Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.
Modeling networks of coupled enzymatic reactions using the total quasi-steady state approximation.
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DOI：
10.1371/journal.pcbi.0030045
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发表时间：
2007-03-16
影响因子：
4.3
通讯作者：
Tyson JJ
中科院分区：
文献类型：
--
作者：
Ciliberto A;Capuani F;Tyson JJ
在代谢网络中，代谢物的存在通常大大超过催化它们相互转化的酶，用米氏速率定律描述这些反应的速率是完全有效的。该速率定律假定酶-底物复合物的浓度（C）远小于游离底物的浓度（S 0）。然而，在蛋白质相互作用网络中，酶和底物都是浓度相当的蛋白质，忽略C相对于S 0是无效的。Borghans，DeBoer和Segel开发了一种酶动力学的替代描述，当C与S 0相当时有效。我们扩展这种描述，Borghans等人。调用总准稳态近似，耦合酶反应的网络。首先，我们分析了一个孤立的Goldbeter-Koshland开关时，酶和底物的浓度相当。然后，基于一个真实的调控细胞周期进程的分子网络的例子，我们将两个和三个Goldbeter-Koshland开关耦合在一起，研究反馈在蛋白激酶和磷酸酶网络中的作用。我们的分析表明，总准稳态近似为蛋白质相互作用网络提供了一个很好的动力学形式，因为（1）它揭示了酶促反应的模块结构，（2）它提出了一个简单的算法来制定正确的动力学方程，（3）与经典的Michaelis-Menten动力学相反，它成功地忠实地再现了网络的动力学定性和定量。细胞对环境的生理反应是由非常复杂的基因-蛋白质相互作用网络控制的。要理解信息在这些网络中是如何处理的，需要对大量耦合化学反应的动力学行为建立精确的数学模型。为了避免产生大型且难以管理的模型，这些反应网络通常使用唯象反应速率定律来简化，例如用于酶催化反应的米氏速率定律。我们表明，在调节网络中，蛋白质交换酶和底物的地方，这样的简化必须进行小心，保持跟踪酶-底物复合物。这样做的风险在于，它提供了一个简化的分子网络描述，这种描述最多只能反映长期行为，而不能反映真实的网络的短期动态。为了避免这种可能性，我们建议使用一种替代方法称为总准稳态近似。我们将这种替代形式应用于控制真核细胞周期进入有丝分裂的网络模型，该模型由三个耦合的蛋白质修饰周期组成。而经典的Michaelis-Menten形式主义未能正确地表示这个网络的动态，我们提出的一个捕获的行为与经济和准确性。
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 (S 0). However, in protein interaction networks, the enzymes and substrates are all proteins in comparable concentrations, and neglecting C with respect to S 0 is not valid. Borghans, DeBoer, and Segel developed an alternative description of enzyme kinetics that is valid when C is comparable to S 0. 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. The physiological responses of a cell to its environment are controlled by gene–protein interaction networks of great complexity. To understand how information is processed in these networks requires accurate mathematical models of the dynamical behavior of large sets of coupled chemical reactions. To avoid producing large and hardly manageable models, such reaction networks are often simplified using phenomenological reaction rate laws, such as the Michaelis–Menten rate law for an enzyme-catalyzed reaction. We show that, in regulatory networks where proteins swap places as enzymes and substrates, such simplifications must be carried out with care, keeping track of enzyme–substrate complexes. The risk is to provide a simplified description of the molecular networks that at best is correct for the long-term behavior but fails to represent the short-term dynamics of the real network. To avoid such a possibility, we suggest using an alternative approach called the total quasi-steady state approximation. We apply this alternative formalism to a model of the network controlling the entry into mitosis in the eukaryotic cell cycle, composed of three coupled protein modification cycles. Whereas the classical Michaelis–Menten formalism fails to represent the dynamics of this network correctly, the one we propose captures the behavior with economy and accuracy.
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影响因子：
21.3
作者：
Pomerening, JR;Sontag, ED;Ferrell, JE
通讯作者：
Ferrell, JE
影响因子：
5.4
作者：
Blüthgen, N;Bruggeman, FJ;Kholodenko, BN
通讯作者：
Kholodenko, BN
影响因子：
2
作者：
Tzafriri, AR;Edelman, ER
通讯作者：
Edelman, ER
影响因子：
64.8
作者：
Hartwell, LH;Hopfield, JJ;Murray, AW
通讯作者：
Murray, AW
DOI：
10.1073/pnas.0235349100
发表时间：
2003-02-04
影响因子：
11.1
作者：
Sha, W;Moore, J;Sible, JC
通讯作者：
Sible, JC