Systems Biology Graphical Notation: Activity Flow 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, 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

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

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://sbml.org/

Beyond Structure: KiSAO and TEDDY -- Two Ontologies Addressing Pragmatical and Dynamical Aspects of Computational Models in Systems Biology

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":http://sourceforge.net/projects/miase, for details on TEDDY see the "project page":http://sourceforge.net/projects/teddyontology

Kinetic Simulation Algorithm Ontology

To enable the accurate and repeatable execution of a computational simulation task, it is important to identify both the algorithm used and the initial setup. These minimum information requirements are described by the MIASE guidelines. Since the details of some algorithms are not always publicly available, and many are implemented only in a limited number of simulation tools, it is crucial to identify alternative algorithms with similar characteristics that may be used to provide comparable results in an equivalent simulation experiment. The Kinetic Simulation Algorithm Ontology (KiSAO) was developed to address this issue by describing existing algorithms and their inter-relationships through their characteristics and parameters. The use of KiSAO in conjunction with simulation descriptions, such as SED-ML, will allow simulation software to automatically choose the best algorithm available to perform a simulation. The availability of algorithm parameters, together with their type may permit the automatic generation of user-interfaces to configure simulators. To enable making queries to KiSAO programmaticaly, from simulation experiment description editors and simulation tools, a java library libKiSAO was implemented

Le français dans le monde

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A propósito del género Paralcamenes Bolívar 1909 (Orth. Acridoidea)

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Impact des TIC sur la réussite et la persévérance

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