Monine Michael I, Haugh Jason M
Department of Chemical and Biomolecular Engineering, North Carolina State University, Box 7905, 911 Partners Way, Raleigh, North Carolina 27695-7905, USA.
J Chem Phys. 2005 Aug 15;123(7):074908. doi: 10.1063/1.2000236.
Biochemical transduction of signals received by living cells typically involves molecular interactions and enzyme-mediated reactions at the cell membrane, a problem that is analogous to reacting species on a catalyst surface or interface. We have developed an efficient Brownian dynamics algorithm that is especially suited for such systems and have compared the simulation results with various continuum theories through prediction of effective enzymatic rate constant values. We specifically consider reaction versus diffusion limitation, the effect of increasing enzyme density, and the spontaneous membrane association/dissociation of enzyme molecules. In all cases, we find the theory and simulations to be in quantitative agreement. This algorithm may be readily adapted for the stochastic simulation of more complex cell signaling systems.
活细胞接收到的信号的生化转导通常涉及细胞膜上的分子相互作用和酶介导的反应,这一问题类似于催化剂表面或界面上的反应物种。我们开发了一种特别适用于此类系统的高效布朗动力学算法,并通过预测有效酶速率常数的值,将模拟结果与各种连续介质理论进行了比较。我们特别考虑了反应与扩散限制、酶密度增加的影响以及酶分子的自发膜结合/解离。在所有情况下,我们发现理论和模拟结果在数量上是一致的。该算法可以很容易地适用于更复杂细胞信号系统的随机模拟。