Zabet Nicolae Radu
Cambridge Systems Biology Centre, University of Cambridge, Tennis Court Road, Cambridge CB2 1QR, UK.
BMC Syst Biol. 2012 Sep 8;6:121. doi: 10.1186/1752-0509-6-121.
Site-specific Transcription Factors (TFs) are proteins that bind to specific sites on the DNA and control the activity of a target gene by enhancing or decreasing the rate at which the gene is transcribed by RNA polymerase. The process by which TF molecules locate their target sites is a key component of transcriptional regulation. Therefore it is essential to gain insight into the mechanisms by which TFs search for the target sites.Research in this area uses experimental and analytical approaches, but also stochastic simulations of the search process. Previous work based on stochastic simulations focussed only on short sequences, primarily for reasons of technical feasibility. Many of these studies had to disregard possible biases introduced by reducing a genome-wide system to a smaller subsystem. In particular, we identified crucial parameters that require adjustment, which were not adequately changed in these previous studies.
We investigated several methods that adequately adapt the parameters of stochastic simulations of the facilitated diffusion, when the full sequence space is reduced to smaller regions of interest. We found two methods that scale the system accordingly: the copy number model and the association rate model. We systematically compared the results produced by simulations of the subsystem with respect to the original system. Our results confirmed that the copy number model is adequate only for high abundance TFs, while for low abundance TFs the association rate model is the only one that reproduces with high accuracy the results of the full system.
We propose a strategy to reduce the size of the system that adequately adapts important parameters to capture the behaviour of the full system. This enables correct simulations of a smaller sequence space (which can be as small as 100 Kbp) and, thus, provides independence from computationally intensive genome-wide simulations of the facilitated diffusion mechanism.
位点特异性转录因子（TFs）是一类蛋白质，它们与DNA上的特定位点结合，并通过增强或降低RNA聚合酶转录基因的速率来控制靶基因的活性。TF分子定位其靶位点的过程是转录调控的关键组成部分。因此，深入了解TFs寻找靶位点的机制至关重要。该领域的研究采用了实验和分析方法，同时也对搜索过程进行了随机模拟。以往基于随机模拟的工作仅聚焦于短序列，主要是出于技术可行性的原因。这些研究中的许多不得不忽略将全基因组系统简化为较小子系统所引入的可能偏差。特别是，我们确定了需要调整的关键参数，而这些参数在以往的研究中并未得到充分改变。
当全序列空间缩减为较小的感兴趣区域时，我们研究了几种能充分调整促进扩散随机模拟参数的方法。我们发现了两种相应地缩放系统的方法：拷贝数模型和缔合速率模型。我们系统地比较了子系统模拟结果与原始系统的结果。我们的结果证实，拷贝数模型仅适用于高丰度TFs，而对于低丰度TFs，缔合速率模型是唯一能高精度重现全系统结果的模型。
我们提出了一种缩小系统规模的策略，该策略能充分调整重要参数以捕捉全系统的行为。这使得能够对较小的序列空间（小至100 Kbp）进行正确模拟，从而无需对促进扩散机制进行计算密集型的全基因组模拟。
