Publications by authors named "Sarah M Keating"

22 Publications

  • Page 1 of 1

SBML Level 3: an extensible format for the exchange and reuse of biological models.

Mol Syst Biol 2020 08;16(8):e9110

Management & IT Consulting Division, Mizuho Information & Research Institute, Inc., Tokyo, Japan.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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http://dx.doi.org/10.15252/msb.20199110DOI Listing
August 2020

Systems Biology Markup Language (SBML) Level 3 Package: Distributions, Version 1, Release 1.

J Integr Bioinform 2020 Jul 20;17(2-3). Epub 2020 Jul 20.

California Institute of Technology, Pasadena, USA.

Biological models often contain elements that have inexact numerical values, since they are based on values that are stochastic in nature or data that contains uncertainty. The Systems Biology Markup Language (SBML) Level 3 Core specification does not include an explicit mechanism to include inexact or stochastic values in a model, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactic constructs. The SBML Distributions package for SBML Level 3 adds the necessary features to allow models to encode information about the distribution and uncertainty of values underlying a quantity.
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http://dx.doi.org/10.1515/jib-2020-0018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756622PMC
July 2020

Systems biology markup language (SBML) level 3 package: multistate, multicomponent and multicompartment species, version 1, release 2.

J Integr Bioinform 2020 Jul 6;17(2-3). Epub 2020 Jul 6.

NIAID/NIH, Bethesda, USA.

Rule-based modeling is an approach that permits constructing reaction networks based on the specification of rules for molecular interactions and transformations. These rules can encompass details such as the interacting sub-molecular domains and the states and binding status of the involved components. Conceptually, fine-grained spatial information such as locations can also be provided. Through "wildcards" representing component states, entire families of molecule complexes sharing certain properties can be specified as patterns. This can significantly simplify the definition of models involving species with multiple components, multiple states, and multiple compartments. The systems biology markup language (SBML) Level 3 Multi Package Version 1 extends the SBML Level 3 Version 1 core with the "type" concept in the Species and Compartment classes. Therefore, reaction rules may contain species that can be patterns and exist in multiple locations. Multiple software tools such as Simmune and BioNetGen support this standard that thus also becomes a medium for exchanging rule-based models. This document provides the specification for Release 2 of Version 1 of the SBML Level 3 Multi package. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the correction of errata and the addition of clarifications.
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http://dx.doi.org/10.1515/jib-2020-0015DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756619PMC
July 2020

Specifications of standards in systems and synthetic biology: status and developments in 2020.

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

University Medicine Greifswald, Greifswald, Germany.

This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.
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http://dx.doi.org/10.1515/jib-2020-0022DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756620PMC
June 2020

The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE).

J Integr Bioinform 2020 Jun 29;17(2-3). Epub 2020 Jun 29.

Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK.

This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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http://dx.doi.org/10.1515/jib-2020-0005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7756615PMC
June 2020

Wikidata as a knowledge graph for the life sciences.

Elife 2020 03 17;9. Epub 2020 Mar 17.

Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, United States.

Wikidata is a community-maintained knowledge base that has been assembled from repositories in the fields of genomics, proteomics, genetic variants, pathways, chemical compounds, and diseases, and that adheres to the FAIR principles of findability, accessibility, interoperability and reusability. Here we describe the breadth and depth of the biomedical knowledge contained within Wikidata, and discuss the open-source tools we have built to add information to Wikidata and to synchronize it with source databases. We also demonstrate several use cases for Wikidata, including the crowdsourced curation of biomedical ontologies, phenotype-based diagnosis of disease, and drug repurposing.
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http://dx.doi.org/10.7554/eLife.52614DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7077981PMC
March 2020

BioModels-15 years of sharing computational models in life science.

Nucleic Acids Res 2020 01;48(D1):D407-D415

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.

Computational modelling has become increasingly common in life science research. To provide a platform to support universal sharing, easy accessibility and model reproducibility, BioModels (https://www.ebi.ac.uk/biomodels/), a repository for mathematical models, was established in 2005. The current BioModels platform allows submission of models encoded in diverse modelling formats, including SBML, CellML, PharmML, COMBINE archive, MATLAB, Mathematica, R, Python or C++. The models submitted to BioModels are curated to verify the computational representation of the biological process and the reproducibility of the simulation results in the reference publication. The curation also involves encoding models in standard formats and annotation with controlled vocabularies following MIRIAM (minimal information required in the annotation of biochemical models) guidelines. BioModels now accepts large-scale submission of auto-generated computational models. With gradual growth in content over 15 years, BioModels currently hosts about 2000 models from the published literature. With about 800 curated models, BioModels has become the world's largest repository of curated models and emerged as the third most used data resource after PubMed and Google Scholar among the scientists who use modelling in their research. Thus, BioModels benefits modellers by providing access to reliable and semantically enriched curated models in standard formats that are easy to share, reproduce and reuse.
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http://dx.doi.org/10.1093/nar/gkz1055DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7145643PMC
January 2020

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019.

J Integr Bioinform 2019 Jul 13;16(2). Epub 2019 Jul 13.

University Medicine Greifswald, Greifswald, Germany.

This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.
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http://dx.doi.org/10.1515/jib-2019-0035DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798822PMC
July 2019

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2.

J Integr Bioinform 2019 Jun 20;16(2). Epub 2019 Jun 20.

NIAID/NIH, Bethesda, MD, USA.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. 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. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.
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http://dx.doi.org/10.1515/jib-2019-0021DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798823PMC
June 2019

Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0.

Nat Protoc 2019 03;14(3):639-702

Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France.

Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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http://dx.doi.org/10.1038/s41596-018-0098-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6635304PMC
March 2019

SBML Level 3 package: Render, Version 1, Release 1.

J Integr Bioinform 2018 Apr 2;15(1). Epub 2018 Apr 2.

University of Hertfordshire, Hertfordshire, United Kingdom of Great Britain and Northern Ireland.

Many software tools provide facilities for depicting reaction network diagrams in a visual form. Two aspects of such a visual diagram can be distinguished: the layout (i.e.: the positioning and connections) of the elements in the diagram, and the graphical form of the elements (for example, the glyphs used for symbols, the properties of the lines connecting them, and so on). This document describes the SBML Level 3 Render package that complements the SBML Level 3 Layout package and provides a means of capturing the precise rendering of the elements in a diagram. The SBML Level 3 Render package provides a flexible approach to rendering that is independent of both the underlying SBML model and the Layout information. There can be one block of render information that applies to all layouts or an additional block for each layout. Many of the elements used in the current render specification are based on corresponding elements from the SVG specification. This allows us to easily convert a combination of layout information and render information into a SVG drawing.
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http://dx.doi.org/10.1515/jib-2017-0078DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167038PMC
April 2018

Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2017.

J Integr Bioinform 2018 Mar 29;15(1). Epub 2018 Mar 29.

University of Rostock, Rostock, Germany.

Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.
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http://dx.doi.org/10.1515/jib-2018-0013DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167034PMC
March 2018

The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core.

J Integr Bioinform 2018 Mar 9;15(1). Epub 2018 Mar 9.

Newcastle University, Newcastle, United Kingdom of Great Britain and Northern Ireland.

Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. 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. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.
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http://dx.doi.org/10.1515/jib-2017-0081DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6167032PMC
March 2018

MOCCASIN: converting MATLAB ODE models to SBML.

Bioinformatics 2016 06 9;32(12):1905-6. Epub 2016 Feb 9.

Department of Neurology, Icahn School of Medicine at Mount Sinai, Mount Sinai Medical Center and School of Medicine, New York, NY 10029, USA.

Unlabelled: MATLAB is popular in biological research for creating and simulating models that use ordinary differential equations (ODEs). However, sharing or using these models outside of MATLAB is often problematic. A community standard such as Systems Biology Markup Language (SBML) can serve as a neutral exchange format, but translating models from MATLAB to SBML can be challenging-especially for legacy models not written with translation in mind. We developed MOCCASIN (Model ODE Converter for Creating Automated SBML INteroperability) to help. MOCCASIN can convert ODE-based MATLAB models of biochemical reaction networks into the SBML format.

Availability And Implementation: MOCCASIN is available under the terms of the LGPL 2.1 license (http://www.gnu.org/licenses/lgpl-2.1.html). Source code, binaries and test cases can be freely obtained from https://github.com/sbmlteam/moccasin

Contact: : mhucka@caltech.edu

Supplementary Information: More information is available at https://github.com/sbmlteam/moccasin.
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http://dx.doi.org/10.1093/bioinformatics/btw056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4908318PMC
June 2016

Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions.

J Integr Bioinform 2015 Sep 4;12(2):271. Epub 2015 Sep 4.

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. 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. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org.
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http://dx.doi.org/10.2390/biecoll-jib-2015-271DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5457286PMC
September 2015

The Systems Biology Markup Language (SBML) Level 3 Package: Qualitative Models, Version 1, Release 1.

J Integr Bioinform 2015 Sep 4;12(2):270. Epub 2015 Sep 4.

Quantitative methods for modelling biological networks require an in-depth knowledge of the biochemical reactions and their stoichiometric and kinetic parameters. In many practical cases, this knowledge is missing. This has led to the development of several qualitative modelling methods using information such as, for example, gene expression data coming from functional genomic experiments. The SBML Level 3 Version 1 Core specification does not provide a mechanism for explicitly encoding qualitative models, but it does provide a mechanism for SBML packages to extend the Core specification and add additional syntactical constructs. The SBML Qualitative Models package for SBML Level 3 adds features so that qualitative models can be directly and explicitly encoded. The approach taken in this package is essentially based on the definition of regulatory or influence graphs. The SBML Qualitative Models package defines the structure and syntax necessary to describe qualitative models that associate discrete levels of activities with entity pools and the transitions between states that describe the processes involved. This is particularly suited to logical models (Boolean or multi-valued) and some classes of Petri net models can be encoded with the approach.
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http://dx.doi.org/10.2390/biecoll-jib-2015-270DOI Listing
September 2015

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

J Integr Bioinform 2015 Sep 4;12(2):266. Epub 2015 Sep 4.

Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. 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. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
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http://dx.doi.org/10.2390/biecoll-jib-2015-266DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5451324PMC
September 2015

SBML qualitative models: a model representation format and infrastructure to foster interactions between qualitative modelling formalisms and tools.

BMC Syst Biol 2013 Dec 10;7:135. Epub 2013 Dec 10.

Instituto Gulbenkian de Ciência, Rua da Quinta Grande 6, 2780-156 Oeiras, Portugal.

Background: Qualitative frameworks, especially those based on the logical discrete formalism, are increasingly used to model regulatory and signalling networks. A major advantage of these frameworks is that they do not require precise quantitative data, and that they are well-suited for studies of large networks. While numerous groups have developed specific computational tools that provide original methods to analyse qualitative models, a standard format to exchange qualitative models has been missing.

Results: We present the Systems Biology Markup Language (SBML) Qualitative Models Package ("qual"), an extension of the SBML Level 3 standard designed for computer representation of qualitative models of biological networks. We demonstrate the interoperability of models via SBML qual through the analysis of a specific signalling network by three independent software tools. Furthermore, the collective effort to define the SBML qual format paved the way for the development of LogicalModel, an open-source model library, which will facilitate the adoption of the format as well as the collaborative development of algorithms to analyse qualitative models.

Conclusions: SBML qual allows the exchange of qualitative models among a number of complementary software tools. SBML qual has the potential to promote collaborative work on the development of novel computational approaches, as well as on the specification and the analysis of comprehensive qualitative models of regulatory and signalling networks.
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http://dx.doi.org/10.1186/1752-0509-7-135DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892043PMC
December 2013

Supporting SBML as a model exchange format in software applications.

Methods Mol Biol 2013 ;1021:201-25

EMBL Outstation-European Bioinformatics Institute, Cambridge, UK.

This chapter describes the Systems Biology Markup Language (SBML) from its origins. It describes the rationale behind and importance of having a common language when it comes to representing models. This chapter mentions the development of SBML and outlines the structure of an SBML model. It provides a section on libSBML, a useful application programming interface (API) library for reading, writing, manipulating and validating content expressed in the SBML format. Finally the chapter also provides a description of the SBML Toolbox which provides a means of facilitating the import and export of SBML from both MATLAB and Octave ( http://www.gnu.org/software/octave/) environments.
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http://dx.doi.org/10.1007/978-1-62703-450-0_11DOI Listing
December 2013

LibSBML: an API library for SBML.

Bioinformatics 2008 Mar 5;24(6):880-1. Epub 2008 Feb 5.

NASA Jet Propulsion Laboratory, Biological Network Modeling Center, California Institute of Technology, Pasadena, CA, USA.

Unlabelled: LibSBML is an application programming interface library for reading, writing, manipulating and validating content expressed in the Systems Biology Markup Language (SBML) format. It is written in ISO C and C++, provides language bindings for Common Lisp, Java, Python, Perl, MATLAB and Octave, and includes many features that facilitate adoption and use of both SBML and the library. Developers can embed libSBML in their applications, saving themselves the work of implementing their own SBML parsing, manipulation and validation software.

Availability: LibSBML 3 was released in August 2007. Source code, binaries and documentation are freely available under LGPL open-source terms from http://sbml.org/software/libsbml.
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http://dx.doi.org/10.1093/bioinformatics/btn051DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2517632PMC
March 2008

Methods for simulating the dynamics of complex biological processes.

Methods Cell Biol 2008 ;84:807-42

Biological and Neural Computation Group, Science and Technology Research Institute, University of Hertfordshire, College Lane, Hatfield AL10 9AB, United Kingdom.

In this chapter, we provide the basic information required to understand the central concepts in the modeling and simulation of complex biochemical processes. We underline the fact that most biochemical processes involve sequences of interactions between distinct entities (molecules, molecular assemblies), and also stress that models must adhere to the laws of thermodynamics. Therefore, we discuss the principles of mass-action reaction kinetics, the dynamics of equilibrium and steady state, and enzyme kinetics, and explain how to assess transition probabilities and reactant lifetime distributions for first-order reactions. Stochastic simulation of reaction systems in well-stirred containers is introduced using a relatively simple, phenomenological model of microtubule dynamic instability in vitro. We demonstrate that deterministic simulation [by numerical integration of coupled ordinary differential equations (ODE)] produces trajectories that would be observed if the results of many rounds of stochastic simulation of the same system were averaged. In Section V, we highlight several practical issues with regard to the assessment of parameter values. We draw some attention to the development of a standard format for model storage and exchange, and provide a list of selected software tools that may facilitate the model building process, and can be used to simulate the modeled systems.
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http://dx.doi.org/10.1016/S0091-679X(07)84025-8DOI Listing
December 2007

SBMLToolbox: an SBML toolbox for MATLAB users.

Bioinformatics 2006 May 30;22(10):1275-7. Epub 2006 Mar 30.

Science and Technology Research Institute, University of Hertfordshire Hatfield, UK.

Summary: We present SBMLToolbox, a toolbox that facilitates importing and exporting models represented in the Systems Biology Markup Language (SBML) in and out of the MATLAB environment and provides functionality that enables an experienced user of either SBML or MATLAB to combine the computing power of MATLAB with the portability and exchangeability of an SBML model. SBMLToolbox supports all levels and versions of SBML.

Availability: SBMLToolbox is freely available from http://sbml.org/software/sbmltoolbox
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http://dx.doi.org/10.1093/bioinformatics/btl111DOI Listing
May 2006