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Items: 1 to 20 of 210

1.

Calibration of dynamic models of biological systems with KInfer.

Lecca P, Palmisano A, Ihekwaba A, Priami C.

Eur Biophys J. 2010 May;39(6):1019-39. doi: 10.1007/s00249-009-0520-3. Epub 2009 Aug 11.

PMID:
19669750
2.

Extraction of elementary rate constants from global network analysis of E. coli central metabolism.

Zhao J, Ridgway D, Broderick G, Kovalenko A, Ellison M.

BMC Syst Biol. 2008 May 7;2:41. doi: 10.1186/1752-0509-2-41.

3.

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems.

Rodriguez-Fernandez M, Egea JA, Banga JR.

BMC Bioinformatics. 2006 Nov 2;7:483.

4.

Dynamic sensitivity analysis of biological systems.

Wu WH, Wang FS, Chang MS.

BMC Bioinformatics. 2008 Dec 12;9 Suppl 12:S17. doi: 10.1186/1471-2105-9-S12-S17.

5.

Improved parameter estimation for systems with an experimentally located Hopf bifurcation.

Cedersund G, Knudsen C.

Syst Biol (Stevenage). 2005 Sep;152(3):161-8.

PMID:
16986279
6.

A system biology approach to understand functional activity of cell communication systems.

Vera J, Wolkenhauer O.

Methods Cell Biol. 2008;90:399-415. doi: 10.1016/S0091-679X(08)00817-0.

PMID:
19195559
7.

A hybrid approach for efficient and robust parameter estimation in biochemical pathways.

Rodriguez-Fernandez M, Mendes P, Banga JR.

Biosystems. 2006 Feb-Mar;83(2-3):248-65. Epub 2005 Oct 19.

PMID:
16236429
8.

Computational procedures for optimal experimental design in biological systems.

Balsa-Canto E, Alonso AA, Banga JR.

IET Syst Biol. 2008 Jul;2(4):163-72. doi: 10.1049/iet-syb:20070069.

PMID:
18681746
9.

Challenges in lin-log modelling of glycolysis in Lactococcus lactis.

del Rosario RC, Mendoza E, Voit EO.

IET Syst Biol. 2008 May;2(3):136-49. doi: 10.1049/iet-syb:20070030.

PMID:
18537454
10.

Computational systems biology and dose-response modeling in relation to new directions in toxicity testing.

Zhang Q, Bhattacharya S, Andersen ME, Conolly RB.

J Toxicol Environ Health B Crit Rev. 2010 Feb;13(2-4):253-76. doi: 10.1080/10937404.2010.483943.

PMID:
20574901
11.

Efficient, sparse biological network determination.

August E, Papachristodoulou A.

BMC Syst Biol. 2009 Feb 23;3:25. doi: 10.1186/1752-0509-3-25.

12.

Dynamic modelling and analysis of biochemical networks: mechanism-based models and model-based experiments.

van Riel NA.

Brief Bioinform. 2006 Dec;7(4):364-74. Epub 2006 Nov 14. Review.

13.

Parameter identification, experimental design and model falsification for biological network models using semidefinite programming.

Hasenauer J, Waldherr S, Wagner K, Allgöwer F.

IET Syst Biol. 2010 Mar;4(2):119-30. doi: 10.1049/iet-syb.2009.0030.

PMID:
20232992
14.

Experiment design through dynamical characterisation of non-linear systems biology models utilising sparse grids.

Donahue MM, Buzzard GT, Rundell AE.

IET Syst Biol. 2010 Jul;4(4):249-62. doi: 10.1049/iet-syb.2009.0031.

PMID:
20632775
15.

Proper lumping in systems biology models.

Dokoumetzidis A, Aarons L.

IET Syst Biol. 2009 Jan;3(1):40-51. doi: 10.1049/iet-syb:20070055.

PMID:
19154083
16.

Optimal sampling time selection for parameter estimation in dynamic pathway modeling.

Kutalik Z, Cho KH, Wolkenhauer O.

Biosystems. 2004 Jul;75(1-3):43-55.

PMID:
15245803
17.

Parameter estimation in modulated, unbranched reaction chains within biochemical systems.

Lall R, Voit EO.

Comput Biol Chem. 2005 Oct;29(5):309-18. Epub 2005 Oct 6.

PMID:
16213792
18.

The sorting direct method for stochastic simulation of biochemical systems with varying reaction execution behavior.

McCollum JM, Peterson GD, Cox CD, Simpson ML, Samatova NF.

Comput Biol Chem. 2006 Feb;30(1):39-49.

PMID:
16321569
19.
20.

WinBEST-KIT: Windows-based biochemical reaction simulator for metabolic pathways.

Sekiguchi T, Okamoto M.

J Bioinform Comput Biol. 2006 Jun;4(3):621-38.

PMID:
16960966
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