Switching regulatory models of cellular stress response
Switching regulatory models of cellular stress response
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DOI：
10.1093/bioinformatics/btp138
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发表时间：
2009-05
期刊：
影响因子：
5.8
通讯作者：
Guido Sanguinetti;Andreas Ruttor;Manfred Opper;Cédric Archambeau
中科院分区：
文献类型：
--
作者：
Guido Sanguinetti;Andreas Ruttor;Manfred Opper;Cédric Archambeau
细胞的应激反应通常是由转录因子（tf）的快速激活介导的。鉴于实验分析TF活性的困难，已经提出了几种统计方法来从微阵列时间过程中推断它们。然而，这些方法往往依赖于先前的假设，排除了在压力反应中观察到的快速反应。我们提出了一个新的统计模型来推断tf如何介导细胞的应激反应。该模型基于感觉tf在活跃和不活跃状态之间快速转换的假设。因此，我们使用双稳态动力系统来模拟mRNA的产生，该系统的行为由潜在随机过程驱动的微分方程系统描述。我们假设随机过程是一个两态连续时间跳跃过程，并设计了推理问题的精确解和有效的近似算法。我们对模拟数据和描述大肠杆菌对突然缺氧反应的真实数据进行了评估。这突出了所提出方法的准确性及其产生新假设和可测试预测的潜力。可用性本文中用到的MATLAB和c++代码可以从http://www.dcs.shef.ac.uk/~guido/下载。
MOTIVATION Stress response in cells is often mediated by quick activation of transcription factors (TFs). Given the difficulty in experimentally assaying TF activities, several statistical approaches have been proposed to infer them from microarray time courses. However, these approaches often rely on prior assumptions which rule out the rapid responses observed during stress response. RESULTS We present a novel statistical model to infer how TFs mediate stress response in cells. The model is based on the assumption that sensory TFs quickly transit between active and inactive states. We therefore model mRNA production using a bistable dynamical systems whose behaviour is described by a system of differential equations driven by a latent stochastic process. We assume the stochastic process to be a two-state continuous time jump process, and devise both an exact solution for the inference problem as well as an efficient approximate algorithm. We evaluate the method on both simulated data and real data describing Escherichia coli's response to sudden oxygen starvation. This highlights both the accuracy of the proposed method and its potential for generating novel hypotheses and testable predictions. AVAILABILITY MATLAB and C++ code used in the article can be downloaded from http://www.dcs.shef.ac.uk/~guido/.