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聚合酶链式反应(PCR)过程通用概率模型参数的验证与估计

Validation and estimation of parameters for a general probabilistic model of the PCR process.

作者信息

Saha Nilanjan, Watson Layne T, Kafadar Karen, Ramakrishnan Naren, Onufriev Alexey, Mane Shrinivasrao, Vasquez-Robinet Cecilia

机构信息

Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA.

出版信息

J Comput Biol. 2007 Jan-Feb;14(1):97-112. doi: 10.1089/cmb.2006.0123.

Abstract

Earlier work rigorously derived a general probabilistic model for the PCR process that includes as a special case the Velikanov-Kapral model where all nucleotide reaction rates are the same. In this model, the probability of binding of deoxy-nucleoside triphosphate (dNTP) molecules with template strands is derived from the microscopic chemical kinetics. A recursive solution for the probability function of binding of dNTPs is developed for a single cycle and is used to calculate expected yield for a multicycle PCR. The model is able to reproduce important features of the PCR amplification process quantitatively. With a set of favorable reaction conditions, the amplification of the target sequence is fast enough to rapidly outnumber all side products. Furthermore, the final yield of the target sequence in a multicycle PCR run always approaches an asymptotic limit that is less than one. The amplification process itself is highly sensitive to initial concentrations and the reaction rates of addition to the template strand of each type of dNTP in the solution. This paper extends the earlier Saha model with a physics based model of the dependence of the reaction rates on temperature, and estimates parameters in this new model by nonlinear regression. The calibrated model is validated using RT-PCR data.

摘要

早期的工作严格推导了PCR过程的一般概率模型,其中包括Velikanov-Kapral模型作为特殊情况,在该模型中所有核苷酸反应速率相同。在这个模型中,脱氧核苷三磷酸(dNTP)分子与模板链结合的概率是从微观化学动力学推导出来的。针对单个循环开发了dNTP结合概率函数的递归解,并用于计算多循环PCR的预期产量。该模型能够定量再现PCR扩增过程的重要特征。在一组有利的反应条件下,目标序列的扩增速度足够快,以至于能迅速超过所有副产物。此外,多循环PCR运行中目标序列的最终产量总是接近一个小于1的渐近极限。扩增过程本身对初始浓度以及溶液中每种dNTP添加到模板链的反应速率高度敏感。本文用一个基于物理的反应速率对温度依赖性的模型扩展了早期的Saha模型,并通过非线性回归估计这个新模型中的参数。使用RT-PCR数据对校准后的模型进行了验证。

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