Murrugarra David, Veliz-Cuba Alan, Aguilar Boris, Arat Seda, Laubenbacher Reinhard
Department of Mathematics, Virginia Tech, Blacksburg, VA 24061-0123, USA.
EURASIP J Bioinform Syst Biol. 2012 Jun 6;2012(1):5. doi: 10.1186/1687-4153-2012-5.
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.
在分子系统生物学中，对基因调控网络中的随机性进行建模是一个重要且复杂的问题。为了阐明内在噪声，诸如 Gillespie 算法等几种建模策略已被成功应用。本文提出了一种方法作为这些经典方法的替代方案。在离散范式中，基因调控网络的基因、蛋白质和其他分子成分被建模为离散变量，并被赋予逻辑规则来描述它们通过与其他成分相互作用进行的调控。随机性是在生物学功能层面进行建模的，其假设是即使更新规则的输入节点的表达水平保证激活或降解，由于随机效应，该过程仍有可能不会发生。这种方法允许对离散模型进行更精细的分析，并为研究细胞间变异性的细胞群体模拟提供了一个自然的设置。我们将我们的方法应用于两个研究最多的调控网络，即细菌的 lambda 噬菌体感染结果和 p53-mdm2 复合物。
