Escalating model sizes and complexities call for standardized forms of representation

The recent work of Kitano et al on a comprehensive EGFR Pathway Map (Mol Systems Biol, this issue) represents a tremendous amount of intellectual effort. The scale of the model is breathtaking. No doubt some readers will assail the effort on the grounds that models of this size and complexity are difficult to verify, but while this may be true for today’s methods, it is an unhelpful criticism. The inescapable reality in systems biology is that models (that is to say, hypotheses cast in a computational form) will continue to grow in size, complexity, and scope. Rather than grouse, we should be thinking about how to developways of analyzing and verifying models of this scale. We also need to improve our methods of sharing and understanding each other’s work in order to facilitate the iterative processes of review and refinement that are fundamental to modeling

The JWS online simulation database

Summary: JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments. Availability and Implementation: The Simulation Database is available on line at https://jjj.bio.vu. nl/models/experiments/

Reproducible computational biology experiments with SED-ML - The Simulation Experiment Description Markup Language

Background: The increasing use of computational simulation experiments to inform modern biological research creates new challenges to annotate, archive, share and reproduce such experiments. The recently published Minimum Information About a Simulation Experiment (MIASE) proposes a minimal set of information that should be provided to allow the reproduction of simulation experiments among users and software tools. Results: In this article, we present the Simulation Experiment Description Markup Language (SED-ML). SED-ML encodes in a computer-readable exchange format the information required by MIASE to enable reproduction of simulation experiments. It has been developed as a community project and it is defined in a detailed technical specification and additionally provides an XML schema. The version of SED-ML described in this publication is Level 1 Version 1. It covers the description of the most frequent type of simulation experiments in the area, namely time course simulations. SED-ML documents specify which models to use in an experiment, modifications to apply on the models before using them, which simulation procedures to run on each model, what analysis results to output, and how the results should be presented. These descriptions are independent of the underlying model implementation. SED-ML is a software-independent format for encoding the description of simulation experiments; it is not specific to particular simulation tools. Here, we demonstrate that with the growing software support for SED-ML we can effectively exchange executable simulation descriptions. Conclusions: With SED-ML, software can exchange simulation experiment descriptions, enabling the validation and reuse of simulation experiments in different tools. Authors of papers reporting simulation experiments can make their simulation protocols available for other scientists to reproduce the results. Because SED-ML is agnostic about exact modeling language(s) used, experiments covering models from different fields of research can be accurately described and combined

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

SED-ML Web Tools: Generate, modify and export standard-compliant simulation studies

ABSTRACT The Simulation Experiment Description Markup Language (SED-ML) is a standardized format for exchanging simulation studies independently of software tools. We present the SED-ML Web Tools, a software that supports SED-ML Level 1 Version 2, including complex modifications and co-simulation of SBML and CellML models. This online application allows for creating, editing, simulating and validating SED-ML documents. It lowers the bar on working with SED-ML documents and helps users create valid simulation descriptions for models in CellML and SBML formats. Availability and Implementation: sysbioapps.dyndns.org/SED-ML Web Tools. Further information: sysbioapps.dyndns.org/SED-ML Web Tools/Services/SedMLService.asmx

Model storage, exchange and integration

The field of Computational Systems Neurobiology is maturing quickly. If one wants it to fulfil its central role in the new Integrative Neurobiology, the reuse of quantitative models needs to be facilitated. The community has to develop standards and guidelines in order to maximise the diffusion of its scientific production, but also to render it more trustworthy. In the recent years, various projects tackled the problems of the syntax and semantics of quantitative models. More recently the international initiative BioModels.net launched three projects: (1) MIRIAM is a standard to curate and annotate models, in order to facilitate their reuse. (2) The Systems Biology Ontology is a set of controlled vocabularies aimed to be used in conjunction with models, in order to characterise their components. (3) BioModels Database is a resource that allows biologists to store, search and retrieve published mathematical models of biological interests. We expect that those resources, together with the use of formal languages such as SBML, will support the fruitful exchange and reuse of quantitative models

Mathematical modeling of cancer metabolism

Systemic approaches are needed and useful for the study of the very complex issue of cancer. Modeling has a central position in these systemic approaches. Metabolic reprogramming is nowadays acknowledged as an essential hallmark of cancer. Mathematical modeling could contribute to a better understanding of cancer metabolic reprogramming and to identify new potential ways of therapeutic intervention. Herein, I review several alternative approaches to metabolic modeling and their current and future impact in oncology.Supported by grants BIO2014-56092-R (MINECO and FEDER), P12-CTS-1507 (Andalusian Government and FEDER) and funds from group BIO-267 (Andalusian Government). The “CIBER de Enfermedades Raras” is an initiative from the ISCIII (Spain). The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript

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

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