Mazaya Maulida, Trinh Hung-Cuong, Kwon Yung-Keun
Department of Electrical/Electronic and Computer Engineering, University of Ulsan, 93 Daehak-ro, Nam-gu, Ulsan, 44610, Republic of Korea.
BMC Syst Biol. 2017 Dec 21;11(Suppl 7):133. doi: 10.1186/s12918-017-0509-y.
Identification of novel gene-gene relations is a crucial issue to understand system-level biological phenomena. To this end, many methods based on a correlation analysis of gene expressions or structural analysis of molecular interaction networks have been proposed. They have a limitation in identifying more complicated gene-gene dynamical relations, though.
To overcome this limitation, we proposed a measure to quantify a gene-gene dynamical influence (GDI) using a Boolean network model and constructed a GDI network to indicate existence of a dynamical influence for every ordered pair of genes. It represents how much a state trajectory of a target gene is changed by a knockout mutation subject to a source gene in a gene-gene molecular interaction (GMI) network. Through a topological comparison between GDI and GMI networks, we observed that the former network is denser than the latter network, which implies that there exist many gene pairs of dynamically influencing but molecularly non-interacting relations. In addition, a larger number of hub genes were generated in the GDI network. On the other hand, there was a correlation between these networks such that the degree value of a node was positively correlated to each other. We further investigated the relationships of the GDI value with structural properties and found that there are negative and positive correlations with the length of a shortest path and the number of paths, respectively. In addition, a GDI network could predict a set of genes whose steady-state expression is affected in E. coli gene-knockout experiments. More interestingly, we found that the drug-targets with side-effects have a larger number of outgoing links than the other genes in the GDI network, which implies that they are more likely to influence the dynamics of other genes. Finally, we found biological evidences showing that the gene pairs which are not molecularly interacting but dynamically influential can be considered for novel gene-gene relationships.
Taken together, construction and analysis of the GDI network can be a useful approach to identify novel gene-gene relationships in terms of the dynamical influence.
识别新的基因-基因关系是理解系统层面生物学现象的关键问题。为此，人们提出了许多基于基因表达相关性分析或分子相互作用网络结构分析的方法。然而，它们在识别更复杂的基因-基因动态关系方面存在局限性。
为克服这一局限性，我们提出了一种使用布尔网络模型量化基因-基因动态影响（GDI）的方法，并构建了一个GDI网络来表示每对有序基因之间动态影响的存在。它表示在基因-基因分子相互作用（GMI）网络中，目标基因的状态轨迹因源基因的敲除突变而改变的程度。通过对GDI网络和GMI网络的拓扑比较，我们观察到前者网络比后者网络更密集，这意味着存在许多动态影响但分子上不相互作用的基因对。此外，GDI网络中产生了更多的枢纽基因。另一方面，这些网络之间存在相关性，即节点的度值彼此正相关。我们进一步研究了GDI值与结构特性的关系，发现它分别与最短路径长度和路径数量存在负相关和正相关。此外，GDI网络可以预测在大肠杆菌基因敲除实验中其稳态表达受到影响的一组基因。更有趣的是，我们发现有副作用的药物靶点在GDI网络中的出链数量比其他基因更多，这意味着它们更有可能影响其他基因的动态。最后，我们发现生物学证据表明，那些分子上不相互作用但动态有影响的基因对可被视为新的基因-基因关系。
综上所述，构建和分析GDI网络可以成为一种从动态影响角度识别新的基因-基因关系的有用方法。
