http://precedings.nature.com/users/523e81fd8e23b92da962e55f52ce35a1/ Documents posted by Nicolas Le Novere Nature Publishing Group en Nature Precedings http://precedings.nature.com/images/header_logo.gif http://precedings.nature.com http://dx.doi.org/10.1038/npre.2009.3724.1 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, specialized 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 ranging from textbooks and teaching in high schools to peer reviewed articles in scientific journals. The first level of the SBGN Activity Flow language has been publicly released. Shared by the communities of biochemists, genomic scientists, theoreticians and computational biologists, SBGN languages will foster efficient storage, exchange and reuse of information on signaling pathways, metabolic networks and gene regulatory maps. http://dx.doi.org/10.1038/npre.2009.3724.1 Mon, 07 Sep 2009 19:43:48 UTC Systems Biology Graphical Notation: Activity Flow language Level 1 doi:10.1038/npre.2009.3724.1 2009-09-07 Huaiyu Mi Nature Precedings 2009-09-07T19:43:48Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3719.1 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 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 first level of the SBGN Entity Relationship language has been publicly released. 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. http://dx.doi.org/10.1038/npre.2009.3719.1 Fri, 04 Sep 2009 15:21:22 UTC Systems Biology Graphical Notation: Entity Relationship language Level 1 doi:10.1038/npre.2009.3719.1 2009-09-04 Nicolas Le Novère Nature Precedings 2009-09-04T15:21:22Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3124.1 BioModels Database (http://www.ebi.ac.uk/biomodels/) is a free resource for storing, viewing and retrieving published, peer-reviewed, quantitative models of biochemical and cellular systems. As a storage format, BioModels Database uses the Systems Biology Markup Language (SBML), but also allows submission and export of models in various other commonly used formats.To offer scientists reliable information, models are curated to comply with the MIRIAM (Minimal Information Requested In the Annotation of biochemical Models) standard. This curation process involves verification of the model structure, the parameter and variable values and its mathematical relations. Furthermore reproduction of results in the reference publication is checked.The different elements of the models are extensively annotated with references to controlled vocabularies and links to other databases, to allow for identification and search. Those references and links are provided in the exported SBML files as a URN (Uniform Resource Name), identifying the data-type and the data-set, and a qualifier, indicating the relation between the element and the referenced data-set. The URNs follow the MIRIAM scheme and are resolved, for instance to URLs, using the Web Services of MIRIAM Resources (http://www.ebi.ac.uk/miriam/). http://dx.doi.org/10.1038/npre.2009.3124.1 Wed, 22 Apr 2009 12:58:44 UTC Curation and annotation for BioModels Database, a resource of published quantitative kinetic models doi:10.1038/npre.2009.3124.1 2009-05-06 Lukas Endler Nature Precedings 2009-04-22T12:58:44Z Poster Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2009.3137.1 Computational models are becoming more and more the central scientific paradigm for understanding the complexity of living systems. With the increasing number and size of these models there is a growing need for model reuse and exchange. Furthermore, detailed models are not manageable without computer support. There are efforts to formalise the mathematical structure of models (e.g. SBML) and to standardise the kinetic and biological meaning of model components (e.g. SBO, GO, UniProt). However, formalising only the structure of computational models is not sufficient to easily exchange and reuse models and to achieve full computer support for modelling. We also need to formalise the pragmatical and dynamical aspects of models.For this purpose we propose two ontologies: The Kinetic Simulation Algorithm Ontology (KiSAO) and the TErminology for the Description of DYnamics (TEDDY). KiSAO covers algorithms used for simulation of computational models. The ontology classifies and puts into context existing simulation algorithms. For the classification, it uses several criteria such as deterministic/stochastic or spatial/nonspatial. The aim of TEDDY is to provide terms for describing and characterising dynamical behaviours, observable dynamical phenomena, and control elements of biological models and biological systems in Systems Biology and Synthetic Biology.We demonstrate how these new ontologies can extend the formalisation of models beyond structure, using the well-known repressilator model as an example. The simulation results depend pragmatically on the used algorithm: We compare the simulation results of the deterministic Livermore solver for ordinary differential equations (KiSAO:0000071) to the simulation results of the stochastic Gibson and Bruck’s next reaction method (KiSAO:0000027). The simulation results depend dynamically on the parameter setting: While parameter * (maximum number of produced proteins per promotor) is increased the modelled dynamical system undergoes a Supercritical Hopf Bifurcation (TEDDY_0000074). Below the critical value of * the system exhibits Damped Oscillation (TEDDY_0000063) converging to a Stable Spiral Point (TEDDY_0000126). Above the bifurcation the system possesses a Stable Limit Cycle (TEDDY_0000114), i.e. it shows Sustained Oscillation. The Negative Feedback (TEDDY_0000034) of the system is a necessary precondition for the ability of the system to oscillate.For details on KiSAO see the MIASE project page, for details on TEDDY see the project page. http://dx.doi.org/10.1038/npre.2009.3137.1 Wed, 22 Apr 2009 21:16:54 UTC Beyond Structure: KiSAO and TEDDY—Two Ontologies Addressing Pragmatical and Dynamical Aspects of Computational Models in Systems Biology doi:10.1038/npre.2009.3137.1 2009-04-22 Christian Knüpfer Nature Precedings 2009-04-22T21:16:54Z Poster Molecular Cell Biology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/58/version/2 With the rise of Systems Biology as a new paradigm for understanding biological processes, the development of quantitative models is no longer restricted to a small circle of theoreticians. The dramatic increase in the number of these models precipitates the need to exchange and reuse both existing and newly created models. The Systems Biology Markup Language (SBML) is a free, open, XML-based format for representing quantitative models of biological interest that advocates the consistent specification of such models and thus facilitates both software development and model exchange.Principally oriented towards describing systems of biochemical reactions, such as cell signalling pathways, metabolic networks and gene regulation etc., SBML can also be used to encode any kinetic model. SBML offers mechanisms to describe biological components by means of compartments and reacting species, as well as their dynamic behaviour, using reactions, events and arbitrary mathematical rules. SBML also offers all the housekeeping structures needed to ensure an unambiguous understanding of quantitative descriptions.This specification presents the structures of the language and the rules used to build a valid model. SBML XML Schema and other related documents and software are also available from the SBML project web site, http://sbml.org/. http://precedings.nature.com/documents/58/version/2 Mon, 05 Nov 2007 16:05:47 UTC Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions hdl:10101/npre.2007.58.2 2009-01-10 Nicolas Le Novere Nature Precedings 2007-11-05T16:05:47Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2008.2715.1 With the rise of Systems Biology as a new paradigm for understanding biological processes, the development of quantitative models is no longer restricted to a small circle of theoreticians. The dramatic increase in the number of these models precipitates the need to exchange and reuse both existing and newly created models. The Systems Biology Markup Language (SBML) is a free, open, XML-based format for representing quantitative models of biological interest that advocates the consistent specification of such models and thus facilitates both software development and model exchange.Principally oriented towards describing systems of biochemical reactions, such as cell signalling pathways, metabolic networks and gene regulation etc., SBML can also be used to encode any kinetic model. SBML offers mechanisms to describe biological components by means of compartments and reacting species, as well as their dynamic behaviour, using reactions, events and arbitrary mathematical rules. SBML also offers all the housekeeping structures needed to ensure an unambiguous understanding of quantitative descriptions.This is Release 1 of the specification for SBML Level 2 Version 4, describing the structures of the language and the rules used to build a valid model. SBML XML Schema and other related documents and software are also available from the SBML project web site, http://sbml.org/. http://dx.doi.org/10.1038/npre.2008.2715.1 Wed, 24 Dec 2008 15:41:15 UTC Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions doi:10.1038/npre.2008.2715.1 2008-12-24 Nicolas Le Novère Nature Precedings 2008-12-24T15:41:15Z Manuscript Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://precedings.nature.com/documents/2320/version/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. http://precedings.nature.com/documents/2320/version/1 Tue, 23 Sep 2008 20:07:50 UTC Systems Biology Graphical Notation: Process Diagram Level 1 hdl:10101/npre.2008.2320.1 2008-09-23 Nicolas Le Novere Nature Precedings 2008-09-23T20:07:50Z Manuscript Biotechnology Bioinformatics http://creativecommons.org/licenses/by/3.0/ http://dx.doi.org/10.1038/npre.2006.10.1 The practise of Systems Biology relies on interfaces. Interfacesbetween the entities we study: the paradigm moved from a physicalobject centric view toward a relationship-centric one; interfacesbetween tools: From the retrieval of the primary data to the fineanalysis of a model’s behaviour, one uses many tools, more or lesswell connected; interfaces between individuals: To build anynon-trivial mechanistic model requires to merge existing work andgather external expertise.If we want these interfaces to be generic enough to allow for anybodyto leverage on existing toolkits, a fundamental requirement is theexistence of community-developed well supported standards, but alsoopen resources where to find the “lego” blocks. Over the lasthalf-decade, several efforts have been launched in that direction,whether concerning encoding format, ontologies or databases. Some ofthem are now well-established in the field and play a significant roleto improve its coherence but also to increase the size and the qualityof quantitative models. http://dx.doi.org/10.1038/npre.2006.10.1 Thu, 30 Nov 2006 15:13:38 UTC Open Standards and Resources in Systems Biology: collaborative scale-up toward virtual life doi:10.1038/npre.2006.10.1 2006-11-30 Nicolas Le Novere Nature Precedings 2006-11-30T15:13:38Z Presentation Biotechnology http://creativecommons.org/licenses/by/2.5/ http://dx.doi.org/10.1038/npre.2006.9.1 Integration of neurotransmitter and neuromodulator signals in the striatum plays a central role in the functions and dysfunctions of the basal ganglia. DARPP-32 is a key actor of this integration in the GABAergic medium-size spiny neurons, in particular in response to dopamine and glutamate. When phosphorylated by cAMP-dependent protein kinase (PKA) DARPP-32 inhibits protein phosphatase-1 (PP1), whereas when phosphorylated by cyclin-dependent kinase 5 (CDK5) it inhibits PKA. DARPP-32 is also regulated by casein kinases and by several protein phosphatases. These complex and intricate regulations make simple predictions of DARPP-32 dynamic behaviour virtually impossible. We used detailed quantitative modelling of the regulation of DARPP-32 phosphorylation to improve our understanding of its function. The models included all the combinations of the three best characterized phosphorylation sites of DARPP-32, their regulation by kinases and phosphatases, and the regulation of those enzymes by cAMP and Ca2+ signals. Dynamic simulations allowed to observe the temporal relationships between cAMP and Ca2+ signals. We confirmed that the proposed regulation of protein phosphatase-2A (PP2A) by calcium can account for the observed decrease of Threonine 75 phosphorylation upon glutamate receptor activation. Sensitivity analysis showed that CDK5 activity is a major regulator of the response, as previously suggested. Conversely, the regulation of PP2A by PKA or by calcium, had little effect on the function of DARPP-32 in these conditions. The simulations showed that DARPP-32 is not only a robust signal integrator, but also a coincidence detector, the delay between cAMP and calcium signals affecting the response to the latter. This integration did not depend on the concentration of DARPP-32, while the absolute response on PP1 varied linearly. In silico mutants showed that Ser137 phosphorylation affects the coincidence detector function, and that constitutive phosphorylation in Ser137 transforms DARPP-32 in a quasi-irreversible switch. This work is a first attempt to better understand the complex interactions between cAMP and Ca2+ regulation of DARPP-32. Progressive inclusion of additional components should lead to a realistic model of signalling networks underlying the function of striatal neurons. http://dx.doi.org/10.1038/npre.2006.9.1 Thu, 30 Nov 2006 15:06:03 UTC DARPP-32 is a robust integrator of dopamine and glutamate signals doi:10.1038/npre.2006.9.1 2006-11-30 Nicolas Le Novere Nature Precedings 2006-11-30T15:06:03Z Poster Neuroscience http://creativecommons.org/licenses/by/2.5/