CoryneCenter – An online resource for the integrated analysis of corynebacterial genome and transcriptome data

Neuweger H, Baumbach J, Albaum S, et al. CoryneCenter: an online resource for the integrated analysis of corynebacterial genome and transcriptome data. BMC Systems Biology. 2007;1(1): 55.Background: The introduction of high-throughput genome sequencing and post-genome analysis technologies, e.g. DNA microarray approaches, has created the potential to unravel and scrutinize complex gene-regulatory networks on a large scale. The discovery of transcriptional regulatory interactions has become a major topic in modern functional genomics. Results: To facilitate the analysis of gene-regulatory networks, we have developed CoryneCenter, a web-based resource for the systematic integration and analysis of genome, transcriptome, and gene regulatory information for prokaryotes, especially corynebacteria. For this purpose, we extended and combined the following systems into a common platform: (1) GenDB, an open source genome annotation system, (2) EMMA, a MAGE compliant application for high-throughput transcriptome data storage and analysis, and (3) CoryneRegNet, an ontology-based data warehouse designed to facilitate the reconstruction and analysis of gene regulatory interactions. We demonstrate the potential of CoryneCenter by means of an application example. Using microarray hybridization data, we compare the gene expression of Corynebacterium glutamicum under acetate and glucose feeding conditions: Known regulatory networks are confirmed, but moreover CoryneCenter points out additional regulatory interactions. Conclusion: CoryneCenter provides more than the sum of its parts. Its novel analysis and visualization features significantly simplify the process of obtaining new biological insights into complex regulatory systems. Although the platform currently focusses on corynebacteria, the integrated tools are by no means restricted to these species, and the presented approach offers a general strategy for the analysis and verification of gene regulatory networks. CoryneCenter provides freely accessible projects with the underlying genome annotation, gene expression, and gene regulation data. The system is publicly available at http://www.CoryneCenter.d

How to Predict Molecular Interactions between Species?

Organisms constantly interact with other species through physical contact which leads to chan-ges on the molecular level, for example the transcriptome. These changes can be monitored forall genes, with the help of high-throughput experiments such as RNA-seq or microarrays. Theadaptation of the gene expression to environmental changes within cells is mediated throughcomplex gene regulatory networks. Often, our knowledge of these networks is incomplete. Netw-ork inference predicts gene regulatory interactions based on transcriptome data. An emergingapplication of high-throughput transcriptome studies are dual transcriptomics experiments. Here,the transcriptome of two or more interacting species is measured simultaneously. Based ona dual RNA-seq data set of murine dendritic cells infected with the fungal pathogen Candidaalbicans, the software tool NetGenerator was applied to predict an inter-species gene regulatorynetwork. To promote further investigations of molecular inter-species interactions, we recentlydiscussed dual RNA-seq experiments for host-pathogen interactions and extended the appliedtool NetGenerator (Schulze et al., 2015). The updated version of NetGenerator makes use ofmeasurement variances in the algorithmic procedure and accepts gene expression time seriesdata with missing values. Additionally, we tested multiple modeling scenarios regarding the stimulifunctions of the gene regulatory network. Here, we summarize the work by Schulze et al. (2015)and put it into a broader context. We review various studies making use of the dual transcriptomicsapproach to investigate the molecular basis of interacting species. Besides the application tohost-pathogen interactions, dual transcriptomics data are also utilized to study mutualistic andcommensalistic interactions. Furthermore, we give a short introduction into additional approachesfor the prediction of gene regulatory networks and discuss their application to dual transcriptomicsdata. We conclude that the application of network inference on dual-transcriptomics data is apromising approach to predict molecular inter-species interactions

Network estimation in State Space Model with L1-regularization constraint

Biological networks have arisen as an attractive paradigm of genomic science ever since the introduction of large scale genomic technologies which carried the promise of elucidating the relationship in functional genomics. Microarray technologies coupled with appropriate mathematical or statistical models have made it possible to identify dynamic regulatory networks or to measure time course of the expression level of many genes simultaneously. However one of the few limitations fall on the high-dimensional nature of such data coupled with the fact that these gene expression data are known to include some hidden process. In that regards, we are concerned with deriving a method for inferring a sparse dynamic network in a high dimensional data setting. We assume that the observations are noisy measurements of gene expression in the form of mRNAs, whose dynamics can be described by some unknown or hidden process. We build an input-dependent linear state space model from these hidden states and demonstrate how an incorporated $L_{1}$ regularization constraint in an Expectation-Maximization (EM) algorithm can be used to reverse engineer transcriptional networks from gene expression profiling data. This corresponds to estimating the model interaction parameters. The proposed method is illustrated on time-course microarray data obtained from a well established T-cell data. At the optimum tuning parameters we found genes TRAF5, JUND, CDK4, CASP4, CD69, and C3X1 to have higher number of inwards directed connections and FYB, CCNA2, AKT1 and CASP8 to be genes with higher number of outwards directed connections. We recommend these genes to be object for further investigation. Caspase 4 is also found to activate the expression of JunD which in turn represses the cell cycle regulator CDC2.Comment: arXiv admin note: substantial text overlap with arXiv:1308.359

A structured approach for the engineering of biochemical network models, illustrated for signalling pathways

http://dx.doi.org/10.1093/bib/bbn026Quantitative models of biochemical networks (signal transduction cascades, metabolic pathways, gene regulatory circuits) are a central component of modern systems biology. Building and managing these complex models is a major challenge that can benefit from the application of formal methods adopted from theoretical computing science. Here we provide a general introduction to the field of formal modelling, which emphasizes the intuitive biochemical basis of the modelling process, but is also accessible for an audience with a background in computing science and/or model engineering. We show how signal transduction cascades can be modelled in a modular fashion, using both a qualitative approach { Qualitative Petri nets, and quantitative approaches { Continuous Petri Nets and Ordinary Differential Equations. We review the major elementary building blocks of a cellular signalling model, discuss which critical design decisions have to be made during model building, and present ..

Genome Binding and Gene Regulation by Stem Cell Transcription Factors

Nearly all cells of an individual organism contain the same genome. However, each cell type transcribes a different set of genes due to the presence of different sets of cell type-specific transcription factors. Such transcription factors bind to regulatory regions such as promoters and enhancers and regulate their activity in gene transcription. Transcription factors interact with each other and form tissue-specific transcription factor networks. Identification of genome binding and gene regulation by transcription factors will enhance our understanding of how transcription factors specify cell types. Chapter 1 serves as a general introduction into eukaryotic transcription regulation and describes the role of enhancers and transcription factors. Chapters 2 – 4 describe the experimental work of this thesis. Chapte