The systems biology markup language (SBML): a medium for
representation and exchange of biochemical network models

Hucka, Michael; Finney, Andrew; Sauro, Herbert M.; Bolouri, Hamid; Doyle, John C.; Kitano, Hiroaki; Arkin, Adam P.; Bornstein, B.; Bray, Dennis; Cornish‐Bowden, Athel; Cuellar, Autumn; Dronov, S.; Gilles, Ernst Dieter; Ginkel, Martin; Gor, V.; Goryanin, Igor; Hedley, Warren J.; Hodgman, Charlie; Hofmeyr, Jan-Hendrik S.; Hunter, Peter; Juty, Nick; Kasberger, Jay; Kremling, Andreas; Kummer, Ursula; Novère, Nicolas Le; Loew, Leslie M.; Lucio, Daniel; Mendes, Pedro; Minch, Eric; Mjolsness, Eric; Nakayama, Yoichi; Nelson, MichaelT .; Nielsen, Poul; Sakurada, Takeshi; Schaff, James C.; Shapiro, Bruce E.; Shimizu, Thomas S.; Spence, H. D.; Stelling, Jörg; Takahashi, Koichi; Tomita, Masaru; Wagner, John; Wang, J.

doi:10.1093/bioinformatics/btg015

Cited by 2,742 publications

(1,983 citation statements)

References 13 publications

Supporting

Mentioning

1,951

Contrasting

Unclassified

Order By: Relevance

“…Recently, a new method and tool have been developed to map the sensitivity of the model outputs by sensitivity heat maps and a global summation law [88]. Most of these sensitivity analysis tools support the models encoded in systems biology markup language (SBML) [89]. SBML is a standard markup language for the systems biology community to represent and exchange biological models.…”

Section: Software Tools For Sensitivity Analysis Of Systems Biology Mmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

View full text Add to dashboard Cite

With the rising application of systems biology, sensitivity analysis methods have been widely applied to study the biological systems, including metabolic networks, signalling pathways and genetic circuits. Sensitivity analysis can provide valuable insights about how robust the biological responses are with respect to the changes of biological parameters and which model inputs are the key factors that affect the model outputs. In addition, sensitivity analysis is valuable for guiding experimental analysis, model reduction and parameter estimation. Local and global sensitivity analysis approaches are the two types of sensitivity analysis that are commonly applied in systems biology. Local sensitivity analysis is a classic method that studies the impact of small perturbations on the model outputs. On the other hand, global sensitivity analysis approaches have been applied to understand how the model outputs are affected by large variations of the model input parameters. In this review, the author introduces the basic concepts of sensitivity analysis approaches applied to systems biology models. Moreover, the author discusses the advantages and disadvantages of different sensitivity analysis methods, how to choose a proper sensitivity analysis approach, the available sensitivity analysis tools for systems biology models and the caveats in the interpretation of sensitivity analysis results.

show abstract

Section: Software Tools For Sensitivity Analysis Of Systems Biology Mmentioning

confidence: 99%

Sensitivity analysis approaches applied to systems biology models

2011

IET Syst. Biol.

339

263

View full text Add to dashboard Cite

show abstract

“…SBOL's community engagement and outreach efforts have been inspired by the tremendous success of the 'Systems Biology Markup Language' (SBML) 17 . The SBOL Developers Group took advantage of the lessons learned from the SBML community, including establishing an open, democratic organization; early inclusion of and engagement with young scientists; and regular meetings to build up and maintain excitement and consensus within the community.…”

Section: Sbol Developers Groupmentioning

confidence: 99%

“…In this way, the modeling extension leverages the significant work done in the development of standards for modeling biological organisms, such as SBML 17 , the 'Biological Pathways Exchange' (BioPAX) 30 , and the 'Systems Biology Graphical Notation' (SBGN) 31 . The extension identifies the modeling language (for example, SBML 32 , CellML 33 , MATLAB, BNGL 34 ) of the linked model, as well as its modeling framework (for example, ODE, Stochastic, Boolean).…”

Section: Sbol Demonstrationmentioning

confidence: 99%

The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology

et al. 2014

View full text Add to dashboard Cite

The re-use of previously validated designs is critical to the evolution of synthetic biology from a research discipline to an engineering practice. Here we describe the Synthetic Biology Open Language (SBOL), a proposed data standard for exchanging designs within the synthetic biology community. SBOL represents synthetic biology designs in a communitydriven, formalized format for exchange between software tools, research groups and commercial service providers. The SBOL Developers Group has implemented SBOL as an XML/RDF serialization and provides software libraries and specification documentation to help developers implement SBOL in their own software. We describe early successes, including a demonstration of the utility of SBOL for information exchange between several different software tools and repositories from both academic and industrial partners. As a community-driven standard, SBOL will be updated as synthetic biology evolves to provide specific capabilities for different aspects of the synthetic biology workflow.Synthetic biology treats biological organisms as a new technological medium with a unique set of characteristics, such as the ability to self-repair, evolve and replicate. These characteristics create their own engineering challenges, but offer a rich and largely untapped source of potential applications across a broad range of sectors 1,2 . Applications such as biomolecular computing 3 , metabolic engineering 4 , or reconstruction and exploration of natural cell biology 5,6 commonly require the design of new genetically encoded systems. As engineers, synthetic biologists most often base their designs on previously described 'DNA segments' (see Supplementary Table 1 for definitions of selected terms) to meet their design requirements. Reuse of the DNA sequence for these segments involves their exchange between laboratories and their hierarchical composition to form devices and systems with higher level function.Every engineering field relies on a set of 'standards' 7 that practitioners follow to enable the exchange and reuse of designs for 'systems' , 'devices' and 'components' . Similarly, the representation of synthetic biology designs using computer-readable 'data standards' has the potential to facilitate the forward engineering of novel biological systems from previously characterized devices and components. For example, such standards could enable synthetic biology companies to offer catalogs of devices and components by means of computerreadable data sheets, just as modern semiconductor companies do for electronics. Such standards could also enable a synthetic biologist to develop portions of a design using one software tool, refine the design using another tool, and finally transmit it electronically to a colleague or commercial fabrication company.In order for synthetic biology designs to scale up in complexity, researchers will need to make greater use of specialized design tools and parts repositories. Seamless inter-tool communication would, for example, allow the separation of gene...

show abstract

“…This arrow represents a bridge between the realms of high-throughput data/bioinformatics on one hand and systems science on the other. A network reconstruction (or BiGG knowledge base) is accessible to all and significant strides have been made to make computation with GEMs more readily accessible and free of use [6][7][8][9][10][11] . This availability of both genome-scale reconstruction and GEMs has unleashed creativity in research groups around the world and resulted in the series of studies reviewed below.…”

Section: Nih-pa Author Manuscriptmentioning

confidence: 99%

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

2008

View full text Add to dashboard Cite

The number and scope of methods developed to interrogate and use metabolic network reconstructions has significantly expanded since the first review of the use of constraint-based analysis in Nature Biotechnology some 14 years ago. In particular, the Escherichia coli metabolic network reconstruction has reached the genome-scale and has been broadly adapted. Specifically, it has been used to address a broad spectrum of basic and practical applications, falling into five main categories: 1) metabolic engineering, 2) model-directed discovery, 3) interpretations of phenotypic screens, 4) analysis of network properties, and 5) studies of evolutionary processes. With these accomplishments in hand, the field is expected to move forward and seek to further, i) broaden the scope and content of network reconstructions, ii) develop new and novel in silico analysis tools, and iii) expand in adaptation to uses of proximal and distal causation in biology. Taken together, these efforts will solidify a mechanistic genotype-phenotype relationship for microbial metabolism.The availability of reconstructed metabolic networks for microorganisms has increased rapidly in recent years, and a growing number of research groups are reconstructing metabolic networks for organisms of interest 1 . A network reconstruction represents a highly curated set of primary biological information for a particular organism and thus can be considered a biochemically, genetically and genomically structured (BiGG) data base 1, 2 . A curated BiGG data base (de facto a knowledge base) can be converted into a mathematical format (i.e., an in silico model), and used to computationally assess phenotypic properties using a variety of computational methods 2,3 . Genome-scale reconstructions are thus, a key step in quantifying the genotype-phenotype relationship and can be used to 'bring genomes to life' 4 . The purpose of this review is to summarize and classify applications utilizing the E. coli reconstruction to answer a broad spectrum of biological questions. These studies provide both an up to date review of the applications of constraint-based analysis and a guide to similar applications for the growing number of organisms for which genome-scale reconstructions are becoming available. The Key Steps in the Formulation of Genome-scale Metabolic Network ModelsThe four key steps in the formulation and use of genome-scale models are illustrated in Fig. 1. Foundational to the process is the generation of global, or genome-scale, omics data. Omics data, along with legacy information (i.e., the 'bibliome') and small-scale detailed experiments, can be used to define the interactions amongst the biological components that are used to reconstruct organism-specific networks 1 . Network reconstruction is also an iterative, on-going process that continually integrates data in a formal fashion as it becomes available 5 . As a result, a current and well curated genome-scale network reconstruction is a common denominator for those studying systems biology of an or...

show abstract

The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models

Abstract: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Cited by 2,742 publications

References 13 publications

Sensitivity analysis approaches applied to systems biology models

Sensitivity analysis approaches applied to systems biology models

The Synthetic Biology Open Language (SBOL) provides a community standard for communicating designs in synthetic biology

The growing scope of applications of genome-scale metabolic reconstructions using Escherichia coli

Contact Info

Product

Resources

About