Wu Xiaogang, Huan Tianxiao, Pandey Ragini, Zhou Tianshou, Chen Jake Y
School of Informatics, Indiana University, Indianapolis, IN 46202, USA.
Int J Comput Biol Drug Des. 2009;2(4):340-52. doi: 10.1504/IJCBDD.2009.030765. Epub 2009 Jan 4.
The identification of molecular entities involved in human diseases has been a primary focus of post-genomic biomedicine for pursuing the clinical goals of diagnosis and therapeutic treatment. An emerging perspective in systems biology is that the essential biological roles of molecular entities seem to be well correlated with general molecular network properties. Several types of biological complex networks, including protein interaction networks, have a feature of scale-free networks that relates to fractals (multi-scale self-similarity). Using Alzheimer's Disease (AD) as a case study, we constructed an AD-relevant protein interaction subnetwork. We further developed a computational framework based on Ant Colony Optimisation (ACO) to rank disease network relevant nodes. In this framework, the task of ranking nodes is represented as the problem of finding optimal density distributions of 'ant colonies' on all nodes of the network. Our results also revealed fractal-like properties of the network.
识别参与人类疾病的分子实体一直是后基因组生物医学为实现诊断和治疗的临床目标而关注的主要焦点。系统生物学中一个新兴的观点是，分子实体的基本生物学作用似乎与一般分子网络特性密切相关。几种类型的生物复杂网络，包括蛋白质相互作用网络，具有与分形（多尺度自相似性）相关的无标度网络特征。以阿尔茨海默病（AD）为例，我们构建了一个与AD相关的蛋白质相互作用子网。我们进一步开发了一个基于蚁群优化（ACO）的计算框架，用于对疾病网络相关节点进行排序。在此框架中，节点排序任务被表示为在网络所有节点上寻找“蚁群”最优密度分布的问题。我们的结果还揭示了该网络的类分形特性。
