ReactionRate typed again by BJ,2008 based on paper eq.A9-12 (the one contained in the Teusink-file was suspciously complex, eg. conained[ADP])
added by BJ: to make the step F6P->F16BP reversible in Glycolysis
introduce new constant VmGAPDHall "overall Vm" (original has forward and reverse separately) only to have a constant to be multiplied when modelling genetic regulation
added by BJ: respiration(oxidative phosphorylation) - rate will be multiplied with relativ Oxygen level
added by BJ: Glycerol transport reaction to compare with Glycerol_outside concentration which is measured in [Wiebe]
Kinetics by BJ: to make the step PDC reversible a negative term was added to the original irreversible hill kinetics of [Teusink] with the new constant KmPDCACE
Exploring the effect of variable enzyme concentrations in a kinetic model of yeast glycolysis Jozsef Bruck, Wolfram Liebermeister, Edda Klipp, Genome Inform 2008 20:1-14 Abstract: Metabolism is one of the best studied fields of biochemistry, but its regulation involves processes on many different levels, some of which are still not understood well enough to allow for quantitative modeling and prediction. Glycolysis in yeast is a good example: although high-quality quantitative data are available, well-established mathematical models typically only cover direct regulation of the involved enzymes by metabolite binding. The effect of various metabolites on the enzyme kinetics is summarized in carefully developed mathematical formulae. However, this approach implicitly assumes that the enzyme concentrations themselves are constant, thus neglecting other regulatory levels - e.g. transcriptional and translational regulation--involved in the regulation of enzyme activities. It is believed, however, that different experimental conditions result in different enzyme activities regulated by the above mechanisms. Detailed modeling of all regulatory levels is still out of reach since some of the necessary data - e.g. quantitative large scale enzyme concentration data sets - are lacking or rare. Nevertheless, a viable approach is to include the regulation of enzyme concentrations into an established model and to investigate whether this improves the predictive capabilities. Proteome data are usually hard to obtain, but levels of mRNA transcripts may be used instead as clues for changes in enzyme concentrations. Here we investigate whether including mRNA data into an established model of yeast glycolysis allows to predict the steady state metabolic concentrations for different experimental conditions. To this end, we modified an established ODE model for the glycolytic pathway of yeast to include changes of enzyme concentrations. Presumable changes were inferred from mRNA transcript level measurement data. We investigate how this approach can be used to predict metabolite concentrations for steady-state yeast cultures at five different oxygen levels ranging from anaerobic to fully aerobic conditions. We were partly able to reproduce the experimental data and present a number of changes that were necessary to improve the modeling result. Further modifications to this model and improved data fit will be presented soon. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. . To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.