生化过程的特异性和完成时间分布
Specificity and Completion Time Distributions of Biochemical Processes
摘要：
为了从大量相似的生物化学构件中产生特定的复杂结构，许多生物化学系统需要对小分子差异具有高灵敏度。用于解释这种高特异性的第一个也是最常见的模型是动力学校正，该模型已扩展到从DNA错配检测到细胞信号传导过程的各种系统。虽然动力学校对模型的规范属性是众所周知的，并在各种情况下进行了研究，很少有人知道它的时间行为。本文研究了离散随机两分支动力学校正格式的动力学性质。利用相应的化学主方程的拉普拉斯变换，我们得到了完成时间分布的解析解。特别是，我们提供了表达式的特异性和过程完成时间的均值和方差。我们还表明，在很宽的参数范围内，区分两种不同产品的过程可以减少到一个更简单的三点过程。我们的研究结果允许特异性和完成时间之间的相互作用的系统研究，以及在生物系统中的动力学校正模型的有效性进行测试。
英文摘要：
In order to produce specific complex structures from a large set of similar biochemical building blocks, many biochemical systems require high sensitivity to small molecular differences. The first and most common model used to explain this high specificity is kinetic proofreading, which has been extended to a variety of systems from detection of DNA mismatch to cell signaling processes. While the specification properties of the kinetic proofreading model are well known and were studied in various contexts, very little is known about its temporal behavior. In this work, we study the dynamical properties of discrete stochastic two branch kinetic proofreading schemes. Using the Laplace transform of the corresponding chemical master equation, we obtain an analytical solution for the completion time distribution. In particular we provide expressions for the specificity and the mean and the variance of the process completion times. We also show that, for a wide range of parameters a process distinguishing between two different products can be reduced to a much simpler three point process. Our results allow for the systematic study of the interplay between specificity and completion times as well as testing the validity of the kinetic proofreading model in biological systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
