.
50. Van Nimwegen,E. (2003) Scaling laws in the functional content of genomes. Trends Genet., 9, 479-484.
51. Wilhelm,B. et al. (2008) Dynamic repertoire of a eukaryotic transcriptome surveyed at single-nucleotide resolution. Nature, 453, 1239-1243.
52. Wishart,D. et al. (2007) HMDB: The human metabolome database. Nucleic Acids Res., 35, D521-D526.
53. Wu,Z. and Irizarry,R.A. (2005) Stochastic models inspired by hybridization theory for short oligonucleotide arrays. J. Comput. Biol., 12, 882-893.
54. Yule,G. (1925) Amathematical theory of evolution based on the conclusions of Dr. J.C. Willis. Philos. Trans. R. Soc. Lond. B, 213, 21-87.