非行少年の司法改革に関する研究

Articledepartmental bulletin pape

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.journal articl

ニュー・レイバーのヘゲモニック・プロジェクト(四・完) : 「新しい政治」の左派党戦略

2001-03-25departmental bulletin pape

Bioinformatic analysis pipeline for dual RNA-seq datasets.

Quality-filtered RNA-seq reads are aligned in parallel against the respective host and pathogen replicons. Reads mapping equally well to both reference organisms (“cross-mappings”) are quantified and discarded from downstream analyses. Reads unequivocally mapped to either the bacterial or host reference are used for quantification and functional analyses. Dual RNA-seq enables a wide range of downstream analyses, discussed in detail in the text. “MT,” mitochondrial genome.</p

READemption v0.3.3

<p>This is the version 0.3.3 the tool READemption. Please visit the following web site for current versions and documentation:</p> <p>https://github.com/konrad/READemption</p> <p>http://pythonhosted.org/READemption</p> <p> The READemption is a pipeline for the computational evaluation of RNA-Seq data. It was originally developed at the IMIB/ZINF to process dRNA-Seq reads (as introduced by Sharma et al., Nature, 2010 originating from bacterial samples. Meanwhile is has been extended to process data generated in different experimental setups and originating from all domains of life and is under active development. The subcommands which are provided by command-line interface cover read processing and aligning, coverage plot generation, gene expression quantification as well as differential gene expression analysis. READemption was applied to analyze numerous data sets. In order to set up analyses quickly READemption follows the principal of "convention over configuration": Once the input files are copied into defined folders no further parameters have to be given. Still, READemption's behavior can be adapted to specific needs of the user.</p

A generic dual RNA-seq workflow analyzing total mixed RNA after double rRNA depletion that discovered the role of PinT small regulatory RNA (sRNA) during <i>Salmonella</i> infection of host cells [13].

<p><i>Salmonella</i> having <i>gfp</i> stably integrated in their chromosome and expressed from a constitutive promoter were used to infect cultures of HeLa cells. RNA-seq of the bacterial input (1) or mock-infected HeLa cells (2) served as reference controls for <i>Salmonella</i> or human gene expression analysis, respectively. Infection was carried out in parallel with wild-type and sRNA mutant <i>Salmonella</i> strains, and samples were taken over a time-course of infection. The resulting cell samples constituted a mixed population consisting of both invaded (GFP-positive) and uninfected bystander (GFP-negative) cells (3). To obtain a homogeneous population of invaded cells, the samples were sorted based on the emitted GFP fluorescence (4). Total RNA was extracted from the thus enriched cells, rRNA from both infection partners was depleted (5), and rRNA-free samples were converted into cDNA libraries and sequenced. The resulting sequencing reads were mapped in parallel against the <i>Salmonella</i> and human (core and mitochondrial) genome. Differential expression analysis of the time course revealed the strong induction over time of a novel <i>Salmonella</i> sRNA, PinT, and comparative analysis unraveled the molecular footprint of this sRNA in the bacterial transcriptome (6). Likewise, comparison of the host transcriptome between wild-type and Δ<i>pinT</i> infections revealed PinT-dependent changes in the immune response, including a differential activation of Janus kinase-Signal Transducer and Activator of Transcription (JAK-STAT) signaling as well as changes with respect to the expression of host long noncoding RNAs (lncRNAs) and microRNAs (miRNAs) (7). In addition, the <i>pinT</i> status of the infecting bacterium influenced mitochondrial gene expression, and infection with Δ<i>pinT Salmonella</i> led to the relocalization of mitochondria in invaded host cells (8).</p

Methods for RNA sequencing of bacterial infections.

A. Concept of dual RNA-seq. Total RNA is extracted from infected cells and analyzed by RNA-seq. The mixed sequencing reads are assigned to their originating genomes in silico. B. Different approaches to quantify gene expression of bacteria in context with mammalian host cells. Traditionally, host material was depleted prior to analysis, either by detergent-mediated lysis of host cells (left) or by sequence-specific removal of host transcripts (middle). Instead, dual RNA-seq omits host depletion (right) and analyzes pathogen and host gene expression in parallel.</p

Overview of dual RNA-seq and related studies published to date.

<p>“Dual SAGE” refers to the simultaneous analysis of host and pathogen by Serial Analysis of Gene Expression (SAGE), and “Multi RNA-seq” refers to a metatranscriptomic analysis of bacterial species constituting the airway microbiota in conjunction with nasal epithelial host cells. “M,” million; “TPM,” transcripts per million; “RPKMO,” reads per kilobase pairs of a gene per million reads aligning to annotated ORFs. Databases containing raw sequencing data: NCBI (National Center for Biotechnology Information), ENA (European Nucleotide Archive), GEO (Gene Expression Omnibus).</p

Illustration of biological insights obtained from dual RNA-seq studies in four different bacterial infection models.

<p>HEp-2 cells infected with obligate intracellular <i>Chlamydia trachomatis</i> [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006033#ppat.1006033.ref010" target="_blank">10</a>], primary airway epithelial cells with nontypeable <i>Haemophilus influenzae</i> [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006033#ppat.1006033.ref016" target="_blank">16</a>], primary murine bone marrow macrophages with uropathogenic <i>E</i>. <i>coli</i> (UPEC) [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006033#ppat.1006033.ref011" target="_blank">11</a>], and diverse human, mouse, and porcine cell lines with <i>Salmonella</i> Typhimurium [<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006033#ppat.1006033.ref013" target="_blank">13</a>]. See main text for details.</p