The radio block center (RBC) is one of the most essential ground systems in a high-speed train control system both in Europe and in China. The RBC handover procedure is an important function of RBC, which affects the transport efficiency, reliability and safety of railways. Analysis of crucial states in the RBC handover procedure is helpful to determine whether there are potential risks in the procedure, and to locate the fault in time when a fault occurs. In this paper, we study a property, called C-detectability, of the RBC handover. This property has been defined in discrete event systems and requires that the crucial states can be determined uniquely by observing the system output. Taking the RBC handover procedure in the Chinese train control system level 3 (CTCS-3) as an example, we first model the RBC handover procedure using Petri nets, which are a graphical and mathematical modeling tool to formalize the behavior of discrete event systems. Then, based on the notion of basis reachability graph, an efficient approach is used to check C-detectability of the Petri net modeling the handover procedure.Note to Practitioners—The railway system is a safety-critical system. System safety is essential to the railway system. It is necessary to provide formal methods to model the system and analyze its properties. The motivation of this work is to present a general modeling framework of railway systems for the crucial states estimation. The crucial states estimation of the railway system has not been discussed yet in the literature despite its importance due to its relationship with the safety of the railway. In particular, estimating the crucial states in the railway system helps to determine whether there are potential risks in the system. In this paper, we investigate a new state estimation property (C-detectability), which is related to fault diagnosis, marking estimation and fault identification. Future research will consider the use of timed Petri nets to increase the modeling power and to allow the performance analysis of the railway system.

The consensusability and global optimality problems are solved for the discrete-time linear multiagent system (MAS) with marginally stable and strictly unstable dynamics. A unified framework is proposed by capturing the maximal disc-guaranteed gain margin (GGM) of the discrete-time linear quadratic regulator (LQR). Sufficient and necessary conditions on consensusability are established. Two bounds of the consensus region are derived only in terms of the unstable eigenvalues of the agent' dynamics. For the single-input MAS, by proving that the radius of the consensus region exactly equals the reciprocal of the Mahler measure of the agent' dynamics, we incidentally reveal the relation between the maximal GGM and the intrinsic entropy rate of the system dynamics for single-input discrete-time linear systems. By employing the inverse optimal control approach, it is proved that the globally optimal consensus is achieved, if and only if the associated Laplacian matrix is a simple matrix and all its nonzero eigenvalues can be radially projected into a specific subset of the consensus region. Moreover, the limitation on the eigenvalues vanishes for the marginally stable MAS. As an application of the global optimality, the minimum-energy-distributed consensus control problem is solved for the marginally stable MAS. Finally, a numerical example is given to demonstrate the effectiveness of the obtained results.

MOTIVATION: Chromatin regulators (CRs) are frequently dysregulated to reprogram the epigenetic landscape of the cancer genome. However, the underpinnings of the dysregulation of CRs and their downstream effectors remain to be elucidated. RESULTS: Here, we designed an integrated framework based on multi-omics data to identify candidate master regulatory CRs affected by genomic alterations across eight cancer types in The Cancer Genome Atlas. Most of them showed consistent activated or repressed (i.e. oncogenic or tumor-suppressive) roles in cancer initiation and progression. In order to further explore the insight mechanism of the dysregulated CRs, we developed an R package ModReg based on differential connectivity to identify CRs as modulators of transcription factors (TFs) involved in tumorigenesis. Our analysis revealed that the connectivity between TFs and their target genes (TGs) tended to be disrupted in the patients who had a high expression of oncogenic CRs or low-expression of tumor-suppressive CRs. As a proof-of-principle study, 14 (82.4%) of the top-ranked 17 driver CRs in liver cancer were able to be validated by literature mining or experiments including shRNA knockdown and dCas9-based epigenetic editing. Moreover, we confirmed that CR SIRT7 physically interacted with TF NFE2L2, and positively modulated the transcriptional program of NFE2L2 by affecting ∼64% of its TGs. AVAILABILITY AND IMPLEMENTATION: ModReg is freely accessible at http://cis.hku.hk/software/ModReg.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community. RESULTS: First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention. AVAILABILITY AND IMPLEMENTATION: http://cis.hku.hk/MR4Cancer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: The interaction between tumor and immune system plays a crucial role in both cancer development and treatment response. To facilitate comprehensive investigation of tumor-immune interactions, we have designed a user-friendly web portal TISIDB, which integrated multiple types of data resources in oncoimmunology. First, we manually curated 4176 records from 2530 publications, which reported 988 genes related to anti-tumor immunity. Second, genes associated with the resistance or sensitivity of tumor cells to T cell-mediated killing and immunotherapy were identified by analyzing high-throughput screening and genomic profiling data. Third, associations between any gene and immune features, such as lymphocytes, immunomodulators and chemokines, were pre-calculated for 30 TCGA cancer types. In TISIDB, biologists can cross-check a gene of interest about its role in tumor-immune interactions through literature mining and high-throughput data analysis, and generate testable hypotheses and high quality figures for publication. AVAILABILITY AND IMPLEMENTATION: http://cis.hku.hk/TISIDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Summary: MicroRNAs play critical roles in oncogenesis by targeting a few key regulators or a large cohort of genes impinging on downstream signaling pathways. Conversely, miRNA activity is also titrated by competitive endogenous RNA such as lncRNA with sponge effect. Web-based server, miRNACancerMap, aims to unravel lncRNA-miRNA-mRNA tripartite complexity to predict the function and clinical relevance of miRNA with network perspective. In conjunction with large-scale data and information integration, miRNACancerMap implements various algorithms and pipelines to construct dynamic miRNA-centered network with rigorous Systems Biology approaches and the state-of-the-art visualization tool. The capability of the server to generate testable hypotheses was exemplified with cases to identify hub miRNAs regulating most of the differentially-expressed genes involved in cancer stage transition, miRNA-TF pairs shared by pan-cancers and lncRNA sponges validated by multiple datasets. LncRNAs sharing the same miRNAs binding sites as mRNAs can sequester miRNAs and indirectly regulate the activity of the related mRNAs. We have re-annotated traditional microarray chips, and included these datasets in the server to enable validation of the predicted lncRNA-miRNA-mRNA regulations derived from TCGA RNA-seq data. Of note, our server enables identifying miRNAs associated with cancer signaling pathways, and related lncRNA sponges from pan-cancers with only a few mouse clicks. Availability and implementation: http://cis.hku.hk/miRNACancerMAP. Supplementary information: Supplementary data are available at Bioinformatics online.

Observation structures usually considered for Petri nets assume that the firing of transitions may be observed through static labels and that the marking of some places may be measurable. These observation structures are rather limited and we consider in this paper more general ones, correspondingly defining two new classes of nets: labeled Petri nets with outputs and adaptive labeled Petri nets. Several examples of nets in these classes are considered. Furthermore we also show the possibility to convert a labeled Petri net into an adaptive Petri net or a labeled Petri net.

MicroRNAs (miRNAs) are a family of endogenous small non-coding RNAs involved in various developmental and physiological processes by negatively regulating gene expression. Single nucleotide polymorphisms (SNPs) are important variations for the diversity among individuals, as well as leading to phenotypes, traits, and diseases. What will happen when SNPs meet miRNAs? To date, a number of studies have demonstrated that SNPs in target sites or miRNA genes were associated with diseases [ 1 – 3 ]. Our aim is to characterize these functional miRNA related SNPs comprehensively. Through mapping SNPs onto miRNAs, we totally identified 757 SNPs (including indel polymorphisms) in 440 human pre-miRNAs, 218 SNPs in pre-miRNAs of other 8 species and thousands of SNPs in pre-miRNAs flanking regions. Of them, 50 SNPs are in the seed regions of 41 human miRNAs. Two different methods (TargetScan and miRanda) were used to predict the target sites for the wild type miRNAs and SNP-miRNAs. By comparing the targets of wild type miRNA with that of SNP-miRNA, we obtained the potential miRNA targets loss and gain. We found about half of the targets will be changed by SNP in miRNA seed region. In addition, we experimentally confirmed seven loss of-function SNPs and one gain-of-function SNP by luciferase report assay. We also mapped SNPs onto gene 3’ untranslated region (3’UTR) and identified tens of thousands of SNPs in 3’UTRs which would affect the miRNA/gene interaction, either destroy a real target or create an illegal target. Among these miRNA related SNPs, we think the SNPs with relatively high minor allele frequencies (MAF), high MAF difference between populations and undergoing positive selection pressure will be important candidates for population phonotype research and complex trait studies. All useful data were compiled into miRNASNP, a user-friendly free online database ( http://www.bioguo.org/miRNASNP/ ). We have systematically identified and analyzed the human polymorphisms in miRNAs and miRNA target sites and analyzed their potential influences on target binding. Our database will be a useful resource for studying miRNA function, identifying disease-associated miRNAs, and further personalized medicine.