Some thoughts about the future of SBML

SBML was designed in 2000, merely 2 years after the first official specification of XML. 11 years afterward, the current structure of the language is largely unchanged. Since then, its user base evolved much, as did the whole field of modeling in biology. 
If one wants SBML to evolve in the next decade, it is time to start discussing the basics now. The presentation covers a series of fundamental issues such as SBML coverage, unity of the language, use of elements and attributes etc

Systems Biology Graphical Notation: Entity Relationship language Level 1 (Version 1.2)

Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Descriptions, the Entity Relationships and the Activity Flows. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The current document presents version 1.2 of the first level of the SBGN Entity Relationship language. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signaling pathways, metabolic networks and gene regulatory maps

Systems Biology Graphical Notation: Process Description language Level 1

Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps

Systems Biology Graphical Notation: Process Description language Level 1

Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps

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

Not applicabl

Systems Biology Graphical Notation: Process Diagram Level 1

Standard graphical representations have played a crucial role in science and engineering throughout the last century. Without electrical symbolism, it is very likely that our industrial society would not have evolved at the same pace. Similarly, specialised notations such as the Feynmann notation or the process flow diagrams did a lot for the adoption of concepts in their own fields. With the advent of Systems Biology, and more recently of Synthetic Biology, the need for precise and unambiguous descriptions of biochemical interactions has become more pressing. While some ideas have been advanced over the last decade, with a few detailed proposals, no actual community standard has emerged. The Systems Biology Graphical Notation (SBGN) is a graphical representation crafted over several years by a community of biochemists, modellers and computer scientists. Three orthogonal and complementary languages have been created, the Process Diagrams, the Entity Relationship Diagrams and the Activity Flow Diagrams. Using these three idioms a scientist can represent any network of biochemical interactions, which can then be interpreted in an unambiguous way. The set of symbols used is limited, and the grammar quite simple, to allow its usage in textbooks and its teaching directly in high schools. The first level of the SBGN Process Diagram has been publicly released. Software support for SBGN Process Diagram was developed concurrently with its specification in order to speed-up public adoption. Shared by the communities of biochemists, genomicians, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signalling pathways, metabolic networks and gene regulatory maps

SBML models and MathSBML

MathSBML is an open-source, freely-downloadable Mathematica package that facilitates working with Systems Biology Markup Language (SBML) models. SBML is a toolneutral,computer-readable format for representing models of biochemical reaction networks, applicable to metabolic networks, cell-signaling pathways, genomic regulatory networks, and other modeling problems in systems biology that is widely supported by the systems biology community. SBML is based on XML, a standard medium for representing and transporting data that is widely supported on the internet as well as in computational biology and bioinformatics. Because SBML is tool-independent, it enables model transportability, reuse, publication and survival. In addition to MathSBML, a number of other tools that support SBML model examination and manipulation are provided on the sbml.org website, including libSBML, a C/C++ library for reading SBML models; an SBML Toolbox for MatLab; file conversion programs; an SBML model validator and visualizer; and SBML specifications and schemas. MathSBML enables SBML file import to and export from Mathematica as well as providing an API for model manipulation and simulation

A novel α-conotoxin, PeIA, cloned from Conus pergrandis, discriminates between Rat α9α10 and α7 nicotinic cholinergic receptors

The α9 and α10 nicotinic cholinergic subunits assemble to form the receptor believed to mediate synaptic transmission between efferent olivocochlear fibers and hair cells of the cochlea, one of the few examples of postsynaptic function for a non-muscle nicotinic acetylcholine receptor (nAChR). However, it has been suggested that the expression profile of α9 and α10 overlaps with that of α7 in the cochlea and in sites such as dorsal root ganglion neurons, peripheral blood lymphocytes, developing thymocytes, and skin. We now report the cloning, total synthesis, and characterization of a novel toxin α-conotoxin PeIA that discriminates between α9α10 and α7 nAChRs. This is the first toxin to be identified from Conus pergrandis, a species found in deep waters of the Western Pacific. α-Conotoxin PeIA displayed a 260-fold higher selectivity for α-bungarotoxin-sensitive α9α10 nAChRs compared with α-bungarotoxin-sensitive α7 receptors. The IC50 of the toxin was 6.9 ± 0.5 nM and 4.4 ± 0.5 nM for recombinant α9α10 and wild-type hair cell nAChRs, respectively. α-Conotoxin PeIA bears high resemblance to α-conotoxins MII and GIC isolated from Conus magus and Conus geographus, respectively. However, neither α-conotoxin MII nor α-conotoxin GIC at concentrations of 10 μM blocked acetylcholine responses elicited in Xenopus oocytes injected with the α9 and α10 subunits. Among neuronal non-α-bungarotoxin- sensitive receptors, α-conotoxin PeIA was also active at α3β2 receptors and chimeric α6/α3β2β3 receptors. α-Conotoxin PeIA represents a novel probe to differentiate responses mediated either through α9α10 or α7 nAChRs in those tissues where both receptors are expressed.Fil: McIntosh, J. Michael. University of Utah; Estados UnidosFil: Plazas, Paola Viviana. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Watkins, Maren. University of Utah; Estados UnidosFil: Gomez Casati, Maria Eugenia. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Olivera, Baldomero M.. University of Utah; Estados UnidosFil: Elgoyhen, Ana Belen. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; Argentin

COMBINE - a vision

A decade ago, the creation of the Systems Biology Markup Language (SBML) changed the way people exchanged, verified and re-used models in systems biology. The robustness and versatility of this format, coupled to a wide software support, fostered the emergence of an entire area of research centered on model processing such as encoding, annotation, merging, comparison and integration with other datasets. Recently, new languages appeared that complement the model description, such as SED-ML to describe the simulation experiments or SBRML to encode the numerical results. In neuroscience, other fledgling efforts cover for instance multi-compartment neurons with NeuroML, and neuronal networks with NineML. More are needed to cover the wide spectrum of computational models used in neuroscience. The developers of those initiatives are in contact, and try to improve the interoperability of the languages, for instance by sharing metadata. Similar development guidelines, governance principles and quality checks are needed, in order to provide the community with a serious infrastructure. One can hope to see, in a not too elusive future, the creation of a coherent set of non-overlapping standards that will support not only the various modeling approaches and scales needed to simulate human functions and dysfunctions, but also cover model structure, parametrization, simulation and numerical output. Such a toolkit will allow the bridging of genomics, computational neuroscience and drug discovery