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Aho T, Smolander OP, Niemi J, et al. RMBNToolbox: random models for biochemical networks. BMC Syst Biol. 2007;1:22doi: 10.1186/1752-0509-1-22.
Aho, T., Smolander, O. P., Niemi, J., & Yli-Harja, O. (2007). RMBNToolbox: random models for biochemical networks. BMC systems biology, 122. https://doi.org/10.1186/1752-0509-1-22
Aho, Tommi, et al. "RMBNToolbox: random models for biochemical networks." BMC systems biology vol. 1 (2007): 22. doi: https://doi.org/10.1186/1752-0509-1-22
Aho T, Smolander OP, Niemi J, Yli-Harja O. RMBNToolbox: random models for biochemical networks. BMC Syst Biol. 2007 May 24;1:22. doi: 10.1186/1752-0509-1-22. PMID: 17524136; PMCID: PMC1896132.
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BMC Syst Biol. 2007 May 24;1:22. doi: 10.1186/1752-0509-1-22.

RMBNToolbox: random models for biochemical networks.

BMC systems biology

Tommi Aho, Olli-Pekka Smolander, Jari Niemi, Olli Yli-Harja

Affiliations

  1. Department of Information Technology, Institute of Signal Processing, Tampere University of Technology, Tampere, Finland. [email protected]

PMID: 17524136 PMCID: PMC1896132 DOI: 10.1186/1752-0509-1-22

Abstract

BACKGROUND: There is an increasing interest to model biochemical and cell biological networks, as well as to the computational analysis of these models. The development of analysis methodologies and related software is rapid in the field. However, the number of available models is still relatively small and the model sizes remain limited. The lack of kinetic information is usually the limiting factor for the construction of detailed simulation models.

RESULTS: We present a computational toolbox for generating random biochemical network models which mimic real biochemical networks. The toolbox is called Random Models for Biochemical Networks. The toolbox works in the Matlab environment, and it makes it possible to generate various network structures, stoichiometries, kinetic laws for reactions, and parameters therein. The generation can be based on statistical rules and distributions, and more detailed information of real biochemical networks can be used in situations where it is known. The toolbox can be easily extended. The resulting network models can be exported in the format of Systems Biology Markup Language.

CONCLUSION: While more information is accumulating on biochemical networks, random networks can be used as an intermediate step towards their better understanding. Random networks make it possible to study the effects of various network characteristics to the overall behavior of the network. Moreover, the construction of artificial network models provides the ground truth data needed in the validation of various computational methods in the fields of parameter estimation and data analysis.

References

  1. Nature. 2000 Feb 10;403(6770):623-7 - PubMed
  2. Bioinformatics. 2005 Apr 1;21(7):1203-10 - PubMed
  3. J Theor Biol. 2000 Apr 7;203(3):229-48 - PubMed
  4. Trends Biochem Sci. 2001 Mar;26(3):179-86 - PubMed
  5. Bioinformatics. 2001 Nov;17(11):1053-7 - PubMed
  6. Science. 2001 Dec 14;294(5550):2364-8 - PubMed
  7. Nucleic Acids Res. 2002 Jan 1;30(1):42-6 - PubMed
  8. Biotechnol Bioeng. 2002 Jul 20;79(2):121-34 - PubMed
  9. Science. 2002 Oct 25;298(5594):799-804 - PubMed
  10. Genome Res. 2003 Feb;13(2):244-53 - PubMed
  11. Bioinformatics. 2003 Mar 1;19(4):524-31 - PubMed
  12. Trends Biochem Sci. 2003 May;28(5):250-8 - PubMed
  13. Genome Biol. 2003;4(9):R54 - PubMed
  14. Bioinformatics. 2003 Oct;19 Suppl 2:ii122-9 - PubMed
  15. BMC Bioinformatics. 2000;1:1 - PubMed
  16. Genome Res. 2003 Nov;13(11):2467-74 - PubMed
  17. Proc Natl Acad Sci U S A. 2003 Dec 9;100(25):14796-9 - PubMed
  18. Science. 2004 Feb 6;303(5659):808-13 - PubMed
  19. Bioinformatics. 2004 Mar 1;20(4):538-46 - PubMed
  20. Genome Res. 2004 Jul;14(7):1298-309 - PubMed
  21. Genome Biol. 2004;5(8):R57 - PubMed
  22. Comput Appl Biosci. 1993 Oct;9(5):563-71 - PubMed
  23. Trends Biochem Sci. 1997 Sep;22(9):361-3 - PubMed
  24. Bioinformatics. 1998;14(10):869-83 - PubMed
  25. Bioinformatics. 1999 Jan;15(1):72-84 - PubMed
  26. Science. 1999 Aug 6;285(5429):901-6 - PubMed
  27. Nucleic Acids Res. 2006 Jan 1;34(Database issue):D689-91 - PubMed
  28. Bioinformatics. 2006 May 15;22(10):1275-7 - PubMed
  29. Bioinformatics. 2006 Jun 1;22(11):1406-7 - PubMed
  30. Bioinformatics. 2006 Aug 1;22(15):1930-1 - PubMed
  31. BMC Bioinformatics. 2006;7:349 - PubMed
  32. Bioinformatics. 2006 Dec 15;22(24):3067-74 - PubMed
  33. Proteomics. 2005 May;5(8):2082-9 - PubMed
  34. Genome Res. 2005 Jun;15(6):820-9 - PubMed
  35. J Theor Biol. 2005 Dec 21;237(4):401-11 - PubMed
  36. Bioinformatics. 2005 Feb 1;21(3):357-63 - PubMed
  37. Genome Res. 2004 Nov;14(11):2367-76 - PubMed
  38. Bioinformatics. 2004 Nov 22;20(17):3289-91 - PubMed
  39. Bioinformatics. 2005 Mar15;21(6):832-4 - PubMed
  40. Nat Biotechnol. 2000 Mar;18(3):326-32 - PubMed

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