Lee Byoungkoo, LeDuc Philip R, Schwartz Russell
654 Mellon Institute, Carnegie Mellon/University of Pittsburgh Joint Program in Computational Biology, 4400 Fifth Avenue, Pittsburgh, Pennsylvania 15213, USA.
Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Oct;80(4 Pt 1):041918. doi: 10.1103/PhysRevE.80.041918. Epub 2009 Oct 14.
The intracellular environment imposes a variety of constraints on biochemical reaction systems that can substantially change reaction rates and equilibria relative to an ideal solution-based environment. One of the most notable features of the intracellular environment is its dense macromolecular crowding, which, among many other effects, tends to strongly enhance binding and assembly reactions. Despite extensive study of biochemistry in crowded media, it remains extremely difficult to predict how crowding will quantitatively affect any given reaction system due to the dependence of the crowding effect on numerous assumptions about the reactants and crowding agents involved. We previously developed a two dimensional stochastic off-lattice model of binding reactions based on the Green's function reaction dynamics method in order to create a versatile simulation environment in which one can explore interactions among many parameters of a crowded assembly system. In the present work, we examine interactions among several critical parameters for a model dimerization system: the total concentration of reactants and inert particles, the binding probability upon a collision between two reactant monomers, the mean time of dissociation reactions, and the diffusion coefficient of the system. Applying regression models to equilibrium constants across parameter ranges shows that the effect of the total concentration is approximately captured by a low-order nonlinear polynomial model, while the other three parameter effects are each accurately captured by a linear model. Furthermore, validation on tests with multi-parameter variations reveals that the effects of these parameters are separable from one another over a broad range of variation in all four parameters. The simulation work suggests that predictive models of crowding effects can accommodate a wider variety of parameter variations than prior theoretical models have so far achieved.
细胞内环境对生化反应系统施加了各种限制，相对于基于理想溶液的环境，这些限制可显著改变反应速率和平衡。细胞内环境最显著的特征之一是其密集的大分子拥挤现象，在许多其他影响中，这种现象往往会强烈增强结合和组装反应。尽管对拥挤介质中的生物化学进行了广泛研究，但由于拥挤效应依赖于有关所涉及反应物和拥挤剂的众多假设，因此预测拥挤将如何定量影响任何给定反应系统仍然极其困难。我们之前基于格林函数反应动力学方法开发了一种结合反应的二维随机非晶格模型，以创建一个通用的模拟环境，在其中可以探索拥挤组装系统许多参数之间的相互作用。在本工作中，我们研究了一个模型二聚化系统的几个关键参数之间的相互作用：反应物和惰性粒子的总浓度、两个反应物单体碰撞时的结合概率、解离反应的平均时间以及系统的扩散系数。对跨参数范围的平衡常数应用回归模型表明，总浓度的影响大约由一个低阶非线性多项式模型捕获，而其他三个参数的影响分别由一个线性模型准确捕获。此外，对多参数变化测试的验证表明，在所有四个参数的广泛变化范围内，这些参数的影响是彼此可分离的。模拟工作表明，与迄今为止先前的理论模型相比，拥挤效应的预测模型可以适应更多种参数变化。
