BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems

Novère, Nicolas Le; Bornstein, B.; Broicher, Alexander; Courtot, Mélanie; Donizelli, Marco; Dharuri, Harish; Li, Lu; Sauro, Herbert M.; Schilstra, Maria J.; Shapiro, Bruce E.; Snoep, Jacky L.; Hucka, Michael

doi:10.1093/nar/gkj092

Cited by 676 publications

(485 citation statements)

References 9 publications

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“…Unlike the CellML repository (Lloyd et al 2008) or Biomodels.net (Le Novere et al 2006), ModelDB hosts models expressed in any simulator format or programming language. Over 80 simulators or programming languages are represented.…”

Section: Modeldb At Presentmentioning

confidence: 99%

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience

et al. 2016

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Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

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Section: Modeldb At Presentmentioning

confidence: 99%

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience

et al. 2016

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“…net repository [35] were used as benchmark to test our reaction system inference algorithm, and compare the results with the original writing of the models in SBML. Out of those 424 models only 361 define reactions with proper kineticLaws.…”

Section: Global Analysismentioning

confidence: 99%

“…This approach was evolved over the years, see for instance [31] for sparse/dense/core solutions when numerical values are provided for the parameters, or [32] for unicity conditions in the symbolic case, still in the restricted framework of mass action law kinetics. In [19], the authors present an algorithm that uncovers hidden structural information for some Systems Biology Markup Language (SBML) [33,34] models of the biomodels.net repository [35], with restricting to reaction models without inhibitors.…”

Section: Introductionmentioning

confidence: 99%

Inferring reaction systems from ordinary differential equations

Fages

Gay

Soliman

2015

Theoretical Computer Science

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In Mathematical Biology, many dynamical models of biochemical reaction systems are presented with Ordinary Differential Equations (ODE). Once kinetic parameter values are fixed, this simple mathematical formalism completely defines the dynamical behavior of a system of biochemical reactions and provides powerful tools for deterministic simulations, parameter sensitivity analysis, bifurcation analysis, etc. However, without requiring any information on the reaction kinetics and parameter values, various qualitative analyses can be performed using the structure of the reactions, provided the reactants, products and modifiers of each reaction are precisely defined. In order to apply these structural methods to parametric ODE models, we study a mathematical condition for expressing the consistency between the structure and the kinetics of a reaction, without restricting to Mass Action law kinetics. This condition, satisfied in particular by standard kinetic laws, entails a remarkable property of independence of the influence graph from the kinetics of the reactions. We derive from this study a heuristic algorithm which, given a system of ODEs as input, computes a system of reactions with the same ODE semantics, by inferring well-formed reactions whenever possible. We show how this strategy is capable of automatically curating the writing of ODE models in SBML, and present some statistics obtained on the model repository biomodels.net. 1 .

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“…Other languages mainly used for annotations of reactions are the web ontology language (OWL) and the biological pathways exchanging language (Biopax; [24]. A free database containing numerous SBML models is available [16].…”

Section: Modelling Nutritional Processes At the Cellular Levelmentioning

confidence: 99%

“…A framework Fig. 2 The cycle of mining, modelling, measuring and remodelling in a system biology approach (modified according to the concept of [1] architecture where the various models could be inserted, may be constructed where the various nutritional models may be interlinked with other depositories like the biomodels database [16].…”

Section: Modelling Nutritional Processes At the Cellular Levelmentioning

confidence: 99%

The challenges for molecular nutrition research 4: the “nutritional systems biology level”

et al. 2008

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Nutritional systems biology may be defined as the ultimate goal of molecular nutrition research, where all relevant aspects of regulation of metabolism in health and disease states at all levels of its complexity are taken into account to describe the molecular physiology of nutritional processes. The complexity spans from intracellular to interorgan dynamics, and involves iterations between mathematical modelling and analysis employing all profiling methods and other biological read-outs. On the basis of such dynamic models we should be enabled to better understand how the nutritional status and nutritional challenges affect human metabolism and health. Although the achievement of this proposition may currently sound unrealistic, many initiatives in theoretical biology and biomedical sciences work on parts of the solution. This review provides examples and some recommendations for the molecular nutrition research arena to move onto the systems level.

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BioModels Database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems

Cited by 676 publications

References 9 publications

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience

Inferring reaction systems from ordinary differential equations

The challenges for molecular nutrition research 4: the “nutritional systems biology level”

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