Poret Arnaud, Boissel Jean-Pierre
Novadiscovery, 60, avenue Rockefeller, 69008 Lyon, France; UMR CNRS 5558, 43, boulevard du 11-Novembre-1918, 69622 Villeurbanne cedex, France.
Novadiscovery, 60, avenue Rockefeller, 69008 Lyon, France.
C R Biol. 2014 Dec;337(12):661-78. doi: 10.1016/j.crvi.2014.10.002. Epub 2014 Nov 11.
Target identification aims at identifying biomolecules whose function should be therapeutically altered to cure the considered pathology. An algorithm for in silico target identification using Boolean network attractors is proposed. It assumes that attractors correspond to phenotypes produced by the modeled biological network. It identifies target combinations which allow disturbed networks to avoid attractors associated with pathological phenotypes. The algorithm is tested on a Boolean model of the mammalian cell cycle and its applications are illustrated on a Boolean model of Fanconi anemia. 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. Nevertheless, it is expected that the algorithm is of interest for target identification.
靶点识别旨在鉴定那些功能需经治疗性改变以治愈所考虑病症的生物分子。本文提出了一种利用布尔网络吸引子进行计算机模拟靶点识别的算法。该算法假定吸引子对应于所建模生物网络产生的表型。它识别出能使失调网络避免与病理表型相关联的吸引子的靶点组合。该算法在哺乳动物细胞周期的布尔模型上进行了测试，并在范可尼贫血的布尔模型上展示了其应用。结果表明，该算法能返回能够消除与病理表型相关联的吸引子的靶点组合，从而成功实现了所提出的计算机模拟靶点识别。然而，与任何计算机模拟证据一样，理论与实践之间仍有一座桥梁需要跨越。尽管如此，预计该算法在靶点识别方面具有一定价值。
