Li et al. BMC Systems Biology 2010, 4:92
BioModels Database: An enhanced, curated and
annotated resource for published quantitative
© 2010 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attri-
bution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
Chen Li1, Marco Donizelli1, Nicolas Rodriguez1, Harish Dharuri2, Lukas Endler1, Vijayalakshmi Chelliah1, Lu Li1,
Enuo He1,2, Arnaud Henry1, Melanie I Stefan1, Jacky L Snoep3, Michael Hucka2, Nicolas Le Novère1 and Camille Laibe*1
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
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.
Advances in molecular and cellular biology over the past
few decades have triggered tremendous growth in avail-
able experimental data. To generate novel or insightful
hypotheses from this enormous quantity of data is a sig-
nificant challenge. Computational modelling can help
meet this challenge by contributing to a deeper under-
standing of relevant chemical and biological phenomena
based on their underlying mechanisms. Simulations of
models can help investigate a complete biological pro-
cess instead of considering smaller segments or aspects,
detail a segment of a process or simplify a very large
one, suggest or even direct future experiments, and pre-
dict the behaviour of a system under given conditions.
Supporting these goals requires precise models that
* Correspondence: email@example.com
1 European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton,
CB10 1SD, UK
Full list of author information is available at the end of the article
Li et al. BMC Systems Biology 2010, 4:92
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accurately represent biological systems in a quantitative
To construct a large-scale comprehensive view of bio-
logical systems, several smaller models may need to be
integrated. This can be difficult to accomplish, since
models can exhibit significant variations even when pur-
porting to cover the same domain space. They can come
from different modellers, developed at different times
from different perspectives, and be encoded in different
formats. Consequently, some models cannot be practi-
cally reused, or even worse, may be entirely lost due to a
lack of the necessary information that would allow them
to be exchanged or converted.
The definition and adoption of standard and machine-
readable formats for encoding quantitative models has
already been recognised as a crucial first step for efficient
exchange and reuse. CellML  and NeuroML  are
such examples, but the Systems Biology Markup Lan-
guage (SBML) , being adopted by more than 180 soft-
ware systems ranging from simulators to model editors
and databases , has so far been the most successful
standard model exchange format in this field.
The next stage of infrastructure development for com-
putational modelling is the creation of public repositories
where models can be freely deposited and distributed in
standardised formats. Models in these repositories
should be curated according to agreed-upon standards,
and annotated using community-developed controlled
vocabularies, for instance with Gene Ontology [5,6] and
Taxonomy [7,8]. Linking the components to external data
resources, such as protein sequences from UniProt  or
pathways from Reactome , can allow the unambigu-
ous identification of the components. This in turn can
enable members of the biomedical and life science com-
munities to search and retrieve models, or parts of mod-
els, relevant to their research topics, whether that topic is
a disease, a biological process, a given molecular com-
plex, or something else.
We developed BioModels Database [11-13] precisely
with these goals in mind, while other resources, such as
ModelDB , JWS Online  or the CellML Model
Repository [16,17], focus on different aspects. The
resource is part of the BioModels.net initiative [18,19],
which aims to (1) define community standards for model
and simulation curation, (2) provide controlled vocabu-
laries to define and link the terms used in systems biol-
ogy, and (3) provide a free, centralised, publicly-accessible
database for storing, searching and retrieving curated and
annotated computational models. Here we describe the
current structure of BioModels Database as well as its
BioModels Database design and procedures
The BioModels Database server software uses a typical
three-tier architecture, in which the data storage, pro-
cessing and presentation are logically separated. The pro-
gramming language used for the main development is
Java , while some conversion-related processes
(described below) are implemented using a combination
of Extensible Stylesheet Language Transformations
(XSLT)  and shell scripts.
The web interface of BioModels Database is implemented
using JavaServer Pages (JSP) . Asynchronous
interface more dynamic and improve the responsiveness
of many processes, especially in the model annotation
interface where a given page does not have to be reloaded
each time a curator makes an incremental annotation.
AJAX also makes user-level features such as model com-
ponent extraction more interactive.
Authentication and User Roles
There are four main types of user roles defined in Bio-
Models Database: The Public user role is assigned by
default, and requires no registration. Public users can
access the database to search, view and download models,
as well as to run simulations. They can also submit mod-
els. The remaining roles of Curator, Annotator and
Administrator are used by database curators and develop-
ers; they provide additional permissions beyond those of
Public user and require specific registration.
Quantitative information, kinetic laws and model entities
are all stored in SBML files. Model metadata are stored
separately in a MySQL database  and not in the SBML
file. This simplifies the management of annotations, espe-
cially during the annotation phase when the data are
updated frequently. Annotations are re-inserted into the
SBML files during the release process, allowing users to
directly download the fully annotated model files. More-
over, each model's history is tracked using Subversion
An earlier version of BioModels Database stored model
XML  files in Xindice , a native XML database.
However, the increasing popularity of BioModels Data-
base as well as an ever-greater number of models exceed-
ing the file-size limit of Xindice forced us to redesign the
system. The current version parses and builds indexes of
model elements (e.g., name, identifier and notes) using
Apache Lucene . Queries based on these indexes are
fast and efficient in terms of server memory and CPU
The metadata for all models (submission date, modifica-
tion date, model format, authors' information, etc.),
including references, are stored in a set of relational
tables in the MySQL database (Figure 1). The design of
Li et al. BMC Systems Biology 2010, 4:92
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these tables also reflects the different stages of the Bio-
Models Database pipeline.
Servers and Mirrors
BioModels Database runs on a server cluster configured
with a failover mechanism. Faster access for North Amer-
ican users is provided through a mirror at the California
Institute of Technology .
The converters from SBML to CellML  and to SciLab
, as well as the converter from CellML to SBML, are
written in XSLT . The converters from SBML to XPP,
BioPAX [31,32], Portable Network Graphics (PNG)
[33,34] and Scalable Vector Graphics (SVG)  are writ-
ten in Java. The Java converter from SBML to the Virtual
Cell Markup Language (VCML)  is provided by the
Virtual Cell team . Converter details and source code
are available online .
From the model overview interface, one can select spe-
cies, reactions and/or compartments in order to generate
a submodel containing these specific elements. This
function relies on a Java library developed by the Bio-
Models Database team. The library parses a model and
extracts the submodel in a four-step procedure. Firstly, it
extracts the species and compartments selected by the
user, along with the reactions they are involved in; sec-
ondly, it fetches the selected reactions; thirdly, it retrieves
the species and compartments involved in all previously
obtained reactions; finally, it extracts the compartment
types, species types, rules, events, parameters, units, and
function definitions needed to build a model which is
The BioModels Database pipeline
The BioModels Database pipeline (Figure 2) manages all
models from their submission to their publication. Mod-
els submitted to the database are not made publicly visi-
ble immediately upon submission; rather, they undergo a
series of curation and annotation processes in order to
ensure a consistent level of quality. As models pass
through the pipeline, additional information is added to
facilitate their reuse and the ability of software tools to
perform functions such as searching, simulation, conver-
sion or merging.
Model submission is open to the public. BioModels Data-
base currently accepts models encoded in SBML as well
as CellML format.
BioModels Database currently only distributes models
published in peer-reviewed literature. During the submis-
sion process, submitters are required to provide an
appropriate publication reference. This reference can be a
PubMed Identifier (PMID) , a Digital Object Identi-
fier (DOI) , or a URL. The publication reference helps
Figure 1 Database structure. Shown here is a schematic of the relational database structure used by BioModels Database. It depicts the different
database tables and their relationships. The three main steps of the pipeline (curation, annotation and publication) are organized by the three main
tables, cura, anno and publ, respectively. The two branches can be distinguished by the fact that the tables related to the non-curated branch
are prefixed with uncura.
Li et al. BMC Systems Biology 2010, 4:92
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other users to identify the model; it also improves the
accuracy of database search engine. BioModels Database
automatically searches for the reference in CiteXplore
 and stores the retrieved data, including journal
details, authorship, and abstract, in its internal indexes.
Models not yet published will not have a publication ref-
erence; however, they can still be submitted to the data-
base, and the reference data can be added later by the
BioModels Database performs numerous consistency
checks during the model submission procedure. The
model must be syntactically valid XML, as well as valid
with respect to its encoding schema. Errors detected dur-
ing submission are reported to the submitter. A submis-
sion is successful only after all errors have been corrected.
Following successful submission, a confirmation email
notifies the submitter of the unique submission identifier
assigned to the model. Each such identifier is composed
of the character sequence "MODEL" followed by ten dig-
its extracted from the timestamp of submission, and
being unique and perennial, the identifier can be quoted
in subsequent publications. A notification is also sent to
the BioModels Database curator team to inform them
that a new model has entered the curation pipeline.
We distinguish several "actors" in the process from
model creation to its publication on BioModels Database:
• The model's author (s) is (are) the author(s) of the
reference citation (i.e., the peer-reviewed article from
which the model originates). Concerns regarding the
biological basis of the model (e.g., the presence of an
interaction not documented in the scientific litera-
ture, or behaviour differing from that expected of the
biological process) should be directed to the model's
• The model's encoder (s) is (are) the person(s) who
actually encoded the model in its present form. There
may be several encoders, including the BioModels
Database curators if they have to modify a model sig-
nificantly. The encoder(s) should be contacted if there
is a problem with the structure of the model (initial
conditions, kinetics parameters, reaction scheme
• BioModels Database also defines the model submit-
ter (s) as the person or persons who submitted the
Figure 2 BioModels Database pipeline. The BioModels Database pipeline encompasses all the steps undergone by each model, from its submis-
sion to its publication. This figure illustrates the sequence of steps. It encompasses both public branches of the database (curated and non-curated)
as well as the possibility of curating and annotating models already published in the non-curated branch.
Li et al. BMC Systems Biology 2010, 4:92
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model to the repository. The submitter(s) should be
contacted if there is a problem with the original
model encoding or annotation.
Any specific concerns about a model can also be
reported to the BioModels Database curators through an
online form provided for this purpose on the website.
Successfully-submitted models are then queued into the
curation pipeline, where several tasks are performed.
1. If a model is submitted in an old level or version of
the SBML format, BioModels Database curators will
convert it to the latest level and version of SBML,
unless the curators believe that such conversion is
likely to cause information loss or inaccuracies. Mod-
els submitted in CellML are converted to the latest
SBML level and version since the BioModels Data-
base software (annotation interface, simulation tool,
etc.) is built around the SBML standard.
2. Further consistency checks are performed on the
model using libSBML  and SBMLeditor . This
includes checks for identifier and unit consistency as
well as for mathematical expression validity (more
specifically, MathML  validity), among others.
3. Curators manually check that the encoded model
faithfully represents the model described in the refer-
ence publication. This includes verifying the structure
of the model, such as the relationships between vari-
ables and mathematical relationships, as well as the
nomenclature used in the model components. It is
important to emphasise the fact that, during this step,
the structure of the submitted models may be modi-
fied by the encoders to reflect the structure of the
model described in the paper.
4. Curators download the model and run simulation
experiments under the conditions defined in the ref-
erence publication. These tasks are performed using
several simulation tools, at least one of them being
different from the tools used by the original authors
of the model. (The latter requirement helps guard
against software-specific behaviours or hidden
dependencies.) The tools most commonly used are
COPASI [45,46], the SBML ODESolver  or the
facilities provided by the Systems Biology Workbench
. If the results cannot be reproduced, curators
contact the model author(s), for clarification or dis-
cussion regarding any issues that have arisen. Once
the results correspond to the paper, curators upload a
typical results set to the database, together with com-
ments on how, and with which tools, it was obtained.
5. The curators give the model a consistent and mean-
ingful name following the general scheme Author
Year_Topic_Approach. Examples include the names
Levchenko2000_MAPK_noScaffold (referring to the
model identified by the BioModels identifier
Edelstein1996_EPSP_AChEvent (referring to the
After the curation phase, a model is moved into one of
two branches depending on the outcome of curation as
well as certain other criteria. In the curated branch, mod-
els are compliant with the MIRIAM (Minimum Informa-
tion Required in the Annotation of Models) reporting
guidelines . MIRIAM compliance requires models to
(1) be encoded in a public standard format, (2) be clearly
related to a single reference, (3) correspond to the biolog-
ical processes listed in the reference publication, and (4)
produce the simulation results given in the reference
publication using the same values and parameters. Mod-
els placed in the curation branch satisfy these require-
ments because, respectively, (1) each model is converted
into SBML and validated, (2) each comes from a peer-
reviewed published article, and each is verified by the
curators to correspond to its reference description in
both (3) structure and (4) results. By contrast, the non-
curated branch is reserved for models that are valid
SBML but either do not satisfy the full requirements for
MIRIAM compliance, or have not been curated fully due
to limited resources by the BioModels Database curation
team. For example, non-kinetic models such as pathway
and interaction maps, as well as steady-state analysis
models, are stored in this branch because it is generally
not possible to verify their results using simulations.
Other models that are placed in the non-curated branch
include spatial and boolean models that contain proprie-
tary annotations needed for their interpretation, and
models that do not reproduce the required results.
Once a model is moved to the curated branch, a new
BioModels Database identifier is generated and assigned
to it. This identifier is composed of the character
sequence "BIOMD" followed by ten digits reflecting the
model's position the
"BIOMD0000000216" for the 216th model successfully
curated. As is the case for submission identifiers, curation
identifiers are unique and permanent, and will never be
re-assigned to a different model, even if for some reason a
particular model must be retracted from the database.
MIRIAM compliance requires a model to have (1) a
unique meaningful name, (2) a reference citation linking
the model to a unique publication, (3) the name and con-
tact information of the model author(s), (4) the date and
time of model creation and last modification, and (5) a
precise statement about the terms of distribution. This
information is generally the first to be added to a model
during the annotation phase.
In publications describing models, the different ele-
ments such as specific genes, proteins and metabolites, or
the organisms from which the model is derived, are often
branch, for example
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described in the text or just given convenient or biologi-
cally non-meaningful names without any links to refer-
ence external resources. Furthermore, the names of
elements in the models most often do not allow users to
directly relate them to a precise biological function or
physical entity. This can greatly diminish model inter-
pretability by both users and software tools. Annotating
model elements helps avoid these problems, allowing
unambiguous identification through reference to appro-
priate external resources (using perennial URIs), such as
terms from controlled vocabularies (Taxonomy, Gene
Ontology, ChEBI ontology , Enzyme Nomenclature
, etc.) and links to other databases (UniProt, KEGG
, Reactome, etc.). In order to enhance the semantics
of models, terms from the Systems Biology Ontology
(SBO)  may also be added in the annotation phase.
Theoretically, all resources listed in MIRIAM Resources
 could be used for annotating model elements. Anno-
tations are used to improve the accuracy of search proce-
dures, as well as provide additional information about the
model components. They can also be useful in users'
analyses of the models, for instance in clustering  or
merging  procedures.
Annotating each model component with the most rele-
vant resource terms requires great efforts, especially
since the number of submitted models has grown rapidly.
The 17th release of BioModels Database (April 27th 2010),
contains 18,950 cross-references (links to external
resources contained in the annotations). This is a modest
number when compared to the total number of species
(37,852) and reactions (44,886) involved in the existing
473 models. This mostly reflects the lack of annotation in
the non-curated branch, which is mainly due to limited
curator resources (in terms of both time and specific
knowledge about the models). Low annotation is also
sometimes caused by a lack of adequate or suitable
resources, as in the case of molecular entities that exist in
a model only for simulation purposes. Moreover, biologi-
cal data resources are often slightly lagging behind newly
generated knowledge, and it is possible that a particular
resource does not offer the relevant information at the
time the model is annotated. In the case of hierarchical
controlled vocabularies, such as Gene Ontology or
ChEBI, there is the option to use a term at a higher level
of abstraction if the required precise term does not cur-
rently exist. Most often, curators nevertheless find ways
of adding some information, even if not in an optimal
fashion. Model annotation needs to be, and indeed is, a
Following the curation and annotation phases, the final
stage in the model processing pipeline is model publica-
tion. The model is tagged as ready for publication, and
becomes publicly available online with the next release of
the BioModels Database. New releases of the database are
issued two to four times per year.
The large range of features provided by BioModels Data-
base allow users to quickly locate models of relevance for
them, analyse them (and understand their structures),
simulate them, extract submodels, or download them in
various formats (whether text-based or graphical). These
facilities are available via a web browser, or can be directly
accessed from other tools by using the accompanying web
The most basic way of finding a particular model is to
identify it from the list of available models. Links to the
lists of curated models and non-curated models can be
found on the homepage of BioModels Database; the same
links are also available from the menu at the top of each
page of the site. The lists are presented as tables whose
columns display several different model characteristics;
within these tabulated views, a user can sort the list of
models by model identifier, model name, publication
identifier or the date of last modification, by clicking the
appropriate column heading.
An alternative to simply browsing the lists of models is
available in the form of a tree-structured browser based
on the Gene Ontology (GO) terms used in the annotation
of models in the database. A navigable, pruned, subtree of
GO is automatically generated by the system, allowing
users to explore the database thematically. The parenthe-
sised number that appears next to each branch of the GO
tree indicates how many models within that branch con-
tain that particular GO term. Expanding the GO tree
branch allows a user to drill down to child terms and find
models annotated with those more specific GO terms
(Figure 3). The extensive GO term coverage within Bio-
Models Database is illustrated in Figure 4.
Search and Retrieval
BioModels Database incorporates a powerful search
engine that allows users to quickly locate models of inter-
est. In order to find relevant models, the algorithm per-
forms several searches based on different data, then
performs an inclusive disjunction (OR) to combine the
results (Figure 5). The searches are performed sequen-
tially as follows: (1) querying metadata, publications and
annotations, (2) searching the model bodies, and (3)
searching supplementary information from external
resource databases. More specifically:
1. The search begins with the metadata (annotations)
of all models in the database. Model metadata is used
to facilitate the understanding, characteristics, and
management of the model. It may consist of its name,
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identifier, timestamp, comments from curator(s), etc.
The annotations of models include publication infor-
mation, authors, terms from controlled vocabularies,
and links to external resources. Metadata and annota-
tion are supposed to best reflect the nature of a
model, since they represent a verified mixture of
curator input and algorithmic import.
2. The next step consists of searching through the
SBML files of the models. For example, the 'notes'
fields are examined, as they usually contain some
information describing the model elements to which
they are attached.
3. Finally, because it is impractical for BioModels
Database to duplicate and keep up-to-date all relevant
information available from model cross-references,
several external databases are searched on demand
through direct connection or using web services.
During this step, the search engine checks available
supplementary information such as synonyms and
The system performs some post-processing of the
search output in order to deliver better results for user
consumption. For example, when the user performs a
search using a taxonomic term, the engine traces the
whole hierarchy in order to find related models. This
means that a search based on the term mammalia will
return not only models associated with mammalia, but
also models annotated with its descendants and ancestors
Figure 3 Models tree based on Gene Ontology. BioModels Database provides users with three primary facilities for finding and discovering models:
the system's search interface, the browsable list of all models, and an alternative list based on Gene Ontology (GO) terms. A screen image of the last
alternative is shown here. The GO-based view is derived from the annotations of models in the database; the annotations of all models are collected
and used to generate a pruned GO tree, and this tree can be browsed in order to find models annotated with a specific GO term.
Li et al. BMC Systems Biology 2010, 4:92
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(Figure 6). The logic of this is that a model describing, say,
a system of Homo sapiens, or of Rattus norvegicus, is a
model describing a system of mammalia. Similarly, a
model that is valid for all metazoa or all vertebrata will be
valid for mammalia too.
Models can also be retrieved directly by using either of
the two permanent and unique identifiers assigned to the
model: the submission identifier, and the curation identi-
The model presentation page provides access to all of the
information stored about a given model, as well as all the
system actions available to the user (Figure 7). Elements
are hyperlinked between the different views in the pre-
sentation of the model. In addition, each annotation is
hyperlinked to detailed information about the annotated
entity. When an annotation links to an external data
resource, the contents of the linked-to resource entry are
displayed in a new window in the user's web browser.
Within the model presentation page for a given model,
the detailed description is separated into categories
organised into a set of six corresponding tabs (area 3 in
• The Model tab displays general information about
the model and its creation. The uppermost region of
Figure 4 Thematic content of models. Categorisation of models in BioModels Database using the Gene Ontology (GO) terms present in each mod-
el's annotations. This chart was generated by enumerating models in the database whose annotations refer to children of the GO terms listed here,
after first removing certain GO terms (translation, GO:0006350; transcription, GO:0006412; and cellular metabolic process, GO:0044237) that appear
across different categories, and hence would have biased the analysis.
Figure 5 Search engine. The BioModels Database search engine pro-
cesses three different types of data in order to provide an accurate re-
sult. First, it searches the annotations (by querying the internal
database), then the models (using Lucene), and finally, data linked
from external resources. Searching for the last is accomplished via di-
rect connection to remote databases and by using remote web servic-
es. Ultimately, all the results are collected, processed to remove
duplicates, then classified based on which branch the models come
from, ordered, and finally, returned to the user.
Figure 6 Taxonomic search. When a user's search is based on a taxo-
nomic term, the BioModels Database search algorithm considers the
entire taxonomic hierarchy. For example, searching for the term "mam-
malia" will catch not only models annotated with the term Mammalia,
but also models annotated with terms related to it, such as Metazoa,
Homo sapiens or Rattus norvegicus (represented in red in this figure).
Li et al. BMC Systems Biology 2010, 4:92
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the tab summarizes the peer-reviewed, published
article that describes the model. In the region below
the publication information, a link provides access to
the file originally submitted, as well as information
about the encoders and the dates and times of model
creation and last modification. Annotations displayed
with the model refer to the model as a whole and indi-
cate such things as the biological processes being
modelled or the taxonomic coverage of the model.
• The Overview tab provides quick access to all the
model components, that is, the mathematical rela-
tionships, physical entities, parameters and other ele-
ments comprising the model. Users can select
components of interest, and that selection is subse-
quently reflected when they view the other tabbed
panels. Clicking 'Create a submodel with
selected elements' generates a model subset
containing the selected components and all the com-
ponents necessary to build a valid SBML model. This
submodel is displayed in a new tab, where a link is
available to allow the user to download it.
• The Math tab lists all of the mathematical con-
structs used to describe the relationships and the time
evolution of the model's variables. These constructs
include reactions, events, and explicit mathematical
formulae (SBML rules). Each construct is accompa-
Figure 7 View of a model page. This screen image shows the interface of BioModels Database as it displays the model Kholodenko1999 EGFRsig-
naling (BIOMD0000000048). As illustrated here, the display of a model in the system is divided into several areas. The areas have been highlighted and
numbered here for discussion purposes. The first area, across the top, contains general links for accessing the various features of BioModels Database.
The second allows the user to perform actions specific to the model currently displayed (for example, to download the model in various formats or
simulate it online). The third area contains all the different views of the model in separate tabbed window panes; each tabbed area is dedicated to a
given aspect of the model (e.g., overview, mathematics, model entities, parameters, curation information, etc.). The fourth area is used to display con-
tent specific to the currently selected view of the model.
Li et al. BMC Systems Biology 2010, 4:92
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nied by a rendering of the mathematical equation, as
well as relevant hyperlinked annotations.
• The Physical entities tab lists the spatiotemporal
entities (i.e., compartments and entity pools) con-
tained in the model, along with their initial quantities
and relevant annotations.
• The Parameters tab lists all parameters used in
mathematical expressions. Parameters whose scope is
limited to a reaction are grouped together. Parame-
ters whose values are determined by mathematical
expressions are linked to the relevant portion of the
• The Curation tab displays representative curation
results, obtained by the curators by simulating the
model under the conditions defined in the reference
publication. This tab includes graphical plots and
comments from the curator.
The SBML formats menu (area 2 in Figure 7) allows a user
to download the model in various versions of SBML .
The version used to produce the curation figures is
emphasised to indicate it is the only one tested by the
curators. The other SBML versions are generated by an
automatic conversion process.
The Other formats menu provides access to other (non-
SBML) model representation formats, such as CellML
, BioPAX [31,32], and the Virtual Cell Markup Lan-
guage (VCML) . To permit a given model to be simu-
lated conveniently, BioModels Database also provides
downloadable configuration files for open tools such as
XPPAUT [57,58] and SciLab . Finally, a human-read-
able report in the Portable Document Format (PDF), pro-
duced using SBML2LaTeX tool , is also available from
the same menu.
The Actions menu provides access to graphical repre-
sentations of the model's reaction networks, in the form
of both static (PNG -Portable Network Graphics- and
SVG -Scalable Vector Graphics-) as well as dynamic
(interactive Java applet) presentations. A utility to convert
graphs into the Systems Biology Graphical Notation
(SBGN)  is currently being developed. The Actions
menu also provides access to the online simulation tools,
BioModels Database embeds SOSlib  to provide a
basic online simulation tool. A given model can be simu-
lated using this facility by selecting the 'BioModels
Online Simulation' item from the Actions menu
(area 2 in Figure 7). Once the user selects the species to
be displayed and the duration for which the simulation
should be performed, the simulation task is submitted to
a computing cluster on the server side. The results of the
simulation are returned in both graphical and textual
form. For many models, an additional and more flexible
simulation tool is available thanks to a collaboration
between BioModels Database and JWS Online . The
JWS Online simulation system is available from the
Model of the Month
Every month, a modeller picks a model of his/her choice
and writes a short article that elaborates on the model.
The article places the model in its biological and theoreti-
cal background and discusses its structure and the results
of its simulation. This article is then published on the
BioModels Database website as a Model of the Month
. Such articles make selected models more easily
accessible to beginners, and may help them understand
their context and significance.
BioModels Database provides web services with a range
of features to enable other software to programmatically
search and retrieve up-to-date models and their associ-
ated data, and to extract submodels . For example,
tools such as the Virtual Cell , CellDesigner  or
the Systems Biology Workbench  use these services to
provide their users direct access (from within their tool)
to hundreds of models. The services available are defined
in a Web Services Description Language (WSDL)  file
that enables software to easily understand available func-
tions and their usage. BioModels Database web services
use the Simple Object Access Protocol (SOAP)  to
encode requests and responses. This allows standardised
communication through HTTP  without the hin-
drance caused by proxies and firewalls. The complete list
of available methods, as well as a Java library and the
associated documentation, are provided on the BioMod-
els Database website .
BioModels Database has become a recognised database
in the computational systems biology field. It now con-
tains an appreciable number of models, and indeed as far
as we are aware, it is the largest public database of its kind
today. On April 27, 2010, BioModels Database
announced its 17th Release, allowing freely available pub-
lic access to 249 curated and 224 non-curated models.
The number of models deposited in BioModels Database
has nearly doubled on a yearly basis (Figure 8) since its
inception in 2005. The number of reactions, species and
annotations has increased even faster as a consequence of
the fact that larger and more complex models are being
Li et al. BMC Systems Biology 2010, 4:92
Page 11 of 14
The models stored in BioModels Database come from
several sources. Modellers themselves can submit their
own models for inclusion in the database. In addition,
many models are created from journal articles found in
the literature by BioModels Database curators. Other
models come from exchange with other collaborative
model repositories, such as the former SBML model
repository (Caltech, USA), JWS Online [61,69], the Data-
base Of Quantitative Cellular Signaling (DOQCS)
[70,71], and the CellML repository .
Several publishers of scientific journals recommend
model submission to BioModels Database, including
Nature Publishing Group, Public Library of Science, and
BioMed Central. Following deposition, authors can quote
the unique model identifier in their paper, allowing read-
ers to download the model as soon as the paper is pub-
lished. Some other journals, as part of their peer-review
process, advise authors to deposit their computational
models into other databases. JWS Online is used for this
purpose by the journals Microbiology, FEBS Journal, IET
Systems Biology, and Metabolomics. Those models are
incorporated into BioModels Database after conversion
from their native JWS Online format.
At present, BioModels Database focuses on storing
models that can be encoded in SBML. Typically, these
models represent activities, interactions or other
dynamic phenomena in biochemical networks. BioMod-
els Database also accepts other quantitative approaches
such as steady-state models, and qualitative types of
approaches, such as logical model; however, these other
model types are mostly put into the non-curated branch,
because a crucial part of the curation process involves
verifying that a model reproduces the exact numerical
results reported in the reference article describing the
model, and we currently do not have processes for these
other model types.
Future development plans
We envision several improvements and additions to Bio-
Models Database and its facilities. Planned developments
• Implementation of a versioning system to allow
users the ability to retrieve and compare different
revisions of a given model, including its annotations.
This is a much needed feature, specially for efforts
like the Minimum Information About A Simulation
Experiment (MIASE), which aims at enabling the
reproducible description of simulation experiments.
• Additional support for the submission of emerging
or developing formats, such as the recently-released
SBML Level 3  and VCML .
• Improvements to the embedded search engine. One
such improvement will be the introduction of a rele-
Figure 8 Growth of BioModels Database. Graph depicting the number of models (green) and the number of reactions (yellow) stored in BioModels
Database at each release of the database made so far. The number of reactions includes SBML "rate rules", since some models only use rate rules. The
graph illustrates that not only has the number of models increased approximately ten-fold since 2005, but the average complexity of those models
has nearly tripled in the same period.
Li et al. BMC Systems Biology 2010, 4:92
Page 12 of 14
vance ranking scheme for retrieved models, based on
their annotations and data stored by external
• Introduction of an annotation helper tool that will
suggest appropriate annotations to the curator. Such a
feature can incorporate tools such as semanticSBML
, SAINT , or libAnnotationSBML .
• Distribution of more information with the models.
We envision providing SED-ML  files in the
future. This will allow users to download machine-
readable descriptions of the simulation experiments
realised during the course of the work that led to the
publication of the model.
Computational models are becoming ever more impor-
tant in various aspects of the life sciences. This is
reflected in the vast increases in both the number and the
complexity of quantitative kinetic models in BioModels
Database (Figure 8). This in turn necessitates the ability
to reuse model components, and to build upon pre-exist-
ing models. BioModels Database was designed to address
BioModels Database is a freely available resource for
storing curated and annotated versions of peer-reviewed,
quantitative models of biological interest. Models are dis-
tributed in several forms, ranging from standard model
file formats to graphical notations. Besides the analysis
tools built into the web interface, BioModels Database
offers a variety of useful features and tools to enable other
software to programmatically search and retrieve models
or submodels, construct large models from components,
and access additional up-to-date information. Because
the models stored in the database are thoroughly curated
by humans, they can be used for teaching purposes, or to
study specific biological processes. Moreover, since the
models cover a wide range of domains, the whole set can
be used for development and testing of simulation tools.
The BioModels Database pipeline, which encompasses
the curation and annotation processes, ensures the cor-
rectness and quality of the models. The pipeline meticu-
lously ensures syntactic correctness, logical model
composition, the accurate capture of biological informa-
tion, as well as confirmation that the model published
will, within reasonable bounds, reproduce the behaviour
attributed to it. Together with the cross-references that
are embedded into each model, this provides the commu-
nity with reliable and reusable models.
Availability and licensing
All models stored in BioModels Database are freely
accessible and reusable by all commercial and academic
users. Once downloaded, modified models should be
renamed and all author attributions removed prior to dis-
tribution of the modified model. This prevents the modi-
fied model being mistaken for the original present in the
database, and should preclude any ensuing confusion this
link at the bottom of every page on the BioModels Data-
base website .
BioModels Database itself is an open-source project;
the software is distributed under the GNU General Public
License . The database schema and code for both
Web Application and Web Services are available from the
BioModels SourceForge repository . All converters
are also available under the same license. This permits
anyone to download and install a local version of the
complete system, which may be useful for those who wish
to store their own models privately or to integrate part or
all of the system into their own software infrastructure.
The work presented here was carried out by the authors in collaboration: CLi
and MD, original developers of BioModels Database; LL and NR, converter and
export developers; HD, LE, VC and EH, created, curated and annotated models;
MIS, coordinator of the Model of the month; AH, developed the SBML to Bio-
PAX converter; JLS, developed and maintained JWS Online; MH, provided coor-
dination, SBML knowledge and grant support; NLN, curation, project
instigation and coordination; CLaibe, feature development and current project
coordinator. All authors have read and approved the final manuscript.
BioModels Database is being developed by the Computational Systems Neu-
robiology group (EMBL-European Bioinformatics Institute, United-Kingdom).
Collaborators include the SBML Team (California Institute of Technology, USA),
the Database Of Quantitative Cellular Signalling (National Center for Biological
Sciences, India), the Virtual Cell (University of Connecticut Health Center, USA),
JWS Online (Stellenbosch University, ZA) and the CellML team (Auckland Bio-
engineering Institute, NZ).
The development of BioModels Database is funded by the European Molecular
Biology Laboratory (Computational Systems Neurobiology group), the Biotech-
nology and Biological Sciences Research Council (Computational Systems
Neurobiology group, grant BB/F010516/1), the National Institute of General
Medical Sciences (SBML Team and Computational Systems Neurobiology
group, grant R01 GM070923). BioModels Database also benefited from funds
of the DARPA (Herbert Sauro, Washington University, Seattle, USA).
The authors would like to thank the members of the BioModels Database Sci-
entific Advisory Board (SAB): Upinder Bhalla, MH, Pedro Mendes, Ion Moraru,
Herbert Sauro and JLS. All the contributors of the models of the month: VC,
Ranjita Dutta Roy, LE, EH, Noriko Hiroi, Nick Juty, Christian Knüpfer, NLN, LL,
Michele Mattioni, Antonia Mayer, Anika Oellrich, Renaud Schiappa, MIS, Domi-
nic P. Tolle and Judith Zaugg. The authors also thank Nick Juty, who read and
corrected this manuscript thoroughly.
The BioModels Database team would also like to express their gratitude to all
the people who have given BioModels Database the opportunity to keep
improving with their continuous support, including the contribution of mod-
els, software tools and constructive comments and criticisms.
1European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton,
CB10 1SD, UK, 2Division of Engineering and Applied Science, California Institute
of Technology, Pasadena, CA 91125, USA and 3Department of Biochemistry,
Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
Received: 12 March 2010 Accepted: 29 June 2010
Published: 29 June 2010
This article is available from: http://www.biomedcentral.com/1752-0509/4/92 © 2010 Li et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
BMC Systems Biology 2010, 4:92
Li et al. BMC Systems Biology 2010, 4:92
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Cite this article as: Li et al., BioModels Database: An enhanced, curated and
annotated resource for published quantitative kinetic models BMC Systems
Biology 2010, 4:92