179 related articles for article (PubMed ID: 15647297)
1. On the use of qualitative reasoning to simulate and identify metabolic pathways.
King RD; Garrett SM; Coghill GM
Bioinformatics; 2005 May; 21(9):2017-26. PubMed ID: 15647297
[TBL] [Abstract][Full Text] [Related]
2. A structured approach for the engineering of biochemical network models, illustrated for signalling pathways.
Breitling R; Gilbert D; Heiner M; Orton R
Brief Bioinform; 2008 Sep; 9(5):404-21. PubMed ID: 18573813
[TBL] [Abstract][Full Text] [Related]
3. Reverse engineering of biochemical equations from time-course data by means of genetic programming.
Sugimoto M; Kikuchi S; Tomita M
Biosystems; 2005 May; 80(2):155-64. PubMed ID: 15823414
[TBL] [Abstract][Full Text] [Related]
4. In silico simulation of biological network dynamics.
Salwinski L; Eisenberg D
Nat Biotechnol; 2004 Aug; 22(8):1017-9. PubMed ID: 15235611
[TBL] [Abstract][Full Text] [Related]
5. Simulating complex intracellular processes using object-oriented computational modelling.
Johnson CG; Goldman JP; Gullick WJ
Prog Biophys Mol Biol; 2004 Nov; 86(3):379-406. PubMed ID: 15302205
[TBL] [Abstract][Full Text] [Related]
6. Computational methodologies for modelling, analysis and simulation of signalling networks.
Gilbert D; Fuss H; Gu X; Orton R; Robinson S; Vyshemirsky V; Kurth MJ; Downes CS; Dubitzky W
Brief Bioinform; 2006 Dec; 7(4):339-53. PubMed ID: 17116646
[TBL] [Abstract][Full Text] [Related]
7. Qualitative modelling of regulated metabolic pathways: application to the tryptophan biosynthesis in E.coli.
Simão E; Remy E; Thieffry D; Chaouiya C
Bioinformatics; 2005 Sep; 21 Suppl 2():ii190-6. PubMed ID: 16204102
[TBL] [Abstract][Full Text] [Related]
8. A hybrid approach for efficient and robust parameter estimation in biochemical pathways.
Rodriguez-Fernandez M; Mendes P; Banga JR
Biosystems; 2006; 83(2-3):248-65. PubMed ID: 16236429
[TBL] [Abstract][Full Text] [Related]
9. A quadratic programming approach for decomposing steady-state metabolic flux distributions onto elementary modes.
Schwartz JM; Kanehisa M
Bioinformatics; 2005 Sep; 21 Suppl 2():ii204-5. PubMed ID: 16204104
[TBL] [Abstract][Full Text] [Related]
10. New approaches to modelling and analysis of biochemical reactions, pathways and networks.
Crampin EJ; Schnell S
Prog Biophys Mol Biol; 2004 Sep; 86(1):1-4. PubMed ID: 15261523
[No Abstract] [Full Text] [Related]
11. The need for speed in stochastic simulation.
Lok L
Nat Biotechnol; 2004 Aug; 22(8):964-5. PubMed ID: 15286647
[No Abstract] [Full Text] [Related]
12. An optimized algorithm for flux estimation from isotopomer distribution in glucose metabolites.
Selivanov VA; Puigjaner J; Sillero A; Centelles JJ; Ramos-Montoya A; Lee PW; Cascante M
Bioinformatics; 2004 Dec; 20(18):3387-97. PubMed ID: 15256408
[TBL] [Abstract][Full Text] [Related]
13. Comparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification.
Geng B; Zhou X; Zhu J; Hung YS; Wong ST
J Biomed Inform; 2008 Apr; 41(2):272-81. PubMed ID: 17950040
[TBL] [Abstract][Full Text] [Related]
14. Ensemble learning of genetic networks from time-series expression data.
Nam D; Yoon SH; Kim JF
Bioinformatics; 2007 Dec; 23(23):3225-31. PubMed ID: 17977884
[TBL] [Abstract][Full Text] [Related]
15. Rapid simulation and analysis of isotopomer distributions using constraints based on enzyme mechanisms: an example from HT29 cancer cells.
Selivanov VA; Meshalkina LE; Solovjeva ON; Kuchel PW; Ramos-Montoya A; Kochetov GA; Lee PW; Cascante M
Bioinformatics; 2005 Sep; 21(17):3558-64. PubMed ID: 16002431
[TBL] [Abstract][Full Text] [Related]
16. HEMET: mathematical model of biochemical pathways for simulation and prediction of HEpatocyte METabolism.
De Maria C; Grassini D; Vozzi F; Vinci B; Landi A; Ahluwalia A; Vozzi G
Comput Methods Programs Biomed; 2008 Oct; 92(1):121-34. PubMed ID: 18640740
[TBL] [Abstract][Full Text] [Related]
17. MMG: a probabilistic tool to identify submodules of metabolic pathways.
Sanguinetti G; Noirel J; Wright PC
Bioinformatics; 2008 Apr; 24(8):1078-84. PubMed ID: 18292114
[TBL] [Abstract][Full Text] [Related]
18. Learning regulatory programs that accurately predict differential expression with MEDUSA.
Kundaje A; Lianoglou S; Li X; Quigley D; Arias M; Wiggins CH; Zhang L; Leslie C
Ann N Y Acad Sci; 2007 Dec; 1115():178-202. PubMed ID: 17934055
[TBL] [Abstract][Full Text] [Related]
19. Cooperativity and saturation in biochemical networks: a saturable formalism using Taylor series approximations.
Sorribas A; Hernández-Bermejo B; Vilaprinyo E; Alves R
Biotechnol Bioeng; 2007 Aug; 97(5):1259-77. PubMed ID: 17187441
[TBL] [Abstract][Full Text] [Related]
20. Gene network inference from incomplete expression data: transcriptional control of hematopoietic commitment.
Missal K; Cross MA; Drasdo D
Bioinformatics; 2006 Mar; 22(6):731-8. PubMed ID: 16332705
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]