Biotechnology and Bioprocess Engineering:BBE

  • 제10권5호
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  • Pages.400-407
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  • 2005
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  • 1226-8372(pISSN)
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  • 1976-3816(eISSN)
한국생물공학회 (The Korean Society for Biotechnology and Bioengineering)
권호 표지

Component-Based Software Architecture for Biosystem Reverse Engineering

  • 발행 : 2005.10.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Reverse engineering is defined as the process where the internal structures and dynamics of a given system are inferred and analyzed from external observations and relevant knowledge. The first part of this paper surveys existing techniques for biosystem reverse engineering. Network structure inference techniques such as Correlation Matrix Construction (CMC), Boolean network and Bayesian network-based methods are explained. After the numeric and logical simulation techniques are briefly described, several representative working software tools were introduced. The second part presents our component-based software architecture for biosystem reverse engineering. After three design principles are established, a loosely coupled federation architecture consisting of 11 autonomous components is proposed along with their respective functions.

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참고문헌

  1. Csete, M. and J. Doyle (2002) Reverse engineering of biological complexity. Science 295: 1664-1669 https://doi.org/10.1126/science.1069981
  2. Kitano, H. (2002) Systems biology: a brief overview. Science 295: 1662-1664 https://doi.org/10.1126/science.1069492
  3. International Human Genome Sequencing Consortium (2004) Finishing the euchromatic sequence of the human genome. Nature 431: 931-945 https://doi.org/10.1038/nature03001
  4. Nam, J. W., K. H. Han, E. S. Yoon, D. I. Shin, J. H. Jin, D. H. Lee, S. Y. Lee, and J. W. Lee (2004) In silico analysis of lactate producing metabolic network in Lactococcus lactis. Enzyme Microb. Technol. 35: 654-662 https://doi.org/10.1016/j.enzmictec.2004.08.032
  5. Jin, J. H, U. S. Jung, J. W. Nam, Y. H. In, S. Y. Lee, D. H. Lee, and J. W. Lee (2005) Construction of comprehensive metabolic network for glycolysis with regulation mechanisms and effectors. J. Microbiol. Biotechnol. 15: 161-174
  6. Di Bernardo, D., M. J. Thompson, T. S. Gardner, S. E. Chobot, E. L. Eastwood, A. P. Wojtovich, S. J. Elliott, S. E. Schaus, and J. J. Collins (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat. Biotechnol. 23: 377-383 https://doi.org/10.1038/nbt1075
  7. Kim, W., K. C. Kim, E. K. Hong, and D. Lee (2000) A component-based architecture for preparing data in data warehousing. J. Obj. Oriented Prog. 13: 43-47
  8. Web Services Activity (2002) http://www.w3c.org/2002/ws
  9. Arkin, A., P. D. Shen, and J. Ross (1997) A test case of correlation metric construction of a reaction pathway from measurements. Science 277: 1275-1279 https://doi.org/10.1126/science.277.5330.1275
  10. Weaver, D. C., C. T. Workman, and G. D. Stormo (1999) Modeling regulatory networks with weight matrices. Proc. Pac. Symp. Biocomput. 112-123
  11. Akutsu, T., S. Miyano, and S. Kuhara (2000) Algorithms for inferring qualitative models of biological networks. Proc. Pac. Symp. Biocomput. 293-304
  12. Lee, D. Y., H. Yun, S. Park, and S. Y. Lee (2003) Meta-FluxNet: the management of metabolic reaction information and quantitative metabolic flux analysis. Bioinformatics 19: 2144-2146 https://doi.org/10.1093/bioinformatics/btg271
  13. Liang, S., S. Fuhrman, and R. Somogyi (1998) REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Proc. Pac. Symp. Biocomput. 18-29
  14. Akutsu, T., S. Miyano, and S. Kuhara (1999) Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Proc. Pac. Symp. Biocomput. 17-28
  15. Kauffman, S. A. (1969) Metabolic stability and epigenesist in randomly constructed genetic nets. J. Theor. Biol. 22: 437-467 https://doi.org/10.1016/0022-5193(69)90015-0
  16. N. Friedman, M. Linial, I. Nachaman, and D. Pe'er (2000) Using Bayesian networks to analyze expression data, J. Comp. Biol., 7: 601-620 https://doi.org/10.1089/106652700750050961
  17. Hartemink, A. J., D. K, Gifford, T. S. Jaakkola, and R. A. Young (2002) Combining location and expression data for principled discovery of genetic regulatory network models. Proc. Pac. Symp. Biocomput. 437-449
  18. Tamada, Y., S. Kim, H. Bannai, S. Imoto, K. Tashiro, S. Kuhara, and S. Miyano (2003) Estimating gene networks from gene expression data by combining Bayesian network model with promoter element detection. Bioinformatics 19(S2): II227- II236 https://doi.org/10.1093/bioinformatics/btg1082
  19. Pe'er, D., A. Regev, G. Elidan, and N. Friedman (2001) Inferring subnetworks from perturbed expression profiles. Bioinformatics 17(S1): S215-S224 https://doi.org/10.1093/bioinformatics/17.suppl_1.S215
  20. Yoo, C., V. Thorsson, and G. F. Cooper (2000) Discovery of causal relationships in a gene regulation pathway from a mixture of experimental and observational DNA microarray data. Proc. Pac. Symp. Biocomput. 498-509
  21. Lee, P. H. and D. H. Lee (2005) Modularized learning of genetic interaction networks from biological annotations and MRNA expression data. Bioinformatics 21: 2739-2747 https://doi.org/10.1093/bioinformatics/bti406
  22. Segal, E., M. Shapira, A. Regev, D. Peer, D. Botstein, D. Koller, and N. Friedman (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat. Genet. 34: 166-176 https://doi.org/10.1038/ng1165
  23. Phuong, T. M., D. Lee, and K. H. Lee (2004) Regression trees for regulatory element identification. Bioinformatics 20: 750-757 https://doi.org/10.1093/bioinformatics/btg480
  24. Voit, E. (2000) Computational analysis of biochemical systems, Cambridge Univ. Press
  25. De Jong, H., J. Geiselmann, C. Hernandez, and M. Page (2003) Genetic network analyzer: qualitative simulation of genetic regulatory networks. Bioinformatics 19: 336-344 https://doi.org/10.1093/bioinformatics/btf851
  26. Goss, P. J. and J. Peccoud (1998) Quantitative modeling of stochastic systems in molecular biology by using stochastic Petri nets. Proc. Natl. Acad. Sci. USA 95: 6750-6755 https://doi.org/10.1073/pnas.95.12.6750
  27. Peleg, M., I. Yeh, and R. B. Altman (2002) Modeling biological processes using workflow and Petri Net models. Bioinformatics 18: 825-837 https://doi.org/10.1093/bioinformatics/18.6.825
  28. Kauffman, S. A. (1991) Antichaos and adaptation. Sci. Am. 265: 78-84 https://doi.org/10.1038/scientificamerican0891-78
  29. Kauffman, S. A. (1993) The Origins of Order: Self- Organization and Selection in Evolution. Oxford University Press, Oxford, UK
  30. Thomas, R. (1991) Regulatory networks seen as asynchronous automata: A logical description. J. Theor. Biol. 153: 1-23 https://doi.org/10.1016/S0022-5193(05)80350-9
  31. Shimulevich, I., E. R. Dougherty, S. Kim, and W. Zhang (2002) Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks. Bioinformatics 18: 261-274 https://doi.org/10.1093/bioinformatics/18.2.261
  32. Brutlag, D. L., A. R. Galper, and D. H. Millis (1991) Knowledge-based simulation of DNA metabolism: prediction of enzyme action. Comput. Appl. Biosci. 7: 9-19
  33. Hofestadt, R. and F. Meineke (1995) Interactive modelling and simulation of biochemical networks. Comput. Biol. Med. 25: 321-334 https://doi.org/10.1016/0010-4825(95)00019-Z
  34. Fukuda, K. and T. Takagi (2001) Knowledge representation of signal transduction pathways. Bioinformatics 17: 829-837 https://doi.org/10.1093/bioinformatics/17.9.829
  35. Mendes, P. (1997) Biochemistry by numbers: simulation of biochemical pathways with Gepasi 3. Trends Biochem. Sci. 22: 361-363 https://doi.org/10.1016/S0968-0004(97)01103-1
  36. Tomita, M., K. Hashimoto, K. Takahashi, T. S. Shimizu, Y. Matsuzaki, F. Miyoshi, K. Saito, S. Tanida, K. Yugi, J. C. Venter, and C. A. 3rd. Hutchison (1999) E-Cell: software environment for whole-cell simulation. Bioinformatics 15: 72-84 https://doi.org/10.1093/bioinformatics/15.1.72
  37. Shapiro, B. and E. Mjolsness (2001) Developmental Simulations with Cellerator. Proc. Second International Conference on Systems Biology, Pasadena, CA, USA
  38. Schaff, J. and L. M. Loew (1999) The Virtual Cell, Pac. Symp. Biocomput. 228-239
  39. Loew, L. M. and J. Schaff (2001) The Virtual Cell: A Software Environment for Computational Cell Biology, Trends Biotechnol. 19: 401-406 https://doi.org/10.1016/S0167-7799(01)01740-1
  40. Hucka, M., A. Finney, H. M, Sauro, H. Bolouri, J. Doyle, and H. Kitano (2002) The ERATO Systems Biology Workbench: enabling interaction and exchange between software tools for computational biology. Proc. Pac. Symp. Biocomput. 450-461
  41. Shannon, P., A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski, and T. Ideker (2003) Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13: 2498-2504 https://doi.org/10.1101/gr.1239303
  42. Nagasaki, M., A. Doi, H. Matsuno, and S. Miyano (2003) Genomic Object Net: A platform for modelling and simulating biopathways. Appl. Bioinformatics 2: 181-184
  43. Hucka, M, A. Finney, H. M. Sauro, H. Bolouri, J. C. Doyle, H. Kitano, A. P. Arkin, B. J. Bornstein, D. Bray, A. Cornish-Bowden, A. A. Cuellar, S. Dronov, E. D. Gilles, M. Ginkel, V. Gor, I. I. Goryanin, W. J. Hedley, T. C. Hodgman, J. H. Hofmeyr, P. J. Hunter, N. S. Juty, J. L. Kasberger, A. Kremling, U. Kummer, N. Le Novere, L. M. Loew, D. Lucio, P. Mendes, E. Minch, E. D. Mjolsness, Y. Nakayama, M. R. Nelson, P. F. Nielsen, T. Sakurada, J. C. Schaff, B. E. Shapiro, T. S. Shimizu, H. D. Spence, J. Stelling, K. Takahashi, M. Tomita, J. Wagner, and J. Wang (2003) The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19: 524-531 https://doi.org/10.1093/bioinformatics/btg015
  44. Yun, H., D. Y. Lee, J. Jeong, S. Lee, and S. Y. Lee (2005) MFAML: a standard data structure for representing and exchanging metabolic flux models. Bioinformatics 21: 3329-3330 https://doi.org/10.1093/bioinformatics/bti502