Title |
Modeling Networks of Coupled Enzymatic Reactions Using the Total Quasi-Steady State Approximation
|
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Published in |
PLoS Computational Biology, March 2007
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DOI | 10.1371/journal.pcbi.0030045 |
Pubmed ID | |
Authors |
Andrea Ciliberto, Fabrizio Capuani, John J Tyson |
Abstract |
In metabolic networks, metabolites are usually present in great excess over the enzymes that catalyze their interconversion, and describing the rates of these reactions by using the Michaelis-Menten rate law is perfectly valid. This rate law assumes that the concentration of enzyme-substrate complex (C) is much less than the free substrate concentration (S0). However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S0. We extend this description, which Borghans et al. call the total quasi-steady state approximation, to networks of coupled enzymatic reactions. First, we analyze an isolated Goldbeter-Koshland switch when enzymes and substrates are present in comparable concentrations. Then, on the basis of a real example of the molecular network governing cell cycle progression, we couple two and three Goldbeter-Koshland switches together to study the effects of feedback in networks of protein kinases and phosphatases. Our analysis shows that the total quasi-steady state approximation provides an excellent kinetic formalism for protein interaction networks, because (1) it unveils the modular structure of the enzymatic reactions, (2) it suggests a simple algorithm to formulate correct kinetic equations, and (3) contrary to classical Michaelis-Menten kinetics, it succeeds in faithfully reproducing the dynamics of the network both qualitatively and quantitatively. |
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Geographical breakdown
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Germany | 3 | 2% |
Turkey | 1 | <1% |
Netherlands | 1 | <1% |
Switzerland | 1 | <1% |
France | 1 | <1% |
Italy | 1 | <1% |
Australia | 1 | <1% |
Other | 5 | 3% |
Unknown | 122 | 82% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 47 | 32% |
Researcher | 38 | 26% |
Professor > Associate Professor | 15 | 10% |
Student > Bachelor | 9 | 6% |
Professor | 8 | 5% |
Other | 22 | 15% |
Unknown | 10 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 57 | 38% |
Biochemistry, Genetics and Molecular Biology | 16 | 11% |
Physics and Astronomy | 14 | 9% |
Mathematics | 11 | 7% |
Chemistry | 10 | 7% |
Other | 27 | 18% |
Unknown | 14 | 9% |