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Stochastic simulation of Boolean rxncon models: towards quantitative analysis of large signaling networks

机译：布尔rxncon模型的随机模拟：走向大型信号网络的定量分析

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BackgroundCellular decision-making is governed by molecular networks that are highly complex. An integrative understanding of these networks on a genome wide level is essential to understand cellular health and disease. In most cases however, such an understanding is beyond human comprehension and requires computational modeling. Mathematical modeling of biological networks at the level of biochemical details has hitherto relied on state transition models. These are typically based on enumeration of all relevant model states, and hence become very complex unless severely – and often arbitrarily – reduced. Furthermore, the parameters required for genome wide networks will remain underdetermined for the conceivable future. Alternatively, networks can be simulated by Boolean models, although these typically sacrifice molecular detail as well as distinction between different levels or modes of activity. However, the modeling community still lacks methods that can simulate genome scale networks on the level of biochemical reaction detail in a quantitative or semi quantitative manner.
机译：背景细胞的决策是由高度复杂的分子网络控制的。在基因组范围内对这些网络的综合理解对于理解细胞的健康和疾病至关重要。但是，在大多数情况下，这种理解是人类无法理解的，需要进行计算建模。迄今为止，生物网络在生化细节水平上的数学建模一直依赖于状态转换模型。这些通常基于所有相关模型状态的枚举，因此会变得非常复杂，除非进行严重（通常是任意地）减少。此外，在可能的将来，全基因组网络所需的参数仍将不确定。或者，可以通过布尔模型来模拟网络，尽管这些模型通常会牺牲分子细节以及不同级别或活动模式之间的区别。然而，建模界仍然缺乏能够以定量或半定量方式在生化反应细节水平上模拟基因组规模网络的方法。

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