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首页> 外文期刊>BMC Bioinformatics >A CoD-based stationary control policy for intervening in large gene regulatory networks
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A CoD-based stationary control policy for intervening in large gene regulatory networks

机译:基于CoD的固定控制策略,用于干预大型基因调控网络

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BackgroundOne of the most important goals of the mathematical modeling of gene regulatory networks is to alter their behavior toward desirable phenotypes. Therapeutic techniques are derived for intervention in terms of stationary control policies. In large networks, it becomes computationally burdensome to derive an optimal control policy. To overcome this problem, greedy intervention approaches based on the concept of the Mean First Passage Time or the steady-state probability mass of the network states were previously proposed. Another possible approach is to use reduction mappings to compress the network and develop control policies on its reduced version. However, such mappings lead to loss of information and require an induction step when designing the control policy for the original network.ResultsIn this paper, we propose a novel solution, CoD-CP, for designing intervention policies for large Boolean networks. The new method utilizes the Coefficient of Determination (CoD) and the Steady-State Distribution (SSD) of the model. The main advantage of CoD-CP in comparison with the previously proposed methods is that it does not require any compression of the original model, and thus can be directly designed on large networks. The simulation studies on small synthetic networks shows that CoD-CP performs comparable to previously proposed greedy policies that were induced from the compressed versions of the networks. Furthermore, on a large 17-gene gastrointestinal cancer network, CoD-CP outperforms other two available greedy techniques, which is precisely the kind of case for which CoD-CP has been developed. Finally, our experiments show that CoD-CP is robust with respect to the attractor structure of the model.ConclusionsThe newly proposed CoD-CP provides an attractive alternative for intervening large networks where other available greedy methods require size reduction on the network and an extra induction step before designing a control policy.
机译:背景技术基因调控网络的数学建模的最重要目标之一是将其行为改变为理想的表型。从固定控制策略的角度出发,得出了用于干预的治疗技术。在大型网络中,获得最佳控制策略在计算上变得繁重。为了克服这个问题,先前提出了基于平均首次通过时间或网络状态的稳态概率质量的贪婪干预方法。另一种可能的方法是使用缩减映射来压缩网络并针对其缩减版本开发控制策略。但是,这种映射会导致信息丢失,并且在设计原始网络的控制策略时需要一个归纳步骤。结果在本文中,我们提出了一种新颖的解决方案CoD-CP,用于设计大型布尔网络的干预策略。新方法利用了模型的确定系数(CoD)和稳态分布(SSD)。与以前提出的方法相比,CoD-CP的主要优点是它不需要对原始模型进行任何压缩,因此可以直接在大型网络上进行设计。对小型合成网络的仿真研究表明,CoD-CP的性能可与以前提议的贪婪策略(从网络的压缩版本中导出)相比。此外,在一个由17个基因组成的大型胃肠道癌症网络上,CoD-CP优于其他两种可用的贪婪技术,而这正是CoD-CP已开发出来的一种情况。最后,我们的实验表明CoD-CP在模型的吸引子结构方面具有较强的鲁棒性。结论新提出的CoD-CP为干预大型网络提供了一种有吸引力的替代方法,在这种情况下,其他可用的贪婪方法需要减小网络的大小并进行额外归纳在设计控制策略之前执行步骤。

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