Exploiting Binary Abstractions in Deciphering Gene Interactions

We propose a dynamic differential Bayesian networks (DDBNs) and nonparametric regression model. This model is an extended model of traditional dynamic Bayesian networks (DBNs), which can incorporate temporal information in a natural way and directly handle real-valued data obtained from microarrays without any transformation. In addition, it can cope with differential information between gene expression levels, without any loss to the traditional advantage, i.e., the capability of estimating non-linear relationships between genes. We apply DDBNs to analyze simulated data and real data, i.e., Saccharomyces cerevisiae cell cycle gene expression data. We have confirmed the effectiveness of our approach in the sense that some edges have been successfully detected only by DDBNs, not by DBNs.

MotivationDeciphering gene interaction networks (GINs) from time-course gene expression (TCGx) data is highly valuable to understand gene behaviors (e.g., activation, inhibition, time-lagged causality) at the system level. Existing methods usually use a global or local proximity measure to infer GINs from a single dataset. As the noise contained in a single data set is hardly self-resolved, the results are sometimes not reliable. Also, these proximity measurements cannot handle the co-existence of the various in vivo positive, negative and time-lagged gene interactions.Methods and resultsWe propose to infer reliable GINs from multiple TCGx datasets using a novel conserved subsequential pattern of gene expression. A subsequential pattern is a maximal subset of genes sharing positive, negative or time-lagged correlations of one expression template on their own subsets of time points. Based on these patterns, a GIN can be built from each of the datasets. It is assumed that reliable gene interactions would be detected repeatedly. We thus use conserved gene pairs from the individual GINs of the multiple TCGx datasets to construct a reliable GIN for a species. We apply our method on six TCGx datasets related to yeast cell cycle, and validate the reliable GINs using protein interaction networks, biopathways and transcription factor-gene regulations. We also compare the reliable GINs with those GINs reconstructed by a global proximity measure Pearson correlation coefficient method from single datasets. It has been demonstrated that our reliable GINs achieve much better prediction performance especially with much higher precision. The functional enrichment analysis also suggests that gene sets in a reliable GIN are more functionally significant. Our method is especially useful to decipher GINs from multiple TCGx datasets related to less studied organisms where little knowledge is available except gene expression data.

Integrative clinical outcome prediction model called gene interaction regularized elastic net (GIREN) method is proposed in this paper. GIREN combines gene expression, methylation profiles, and gene interaction networks in order to reveal genomic and epigenomic features that bear important prognostic value. With GIREN, gene expression and DNA methylation profiles are first jointly analyzed in a linear regression model, and additional gene interaction network is simultaneously integrated as a regularizing penalty that follow an elastic net formulation. Such regularization also enforce sparsity in the solution so that features with prognostic values are automatically selected. To solve the regularized optimization, an iterative gradient descent algorithm is also developed. We applied GIREN to a set of 87 human ovarian cancer samples, which underwent a rigorous sample selection. The predicted outcome was used to group patients into high-risk vs. low-risk. Validation showed that GIREN outperformed other competing algorithms including SuperPCA.

BackgroundIntact Toll-like receptor 4 (TLR4) has been identified in hepatic stellate cells (HSCs), the primary fibrogenic cell type in liver. Here, we investigated the impact of TLR4 signaling on the gene expression network of HSCs by comparing the transcriptomic changes between wild-type (JS1) and TLR4 knockout (JS2) murine HSCs in response to two TLR4 ligands, lipopolysacchride (LPS), or high-mobility group box 1 (HMGB1).ResultsWhole mouse genome microarray was performed for gene expression analysis. Gene interaction and co-expression networks were built on the basis of ontology and pathway analysis by Kyoto Encyclopedia of Genes and Genomes (KEGG). Gene expression profiles are markedly different between Wild type (JS1) and TLR4 knockout (JS2) HSCs under basal conditions or following stimulation with LPS or HMGB1. The differentially expressed genes between TLR4 intact and null HSCs were enriched in signaling pathways including p53, mTOR, NOD-like receptor, Jak-STAT, chemokine, focal adhesion with some shared downstream kinases, and transcriptional factors. Venn analysis revealed that TLR4-dependent, LPS-responsive genes were clustered into pathways including Toll-like receptor and PI3K-Akt, whereas TLR4-dependent, HMGB1-responsive genes were clustered into pathways including metabolism and phagosome signaling. Genes differentially expressed that were categorized to be TLR4-dependent and both LPS- and HMGB1-responsive were enriched in cell cycle, ubiquitin mediated proteolysis, and mitogen-activated protein kinase (MAPK) signaling pathways.ConclusionsTLR4 mediates complex gene expression alterations in HSCs. The affected pathways regulate a wide spectrum of HSC functions, including inflammation, fibrogenesis, and chemotaxis, as well as cell growth and metabolism. There are common and divergent regulatory signaling downstream of LPS and HMGB1 stimulation via TLR4 on HSCs. These findings emphasize the complex cascades downstream of TLR4 in HSCs that could influence their cellular biology and function.Electronic supplementary materialThe online version of this article (doi:10.1186/s13069-016-0039-z) contains supplementary material, which is available to authorized users.

Dynamic aspects of regulatory networks are typically investigated by measuring relevant variables at multiple points in time. Current state-of-the-art approaches for gene network reconstruction directly build on such data, making the strong assumption that the system evolves in a synchronous fashion and in discrete time. However, omics data generated with increasing time-course granularity allow to model gene networks as systems whose state evolves in continuous time, thus improving the model’s expressiveness. In this work continuous time Bayesian networks are proposed as a new approach for regulatory network reconstruction from time-course expression data. Their performance is compared to that of two state-of-the-art methods: dynamic Bayesian networks and Granger causality. The comparison is accomplished using both simulated and experimental data. Continuous time Bayesian networks achieve the highest F-measure on both datasets. Furthermore, precision, recall and F-measure degrade in a smoother way than those of dynamic Bayesian networks and Granger causality, when the complexity of the gene regulatory network increases.

Aims/hypothesisB cells play an important role in driving the development of type 1 diabetes; however, it remains unclear how they contribute to local beta cell destruction during disease progression. Here, we use gene expression profiling of B cell subsets identified in inflamed pancreatic tissue to explore their primary functional role during the progression of autoimmune diabetes.MethodsTranscriptional profiling was performed on FACS-sorted B cell subsets isolated from pancreatic islets and the pancreatic lymph nodes of NOD mice.ResultsB cells are highly modified by the inflamed pancreatic tissue and can be distinguished by their transcriptional profile from those in the lymph nodes. We identified both a discrete and a core shared gene expression profile in islet CD19+CD138– and CD19+CD138+ B cell subsets, the latter of which is known to have enriched autoreactivity during diabetes development. On localisation to pancreatic islets, compared with CD138– B cells, CD138+ B cells overexpress genes associated with adhesion molecules and growth factors. Their shared signature consists of gene expression changes related to the differentiation of antibody-secreting cells and gene regulatory networks associated with IFN signalling pathways, proinflammatory cytokines and Toll-like receptor (TLR) activation. Finally, abundant TLR7 expression was detected in islet B cells and was enhanced specifically in CD138+ B cells.Conclusions/interpretationOur study provides a detailed transcriptional analysis of islet B cells. Specific gene signatures and interaction networks have been identified that point towards a functional role for B cells in driving autoimmune diabetes.Graphical abstract

Dynamic Bayesian network (DBN) methods have shown great promise in regulatory network reconstruction because of their capability of modeling causality and cyclic networks, and handling data with noises found in biological experiments. However, they tend to produce relative high false positives and are not computationally efficient even for networks of moderate size. This paper presents a novel DBN-based approach to address these issues. For each node, a differential mutual information is used to select potential parents and a Bayesian scoring metric with a Dirichlet prior for regulation is applied to evaluate its parents. The proposed method is applied to recover a network structure from simulated data with higher accuracy and computational efficiency compared to DBNs with other scoring metrics. When applied to infer a cell cycle pathway of Saccharomyces cerevisiae using real time-series expression data, the proposed method is capable of identifying most gene interactions in the pathways.

BackgroundPathway alterations reflected as changes in gene expression regulation and gene interaction can result from cellular exposure to toxicants. Such information is often used to elucidate toxicological modes of action. From a risk assessment perspective, alterations in biological pathways are a rich resource for setting toxicant thresholds, which may be more sensitive and mechanism-informed than traditional toxicity endpoints. Here we developed a novel differential networks (DNs) approach to connect pathway perturbation with toxicity threshold setting.MethodsOur DNs approach consists of 6 steps: time-series gene expression data collection, identification of altered genes, gene interaction network reconstruction, differential edge inference, mapping of genes with differential edges to pathways, and establishment of causal relationships between chemical concentration and perturbed pathways. A one-sample Gaussian process model and a linear regression model were used to identify genes that exhibited significant profile changes across an entire time course and between treatments, respectively. Interaction networks of differentially expressed (DE) genes were reconstructed for different treatments using a state space model and then compared to infer differential edges/interactions. DE genes possessing differential edges were mapped to biological pathways in databases such as KEGG pathways.ResultsUsing the DNs approach, we analyzed a time-series Escherichia coli live cell gene expression dataset consisting of 4 treatments (control, 10, 100, 1000 mg/L naphthenic acids, NAs) and 18 time points. Through comparison of reconstructed networks and construction of differential networks, 80 genes were identified as DE genes with a significant number of differential edges, and 22 KEGG pathways were altered in a concentration-dependent manner. Some of these pathways were perturbed to a degree as high as 70% even at the lowest exposure concentration, implying a high sensitivity of our DNs approach.ConclusionsFindings from this proof-of-concept study suggest that our approach has a great potential in providing a novel and sensitive tool for threshold setting in chemical risk assessment. In future work, we plan to analyze more time-series datasets with a full spectrum of concentrations and sufficient replications per treatment. The pathway alteration-derived thresholds will also be compared with those derived from apical endpoints such as cell growth rate.

Microarray experiments are proficient of yielding observations for thousands of genes those are differentially expressed under several conditions. Although it is possible to measure simultaneously the changes in gene expression profiles at whole genomic scale, interpreting individual gene expression profile in terms of its actual biological function or associated biochemical processes remains challenging. Exploratory multivariate statistical techniques such as principal component analysis have been extensively used to reduce the complexity of large size microarray data. Although Saccaromycea Cerevisae is the most widely studied species using microarray techniques, a complete understanding of the efficacy of principal component analysis and data pre-processing is still lacking for clustering and functional mapping of yeast gene expression profiles, reported in various studies. Therefore in this work, we evaluate the impact of data pre-processing and principal component analysis on k-means clustering-based functional mapping of yeast gene expression profiles observed during diauxic-shift. Two time-series gene expression datasets were chosen such as, (1) yeast diauxic-shift data and (2) yeast sporulation data to examine the efficacy of principal component analysis in interpreting gene-based or score-based clusters and their relationship with known pathways. It was shown that unlike conventional pre-processing, principal component analysis provides a powerful tool to capture most of the information using only two component variables for inferring gene expression time-course data. Using yeast genome databases, it was demonstrated that clustering with principal components instead of the original variables does not necessarily improve the cluster quality but helps in identifying the relationships between genes of a cluster and key biological process of diauxic shift. Overall, the present analysis is useful in mining high dimensional microarray data at a reduced computational cost associated with functional enrichment of expression time-series, regardless of species or experimental conditions.

In order to find out the problems of students' learning in the online learning process in time and ensure that students can successfully complete the online learning course, the dynamic Bayesian network is used to predict students' online learning scores. First, the online course Bayesian network is established, and then the dynamic Bayesian network is established by combining the Bayesian network and hidden Markov according to the time slice. The maximum expectation algorithm is used for Bayesian parameter learning, and the scores are predicted according to video learning, chapter test and other influencing factors. The results show that the dynamic Bayesian network can effectively predict the online learning results, the prediction results provide teachers with decision-making, and students can carry out targeted reinforcement learning according to the prediction results in the learning process.

Packed Red Blood Cells Modulate Expression Of Dendritic Cell Signal Transduction and Antigen Presentation Pathways

Purpose – Change propagation is the major source of schedule delays and cost overruns in design projects. One way to mitigate the risk of change propagation is to impose a design freeze on components at some point prior to completion of the process. The purpose of this paper is to propose a model-driven approach to optimal freeze sequence identification based on change propagation risk. Design/methodology/approach – A dynamic Bayesian network was used to represent the change propagation process within a system. According to the model, when a freeze decision is made with respect to a component, a probabilistic inference algorithm within the Bayesian network updates the uncertain state of each component. Based on this mechanism, a set of algorithm was developed to derive optimal freeze sequence. Findings – The authors derived the optimal freeze sequence of a helicopter design project from real product development process. The experimental result showed that our proposed method can significantly improve the effectiveness of freeze sequencing compared with arbitrary freeze sequencing. Originality/value – The methodology identifies the optimal sequence for resolution of entire-system uncertainty in the most effective manner. This mechanism, in progressively updating the state of each component, enables an analyzer to continuously evaluate the effectiveness of the freeze sequence.

Modulators (Ms) are proteins that modify the activity of transcription factors (TFs) and influence expression of their target genes (TGs). To discover modulators of NF-κB/RelA, we first identified 365 NF-κB/RelA-binding proteins using liquid chromatography-tandem mass spectrometry (LC-MS/MS). We used a probabilistic model to infer 8349 (M, NF-κB/RelA, TG) triplets and their modes of modulatory action from our combined LC-MS/MS and ChIP-Seq (ChIP followed by next generation sequencing) data, published RelA modulators and TGs, and a compendium of gene expression profiles. Hierarchical clustering of the derived modulatory network revealed functional subnetworks and suggested new pathways modulating RelA transcriptional activity. The modulators with the highest number of TGs and most non-random distribution of action modes (measured by Shannon entropy) are consistent with published reports. Our results provide a repertoire of testable hypotheses for experimental validation. One of the NF-κB/RelA modulators we identified is STAT1. The inferred (STAT1, NF-κB/RelA, TG) triplets were validated by LC-selected reaction monitoring-MS and the results of STAT1 deletion in human fibrosarcoma cells. Overall, we have identified 562 NF-κB/RelA modulators, which are potential drug targets, and clarified mechanisms of achieving NF-κB/RelA multiple functions through modulators. Our approach can be readily applied to other TFs.

Nowadays, several components and systems are designed and produced based on reliability. Since the reliability criterion has an important role in purchasing and implementation of these systems. In the design of a reliable system, fault and failure analysis must be carried out in order to reduce fault probability of the system. When dependency and the relation between components of a complex system are important and should be mentioned, determination of system reliability is very difficult. In this paper, dynamic fault tree is used to evaluate the systems reliability that their behavior is varied with time. Dynamic fault tree is constructed and then it converted to dynamic bayesian network. In this paper, the principle of dynamic fault tree gates and their mapping into dynamic bayesian are explained and some new relations between events and gates for this mapping are proposed. GeNIe package is used to determine dynamic bayesian network based on stochastic sampling algorithms. Four systems (cardiac assist system, hypothetical cascaded priority-and system, inertial navigation system/ global positioning system integrated, and emergency detection system) are investigated; reliability and fault probability of these systems are calculated. Comparison of the results with those obtained by other researches shows the proposed method effectiveness for systems reliability modeling and assessment via dynamic bayesian network.

Clustering of gene expression time series gives insight into which genes may be co-regulated, allowing us to discern the activity of pathways in a given microarray experiment. Of particular interest is how a given group of genes varies with different conditions or genetic background. This paper develops a new clustering method that allows each cluster to be parameterised according to whether the behaviour of the genes across conditions is correlated or anti-correlated. By specifying correlation between such genes,more information is gain within the cluster about how the genes interrelate. Amyotrophic lateral sclerosis (ALS) is an irreversible neurodegenerative disorder that kills the motor neurons and results in death within 2 to 3 years from the symptom onset. Speed of progression for different patients are heterogeneous with significant variability. The SOD1G93A transgenic mice from different backgrounds (129Sv and C57) showed consistent phenotypic differences for disease progression. A hierarchy of Gaussian isused processes to model condition-specific and gene-specific temporal co-variances. This study demonstrated about finding some significant gene expression profiles and clusters of associated or co-regulated gene expressions together from four groups of data (SOD1G93A and Ntg from 129Sv and C57 backgrounds). Our study shows the effectiveness of sharing information between replicates and different model conditions when modelling gene expression time series. Further gene enrichment score analysis and ontology pathway analysis of some specified clusters for a particular group may lead toward identifying features underlying the differential speed of disease progression.