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Items: 1 to 20 of 117

1.

Classic and contemporary approaches to modeling biochemical reactions.

Chen WW, Niepel M, Sorger PK.

Genes Dev. 2010 Sep 1;24(17):1861-75. doi: 10.1101/gad.1945410. Review.

2.

The total quasi-steady-state approximation is valid for reversible enzyme kinetics.

Tzafriri AR, Edelman ER.

J Theor Biol. 2004 Feb 7;226(3):303-13.

PMID:
14643644
3.

The total quasi-steady-state approximation for fully competitive enzyme reactions.

Pedersena MG, Bersani AM, Bersani E.

Bull Math Biol. 2007 Jan;69(1):433-57. Epub 2006 Jul 19.

PMID:
16850351
4.

An introduction to dynamical systems.

Sobie EA.

Sci Signal. 2011 Sep 13;4(191):tr6. doi: 10.1126/scisignal.2001982.

5.

New types of experimental data shape the use of enzyme kinetics for dynamic network modeling.

Tummler K, Lubitz T, Schelker M, Klipp E.

FEBS J. 2014 Jan;281(2):549-71. doi: 10.1111/febs.12525. Epub 2013 Nov 4. Review.

6.

Estimating parameters for generalized mass action models using constraint propagation.

Tucker W, Kutalik Z, Moulton V.

Math Biosci. 2007 Aug;208(2):607-20. Epub 2006 Dec 8.

PMID:
17306307
7.

Simplifying principles for chemical and enzyme reaction kinetics.

Klonowski W.

Biophys Chem. 1983 Sep;18(2):73-87.

PMID:
6626688
8.

Graphical reduction of reaction networks by linear elimination of species.

Sáez M, Wiuf C, Feliu E.

J Math Biol. 2017 Jan;74(1-2):195-237. doi: 10.1007/s00285-016-1028-y. Epub 2016 May 24.

PMID:
27221101
9.

Legitimacy of the stochastic Michaelis-Menten approximation.

Sanft KR, Gillespie DT, Petzold LR.

IET Syst Biol. 2011 Jan;5(1):58. doi: 10.1049/iet-syb.2009.0057.

PMID:
21261403
10.

Mathematical modelling of dynamics and control in metabolic networks. I. On Michaelis-Menten kinetics.

Palsson BO, Lightfoot EN.

J Theor Biol. 1984 Nov 21;111(2):273-302.

PMID:
6513572
11.

Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.

Ciliberto A, Capuani F, Tyson JJ.

PLoS Comput Biol. 2007 Mar 16;3(3):e45.

12.

Reduced models of networks of coupled enzymatic reactions.

Kumar A, Josić K.

J Theor Biol. 2011 Jun 7;278(1):87-106. doi: 10.1016/j.jtbi.2011.02.025. Epub 2011 Mar 4.

PMID:
21377474
13.

A generalised enzyme kinetic model for predicting the behaviour of complex biochemical systems.

Wong MK, Krycer JR, Burchfield JG, James DE, Kuncic Z.

FEBS Open Bio. 2015 Mar 9;5:226-39. doi: 10.1016/j.fob.2015.03.002. eCollection 2015.

14.

Modeling of uncertainties in biochemical reactions.

Mišković L, Hatzimanikatis V.

Biotechnol Bioeng. 2011 Feb;108(2):413-23. doi: 10.1002/bit.22932.

PMID:
20830674
15.

How molecular should your molecular model be? On the level of molecular detail required to simulate biological networks in systems and synthetic biology.

Gonze D, Abou-Jaoudé W, Ouattara DA, Halloy J.

Methods Enzymol. 2011;487:171-215. doi: 10.1016/B978-0-12-381270-4.00007-X.

PMID:
21187226
16.

Michaelis-Menten relations for complex enzymatic networks.

Kolomeisky AB.

J Chem Phys. 2011 Apr 21;134(15):155101. doi: 10.1063/1.3580564.

17.
18.

Accuracy of the Michaelis-Menten approximation when analysing effects of molecular noise.

Lawson MJ, Petzold L, Hellander A.

J R Soc Interface. 2015 May 6;12(106). pii: 20150054. doi: 10.1098/rsif.2015.0054.

19.
20.

Michaelis-Menten dynamics in protein subnetworks.

Rubin KJ, Sollich P.

J Chem Phys. 2016 May 7;144(17):174114. doi: 10.1063/1.4947478.

PMID:
27155632

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