455 research outputs found
Functional States of the Genome-Scale Escherichia Coli Transcriptional Regulatory System
A transcriptional regulatory network (TRN) constitutes the collection of regulatory rules that link environmental cues to the transcription state of a cell's genome. We recently proposed a matrix formalism that quantitatively represents a system of such rules (a transcriptional regulatory system [TRS]) and allows systemic characterization of TRS properties. The matrix formalism not only allows the computation of the transcription state of the genome but also the fundamental characterization of the input-output mapping that it represents. Furthermore, a key advantage of this “pseudo-stoichiometric” matrix formalism is its ability to easily integrate with existing stoichiometric matrix representations of signaling and metabolic networks. Here we demonstrate for the first time how this matrix formalism is extendable to large-scale systems by applying it to the genome-scale Escherichia coli TRS. We analyze the fundamental subspaces of the regulatory network matrix (R) to describe intrinsic properties of the TRS. We further use Monte Carlo sampling to evaluate the E. coli transcription state across a subset of all possible environments, comparing our results to published gene expression data as validation. Finally, we present novel in silico findings for the E. coli TRS, including (1) a gene expression correlation matrix delineating functional motifs; (2) sets of gene ontologies for which regulatory rules governing gene transcription are poorly understood and which may direct further experimental characterization; and (3) the appearance of a distributed TRN structure, which is in stark contrast to the more hierarchical organization of metabolic networks
Reconciliation of Genome-Scale Metabolic Reconstructions for Comparative Systems Analysis
In the past decade, over 50 genome-scale metabolic reconstructions have been
built for a variety of single- and multi- cellular organisms. These
reconstructions have enabled a host of computational methods to be leveraged for
systems-analysis of metabolism, leading to greater understanding of observed
phenotypes. These methods have been sparsely applied to comparisons between
multiple organisms, however, due mainly to the existence of differences between
reconstructions that are inherited from the respective reconstruction processes
of the organisms to be compared. To circumvent this obstacle, we developed a
novel process, termed metabolic network reconciliation, whereby non-biological
differences are removed from genome-scale reconstructions while keeping the
reconstructions as true as possible to the underlying biological data on which
they are based. This process was applied to two organisms of great importance to
disease and biotechnological applications, Pseudomonas
aeruginosa and Pseudomonas putida, respectively.
The result is a pair of revised genome-scale reconstructions for these organisms
that can be analyzed at a systems level with confidence that differences are
indicative of true biological differences (to the degree that is currently
known), rather than artifacts of the reconstruction process. The reconstructions
were re-validated with various experimental data after reconciliation. With the
reconciled and validated reconstructions, we performed a genome-wide comparison
of metabolic flexibility between P. aeruginosa and P.
putida that generated significant new insight into the underlying
biology of these important organisms. Through this work, we provide a novel
methodology for reconciling models, present new genome-scale reconstructions of
P. aeruginosa and P. putida that can be
directly compared at a network level, and perform a network-wide comparison of
the two species. These reconstructions provide fresh insights into the metabolic
similarities and differences between these important
Pseudomonads, and pave the way towards full comparative
analysis of genome-scale metabolic reconstructions of multiple species
Matrix Formalism to Describe Functional States of Transcriptional Regulatory Systems
Complex regulatory networks control the transcription state of a genome. These transcriptional regulatory networks (TRNs) have been mathematically described using a Boolean formalism, in which the state of a gene is represented as either transcribed or not transcribed in response to regulatory signals. The Boolean formalism results in a series of regulatory rules for the individual genes of a TRN that in turn can be used to link environmental cues to the transcription state of a genome, thereby forming a complete transcriptional regulatory system (TRS). Herein, we develop a formalism that represents such a set of regulatory rules in a matrix form. Matrix formalism allows for the systemic characterization of the properties of a TRS and facilitates the computation of the transcriptional state of the genome under any given set of environmental conditions. Additionally, it provides a means to incorporate mechanistic detail of a TRS as it becomes available. In this study, the regulatory network matrix, R, for a prototypic TRS is characterized and the fundamental subspaces of this matrix are described. We illustrate how the matrix representation of a TRS coupled with its environment (R*) allows for a sampling of all possible expression states of a given network, and furthermore, how the fundamental subspaces of the matrix provide a way to study key TRS features and may assist in experimental design
Rapidly exploring structural and dynamic properties of signaling networks using PathwayOracle
<p>Abstract</p> <p>Background</p> <p>In systems biology the experimentalist is presented with a selection of software for analyzing dynamic properties of signaling networks. These tools either assume that the network is in steady-state or require highly parameterized models of the network of interest. For biologists interested in assessing how signal propagates through a network under specific conditions, the first class of methods does not provide sufficiently detailed results and the second class requires models which may not be easily and accurately constructed. A tool that is able to characterize the dynamics of a signaling network using an unparameterized model of the network would allow biologists to quickly obtain insights into a signaling network's behavior.</p> <p>Results</p> <p>We introduce <it>PathwayOracle</it>, an integrated suite of software tools for computationally inferring and analyzing structural and dynamic properties of a signaling network. The feature which differentiates <it>PathwayOracle </it>from other tools is a method that can predict the response of a signaling network to various experimental conditions and stimuli using only the connectivity of the signaling network. Thus signaling models are relatively easy to build. The method allows for tracking signal flow in a network and comparison of signal flows under different experimental conditions. In addition, <it>PathwayOracle </it>includes tools for the enumeration and visualization of coherent and incoherent signaling paths between proteins, and for experimental analysis – loading and superimposing experimental data, such as microarray intensities, on the network model.</p> <p>Conclusion</p> <p><it>PathwayOracle </it>provides an integrated environment in which both structural and dynamic analysis of a signaling network can be quickly conducted and visualized along side experimental results. By using the signaling network connectivity, analyses and predictions can be performed quickly using relatively easily constructed signaling network models. The application has been developed in Python and is designed to be easily extensible by groups interested in adding new or extending existing features. <it>PathwayOracle </it>is freely available for download and use.</p
Genome-wide real-time PCR for West Nile virus reduces the false-negative rate and facilitates new strain discovery
West-Nile virus (WNV) causes significant morbidity and mortality worldwide. Transplant and transfusion recipients as well as the elderly are particularly at risk. WNV shows strain variation from season to season and from locale to locale. This poses a significant problem for diagnosis. Most assays use a single primer pair to detect WNV by QPCR, and can fail to detect novel stains. To overcome this limitation, a genome-wide, multiple primer-based real-time QPCR assay was developed for WNV. The same assay can be used for quantitation, viral variant discovery as well as for amplification of the entire viral genome using a single annealing temperature. It improves upon routine diagnosis as well as facilitates laboratory investigations of the pathology of WNV
Langerin (CD207) represents a novel interferon-stimulated gene in Langerhans cells
Interferons (IFNs) are “warning signal” cytokines released upon pathogen sensing. IFNs control the expression of interferon-stimulated genes (ISGs), which are often crucial to restrict viral infections and establish a cellular antiviral state.1,2 Langerin (CD207), a well-known surface receptor on Langerhans cells (LC), belongs to the C-type lectin receptor (CLR) family and constitutes a major pathogen binding receptor able to regulate both innate and adaptive immune responses.3,4 Importantly, this CLR was reported as an antiviral receptor, notably able to bind and internalize incoming human immunodeficiency virus (HIV) virions in Birbeck granules for degradation.5,6 However, langerin was never viewed as a contributor to the interferon-mediated antiviral immune response. We now provide evidence that langerin is an ISG showing upregulated expression upon IFN treatment in monocyte-derived and ex vivo human skin-isolated LCs
The NASA Exoplanet Archive: Data and Tools for Exoplanet Research
We describe the contents and functionality of the NASA Exoplanet Archive, a
database and tool set funded by NASA to support astronomers in the exoplanet
community. The current content of the database includes interactive tables
containing properties of all published exoplanets, Kepler planet candidates,
threshold-crossing events, data validation reports and target stellar
parameters, light curves from the Kepler and CoRoT missions and from several
ground-based surveys, and spectra and radial velocity measurements from the
literature. Tools provided to work with these data include a transit ephemeris
predictor, both for single planets and for observing locations, light curve
viewing and normalization utilities, and a periodogram and phased light curve
service. The archive can be accessed at
http://exoplanetarchive.ipac.caltech.edu.Comment: Accepted for publication in the Publications of the Astronomical
Society of the Pacific, 4 figure
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