Missiuro Patrycja Vasilyev, Liu Kesheng, Zou Lihua, Ross Brian C, Zhao Guoyan, Liu Jun S, Ge Hui
Whitehead Institute for Biomedical Research, Cambridge, Massachusetts, USA.
PLoS Comput Biol. 2009 Apr;5(4):e1000350. doi: 10.1371/journal.pcbi.1000350. Epub 2009 Apr 10.
Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.
近期对细胞网络的研究揭示了基因和蛋白质的模块化组织。例如，在相互作用组网络中，一个模块指的是一组相互作用的蛋白质，它们形成分子复合物和/或生化途径，并共同介导一个生物学过程。然而，目前对于生物信息如何在不同模块之间传递仍知之甚少。我们开发了信息流分析方法，这是一种新的计算方法，可识别在整个网络中生物信息传递的核心蛋白质。在信息流分析中，我们将相互作用组网络表示为一个电路，其中相互作用被建模为电阻，蛋白质被建模为互连节点。将生物信号的传播解释为电流流动，我们的方法为每个蛋白质计算一个信息流分数。与以往仅考虑拓扑特征的网络中心性指标（如度或介数）不同，我们的方法纳入了蛋白质 - 蛋白质相互作用的置信度分数，并在评估每个蛋白质的重要性时自动考虑网络中的所有可能路径。我们将我们的方法应用于酿酒酵母和秀丽隐杆线虫的相互作用组网络。我们发现，当一个蛋白质被去除时观察到致死性和多效性的可能性与该蛋白质的信息流分数呈正相关。即使在度或介数较低的蛋白质中，高信息流分数也可作为功能丧失致死性或多效性的有力预测指标。信息流分数与表型之间的相关性支持了我们的假设，即高信息流的蛋白质位于相互作用组网络的中心位置。我们还表明，当向相互作用组添加大量噪声数据时，信息流分数的排名比介数的排名更一致。最后，我们将基因表达数据与秀丽隐杆线虫的相互作用数据相结合，构建了肌肉特异性基因的相互作用组网络。我们发现，在肌肉相互作用组网络中信息流排名高但在整个网络中排名不高的基因往往在肌肉功能中发挥重要作用。这种通过信息流模型研究组织特异性网络的框架也可应用于其他组织和其他生物体。
