10,679 research outputs found
Controlled vocabularies and semantics in systems biology
The use of computational modeling to describe and analyze biological systems is at the heart of systems biology. Model structures, simulation descriptions and numerical results can be encoded in structured formats, but there is an increasing need to provide an additional semantic layer. Semantic information adds meaning to components of structured descriptions to help identify and interpret them unambiguously. Ontologies are one of the tools frequently used for this purpose. We describe here three ontologies created specifically to address the needs of the systems biology community. The Systems Biology Ontology (SBO) provides semantic information about the model components. The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. The Terminology for the Description of Dynamics (TEDDY) categorizes dynamical features of the simulation results and general systems behavior. The provision of semantic information extends a model's longevity and facilitates its reuse. It provides useful insight into the biology of modeled processes, and may be used to make informed decisions on subsequent simulation experiments
Uniformly curated signaling pathways reveal tissue-specific cross-talks and support drug target discovery
Motivation: Signaling pathways control a large variety of cellular processes.
However, currently, even within the same database signaling pathways are often
curated at different levels of detail. This makes comparative and cross-talk
analyses difficult. Results: We present SignaLink, a database containing 8
major signaling pathways from Caenorhabditis elegans, Drosophila melanogaster,
and humans. Based on 170 review and approx. 800 research articles, we have
compiled pathways with semi-automatic searches and uniform, well-documented
curation rules. We found that in humans any two of the 8 pathways can
cross-talk. We quantified the possible tissue- and cancer-specific activity of
cross-talks and found pathway-specific expression profiles. In addition, we
identified 327 proteins relevant for drug target discovery. Conclusions: We
provide a novel resource for comparative and cross-talk analyses of signaling
pathways. The identified multi-pathway and tissue-specific cross-talks
contribute to the understanding of the signaling complexity in health and
disease and underscore its importance in network-based drug target selection.
Availability: http://SignaLink.orgComment: 9 pages, 4 figures, 2 tables and a supplementary info with 5 Figures
and 13 Table
BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models
Background: Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the
biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in
the use of models as well as the development of improved software systems and the availability of better, cheaper
computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model
repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in
these repositories should be extensively tested and encoded in community-supported and standardised formats. In
addition, the models and their components should be cross-referenced with other resources in order to allow their
unambiguous identification.
Description: BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a
freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative
models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by
BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled
vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various
formats. Reaction network diagrams generated from the models are also available in several formats. BioModels
Database also provides features such as online simulation and the extraction of components from large scale models
into smaller submodels. Finally, the system provides a range of web services that external software systems can use to
access up-to-date data from the database.
Conclusions: BioModels Database has become a recognised reference resource for systems biology. It is being used by
the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the
clustering of models based upon their annotations. Model deposition to the database today is advised by several
publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying
software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU
General Public License
BacillOndex: An Integrated Data Resource for Systems and Synthetic Biology
BacillOndex is an extension of the Ondex data integration system, providing a semantically annotated, integrated knowledge base for the model Gram-positive bacterium Bacillus subtilis. This application allows a user to mine a variety of B. subtilis data sources, and analyse the resulting integrated dataset, which contains data about genes, gene products and their interactions. The data can be analysed either manually, by browsing using Ondex, or computationally via a Web services interface. We describe the process of creating a BacillOndex instance, and describe the use of the system for the analysis of single nucleotide polymorphisms in B. subtilis Marburg. The Marburg strain is the progenitor of the widely-used laboratory strain B. subtilis 168. We identified 27 SNPs with predictable phenotypic effects, including genetic traits for known phenotypes. We conclude that BacillOndex is a valuable tool for the systems-level investigation of, and hypothesis generation about, this important biotechnology workhorse. Such understanding contributes to our ability to construct synthetic genetic circuits in this organism
TinkerCell: Modular CAD Tool for Synthetic Biology
Synthetic biology brings together concepts and techniques from engineering
and biology. In this field, computer-aided design (CAD) is necessary in order
to bridge the gap between computational modeling and biological data. An
application named TinkerCell has been created in order to serve as a CAD tool
for synthetic biology. TinkerCell is a visual modeling tool that supports a
hierarchy of biological parts. Each part in this hierarchy consists of a set of
attributes that define the part, such as sequence or rate constants. Models
that are constructed using these parts can be analyzed using various C and
Python programs that are hosted by TinkerCell via an extensive C and Python
API. TinkerCell supports the notion of a module, which are networks with
interfaces. Such modules can be connected to each other, forming larger modular
networks. Because TinkerCell associates parameters and equations in a model
with their respective part, parts can be loaded from databases along with their
parameters and rate equations. The modular network design can be used to
exchange modules as well as test the concept of modularity in biological
systems. The flexible modeling framework along with the C and Python API allows
TinkerCell to serve as a host to numerous third-party algorithms. TinkerCell is
a free and open-source project under the Berkeley Software Distribution
license. Downloads, documentation, and tutorials are available at
www.tinkercell.com.Comment: 23 pages, 20 figure
Spectral analysis of gene expression profiles using gene networks
Microarrays have become extremely useful for analysing genetic phenomena, but
establishing a relation between microarray analysis results (typically a list
of genes) and their biological significance is often difficult. Currently, the
standard approach is to map a posteriori the results onto gene networks to
elucidate the functions perturbed at the level of pathways. However,
integrating a priori knowledge of the gene networks could help in the
statistical analysis of gene expression data and in their biological
interpretation. Here we propose a method to integrate a priori the knowledge of
a gene network in the analysis of gene expression data. The approach is based
on the spectral decomposition of gene expression profiles with respect to the
eigenfunctions of the graph, resulting in an attenuation of the high-frequency
components of the expression profiles with respect to the topology of the
graph. We show how to derive unsupervised and supervised classification
algorithms of expression profiles, resulting in classifiers with biological
relevance. We applied the method to the analysis of a set of expression
profiles from irradiated and non-irradiated yeast strains. It performed at
least as well as the usual classification but provides much more biologically
relevant results and allows a direct biological interpretation
Modeling dependent gene expression
In this paper we propose a Bayesian approach for inference about dependence
of high throughput gene expression. Our goals are to use prior knowledge about
pathways to anchor inference about dependence among genes; to account for this
dependence while making inferences about differences in mean expression across
phenotypes; and to explore differences in the dependence itself across
phenotypes. Useful features of the proposed approach are a model-based
parsimonious representation of expression as an ordinal outcome, a novel and
flexible representation of prior information on the nature of dependencies, and
the use of a coherent probability model over both the structure and strength of
the dependencies of interest. We evaluate our approach through simulations and
in the analysis of data on expression of genes in the Complement and
Coagulation Cascade pathway in ovarian cancer.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS525 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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