使用布尔网络吸引子发现治疗靶点：避免病理表型
Therapeutic target discovery using Boolean network attractors: avoiding pathological phenotypes
摘要：
靶点鉴定是药物发现的步骤之一，其目的是鉴定其功能应在治疗上改变以治愈所考虑的病理的生物分子。本文提出了一种基于布尔网络吸引子的计算机目标识别算法。它假设动力系统（如布尔网络）的吸引子对应于由建模的生物系统产生的表型。在此假设下，并给定对病理生理学建模的布尔网络，该算法识别能够去除与病理表型相关的吸引子的目标组合。它是一个布尔模型的哺乳动物细胞周期轴承组成性失活的视网膜母细胞瘤蛋白，如在癌症中所见，其应用程序的范科尼贫血的布尔模型上进行了测试。结果表明，该算法返回的目标组合能够去除与病理表型相关联的吸引子，然后成功地执行所提出的在硅片上的目标识别。然而，与任何计算机证据一样，理论与实践之间存在一座桥梁，因此需要将其与湿实验室实验结合使用。尽管如此，预计该算法是感兴趣的目标识别，特别是通过利用计算方法的廉价性和预测能力，以优化昂贵的湿实验室实验的效率。
英文摘要：
Target identification, one of the steps of drug discovery, aims at identifying biomolecules whose function should be therapeutically altered in order to cure the considered pathology. This work proposes an algorithm for in silico target identification using Boolean network attractors. It assumes that attractors of dynamical systems, such as Boolean networks, correspond to phenotypes produced by the modeled biological system. Under this assumption, and given a Boolean network modeling a pathophysiology, the algorithm identifies target combinations able to remove attractors associated with pathological phenotypes. It is tested on a Boolean model of the mammalian cell cycle bearing a constitutive inactivation of the retinoblastoma protein, as seen in cancers, and its applications are illustrated on a Boolean model of Fanconi anemia. The results show that the algorithm returns target combinations able to remove attractors associated with pathological phenotypes and then succeeds in performing the proposed in silico target identification. However, as with any in silico evidence, there is a bridge to cross between theory and practice, thus requiring it to be used in combination with wet lab experiments. Nevertheless, it is expected that the algorithm is of interest for target identification, notably by exploiting the inexpensiveness and predictive power of computational approaches to optimize the efficiency of costly wet lab experiments.
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