Editorial: part 2: network construction and mining for systems biology.

Abstract

The molecular networks provide the context in which the molecules interact with each other and can therefore help us understand the functions of these molecules. Unfortunately, the current knowledge about molecular networks is far from complete. It is time consuming and expensive to screen possible interactions between distinct molecules in a lab. Furthermore, the current interactomes deposited in popular public databases are generally static snapshots of the dynamic molecular interactions that change with time. Therefore, some computational approaches have been proposed for reverse-engineering and mining the interactomes. In this Special Issue, we reported the recent progress on computational approaches that have been developed to reverse-engineer the molecular networks and some algorithms that can be utilised to explore the networks to understand how the biological systems work. Gene regulation is one of the most important processes within the cells, which is extensively involved in development and disease, etc. However, it is not easy to determine which genes regulate the others, considering the possible combinations between tens of thousands of genes. In general, it is assumed that the transcription is mainly regulated by transcription factors (TFs). Recently, it is recognised that the microRNAs(miRNAs) also play important roles in the transcription and post-transcription processes, which makes it more difficult to recover the regulation networks underlying the transcription process. In their paper, “Construction and analysis of microRNA-transcription factor regulation network in arabidopsis”, Tang et al. developed a pipeline to construct the miRNA-TF regulation network for Arabidopsis, and the analysis of the regulation network provides insights into the mechanism underlying transcription within Arabidopsis. Deng et al. constructed a combinatorial regulatory network regulated by both transcription factors and miRNAs with a Graphical Adaptive Lasso model in breast cancer, in their paper entitled, “Using graphical adaptive Lasso approach to construct transcription factor and microRNA's combinatorial regulatory network in breast cancer”. With the regulation network, they found some important dysregulations that can help us understand the initiation and progress of breast cancer. More recently, it was found that the competing endogenous RNAs (ceRNAs) regulate the transcript expression via competition for common miRNAs, which provides insights into the complex regulation networks from another perspective. In their paper, “Construction and investigation of breast-cancer-specific ceRNA network based on the mRNA and miRNA expression data”, Zhou et al. developed a new approach to construct a breast cancer specific ceRNA network based on the mRNA and miRNA expression data, and found some novel regulations involved in the breast cancer. Beyond the genetic factors involved in the transcription, it is established that the epigenetic factors are also widely involved in the regulation of transcript expression. In their paper, “Modelling epigenetic regulation of gene expression in 12 human cell types reveals combinatorial patterns of cell-type-specific genes”, Lu et al. proposed a new mathematical model to bridge the histone modifications and the gene expression. With data from 12 human cell types, they found some histone modification markers for tissue specificity. The exploration of various types of existing networks indicates that most networks have specific topological patterns, where the community structure is among the most known ones. Finally, in “Anti-triangle centrality-based community detection in complex networks”, Jia et al. proposed a new algorithm to detect communities from complex networks based on anti-triangle centrality, and obtained some promising results. The new algorithm can be utilised to identify new functional modules from the molecular networks in the future. Xing-Ming Zhao received his Ph.D. degree from the University of Science and Technology of China in 2005. He was a postdoc fellow at the University of Tokyo during 2006-2008, and joined the Institute of Systems Biology at Shanghai University in 2008 as an Associate Professor. In 2012, he moved to the School of Electronics and Information Engineering, Tongji University. His research focuses on inference and analysis of molecular interaction networks, identification of signalling pathways, prediction of drug-protein interactions and drug combinations. He has published more than 50 journal papers, and is editorial board member of several journals.