Modeling stochasticity and variability in gene regulatory networks.

David Murrugarra, Alan Veliz-Cuba, Boris Aguilar, Seda Arat, Reinhard Laubenbacher
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引用次数: 82

Abstract

Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such as the Gillespie algorithm have been used successfully. This article contributes an approach as an alternative to these classical settings. Within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory networks are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components. Stochasticity is modeled at the biological function level under the assumption that even if the expression levels of the input nodes of an update rule guarantee activation or degradation there is a probability that the process will not occur due to stochastic effects. This approach allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability. We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53-mdm2 complex.

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基因调控网络的随机性和可变性建模。
基因调控网络的随机性建模是分子系统生物学中一个重要而复杂的问题。为了阐明固有噪声,一些建模策略如Gillespie算法已被成功地应用。本文提供了一种替代这些经典设置的方法。在离散范式中,基因、蛋白质和基因调控网络的其他分子成分被建模为离散变量,并被分配为逻辑规则,通过与其他成分的相互作用来描述它们的调控。假设即使更新规则的输入节点的表达水平保证激活或退化,在生物功能水平上对随机性进行建模,但由于随机效应,该过程可能不会发生。这种方法允许对离散模型进行更精细的分析,并为细胞群模拟提供自然设置,以研究细胞间的可变性。我们将我们的方法应用于两个研究最多的调控网络,细菌的lambda噬菌体感染的结果和p53-mdm2复合物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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