SBML Models Research Articles

Annotation-based feature extraction from sets of SBML models.

BackgroundModel repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models.ResultsIn this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate.ConclusionsAnnotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0014-4) contains supplementary material, which is available to authorized users.

Hybrid Simulations of Heterogeneous Biochemical Models in SBML

Models of biochemical systems presented as a set of formal reaction rules can be interpreted in different formalisms, most notably as either deterministic Ordinary Differential Equations, stochastic continuous-time Markov Chains, Petri nets, or Boolean transition systems. While the formal composition of reaction systems can be syntactically defined as the (multiset) union of the reactions, the composition and simulation of models in different formalisms remain a largely open issue. In this article, we show that the combination of reaction rules and events, as already present in SBML, can be used in a nonstandard way to define stochastic and Boolean simulators and give meaning to the hybrid composition and simulation of heterogeneous models of biochemical processes. In particular, we show how two SBML reaction models can be composed into one hybrid continuous--stochastic SBML model through a high-level interface for composing reaction models and specifying their interpretation. Furthermore, we describe dynamic strategies for automatically partitioning reactions with stochastic or continuous interpretations according to dynamic criteria. The performances are then compared to static partitioning. The proposed approach is illustrated and evaluated on several examples, including the reconstructions of the hybrid model of the mammalian cell cycle regulation of Singhania et al. as the composition of a Boolean model of cell cycle phase transitions with a continuous model of cyclin activation, the hybrid stochastic--continuous models of bacteriophage T7 infection of Alfonsi et al., and the bacteriophage λ model of Goutsias, showing the gain in both accuracy and simulation time of the dynamic partitioning strategy.

Integrating Systems Biology Markup Language(SBML) with NEURON

The NEURON simulator software is widely used by the computational neuroscience community for neuronal electrophysiology modeling and we have recently extended it to support reaction-diffusion dynamics [1]. This class of dynamics is ideal for studying intracellular dynamics, such as calcium-induced calcium release. NEURON’s original reaction-diffusion support introduced a custom syntax; although this allowed taking advantage of NEURON’s full capabilities, it required existing cell biology models to be rewritten. To eliminate this barrier, we have introduced SBML support for NEURON. SBML [2] is an XML-based representation format used for specifying computational models of biological processes. This format is one of the most widely used, and is supported by over 200 software packages. It has become a standard used by systems biologists for the exchange of information about chemical reaction dynamics. We have introduced SBML support to the reaction diffusion module of the NEURON simulation software. This enables NEURON users to import a large number of previously developed cell biology models and use them in computational neuroscience research. We have added the capability to import and export models in SBML format to NEURON’s reaction-diffusion module using the libSBML library [3]. This allows one to combine models from ModelDB [4] with cell models from the BioModels database [5] in a way that makes it possible to match state variables and to readily change parameters. The procedure is as follows: • Electrophysiology model is loaded from ModelDB or constructed de novo. • NEURON loads SBML data and instantiates appropriate reaction diffusion objects: rxd.Region, rxd.Species, rxd.Reaction. User must interactively or preemptively match state-variable names across combined models to combine the two models into one. • SBML models do not include diffusion so diffusion constants can be added to provide the spatial element of the combined model. • User can adjust the parameters and make simulation runs of the model. We have used these new features to study the interaction of a calcium induced calcium release model from BioModels with electrical activity in a detailed model of a hippocampal CA1 pyramidal neuron.

Identification of components in biochemical pathways: extensive application to SBML models

Reactions forming a pathway can be rewritten by making explicit the different molecular components involved in them. A molecular component represents a biological entity, such as a protein, in all its states (free, bound, degraded, etc.). Component identification, which is made possible by the mass conservation principle, allows subpathways to be computed to better understand the pathway functioning. In this paper we show the application of a previously-defined component identification algorithm to a number of real-world models to experimentally validate the approach; precisely, we have processed all the curated SBML models of the BioModels repository. In order to extend the applicability of our approach, we propose both (i) a preprocessing phase aiming at transforming a given pathway into a format suitable as input for the algorithm, and (ii) a dynamic model correction procedure that could allow some erroneous situations to be solved. We also prove the correctness of the preprocessing phase, and a property characterizing the structure of pathways with erroneous reactions.

SensA: web-based sensitivity analysis of SBML models.

Summary: SensA is a web-based application for sensitivity analysis of mathematical models. The sensitivity analysis is based on metabolic control analysis, computing the local, global and time-dependent properties of model components. Interactive visualization facilitates interpretation of usually complex results. SensA can contribute to the analysis, adjustment and understanding of mathematical models for dynamic systems.Availability and implementation: SensA is available at http://gofid.biologie.hu-berlin.de/ and can be used with any modern browser. The source code can be found at https://bitbucket.org/floettma/sensa/ (MIT license)Contact: max.floettmann@biologie.hu-berlin.de or thomas.spiesser@biologie.hu-berlin.de

CSML2SBML: a novel tool for converting quantitative biological pathway models from CSML into SBML.

CSML and SBML are XML-based model definition standards which are developed with the aim of creating exchange formats for modeling, visualizing and simulating biological pathways. In this article we report a release of a format convertor for quantitative pathway models, namely CSML2SBML. It translates models encoded by CSML into SBML without loss of structural and kinetic information. The simulation and parameter estimation of the resulting SBML model can be carried out with compliant tool CellDesigner for further analysis. The convertor is based on the standards CSML version 3.0 and SBML Level 2 Version 4. In our experiments, 11 out of 15 pathway models in CSML model repository and 228 models in Macrophage Pathway Knowledgebase (MACPAK) are successfully converted to SBML models. The consistency of the resulting model is validated by libSBML Consistency Check of CellDesigner. Furthermore, the converted SBML model assigned with the kinetic parameters translated from CSML model can reproduce the same dynamics with CellDesigner as CSML one running on Cell Illustrator. CSML2SBML, along with its instructions and examples for use are available at http://csml2sbml.csml.org.

Application of a Semi-automatic Algorithm for Identification of Molecular Components in SBML Models

Reactions forming a pathway can be rewritten by making explicit the different molecular components involved in them. A molecular component represents a biological entity (e.g. a protein) in all its states (free, bound, degraded, etc.). In this paper we show the application of a component identification algorithm to a number of real-world models to experimentally validate the approach. Components identification allows subpathways to be computed to better understand the pathway functioning.

The systems biology simulation core algorithm.

BackgroundWith the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases.ResultsThis article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database.ConclusionsThe formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net.

CySBGN: A Cytoscape plug-in to integrate SBGN maps

BackgroundA standard graphical notation is essential to facilitate exchange of network representations of biological processes. Towards this end, the Systems Biology Graphical Notation (SBGN) has been proposed, and it is already supported by a number of tools. However, support for SBGN in Cytoscape, one of the most widely used platforms in biology to visualise and analyse networks, is limited, and in particular it is not possible to import SBGN diagrams.ResultsWe have developed CySBGN, a Cytoscape plug-in that extends the use of Cytoscape visualisation and analysis features to SBGN maps. CySBGN adds support for Cytoscape users to visualize any of the three complementary SBGN languages: Process Description, Entity Relationship, and Activity Flow. The interoperability with other tools (CySBML plug-in and Systems Biology Format Converter) was also established allowing an automated generation of SBGN diagrams based on previously imported SBML models. The plug-in was tested using a suite of 53 different test cases that covers almost all possible entities, shapes, and connections. A rendering comparison with other tools that support SBGN was performed. To illustrate the interoperability with other Cytoscape functionalities, we present two analysis examples, shortest path calculation, and motif identification in a metabolic network.ConclusionsCySBGN imports, modifies and analyzes SBGN diagrams in Cytoscape, and thus allows the application of the large palette of tools and plug-ins in this platform to networks and pathways in SBGN format.

Computational modelling of the regulation of Insulin signalling by oxidative stress

BackgroundExisting models of insulin signalling focus on short term dynamics, rather than the longer term dynamics necessary to understand many physiologically relevant behaviours. We have developed a model of insulin signalling in rodent adipocytes that includes both transcriptional feedback through the Forkhead box type O (FOXO) transcription factor, and interaction with oxidative stress, in addition to the core pathway. In the model Reactive Oxygen Species are both generated endogenously and can be applied externally. They regulate signalling though inhibition of phosphatases and induction of the activity of Stress Activated Protein Kinases, which themselves modulate feedbacks to insulin signalling and FOXO.ResultsInsulin and oxidative stress combined produce a lower degree of activation of insulin signalling than insulin alone. Fasting (nutrient withdrawal) and weak oxidative stress upregulate antioxidant defences while stronger oxidative stress leads to a short term activation of insulin signalling but if prolonged can have other effects including degradation of the insulin receptor substrate (IRS1) and FOXO. At high insulin the protective effect of moderate oxidative stress may disappear.ConclusionOur model is consistent with a wide range of experimental data, some of which is difficult to explain. Oxidative stress can have effects that are both up- and down-regulatory on insulin signalling. Our model therefore shows the complexity of the interaction between the two pathways and highlights the need for such integrated computational models to give insight into the dysregulation of insulin signalling along with more data at the individual level.A complete SBML model file can be downloaded from BIOMODELS (https://www.ebi.ac.uk/biomodels-main) with unique identifier MODEL1212210000.Other files and scripts are available as additional files with this journal article and can be downloaded from https://github.com/graham1034/Smith2012_insulin_signalling.

SBML Reaction Finder: Retrieve and extract specific reactions from the BioModels database

AbstractSummary: The SBML Reaction Finder (SRF) application leverages the deep semantic annotations in the BioModels database to provide efficient retrieval and extraction of individual reactions from SBML models. We hope that the SRF will be useful to quantitative modelers who seek to accelerate their modeling efforts by reusing previously published representations of specific chemical reactions.Availability and Implementation: The SRF is open source, coded in Java, and distributed under the Mozilla Pubic License Version 1.1. Windows, Macintosh and Linux distributions are available for download at http://sourceforge.net/projects/sbmlrxnfinder.

Application of string similarity ratio and edit distance in automatic metabolite reconciliation comparing reconstructions and models

Increasing numbers of biochemical network models become available and reuse of these models is becoming more common. As a consequence, tools to compare models are needed. Comparison can be difficult because model builders often use different standards during reconstruction, metabolite formulas are not always indicated, IDs and names of metabolites are different, and models are stored in different formats (SBML, COBRA and others). Herein, a model comparison algorithm for SBML and COBRA format models is presented, called ModeRator. Precondition for correct matching of reactions is the comparison of the participating metabolites. ModeRator is based on the comparison of metabolite names as text strings. An automatic three level filtering approach is implemented in the software, which rejects pairs of potentially equal metabolites and builds an opinion about metabolite pairs with high similarity in metabolite names. ModeRator was applied to two test cases, comparing two models of each, E.coli and S.cerevisiae. Matches of the automatic mapping were manually inspected and compared with the automatic predictions. Automatic metabolite mapping of E.coli models (1314 and 1704 metabolites) comparing only identifiers revealed a high number of accordant metabolites. Both models originate from the same source (BioCyc database). No significant difference between automatic mapping and manual curation are observed. For the comparison of two S.cerevisiae models (679 and 1061 metabolites), three level filtration by metabolite name is used. The discrepancy between manual curated predictions and ModeRator predictions was 7%.

Managing Bio-Models: *Model Storage, Retrieval, Ranking and Versioning

AbstractIn this poster, we present our experience with techniques for the management of bio-models (i.e. XML-encoded computational models of biological systems) in repositories. These techniques support users to find, access, adapt and then re-submit models - tasks which are essential for the reuse of models in systems biology. Here, we focus on model retrieval, the ranking of search results and concepts to track the history of a model through version control. These concepts have so far not been sufficiently addressed in existing systems.To validate our techniques, we have been implemented a framework called Sombi. The underlying database called mDB is tailored towards a fine-grained storage of the model and its annotations. The design is applicable to different representation formats but, to begin with, has been tested with SBML models (Köhn et al., 2009).We furthermore present a retrieval algorithm for bio-model repositories (Henkel et al., 2010). The algorithm has been implemented and validated with mDB and the BioModels Database (www.ebi.ac.uk/biomodels-main/). The ranked retrieval enhances the result set by incorporating expert knowledge in the annotations. In contrast to existing systems, our approach provides a ranked result which is justified and explains the ordering of retrieved models to the users. The mDB design furthermore enables the application of structured queries on the stored model data.Finally, the poster proposes a solution to model version control. We introduce a library called BiVeS for tracking a model’s history in a diff tree; i.e. a tree-like storage of changes between model versions. These changes are additionally classified into modifications to XML structure, biology and mathematics. This information helps to understand existing models.All approaches are independent of the SBML format and can therefore be applied to other XML based exchange formats. All code is freely available on Sourceforge from http://bives.sourceforge.net/ and http://sombi.sourceforge.net/.

Retrieval, alignment, and clustering of computational models based on semantic annotations

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.

Bridging the gap between Computational Neuroscience and Systems Biology modelling

Bridging the gap between Computational Neuroscience and Systems Biology modelling

Integrating systems biology models and biomedical ontologies.

BackgroundSystems biology is an approach to biology that emphasizes the structure and dynamic behavior of biological systems and the interactions that occur within them. To succeed, systems biology crucially depends on the accessibility and integration of data across domains and levels of granularity. Biomedical ontologies were developed to facilitate such an integration of data and are often used to annotate biosimulation models in systems biology.ResultsWe provide a framework to integrate representations of in silico systems biology with those of in vivo biology as described by biomedical ontologies and demonstrate this framework using the Systems Biology Markup Language. We developed the SBML Harvester software that automatically converts annotated SBML models into OWL and we apply our software to those biosimulation models that are contained in the BioModels Database. We utilize the resulting knowledge base for complex biological queries that can bridge levels of granularity, verify models based on the biological phenomenon they represent and provide a means to establish a basic qualitative layer on which to express the semantics of biosimulation models.ConclusionsWe establish an information flow between biomedical ontologies and biosimulation models and we demonstrate that the integration of annotated biosimulation models and biomedical ontologies enables the verification of models as well as expressive queries. Establishing a bi-directional information flow between systems biology and biomedical ontologies has the potential to enable large-scale analyses of biological systems that span levels of granularity from molecules to organisms.

Context-based generation of kinetic equations with SBMLsqueezer 1.3

AbstractThe development of predictive, quantitative models constitutes a common task in today‘s systems biology. To obtain a mathematical model description for the simulation of gene-regulatory, signaling, and metabolic networks, kinetic equations are required for each reaction within the network. Deriving and assembling these formulas is a complicated, time-consuming, and error-prone process that requires knowledge about the structure of interactions, consistently choosing a rate law for each type of reaction, and assignment of appropriate units to all parameters. In many cases, thermodynamic dependencies between the parameters have to be taken into account. For multi compartment models, the concentration units of reacting species have to be converted into molecular amounts. Here we present version 1.3 of the program SBMLsqueezer that generates kinetic equations for SBML models based on the definition of the network's topology and annotations of the elements therein, i.e., Systems Biology Ontology (SBO) and Minimal Information Requested In the Annotation of Models (MIRIAM) annotations.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core

AbstractThis is the final specification for Release 1 of *SBML Level 3 Version 1 Core*, an electronic model representation format for systems biology.SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. SBML is defined neutrally with respect to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML. This document contains many examples of SBML models written in XML.More information about SBML and this specification is available online at "http://sbml.org/Documents/Specifications/":http://sbml.org/Documents/Specifications/.

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core

This is the final specification for Release 1 of *SBML Level 3 Version 1 Core*, an electronic model representation format for systems biology.SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. SBML is defined neutrally with respect to programming languages and software encoding; however, it is oriented primarily towards allowing models to be encoded using XML. This document contains many examples of SBML models written in XML.More information about SBML and this specification is available online at "http://sbml.org/Documents/Specifications/":http://sbml.org/Documents/Specifications/.

A graphical method for reducing and relating models in systems biology

Motivation: In Systems Biology, an increasing collection of models of various biological processes is currently developed and made available in publicly accessible repositories, such as biomodels.net for instance, through common exchange formats such as SBML. To date, however, there is no general method to relate different models to each other by abstraction or reduction relationships, and this task is left to the modeler for re-using and coupling models. In mathematical biology, model reduction techniques have been studied for a long time, mainly in the case where a model exhibits different time scales, or different spatial phases, which can be analyzed separately. These techniques are however far too restrictive to be applied on a large scale in systems biology, and do not take into account abstractions other than time or phase decompositions. Our purpose here is to propose a general computational method for relating models together, by considering primarily the structure of the interactions and abstracting from their dynamics in a first step.Results: We present a graph-theoretic formalism with node merge and delete operations, in which model reductions can be studied as graph matching problems. From this setting, we derive an algorithm for deciding whether there exists a reduction from one model to another, and evaluate it on the computation of the reduction relations between all SBML models of the biomodels.net repository. In particular, in the case of the numerous models of MAPK signalling, and of the circadian clock, biologically meaningful mappings between models of each class are automatically inferred from the structure of the interactions. We conclude on the generality of our graphical method, on its limits with respect to the representation of the structure of the interactions in SBML, and on some perspectives for dealing with the dynamics.Availability: The algorithms described in this article are implemented in the open-source software modeling platform BIOCHAM available at http://contraintes.inria.fr/biocham The models used in the experiments are available from http://www.biomodels.net/Contact: francois.fages@inria.fr