Reconstructing nonlinear dynamic models of gene regulation using stochastic sampling

BMC Bioinformatics

Volumn 10, Issue , 2009, Pages

  Mazur, Johanna ^a   Ritter, Daniel ^a   Reinelt, Gerhard ^a   Kaderali, Lars ^a  

^a UNIVERSITY OF HEIDELBERG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL EXPLOSION; GENE REGULATORY NETWORKS; NONLINEAR DIFFERENTIAL EQUATION; POSTERIOR DISTRIBUTIONS; QUANTITATIVE DYNAMICS; REGULATORY INTERACTIONS; STOCHASTIC INFERENCE; TIME-SERIES GENE EXPRESSION DATA;

COMPLEX NETWORKS; DIFFERENTIAL EQUATIONS; DYNAMIC MODELS; ELECTRIC NETWORK TOPOLOGY; GENE EXPRESSION; PROBABILITY DISTRIBUTIONS; STOCHASTIC SYSTEMS; TOPOLOGY;

STOCHASTIC MODELS;

ARTICLE; BIOLOGY; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; METHODOLOGY; NONLINEAR SYSTEM;

COMPUTATIONAL BIOLOGY; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; NONLINEAR DYNAMICS;

EID: 75649151459     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/1471-2105-10-448     Document Type: Article

References (53)

1. de Jong H. Modeling and Simulation of Genetic Regulatory Systems: A Literature Review. Journal of Computational Biology 2002, 9:67-103. 10.1089/10665270252833208, 11911796.
2. Garner TS, Faith JJ. Reverse-engineering transcription control networks. Physics of Life Reviews 2005, 2:65-88.
3. Filkov V. Identifying Gene Regulatory Networks from Gene Expression Data. Handbook of Computational Molecular Biology 2005, 27.1-27.29. Boca Raton, FL, USA: Chapman & Hall/CRC Press, Aluru S.
4. Goutsias J, Lee NH. Computational and Experimental Approaches for Modeling Gene Regulatory Networks. Current Pharmaceutical Design 2007, 13:1415-1436. 10.2174/138161207780765945, 17504165.
5. Bansal M, Belcastro V, Ambesi-Impiombato A, di Bernardo D. How to infer gene networks from expression profiles. Molecular Systems Biology 2007, 3(78). 1828749, 17299415.
6. Cho KH, Choo SM, Jung SH, Kim JR, Choi HS, Kim J. Reverse engineering of gene regulatory networks. IET Systems Biology 2007, 1(3):149-163. 10.1049/iet-syb:20060075, 17591174.
7. Markowetz F, Spang R. Inferring cellular networks - a review. BMC Bioinformatics 2007, 8(Suppl 6):S5. 10.1186/1471-2105-8-S6-S5, 1995541, 17903286.
8. Kaderali L, Radde N. Inferring Gene Regulatory Networks from Expression Data. Computational Intelligence in Bioinformatics, Volume 94 of Studies in Computational Intelligence 2008, 33-74. Berlin: Springer, Kelemen A, Abraham A, Chen Y., http://www.springerlink.com/content/t100323m8141840k/?p=9f516592e7b44869 91d1928407f55d48&pi=4
9. Voit EO. Computational Analysis of Biochemical Systems: A Practical Guide for Biochemists and Molecular Biologists 2000, Cambridge: Cambridge University Press.
10. Wiggins S. Introduction to applied nonlinear dynamical systems and chaos 2003, New York: Springer.
11. Heuser H. Gewöhnliche Differentialgleichungen 2006, Wiesbaden: Teubner.
12. Chen T, He H, Church G. Modeling gene expression with differential equations. Pacific Symposium on Biocomputing 1999, 4:29-40.
13. Gardner TS, di Bernardo D, Lorenzo D, Collins JJ. Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling. Science 2003, 301(5629):102-105. 10.1126/science.1081900, 12843395.
14. Savageau MA. Biochemical systems analysis 1976, London [u.a.]: Addison-Wesley.
15. Heinrich R, Schuster S. The regulation of cellular systems 1996, NewYork [u.a.]: Chapman & Hall.
16. Yeung MKS, Tegnér J, Collins JJ. Reverse engineering gene networks using singular value decomposition and robust regression. PNAS 2002, 99(9):6163-6168. 10.1073/pnas.092576199, 122920, 11983907.
17. Kholodenko BN, Kiyatkin A, Bruggeman FJ, Sontag E, Westerhoff HV. Untangling the wires: A strategy to trace functional interactions in signaling and gene networks. PNAS 2002, 99(20):12841-12846. 10.1073/pnas.192442699, 130547, 12242336.
18. Sontag E, Kiyatkin A, Kholodenko BN. Inferring dynamic architecture of cellular networks using time series of gene expression, protein and metabolite data. Bioinformatics 2004, 20(12):1877-1886. 10.1093/bioinformatics/bth173, 15037511.
19. Schmidt H, Cho KH, Jacobson EW. Identification of small scale biochemical networks based on general type systems perturbations. FEBS Journal 2005, 272:2141-2151. 10.1111/j.1742-4658.2005.04605.x, 15853799.
20. Steinke F, Seeger M, Tsuda K. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models. BMC Systems Biology 2007, 1(51). 2233642, 18021391.
21. de Jong H, Page M. Qualitative simulation of large and complex genetic regulatory systems. Proceedings of the 14th European Conference on Artificial Intelligence 2000, 141-145. Amsterdam: IOS Press, Horn W.
22. Bongard J, Lipson H. Automated reverse engineering of nonlinear dynamical systems. Proceedings of the National Academy of Sciences USA 2007, 104:9943-9948.
23. Savageau MA. Biochemical Systems Analysis: III. Dynamic Solutions using a Power-law Approximation. Journal of theoretical Biology 1970, 26(2):215-226. 10.1016/S0022-5193(70)80013-3, 5434343.
24. Savegeau MA. Biochemical Systems Theory: Operational Differences Among Variant Representations and their Significance. Journal of theoretical Biology 1991, 151(4):509-530. 10.1016/S0022-5193(05)80367-4, 1943154.
25. Weaver D, Workman C, Stormo G. Modeling regulatory networks with weight matrices. Pacific Symposium on Biocomputing 1999, 4:112-123. World Scientific.
26. Spieth C, Streichert F, Speer N, Zell A. A memetic inference method for gene regulatory networks based on S-Systems. Evolutionary Computation 2004, 1:152-157.
27. Spieth C, Streichert F, Speer N, Zell A. Multi-Objective Model Optimization for Inferring Gene Regulatory Networks. Lecture Notes in Computer Science, Volume 3410/2005 2005, 607-620. Springer.
28. Spieth C, Hassis N, Streichert F. Comparing Mathematical Models on the Problem of Network Inference. Proceedings 8th Annual Conference Genetic and evolutionary computation (GECCO 2006) 2006, 279-285., http://portal.acm.org/citation.cfm?id=1143997.1144045
29. Kimura S, Ide K, Kashihara A, Kano M, Hatakeyama M, Masui R, Nakagawa N, Yokoyama S, Kuramitsu S, Konagaya A. Inference of S-system models of genetic networks using a cooperative coevolutionary algorithm. Bioinformatics 2005, 21(7):1154-1163. 10.1093/bioinformatics/bti071, 15514004.
30. Perkins TJ, Jaeger J, Reinitz J, Glass L. Reverse engineering the Gap gene network of drosophila melanogaster. PLoS Computational Biology 2006, 2:e51. 10.1371/journal.pcbi.0020051, 1463021,1463021, 16710449.
31. Busch H, Camacho-Trullio D, Rogon Z, Breuhahn K, Angel P, Eils R, Szabowski A. Gene Network Dynamics controlling Keratinocyte Migration. Molecular Systems Biology 2008, 4(199). 2516358, 18594517.
32. Chen KC, Wang TY, Tseng HH, Huang CYF, Kao CY. A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics 2005, 21(12):2883-2890. 10.1093/bioinformatics/bti415, 15802287.
33. Nachman I, Regev A, Friedman N. Inferring quantitative models of regulatory networks from expression data. Bioinformatics 2004, 201:i248-i256.
34. van Someren EP, Vaes BLT, Steegenga WT, Sijbers AM, Dechering KJ, Reinders MJT. Least absolute regression network analysis of the murine osteoblast differentiation network. Bioinformatics 2006, 22:477-484. 10.1093/bioinformatics/bti816, 16332709.
35. Wehrli AV, Husmeier D. Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology 2007, 6:15.
36. Ramsay JO, Hooker G, Campbell D, Cao J. Parameter estimation for differential equations: a generalized smoothing approach. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2007, 69(5):741-796.
37. Varah JM. A spline least squares method for numerical parameter estimation in differential equations. SIAM Journal on Scientific & Statistical Computing 1982, 3:28-46.
38. Poyton AA, Varziri MS, McAuley KB, McLellan PJ, Ramsay JO. Parameter estimation in continuous-time dynamic models using principal differential analysis. Computers and Chemical Engineering 2006, 30:698-708.
39. Radde N, Kaderali L. Bayesian Inference of Gene Regulatory Networks Using Gene Expression Time Series Data. BIRD, Volume 4414 of Lecture Notes in Computer Science 2007, 1-15. Berlin: Springer, Hochreiter S, Wagner R., http://www.springerlink.com/content/k69807u72u21458u/?p=9f516592e7b44869 91d1928407f55d48&pi=1
40. Alon U. An Introduction to Systems Biology: Design Principles of Biological Circuits 2006, Boca Raton, FL, USA: Chapman & Hall/CRC.
41. Jacob F, Monod J. Genetic regulatory mechanisms in the synthesis of proteins. Journal of Molecular Biology 1961, 3:318-356.
42. Yagil G, Yagil E. On the Relation between Effector Concentration and the Rate of Induced Enzyme Synthesis. Biophysical Journal 1971, 11:11-27. 10.1016/S0006-3495(71)86192-1, 1484024, 4923389.
43. Arnone MI, Davidson EH. The hardwiring of development: organization and function of genomic regulatory systems. Development 1997, 124:1851-1864.
44. Liu Y, Zhang HH, Park C, Ahn J. Support vector machines with adaptive Lq penalty. Comput Stat Data Anal 2007, 51(12):6380-6394.
45. Kaderali L, Zander T, Faigle U, Wolf J, Schultze JL, Schrader R. CASPAR: A Hierarchical Bayesian Approach to predict Survival Times in Cancer from Gene Expression Data. Bioinformatics 2006, 22:1495-1502. 10.1093/bioinformatics/btl103, 16554338.
46. Duane D, Kennedy AD, Pendleton BJ, Roweth D. Hybrid Monte Carlo. Physics Letters B 1987, 195:216-222.
47. Neal RM. Bayesian Learning for Neural Networks 1996, Secaucus, NJ, USA: Springer-Verlag New York, Inc.
48. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH. Equations of State Calculations by Fast Computing Machines. The Journal of Chemical Physics 1953, 21(6):1087-1092.
49. Hastings WK. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 1970, 57:97-109.
50. Radde N, Kaderali L. Inference of an oscillating model for the yeast cell cycle. Discrete Applied Mathematics 2009, 157(10):2285-2295.
51. Stolovitzky G, Monroe D, Califano A. Dialogue on Reverse Engineering Assessment and Methods: The DREAM of high throughput pathway inference. 2007, 1115:1-22. Annals of the New York Academy of Sciences.
52. Cantone I, Marucci L, Iorio F, Ricci MA, Belcastro V, Bansal M, Santini S, di Bernardo M, di Bernardo D, Cosma MP. A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches. Cell 2009, 137:172-181. 10.1016/j.cell.2009.01.055, 19327819.
53. DREAM2 Challenge Scoring Methodology. , http://wiki.c2b2.columbia.edu/dream/results/