ArticlePDF Available

BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems

Authors:
  • MediaMarktSaturn IT Solutions

Abstract and Figures

BioModels Database (http://www.ebi.ac.uk/biomodels/), part of the international initiative BioModels.net, provides access to published, peer-reviewed, quantitative models of biochemical and cellular systems. Each model is carefully curated to verify that it corresponds to the reference publication and gives the proper numerical results. Curators also annotate the components of the models with terms from controlled vocabularies and links to other relevant data resources. This allows the users to search accurately for the models they need. The models can currently be retrieved in the SBML format, and import/export facilities are being developed to extend the spectrum of formats supported by the resource.
Content may be subject to copyright.
BioModels Database: a free, centralized database of
curated, published, quantitative kinetic models of
biochemical and cellular systems
Nicolas Le Nove
`re*, Benjamin Bornstein
1
, Alexander Broicher, Me
´lanie Courtot,
Marco Donizelli, Harish Dharuri
2
, Lu Li, Herbert Sauro
2
, Maria Schilstra
3
,
Bruce Shapiro
1
, Jacky L. Snoep
4
and Michael Hucka
5
European Bioinformatics Institute, EMBL Wellcome-Trust Genome Campus, Hinxton, CB10 1SD, UK,
1
Jet Propulsion
Laboratory, California Institute of Technology, Pasadena, CA 91109, USA,
2
Keck Graduate Institute, 535 Watson Drive,
Claremont, CA 91711, USA,
3
STRI, University of Hertfordshire, Hatfield, Herts AL10 9AB, UK,
4
Department of
Biochemistry, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa and
5
Control and Dynamical
Systems, California Institute of Technology, Pasadena, CA 91125, USA
Received July 29, 2005; Revised October 4, 2005; Accepted October 16, 2005
ABSTRACT
BioModels Database (http://www.ebi.ac.uk/
biomodels/), part of the international initiative
BioModels.net, provides access to published, peer-
reviewed, quantitative models of biochemical and
cellular systems. Each model is carefully curated
to verify that it corresponds to the reference publica-
tion and gives the proper numerical results. Curators
also annotate the components of the models with
terms from controlled vocabularies and links to
other relevant data resources. This allows the users
to search accurately for the models they need. The
models can currently be retrieved in the SBML format,
and import/export facilities are being developed to
extend the spectrum of formats supported by the
resource.
INTRODUCTION
The number of quantitative models trying to explain various
aspects of the cellular machinery is increasing at a steady pace,
thanks in part to the rising popularity of systems biology (1).
However, as for all types of knowledge, such models will only
be as useful as their access and reuse is easy for all scientists.
A first step was to define standard descriptions to encode
quantitative models in machine-readable formats. Example
of such formats are CellML (2) and the Systems Biology
Markup Language (SBML) (3,4). The biomedical community
now needs public integrated resources, where authors can
deposit, in controlled formats, the models they describe in
scientific publications.
Some general repositories of quantitative models have been
made available, such as the CellML repository CellML repos-
itory [(5), http://www.cellml.org/examples/repository/index.
html] JWS Online (6) and the former SBML repository. In
addition specialist repositories include SenseLab ModelDB
(7), the Database of Quantitative Cellular Signalling
(DOCQS) (8) and SigPath (9). However no general public
resource existed that allowed the user to browse, search and
retrieve annotated models
Here we present BioModels Database, developed as part of
the BioModels.net initiative (http://www.biomodels.net/).
BioModels.net is a collaboration between the SBML Team
(USA), the EMBL-EBI (UK), the Systems Biology Group
of the Keck Graduate Institute (USA), the Systems Biology
Institute (Japan) and JWS Online at Stellenbosch University
(South Africa). Its aims are as follows: (i) to define agreed-
upon standards for model curation, (ii) to define agreed-upon
vocabularies for annotating models with connections to bio-
logical data resources and (iii) to provide a free, centralized,
publicly accessible database of annotated, computational
models in SBML and other structured formats.
BioModels Database is an annotated resource of quantitative
models of biomedical interest. Models are carefully curated to
verify their correspondence to their source articles. They are
also extensively annotated, with (i) terms from controlled
vocabularies, such as disease codes and Gene Ontology terms
and (ii) links to other data resources, such as sequence or path-
way databases. Researchers in the biomedical and life science
communities can then search and retrieve models related to a
particular disease, biological process or molecular complex.
*To whom correspondence should be addressed. Tel: +44 1223 494521; Fax: +44 1223 494468; Email: lenov@ebi.ac.uk
The Author 2006. Published by Oxford University Press. All rights reserved.
The online version of this article has been published under an open access model. Users are entitled to use, reproduce, disseminate, or display the open access
version of this article for non-commercial purposes provided that: the original authorship is properly and fully attributed; the Journal and Oxford University Press
are attributed as the original place of publication with the correct citation details given; if an article is subsequently reproduced or disseminated not in its entirety but
only in part or as a derivative work this must be clearly indicated. For commercial re-use, please contact journals.permissions@oxfordjournals.org
Nucleic Acids Research, 2006, Vol. 34, Database issue D689–D691
doi:10.1093/nar/gkj092
SUBMISSION, CURATION AND ANNOTATION
Models can be submitted by anyone to the curation pipeline of
the database (Figure 1). At present, BioModels Database aims
to store and annotate models that can be encoded with SBML.
CellML models are also accepted. These model formats are
synonymous with models that can be integrated or iterated
forwards in time, such as ordinary differential equation mod-
els. Although we are aware that this means we can cover only a
restricted part of the modeling field, we make this our initial
focus for the following reason: (i) since a crucial part of the
curation process is the verification that the models produce
numerical results similar to the ones described in the reference
article, iterative simulations over ranges of parameter values
and perturbation of simulations at equilibrium are mandatory
and (ii) a very large number of such models have already been
published, and the pace of their publication is increasing stead-
ily. As a consequence, they are sufficient to consume all the
curation workforce we have, and we can envision to gather in
the near future.
To be accepted in BioModels Database, a model must be
compliant with MIRIAM, the Minimal Information Requested
in the Annotation of Models (10). One of the requirements of
MIRIAM is that a model has to be associated with a reference
description that provides directly, or through references, the
structure of the model, the necessary quantitative parameters
and presents the results of numerical analysis of the model.
BioModels Database further refines the notion of reference
description, by considering only models described in the
peer-reviewed scientific litterature.
A series of automated tasks are performed by the pipeline
prior to human intervention (see Materials and Methods for
details):
Verification that the file is well-formed XML.
If necessary, conversion to the latest version of SBML.
Verification of the syntax of SBML.
Series of consistency checks, enforcing the validity of the
model.
If any of those steps is not completed, a member of the
distributed team of curators can reject the model, or instead
correct it and resubmit it to the pipeline. The last and most
important step, of the curation process, is verifying that when
instantiated in a simulation, the model provides results cor-
responding to the reference scientific article. Curators do not
normally challenge the biological relevance of the models, and
assume the peer-review process already filtered out unsuitable
contributions. However, in specific cases, curators can spot
mistakes in an article and, with the agreement of the authors,
modify the model accordingly. Once the model is verified to
be valid SBML, and to correspond well to the article, it is
accepted in the production database for annotation.
In order to be confident in reusing an encoded model, one
should be able to trace its origin, and the people who were
involved in its inception. The following information is there-
fore added to the model: (i) either a PubMed identifier (http://
www.pubmed.gov) or a DOI (http://www.doi.org) or an URL
that permits identifying the peer-review article describing the
model; (ii) name and contact details of the individuals who
actually contributed to the encoding of the model in its present
form; (iii) name and contact of the the person who finally
entered the model in the production database and who should
be contacted if there is a problem with the encoding of the
model or the annotation.
In addition, model components are annotated with refer-
ences to relevant resources, such as terms from controled
vocabularies (Taxonomy, Gene Ontology, ChEBI, etc.) and
links to other databases (UniProt, KEGG, Reactome, etc.).
This annotation is a crucial feature of BioModels Data-
base in that it permits the unambiguous identification of
molecular species or reactions and enables effective search
stategies.
SEARCH AND RETRIEVAL
The thorough annotation of models allows a triple search
strategy to be run in order to retrieve models of interest
(Figure 2).
The models converted to SBML are stored directly in an
XML native database (Xindice, http://xml.apache.org/xindice/),
enabling those models and/or their components to be retrieved
based on the content of their elements and attributes (using
XPath, http://www.w3.org/TR/xpath). For instance, the user
can search for a given string of characters in the id, name and
notes elements of each model component.
Models can be retrieved by searching the annotation data-
base directly, using SQL. Although this search is quick, it
requires knowing the exact identifiers used by curators to
annotate a model and relate it to third party resources, such
as UniProt accession, Gene Ontology Term ID, etc.
We, therefore, implemented a more advanced search sys-
tem. A user can actually search third party resources directly,
such as PubMed, Gene Ontology and UniProt, for instance
with literal text matching. The search system retrieves the
relevant identifiers and then searches BioModels Database
for the models annotated with those identifiers. As a con-
sequence, the user can retrieve all the models dealing with
‘cell cycle’ or ‘MAPK’, without having to type ‘GO:0007049’
or ‘P27361’.
Several searches of any of the three types can also be run in
parallel, the results being thereafter combined with boolean
operators.
Figure 1. Pipeline describing the structure of BioModels database.
D690 Nucleic Acids Research, 2006, Vol. 34, Database issue
Once retrieved, the models of interest can be downloaded in
SBML Level 2 format. A number of export filters are under
development to provide the models in a wider range of
formats.
BioModels Database is copyrighted by The BioModels
Team, i.e. the set of individuals developing the resource. How-
ever, the copyright on the database does not imply copyright of
the original models in BioModels Database. Each individual
model retains the copyright assigned by both the creator(s) of
the model and the author(s) of the reference publication. Users
may distribute verbatim copies of the entire content of Bio-
Models Database, including the models and their annotations,
or a subset of the models. Users may also modify any of the
models in any way, provided that at least one of the following
condition is fulfilled:
The modified model is used only within the user’s
organization.
The modifications are placed in the Public Domain, or other-
wise made Freely Available by allowing the Copyright
Holders of the model to include the modifications in the
standard version of the model.
The modified model is renamed, and both BioModels
Database identifier and any mention of the Copyright Holders
of the model is removed.
Other distribution arrangements are made directly with the
Copyright Holders of the model(s) in question.
This restricted license has been rendered necessary by the
specific nature of the data distributed by BioModels Database.
If a user of BioModels Database downloads a kinetics model
and modifies it, the resulting model could be meaningless, or
even worse, exhibits a behaviour completely different of what
was initially meant by the authors and the creators. Therefore,
we thought that the best compromise was to let complete
freedom of reuse and modification, providing that BioModels
Database is not associated with any modification.
PERSPECTIVE
Although BioModels Database is a very recent resource, it has
already gained momentum thanks to the support of the SBML
community, which has started to submit models, and major
scientific publishing actors such as Nature Publishing Group,
which has publicized the launch of the database. The growth
of BioModels Database is currently limited, by the size of the
curation workforce, to only a dozen new models a month. We
expect that the existence of this public resource will contribute
to an improvement in the quality of the models published by
establishing an additional process for evaluating those models.
The increase in quality and the continuously improved support
of SBML by modelling tools should increase the speed of
curation. Meanwhile, we will continue to improve the search
and retrieval facilities, and support more export formats, so
that users can directy use the models contained in the database
even in non-SBML compliant tools.
ACKNOWLEDGEMENTS
Authors thank G. Bard Ermentrout, Sarah Keating, Joanne
Matthews and Nicolas Rodriguez for sharing their code.
Funding to pay the Open Access publication charges for this
article was provided by EMBL.
Conflict of interest statement. None declared.
REFERENCES
1. Kitano,H. (2005) International alliances for quantitative modeling in
systems biology. Mol. Syst. Biol. doi: 10.1038/msb4100011.
2. Lloyd,C., Halstead,M.D. and Nielsen,P.F. (2004) CellML: its future,
present and past. Prog. Biophys. Mol. Biol.,85, 433–450.
3. Hucka,M., Bolouri,H., Finney,A., Sauro,H.M., Doyle,J.C., Kitano,H.,
Arkin,A.P., Bornstein,B.J., Bray,D. et al. (2003) The systems biology
markup language (SBML): a medium for representation and exchange of
biochemical network models. Bioinformatics,19, 524–531.
4. Finney,A. and Hucka,M. (2003) Systems biology markup language:
level 2 and beyond. Biochem. Soc. Trans.,31, 1472–1473.
5. Lloyd,C. The CellML repository.
6. Olivier,B.G. and Snoep,J.L. (2004) Web-based kinetic modelling using
JWS online. Bioinformatics,20, 2143–2144.
7. Migliore,M., Morse,T.M., Davison,A.P., Marenco,L., Shepherd,G.M.
and Hines,M.L. (2003) ModelDB: making models publicly accessible to
support computational neuroscience. Neuroinformatics,1, 135–139.
8. Sivakumaran,S., Hariharaputran,S., Mishra,J. and Bhalla,U. (2003)
The database of quantitative cellular signaling: management and analysis
of chemical kinetic models of signaling networks. Bioinformatics,
19, 408–415.
9. Campagne,F., Neves,S., Chang,C.W., Skrabanek,L., Ram,P.T.,
Iyengar,R. and Weinstein,H. (2004) Quantitative information
management for the biochemical computation of cellular networks. Sci.
STKE,248, PL11.
10. Le Nove
`re,N., Finney,A., Hucka,M., Bhalla,U., Campagne,F., Collado-
Vides,J., Crampin,E., Halstead,M., Klipp,E. et al. (2005) Minimum
information requested in the annotation of biochemical models
(MIRIAM). Nat. Biotechnol.,23, in press.
Figure 2. Schema representing the cascading search strategy. The result is a list
of BioModels entries.
Nucleic Acids Research, 2006, Vol. 34, Database issue D691
... For large classes of biological, chemical or metabolic reaction networks, detailed numerical data on reaction rates are neither available, nor accessible, by parameter identification. See the large, and growing, data bases on chemical and metabolic pathways like [Le+06,KG00], for thousands of examples. One standard approach to establish, and check, the validity of such networks are knockout experiments: some reaction is obstructed, via the knockout of its catalyzing enzyme, and the response of the network is measured, e.g., in terms of concentration changes of metabolites. ...
... A value of k = 2 127 satisfies the crude requirement (7.2), for any metabolic network in databases like [KG00,Le+06]. Practically, this eliminates the problem of unlucky primes p and unlucky rate entries r jm . The computational overhead over floating point arithmetic turns out to be very moderate for primes of such order. ...
... • Section 7 provides an efficient algorithm to implement our results for the moderately large networks in data bases like [KG00,Le+06]. Our algorithm is based on the Schwartz-Zippel lemma 7.1. ...
Preprint
We present a systematic mathematical analysis of the qualitative steady-state response to rate perturbations in large classes of reaction networks. This includes multimolecular reactions and allows for catalysis, enzymatic reactions, multiple reaction products, nonmonotone rate functions, and non-closed autonomous systems. Our structural sensitivity analysis is based on the stoichiometry of the reaction network, only. It does not require numerical data on reaction rates. Instead, we impose mild and generic nondegeneracy conditions of algebraic type. From the structural data, only, we derive which steady-state concentrations are sensitive to, and hence influenced by, changes of any particular reaction rate - and which are not. We also establish transitivity properties for influences involving rate perturbations. This allows us to derive an influence graph which globally summarizes the influence pattern of the given network. The influence graph allows the computational, but meaningful, automatic identification of functional subunits in general networks, which hierarchically influence each other. We illustrate our results for several variants of the glycolytic citric acid cycle. Biological applications include enzyme knockout experiments, and metabolic control.
... Our collection of 17 systems biology models [2,21,25,28,29,30,31,32,33,34,35,36,37,38,39,40,41] was drawn primarily from the BioModels database [42], an online repository of models encoded in the Systems Biology Markup Language (SBML) [43]. The collected models encompass a diverse range of biological systems, including circadian rhythm, metabolism, and signaling. ...
... Text S5: Eigenvalue Analysis of Brodersen et al. Binding Studies Dataset S1: SBML Files, SloppyCell Scripts, and χ 2 -Hessians A. Accession NumbersModels discussed that are in the BioModels database[42] are: ...
Preprint
Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a substantial obstacle to model development. Directly measuring \emph{in vivo} biochemical parameters is difficult, and collectively fitting them to other data often yields large parameter uncertainties. Nevertheless, in earlier work we showed in a growth-factor-signaling model that collective fitting could yield well-constrained predictions, even when it left individual parameters very poorly constrained. We also showed that the model had a `sloppy' spectrum of parameter sensitivities, with eigenvalues roughly evenly distributed over many decades. Here we use a collection of models from the literature to test whether such sloppy spectra are common in systems biology. Strikingly, we find that every model we examine has a sloppy spectrum of sensitivities. We also test several consequences of this sloppiness for building predictive models. In particular, sloppiness suggests that collective fits to even large amounts of ideal time-series data will often leave many parameters poorly constrained. Tests over our model collection are consistent with this suggestion. This difficulty with collective fits may seem to argue for direct parameter measurements, but sloppiness also implies that such measurements must be formidably precise and complete to usefully constrain many model predictions. We confirm this implication in our signaling model. Our results suggest that sloppy sensitivity spectra are universal in systems biology models. The prevalence of sloppiness highlights the power of collective fits and suggests that modelers should focus on predictions rather than on parameters.
... As the number of the downstream-module promoter (P T ) increases, the amplitude and period of the oscillation in the repressilator can be changed (B and C). A repressilator model was obtained from the BioModels Database BIOMD0000000012[42]. We modified the model to lower the expression levels (by changing translation efficiency to 10 and K M to 10 and the maximum transcription rate to 3 per min per cell) and to add promoter binding-unbinding reactions for TetR repressors (for the detailed model description, refer to the SI). ...
... BIOMD0000000012) [42]. This model is composed of transcription and translation processes for respective lacI, tetR, and cI. ...
Preprint
In synthetic biology, gene regulatory circuits are often constructed by combining smaller circuit components. Connections between components are achieved by transcription factors acting on promoters. If the individual components behave as true modules and certain module interface conditions are satisfied, the function of the composite circuits can in principle be predicted. In this paper, we investigate one of the interface conditions: fan-out. We quantify the fan-out, a concept widely used in electric engineering, to indicate the maximum number of the downstream inputs that an upstream output transcription factor can regulate. We show that the fan-out is closely related to retroactivity studied by Del Vecchio, et al. We propose an efficient operational method for measuring the fan-out that can be applied to various types of module interfaces. We also show that the fan-out can be enhanced by self-inhibitory regulation on the output. We discuss the potential role of the inhibitory regulations found in gene regulatory networks and protein signal pathways. The proposed estimation method for fanout not only provides an experimentally efficient way for quantifying the level of modularity in gene regulatory circuits but also helps characterize and design module interfaces, enabling the modular construction of gene circuits.
... The BioModels Database (https://www.ebi.ac.uk/biomodels/) [6,7] is one of the largest public open-source databases for quantitative biological models, where the models are manually curated and enriched. The curation includes but is not limited to the validity of the model file and whether the model provides results corresponding to the reference scientific article [6]. ...
... The BioModels Database (https://www.ebi.ac.uk/biomodels/) [6,7] is one of the largest public open-source databases for quantitative biological models, where the models are manually curated and enriched. The curation includes but is not limited to the validity of the model file and whether the model provides results corresponding to the reference scientific article [6]. However, there are some limitations of the current curation efforts for the BioModels Database. ...
Article
Full-text available
The reproducibility of computational biology models can be greatly facilitated by widely adopted standards and public repositories. We examined 50 models from the BioModels Database and attempted to validate the original curation and correct some of them if necessary. For each model, we reproduced these published results using Tellurium. Once reproduced we manually created a new set of files, with the model information stored by the Systems Biology Markup Language (SBML), and simulation instructions stored by the Simulation Experiment Description Markup Language (SED-ML), and everything included in an Open Modeling EXchange (OMEX) file, which could be used with a variety of simulators to reproduce the same results. On the one hand, the reproducibility procedure of 50 models developed a manual workflow that we would use to build an automatic platform to help users more easily curate and verify models in the future. On the other hand, these exercises allowed us to find the limitations and possible enhancement of the current curation and tooling to verify and curate models.
... For a broader range of software support for rule-based modeling, the Systems Biology Markup Language (SBML) is used [53]. It is beneficial to use standardized formats because of the availability of databases such as the Bio Models Database archives [53][54][55]. A model that describes the concentrations of populations of chemical species over time comprises nonlinear ordinary differential equations (ODEs), which generate deterministic time courses for each chemical species. ...
Article
Full-text available
Different disciplines are developing various methods for determining and dealing with uncertainties in complex systems. The constrained disorder principle (CDP) accounts for the randomness, variability, and uncertainty that characterize biological systems and are essential for their proper function. Per the CDP, intrinsic unpredictability is mandatory for the dynamicity of biological systems under continuously changing internal and external perturbations. The present paper describes some of the parameters and challenges associated with uncertainty and randomness in biological systems and presents methods for quantifying them. Modeling biological systems necessitates accounting for the randomness, variability, and underlying uncertainty of systems in health and disease. The CDP provides a scheme for dealing with uncertainty in biological systems and sets the basis for using them. This paper presents the CDP-based second-generation artificial intelligence system that incorporates variability to improve the effectiveness of medical interventions. It describes the use of the digital pill that comprises algorithm-based personalized treatment regimens regulated by closed-loop systems based on personalized signatures of variability. The CDP provides a method for using uncertainties in complex systems in an outcome-based manner.
... These models are typically constructed on the basis of mass action kinetics. In many cases, this approach is appropriate and successful (Le Novere et al, 2006;Deuflhard and Röblitz, 2015). However, the ODEs describe the well-mixed concentrations of chemical species and may be inappropriate in the context of a cell because chemical species may be present in low copy numbers. ...
Preprint
Gillespie's direct method for stochastic simulation of chemical kinetics is a staple of computational systems biology research. However, the algorithm requires explicit enumeration of all reactions and all chemical species that may arise in the system. In many cases, this is not feasible due to the combinatorial explosion of reactions and species in biological networks. Rule-based modeling frameworks provide a way to exactly represent networks containing such combinatorial complexity, and generalizations of Gillespie's direct method have been developed as simulation engines for rule-based modeling languages. Here, we provide both a high-level description of the algorithms underlying the simulation engines, termed network-free simulation algorithms, and how they have been applied in systems biology research. We also define a generic rule-based modeling framework and describe a number of technical details required for adapting Gillespie's direct method for network-free simulation. Finally, we briefly discuss potential avenues for advancing network-free simulation and the role they continue to play in modeling dynamical systems in biology.
... As systems biology grows, so we see a proliferation of mathematical models of cell metabolism and signalling -see the many examples at the model repositories BioModels.net [3] and CellML.org [4]. ...
Preprint
Full-text available
We investigate methods for modelling metabolism within populations of cells. Typically one represents the interaction of a cloned population of cells with their environment as though it were one large cell. The question is as to whether any dynamics are lost by this assumption, and as to whether it might be more appropriate to instead model each cell individually. We show that it is sufficient to model at an intermediate level of granularity, representing the population as two interacting lumps of tissue.
Chapter
The term “systems biology” is used to explain the three-dimensional scientific innovations and multidisciplinary research in life sciences. Systems biology is a new field in biological science, offering innovative approaches to usher in a new era in biological systems and to redesign various omics tools. It delivers new solutions to global healthcare, livestock management, and environmental challenges. Systems biology helps in understanding complete biological systems by elucidating, modeling, and predicting the behavior of all interacting partners and also explaining the interactions of biomolecules (genes, proteins, and metabolites) with respect to external stimuli. It provides a better picture of a system’s dynamics under different physiological and environmental conditions. Systems biology platforms are constantly being updated with new information. Researchers use these approaches to identify and visualize undiscovered mechanisms of biological systems. Right now, systems biology-based approaches are facing various challenges related to file format, data integration, modeling and simulation, platform limitation, and computation. There exist numerous opportunities to minimize the limitations of systems biology tools and databases for a better understanding of biological systems. In this chapter, we discuss the advances, applications, challenges, and opportunities of systems biology.
Article
Full-text available
Modelling the dynamics of biological processes is ubiquitous across the ecological and evolutionary disciplines. However, the increasing complexity of these models poses a challenge to the dissemination of model‐derived results. Often only a small subset of model results are made available to the scientific community, with further exploration of the parameter space relying on local deployment of code supplied by the authors. This can be technically challenging, owing to the diversity of frameworks and environments in which models are developed. To address this issue, we developed a platform that serves as an interactive repository of biological models, called modelRxiv. To facilitate adding models to modelRxiv, we utilise large‐language models (LLMs) to make the platform language‐agnostic. The platform provides a unified interface for the analysis of models that do not require any technical understanding of the model implementation, thus improving the accessibility, reproducibility and validation of ecological and evolutionary models.
Chapter
In this chapter, we illustrate the utilization of network analysis and mechanistic modeling, two potent branches of systems biology, to simplify the representation of intricate biological processes such as cell signaling, gene regulation, and metabolic pathways. Specifically, we demonstrate the application of a well-established method to generate a microRNA-transcription factor-gene regulatory feed-forward loop network extracted from the GEO dataset GSE163877. Furthermore, we outline a method for constructing a deterministic model using the LSODA method based on the sub-network. This model furnishes insights into the roles of crucial differentially expressed microRNAs and transcription factors in gene expression associated with Alzheimer’s disease progression. Our analysis of the model reveals elevated kinetics of synthesis for EGR1, miR-6891, miR-4786, and LTBP1. The model suggests the linear upregulation of miR-8080, miR-3921, HSPB6, and downregulation MX2 gene. The rest of the miRNA, TFs, and genes shows a momentary variation in expression and if the system is undisturbed, they attain equilibrium. Thus, we elucidate how mechanistic modeling, along with perturbation studies and network analysis of expression data, can yield diverse insights into the trajectory of disease progression.
Article
Full-text available
Motivation: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. Results: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. Availability: The specification of SBML Level 1 is freely available from http://www.sbml.org/
Article
Full-text available
Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. The specification of SBML Level 1 is freely available from http://www.sbml.org/
Article
Full-text available
Analysis of cellular signaling interactions is expected to pose an enormous informatics challenge, perhaps even larger than analyzing the genome. The complex networks arising from signaling processes are traditionally represented as block diagrams. A key step in the evolution toward a more quantitative understanding of signaling is to explicitly specify the kinetics of all chemical reaction steps in a pathway. Technical advances in proteomics and high-throughput protein interaction assays promise a flood of such quantitative data. While annotations, molecular information and pathway connectivity have been compiled in several databases, and there are several proposals for general cell model description languages, there is currently little experience with databases of chemical kinetics and reaction level models of signaling networks. The Database of Quantitative Cellular Signaling is a repository of models of signaling pathways. It is intended both to serve the growing field of chemical-reaction level simulation of signaling networks, and to anticipate issues in large-scale data management for signaling chemistry. The Database of Quantitative Cellular Signaling is available at http://doqcs.ncbs.res.in. Links to the signaling model simulator, GENESIS/Kinetikit are at http://www.ncbs.res.in/~bhalla/kkit/index.html and are also provided from within the database. The database source code is available under the GNU Public License.
Article
Full-text available
The SBML (systems biology markup language) is a standard exchange format for computational models of biochemical networks. We continue developing SBML collaboratively with themodelling community to meet their evolving needs. The recently introduced SBML Level 2 includes several enhancements to the original Level 1, and features under development for SBML Level 3 include model composition, multistate chemical species and diagrams.
Article
Full-text available
ModelDB provides a resource for the computational neuroscience community that enables investigators to increase their understanding of published models by enabling them o run the models as published and build on them for further research. Its use can aid the field of computational neuroscience to enter a new era of expedited numerical experimentation.
Article
Full-text available
JWS Online is a repository of kinetic models, describing biological systems, which can be interactively run and interrogated over the Internet. It is implemented using a client–server strategy where the clients, in the form of web browser based Java applets, act as a graphical interface to the model servers, which perform the required numerical computations. Availability: The JWS Online website is publicly accessible at http://jjj.biochem.sun.ac.za/ with mirrors at http://www.jjj.bio.vu.nl/ and http://jjj.vbi.vt.edu/
Article
Full-text available
Understanding complex protein networks within cells requires the ability to develop quantitative models and to numerically compute the properties and behavior of the networks. To carry out such computational analysis, it is necessary to use modeling tools and information management systems (IMSs) where the quantitative data, associated to its biological context, can be stored, curated, and reliably retrieved. We have focused on the biochemical computation of cellular interactions and developed an IMS that stores both quantitative information on the cellular components and their interactions, and the basic reactions governing those interactions. This information can be used to construct pathways and eventually large-scale networks. This system, SigPath, is available on the Internet (http://www.sigpath.org). Key features of the approach include (i) the use of background information (for example, names of molecules, aliases, and accession codes) to ease data submission and link this quantitative database with other qualitative databases, (ii) a strategy to allow refinement of information over time by multiple users, (iii) the development of a data representation that stores both qualitative and quantitative information, and (iv) features to assist contributors and users in assembling custom quantitative models from the information stored in the IMS. Currently, models assembled in SigPath can be automatically exported to several computing environments, such as Kinetikit/Genesis, Virtual Cell, Jarnac/JDesigner, and JSim. We anticipate that, when appropriately populated, such a system will be useful for large-scale quantitative studies of cell-signaling networks and other cellular networks. SigPath is distributed under the GNU General Public License.
Article
Full-text available
Most of the published quantitative models in biology are lost for the community because they are either not made available or they are insufficiently characterized to allow them to be reused. The lack of a standard description format, lack of stringent reviewing and authors' carelessness are the main causes for incomplete model descriptions. With today's increased interest in detailed biochemical models, it is necessary to define a minimum quality standard for the encoding of those models. We propose a set of rules for curating quantitative models of biological systems. These rules define procedures for encoding and annotating models represented in machine-readable form. We believe their application will enable users to (i) have confidence that curated models are an accurate reflection of their associated reference descriptions, (ii) search collections of curated models with precision, (iii) quickly identify the biological phenomena that a given curated model or model constituent represents and (iv) facilitate model reuse and composition into large subcellular models.
Article
Advances in biotechnology and experimental techniques have lead to the elucidation of vast amounts of biological data. Mathematical models provide a method of analysing this data; however, there are two issues that need to be addressed: (1) the need for standards for defining cell models so they can, for example, be exchanged across the World Wide Web, and also read into simulation software in a consistent format and (2) eliminating the errors which arise with the current method of model publication. CellML has evolved to meet these needs of the modelling community. CellML is a free, open-source, eXtensible markup language based standard for defining mathematical models of cellular function. In this paper we summarise the structure of CellML, its current applications (including biological pathway and electrophysiological models), and its future development--in particular, the development of toolsets and the integration of ontologies.