Algebraic Models of Biochemical Networks

Methods in Enzymology

Volumn 467, Issue C, 2009, Pages 163-196

  Laubenbacher, Reinhard ^a   Jarrah, Abdul Salam ^a  

^a VIRGINIA BIOINFORMATICS INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FUNCTIONAL GENOMICS; INFORMATION SCIENCE; LACTOSE OPERON; MATHEMATICAL MODEL; MOLECULAR DYNAMICS; PRIORITY JOURNAL; REVIEW; STOCHASTIC MODEL; SYSTEMS BIOLOGY;

ALGORITHMS; COMPUTATIONAL BIOLOGY; COMPUTER SIMULATION; FEEDBACK, PHYSIOLOGICAL; GENE REGULATORY NETWORKS; LAC OPERON; MODELS, BIOLOGICAL; MODELS, THEORETICAL; SIGNAL TRANSDUCTION; STOCHASTIC PROCESSES;

EID: 71549142857     PISSN: 00766879     EISSN: None     Source Type: Book Series    
DOI: 10.1016/S0076-6879(09)67007-5     Document Type: Review

References (95)

1. Akutsu T., Miyano S., et al. Identification of genetic networks from a small number of gene expression patterns under the Boolean network model. Pac. Symp. Biocomput. (1999) 17-28
2. Akutsu T., Miyano S., et al. Algorithms for inferring qualitative models of biological networks. Pac. Symp. Biocomput. (2000) 293-304
3. Akutsu T., Miyano S., et al. Inferring qualitative relations in genetic networks and metabolic pathways. Bioinformatics 16 8 (2000) 727-734
4. Albert R., and Othmer H.G. The topology of the regulatory interactions predicts the expression pattern of the segment polarity genes in Drosophila melanogaster. J. Theor. Biol. 223 1 (2003) 1-18
5. Alon U. An Introduction to Systems Biology: Design Principles of Biological Circuits (2006), CRC Press, Boca Raton, FL
6. Andrec M., Kholodenko B.N., et al. Inference of signaling and gene regulatory networks by steady-state perturbation experiments: Structure and accuracy. J. Theor. Biol. 232 3 (2005) 427
7. Balleza E., Alvarez-Buylla E.R., et al. Critical dynamics in gene regulatory networks: Examples from four kingdoms. PLoS One 3 6 (2008) e2456
8. Bansal M., Gatta G.D., et al. Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22 7 (2006) 815-822
9. Bansal M., Belcastro V., et al. How to infer gene networks from expression profiles. Mol. Syst. Biol. 3 (2007) 78 10.1038/msb4100120
10. Barrett C.B., Herring C.D., et al. The global transcriptional regulatory network for metabolism in Escherichia coli exhibits few dominant functional states. Proc. Natl. Acad. Sci. USA 102 52 (2005) 19103-19108
11. Beal M.J., Falciani F., et al. A Bayesian approach to reconstructing genetic regulatory networks with hidden factors. Bioinformatics 21 3 (2005) 349-356
12. Bentele M., Lavrik I., et al. Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis. J. Cell Biol. 166 6 (2004) 839-851
13. Bernard A., and Hartemink A. Informative structure priors: Joint learning of dynamic regulatory networks from multiple types of data. Pac. Symp. Biocompu. conf. proceedings (2005) pp. 459-470
14. Brenner S. Loose ends. Curr. Biol. (1997) 73
15. Bruggemann F.J., and Westerhoff H. The nature of systems biology. Trends Microbiol. 15 1 (2006) 45-50
16. Butler J.T., Tsutomu S., et al. Average path length of binary decision diagrams. IEEE Trans. Comput. 54 9 (2005) 1041-1053
17. Butte A.J., Tamayo P., et al. Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. PNAS 97 22 (2000) 12182-12186
18. Camacho D., vera-Licona P., et al. Comparison of reverse-engineering method using an in silico network. Ann. NY Acad. Sci. 1115 (2007) 73-89
19. Chang W.-C., Li C.-W., et al. Quantitative inference of dynamic regulatory pathways via microarray data. BMC Bioinform. 6 1 (2005) 44
20. Chaves M., Albert R., et al. Robustness and fragility of Boolean models for genetic regulatory networks. J. Theor. Biol. 235 (2005) 431-449
21. Ching W.K., Ng M.M., Fung E.S., and Akutsu T. On construction of stochastic genetic networks based on gene expression sequences. Int. J. Neural Syst. 15 4 (2005) 297-310
22. Davidich M.I., and Bornholdt S. Boolean network model predicts cell cycle sequence of fission yeast. PLoS One 3 2 (2007) e1672
23. deBoer R.J. Theoretical biology. Undergraduate Course at Utrecht University (2008). http://theory.bio.uu.nl/rdb/books/ available at
24. de la Fuente A., and Mendes P. Quantifying gene networks with regulatory strengths. Mol. Biol. Rep. 29 1-2 (2002) 73-77
25. de la Fuente A., Bing N., et al. Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20 18 (2004) 3565-3574
26. Deng X., Geng H., et al. EXAMINE: A computational approach to reconstructing gene regulatory networks. Biosystems 81 2 (2005) 125
27. Dimitrova E., Jarrah A., et al. A Groebner-fan-based method for biochemical network modeling. Proceedings of the International Symposium on Symbolic and Algebraic Computation (2007), Assoc Comp Mach, Waterloo, CA
28. Dimitrova E., Vera-Licona P., et al. Data discretization for reverse-engineering: A comparative study (2008) (under review)
29. Dimitrova E., Garcia-Puente L., et al. Parameter estimation for Boolean models of biological networks. Theor. Comp. Sci. (2009) (in press)
30. Dojer N., Gambin A., et al. Applying dynamic Bayesian networks to perturbed gene expression data. BMC Bioinform. 7 1 (2006) 249
31. Ernst J., Vainas O., et al. Reconstructing dynamic regulatory maps. Mol. Syst. Biol. 3 (2007) 74
32. Espinosa-Soto C., Padilla-Longoria P., et al. A gene regulatory network model for cell-fate determination during Arabidopsis thaliana flower development that is robust and recovers experimental gene expression profiles. Plant Cell 16 11 (2004) 1923-1939
33. Faure A., Naldi A., et al. Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle. Bioinformatics 22 14 (2006) 124-131
34. Friedman N. Inferring cellular networks using probabilistic graphical models. Science 303 5659 (2004) 799-805
35. Friedman N., Linial M., et al. Using Bayesian networks to analyze expression data. J. Comput. Biol. 7 3-4 (2000) 601-620
36. Gadkar K., Gunawan R., et al. Iterative approach to model identification of biological networks. BMC Bioinform. 6 1 (2005) 155
37. Gardner T.S., di Bernardo D., et al. Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301 5629 (2003) 102-105
38. Gat-Viks I., and Shamir R. Chain functions and scoring functions in genetic networks. Bioinformatics 19 (2003) 108-117
39. Gonzalez A., Chaouiya C., et al. Logical modelling of the role of the Hh pathway in the patterning of the Drosophila wing disc. Bioinformatics 24 234-240 (2008) 16
40. Gupta S., Bisht S.S., et al. Boolean network analysis of a neurotransmitter signaling pathway. J. Theor. Biol. 244 3 (2007) 463-469
41. Harris S.E., Sawhill B.K., et al. A model of transcriptional regulatory networks based on biases in the observed regulation rules. Complex Syst. 7 4 (2002) 23-40
42. Hartemink A., Gifford D., et al. Bayesian methods for elucidating genetic regulatory networks. IEEE Intel. Syst. 17 (2002) 37-43
43. Herrgard M.J., Lee B.S., et al. Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae. Genome Res. 16 (2006) 627-635
44. Ideker T.E., and Lauffenburger D. Building with a scaffold: Emerging strategies for high- to low-level cellular modeling. Trends Biotechnol. 21 6 (2003) 256-262
45. Ideker T.E., Thorsson V., et al. Discovery of regulatory interactions through perturbation: Inference and experimental design. Pac. Symp. Biocomput. 5 (2000) 305-316
46. Ideker T.E., Ozier O., et al. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl. 1 (2002) S233-S240
47. Jarrah A., Raposa B., et al. Nested canalyzing, unate cascade, and polynomial functions. Physica D 233 2 (2007) 167-174
48. Jarrah A., Laubenbacher R., Stigler B., and Stillman M. Reverse-engineering polynomial dynamical systems. Adv. Appl. Math. 39 (2007) 477-489
49. Kauffman S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22 3 (1969) 437-467
50. Kauffman S.A., Peterson C., et al. Random Boolean network models and the yeast transcriptional network. Proc. Natl. Acad. Sci. USA 100 25 (2003) 14796-14799
51. Kauffman S.A., Peterson C., et al. Genetic networks with canalyzing Boolean rules are always stable. Proc. Natl. Acad. Sci. USA 101 49 (2004) 17102-17107
52. Kell D.B. Metabolomics and systems biology: Making sense of the soup. Curr. Opin. Microbiol. 7 3 (2004) 296-307
53. Kim J., Bates D., et al. Least-squares methods for identifying biochemical regulatory networks from noisy measurements. BMC Bioinform. 8 1 (2007) 8
54. Kimura S., Ide K., et al. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 21 7 (2005) 1154-1163
55. Kremling A., Fischer S., et al. A benchmark for methods in reverse engineering and model discrimination: Problem formulation and solutions. Genome Res. 14 9 (2004) 1773-1785
56. Laubenbacher R., and Stigler B. A computational algebra approach to the reverse engineering of gene regulatory networks. J. Theor. Biol. 229 (2004) 523-537
57. Laubenbacher R., and Sturmfels B. Computer algebra in systems biology. Am. Math. Mon. (2009) (in press)
58. Li F., Long T., et al. The yeast cell-cycle network is robustly designed. Proc. Natl. Acad. Sci. USA 101 14 (2004) 4781-4786
59. Li S., Assman S.M., et al. Predicting essential components of signal transduction networks: A dynamic model of guard cell abscisic acid signaling. PLoS Biol. 4 10 (2006) e312
60. Li X., Rao S., et al. Discovery of time-delayed gene regulatory networks based on temporal gene expression profiling. BMC Bioinform. 7 1 (2006) 26
61. Liang S., Fuhrman S., et al. REVEAL, a general reverse engineering algorithm for inference of genetic network architectures. Pac. Symp Biocomput. 3 (1998) 18-29
62. Loomis W.F., and Sternberg P.W. Genetic networks. Science 269 5224 (1995) 649
63. Margolin A.A., Nemenman I., et al. ARACNE: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 7 Suppl. 1 (2006) S7
64. Marino S., and Voit E. An automated procedure for the extraction of metabolic network information from time series data. J. Bioinform. Comp. Biol. 4 3 (2006) 665-691
65. Martin S., Zhang Z., et al. Boolean dynamics of genetic regulatory networks inferred from microarray time series data. Bioinformatics 23 7 (2007) 866-874
66. Mehra S., Hu W.-S., et al. A Boolean algorithm for reconstructing the structure of regulatory networks. Metab. Eng. 6 4 (2004) 326
67. Mendoza L. A network model for the control of the differentiation process in Th cells. Biosystems 84 (2006) 101-114
68. Mendoza L., and Alvarez-Buylla E.R. Genetic regulation of root hair development in Arabidopsis thaliana: A network model. J. Theor. Biol. 204 (2000) 311-326
69. Nariai N., Tamada Y., et al. Estimating gene regulatory networks and protein-protein interactions of Saccharomyces cerevisiae from multiple genome-wide data. Bioinformatics 21 Suppl. 2 (2005) ii206-ii212
70. Nikolayewaa S., Friedela M., et al. Boolean networks with biologically relevant rules show ordered behavior. Biosystems 90 1 (2007) 40-47
71. Pe'er D., Regev A., et al. Inferring subnetworks from perturbed expression profiles. Bioinformatics 17 Suppl. 1 (2001) S215-S224
72. Pournara I., and Wernisch L. Reconstruction of gene networks using Bayesian learning and manipulation experiments. Bioinformatics 20 17 (2004) 2934-2942
73. Raeymaekers L. Dynamics of Boolean networks controlled by biologically meaningful functions. J. Theor. Biol. 218 3 (2002) 331-341
74. Rice J.J., Tu Y., et al. Reconstructing biological networks using conditional correlation analysis. Bioinformatics 21 6 (2005) 765-773
75. Robeva R., and Laubenbacher R. Mathematical biology education: Beyond calculus. Science 325 5940 (2009) 542-543
76. Saez-Rodriguez J., Simeoni L., et al. A logical model provides insights into T cell receptor signaling. PLoS Comp. Biol. 3 8 (2007) e163
77. Samal A., and Jain S. The regulatory network of E. coli metabolism as a Boolean dynamical system exhibits both homeostasis and flexibility of response. BMC Syst. Biol. 2 (2008) 21
78. Sanchez L., and Thieffry D. A logical analysis of the Drosophila gap-gene system. J. Theor. Biol. 211 (2001) 115-141
79. Savageau M.A. Biochemical systems theory: Operational differences among variant representations and their significance. J. Theor. Biol. 151 4 (1991) 509
80. Shmulevich I., Dougherty E.R., et al. Probabilistic Boolean networks: A rule-based uncertainty model for gene regulatory networks. Bioinformatics 18 2 (2002) 261-274
81. Shmulevich I., Gluhovsky I., et al. Steady-state analysis of genetic regulatory networks modelled by probabilistic Boolean networks. Comp. Funct. Genomics 4 6 (2003) 601-608
82. Shmulevich I., Lahdesmaki H., et al. The role of certain Post classes of Boolean network models of genetic networks. Proc. Natl. Acad. Sci. USA 100 19 (2003) 10734-10739
83. Stigler B., and Veliz-Cuba A. Network topology as a driver of bistability in the lac operon (2009). http://arxiv.org/abs/0807.3995
84. Thomas R., and D'Ari R. Biological Feedback (1989), CRC Press
85. Thomas R., Mehrotra S., et al. A model-based optimization framework for the inference on gene regulatory networks from DNA array data. Bioinformatics 20 17 (2004) 3221-3235
86. Tringe S., Wagner A., et al. Enriching for direct regulatory targets in perturbed gene-expression profiles. Genome Biol. 5 4 (2004) R29
87. Vera-Licona P., Jarrah A., et al. An optimization algorithm for the inference of biological networks (2009) (in preparation)
88. Waddington C.H. Canalisation of development and the inheritance of acquired characters. Nature 150 (1942) 563-564
89. Wagner A. How to reconstruct a large genetic network from n gene perturbations in fewer than n(2) easy steps. Bioinformatics 17 12 (2001) 1183-1197
90. Wagner A. Reconstructing pathways in large genetic networks from genetic perturbations. J. Comput. Biol. 11 1 (2004) 53-60
91. Werhli A.V., Grzegorczyk M., et al. Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical Gaussian models and Bayesian networks. Bioinformatics 22 20 (2006) 2523-2531
92. Yeung M.K., Tegner J., et al. Reverse engineering gene networks using singular value decomposition and robust regression. Proc. Natl. Acad. Sci. USA 99 9 (2002) 6163-6168
93. Yu J., Smith V.A., et al. Advances to Bayesian network inference for generating causal networks from observational biological data. Bioinformatics 20 18 (2004) 3594-3603
94. Zhang R., Shah M.V., et al. Network model of survival signaling in large granular lymphocyte leukemia. Proc. Natl. Acad. Sci. USA 105 42 (2008) 16308-16313
95. Zou M., and Conzen S.D. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics 21 1 (2005) 71-79