BioModels: ten-year anniversary

BioModels (http://www.ebi.ac.uk/biomodels/) is a repository of mathematical models of biological processes. A large set of models is curated to verify both correspondence to the biological process that the model seeks to represent, and reproducibility of the simulation results as described in the corresponding peer-reviewed publication. Many models submitted to the database are annotated, cross-referencing its components to external resources such as database records, and terms from controlled vocabularies and ontologies. BioModels comprises two main branches: one is composed of models derived from literature, while the second is generated through automated processes. BioModels currently hosts over 1200 models derived directly from the literature, as well as in excess of 140 000 models automatically generated from pathway resources. This represents an approximate 60-fold growth for literature-based model numbers alone, since BioModels’ first release a decade ago. This article describes updates to the resource over this period, which include changes to the user interface, the annotation profiles of models in the curation pipeline, major infrastructure changes, ability to perform online simulations and the availability of model content in Linked Data form. We also outline planned improvements to cope with a diverse array of new challenges

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

Impact of clinical and genetic findings on the management of young patients with Brugada syndrome.

BACKGROUND: Brugada syndrome (BrS) is an arrhythmogenic disease associated with sudden cardiac death (SCD) that seldom manifests or is recognized in childhood. OBJECTIVES: The objectives of this study were to describe the clinical presentation of pediatric BrS to identify prognostic factors for risk stratification and to propose a data-based approach management. METHODS: We studied 106 patients younger than 19 years at diagnosis of BrS enrolled from 16 European hospitals. RESULTS: At diagnosis, BrS was spontaneous (n = 36, 34%) or drug-induced (n = 70, 66%). The mean age was 11.1 ± 5.7 years, and most patients were asymptomatic (family screening, (n = 67, 63%; incidental, n = 13, 12%), while 15 (14%) experienced syncope, 6(6%) aborted SCD or symptomatic ventricular tachycardia, and 5 (5%) other symptoms. During follow-up (median 54 months), 10 (9%) patients had life-threatening arrhythmias (LTA), including 3 (3%) deaths. Six (6%) experienced syncope and 4 (4%) supraventricular tachycardia. Fever triggered 27% of LTA events. An implantable cardioverter-defibrillator was implanted in 22 (21%), with major adverse events in 41%. Of the 11 (10%) patients treated with hydroquinidine, 8 remained asymptomatic. Genetic testing was performed in 75 (71%) patients, and SCN5A rare variants were identified in 58 (55%); 15 of 32 tested probands (47%) were genotype positive. Nine of 10 patients with LTA underwent genetic testing, and all were genotype positive, whereas the 17 SCN5A-negative patients remained asymptomatic. Spontaneous Brugada type 1 electrocardiographic (ECG) pattern (P = .005) and symptoms at diagnosis (P = .001) were predictors of LTA. Time to the first LTA event was shorter in patients with both symptoms at diagnosis and spontaneous Brugada type 1 ECG pattern (P = .006). CONCLUSION: Spontaneous Brugada type 1 ECG pattern and symptoms at diagnosis are predictors of LTA events in the young affected by BrS. The management of BrS should become age-specific, and prevention of SCD may involve genetic testing and aggressive use of antipyretics and quinidine, with risk-specific consideration for the implantable cardioverter-defibrillator

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

Identifying Cell Types from Spatially Referenced Single-Cell Expression Datasets

<div><p>Complex tissues, such as the brain, are composed of multiple different cell types, each of which have distinct and important roles, for example in neural function. Moreover, it has recently been appreciated that the cells that make up these sub-cell types themselves harbour significant cell-to-cell heterogeneity, in particular at the level of gene expression. The ability to study this heterogeneity has been revolutionised by advances in experimental technology, such as Wholemount in Situ Hybridizations (WiSH) and single-cell RNA-sequencing. Consequently, it is now possible to study gene expression levels in thousands of cells from the same tissue type. After generating such data one of the key goals is to cluster the cells into groups that correspond to both known and putatively novel cell types. Whilst many clustering algorithms exist, they are typically unable to incorporate information about the spatial dependence between cells within the tissue under study. When such information exists it provides important insights that should be directly included in the clustering scheme. To this end we have developed a clustering method that uses a Hidden Markov Random Field (HMRF) model to exploit both quantitative measures of expression and spatial information. To accurately reflect the underlying biology, we extend current HMRF approaches by allowing the degree of spatial coherency to differ between clusters. We demonstrate the utility of our method using simulated data before applying it to cluster single cell gene expression data generated by applying WiSH to study expression patterns in the brain of the marine annelid <i>Platynereis dumereilii</i>. Our approach allows known cell types to be identified as well as revealing new, previously unexplored cell types within the brain of this important model system.</p></div

In-situ hybridization image for rOpsin and rOpsin3 in the full brain at 48hpf (Apical view).

<p>Z-projection of the expression of rOpsin (red) in both the adult eyes and the larval eyes, rOpsin3 (green) specifically in the larval eyes and co-expression areas in some areas of the larval eyes in the full brain of <i>Platynereis</i> at 48hpf. This image been obtained directly from the data obtained in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Tomer1" target="_blank">[3]</a></p

Densities of log luminescence values for two genes (rOpsin, PRDM8) over the voxels.

<p>For <i>rOpsin</i>, the density exhibits two clear peaks making the choice of a binarizing threshold easy. By contrast, for <i>PRDM8</i> there is no such clear threshold, making an automated binarization method hard to implement.</p

Wholemount in-situ hybridization expression data for 86 genes in the full brain of Platynereis.

<p>The whole larvae is hybridized with two dyed probes targetting specific mRNAs, one corresponding to a reference gene and the other a gene of interest. Using confocal microscopy, the whole larvae is visualized slice by slice and the dyed regions are reported with laser light reflecting back to the detector. Every image is then divided into 1 cell large squares which allows the reconstruction of the 3D map of expression for the two genes in the full brain. The process was repeated 86 times for key genes in Platynereis development <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi.1003824-Tomer1" target="_blank">[3]</a>.</p

Validating the estimation of beta.

<p>This figure shows the evolution for of the mean value of across all the clusters. The red dots represent the biological data clustering (i.e the reference in our simulations scheme). The green dots represent the results obtained after clustering simulated data, which shows an underestimation of . To confirm that this underestimation come from the simulation scheme and not the clustering method, we used the simulated data as the reference to generate a "second generation" of simulated data, suppressing the simulation scheme bias (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g007" target="_blank">Figure 7</a>). The results of this re-simulation are shown by the blue dots, which exhibit no underestimation of . Finally the brown dots represent the mean value of on the same simulated data but spatially randomized, as expected the are now estimated to .</p

Decrease in spatial coherency due to the simulation scheme.

<p>For an example cluster , gene may only be expressed in half of the voxels. This will yield . However, in the biological data, the voxels expressing gene may be spatially coherent (i.e., located close to one another), leading to a reduced area of expression discontinuity (the green line). By contrast, in the simulated data the expression of such a gene will lose its spatial coherency, leading to an increased area of expression discontinuity. The number of voxels having a neighbour with some differences in the gene expression pattern is directly linked to the value of through the energy function (<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#s4" target="_blank">Methods</a>). This explains the underestimation of observed in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003824#pcbi-1003824-g006" target="_blank">Figure 6</a>.</p