Using chemical organization theory for model checking

Motivation: The increasing number and complexity of biomodels makes automatic procedures for checking the models' properties and quality necessary. Approaches like elementary mode analysis, flux balance analysis, deficiency analysis and chemical organization theory (OT) require only the stoichiometric structure of the reaction network for derivation of valuable information. In formalisms like Systems Biology Markup Language (SBML), however, information about the stoichiometric coefficients required for an analysis of chemical organizations can be hidden in kinetic laws

OREMPdb: a semantic dictionary of computational pathway models

<p>Abstract</p> <p>Background</p> <p>The information coming from biomedical ontologies and computational pathway models is expanding continuously: research communities keep this process up and their advances are generally shared by means of dedicated resources published on the web. In fact, such models are shared to provide the characterization of molecular processes, while biomedical ontologies detail a semantic context to the majority of those pathways. Recent advances in both fields pave the way for a scalable information integration based on aggregate knowledge repositories, but the lack of overall standard formats impedes this progress. Indeed, having different objectives and different abstraction levels, most of these resources "speak" different languages. Semantic web technologies are here explored as a means to address some of these problems.</p> <p>Methods</p> <p>Employing an extensible collection of interpreters, we developed OREMP (Ontology Reasoning Engine for Molecular Pathways), a system that abstracts the information from different resources and combines them together into a coherent ontology. Continuing this effort we present OREMPdb; once different pathways are fed into OREMP, species are linked to the external ontologies referred and to reactions in which they participate. Exploiting these links, the system builds species-sets, which encapsulate species that operate together. Composing all of the reactions together, the system computes all of the reaction paths from-and-to all of the species-sets.</p> <p>Results</p> <p>OREMP has been applied to the curated branch of BioModels (2011/04/15 release) which overall contains 326 models, 9244 reactions, and 5636 species. OREMPdb is the semantic dictionary created as a result, which is made of 7360 species-sets. For each one of these sets, OREMPdb links the original pathway and the link to the original paper where this information first appeared. </p

The systems biology format converter

BACKGROUND: Interoperability between formats is a recurring problem in systems biology research. Many tools have been developed to convert computational models from one format to another. However, they have been developed independently, resulting in redundancy of efforts and lack of synergy. RESULTS: Here we present the System Biology Format Converter (SBFC), which provide a generic framework to potentially convert any format into another. The framework currently includes several converters translating between the following formats: SBML, BioPAX, SBGN-ML, Matlab, Octave, XPP, GPML, Dot, MDL and APM. This software is written in Java and can be used as a standalone executable or web service. CONCLUSIONS: The SBFC framework is an evolving software project. Existing converters can be used and improved, and new converters can be easily added, making SBFC useful to both modellers and developers. The source code and documentation of the framework are freely available from the project web site. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-016-1000-2) contains supplementary material, which is available to authorized users

The Ligand Gated Ion Channel database: an example of a sequence database in neuroscience.

Multiple comparisons of receptor sequences, or receptor subunit sequences, has proved to be an invaluable tool in modern pharmacological investigations. Although of outstanding importance, general sequence databases suffer from several imperfections due to their size and their non-specificity. Room therefore exists for expert-maintained databases of restricted focus, where knowledge of the research field helps to filter the huge amount of data generated. Accordingly, neuroscientists have designed databases covering several types of proteins, in particular receptors for neurotransmitters. Ligand-gated ion channels are oligomeric transmembrane proteins involved in the fast response to neurotransmitters. All these receptors are formed by the assembly of homologous subunits, and an unexpected wealth of genes coding for these subunits has been revealed during the last two decades. The Ligand Gated Ion Channel database (LGICdb) has been developed to handle this growing body of information. The database aims to provide only one entry for each gene, containing annotated nucleic acid and protein sequences

Completing SBGN-AF Networks by Logic-Based Hypothesis Finding

International audienceThis study considers formal methods for finding unknown interactions of incomplete molecular networks using microarray profiles. In systems biology, a challenging problem lies in the growing scale and complexity of molecular networks. Along with high-throughput experimental tools, it is not straightforward to reconstruct huge and complicated networks using observed data by hand. Thus, we address the completion problem of our target networks represented by a standard markup language, called SBGN (in particular, Activity Flow). Our proposed method is based on logic-based hypothesis finding techniques; given an input SBGN network and its profile data, missing interactions can be logically generated as hypotheses by the proposed method. In this paper, we also show empirical results that demonstrate how the proposed method works with a real network involved in the glucose repression of S. cerevisiae

Transcription of α

The hybrid lager yeast Saccharomyces pastorianus (S. cerevisiae × S. eubayanus) contains several genes encoding proteins responsible for the uptake and metabolism of maltose and maltotriose. In many cases the genes occur as orthologues, that is, the S. cerevisiaegene exists along with the S. eubayanus gene. Prior to formation of the hybrid, these genes existed in organisms, which had been separated for tens of millions of years and were expected to show some level of genetic and functional differentiation. In this study, oligonucleotide probes were designed for TRAC analysis of transcription of the S. cerevisiae and S. eubayanus orthologues of AGT1, MALx1, MALx2 and MALx3 as well as the S. cerevisiae‐derived MPH2/3 genes within the S. pastorianus genome. Specificity of probes was validated using mRNA from S. cerevisiae and from S. eubayanus. Probes were used to analyse gene expression during 15°P wort fermentations conducted at different temperatures (10–20°C). As well as differential expression of different genes, differential expression of orthologues was also observed during fermentation. The differences suggest that, where two forms of the gene exist, either one will dominate (as with AGT1) or expression will be staggered (MALx2), possibly to maximize transport and for efficient degradation of sugars