Dataset Information

BioModels Parameters: a treasure trove of parameter values from published systems biology models.

ABSTRACT: MOTIVATION:One of the major bottlenecks in building systems biology models is identification and estimation of model parameters for model calibration. Searching for model parameters from published literature and models is an essential, yet laborious task. RESULTS:We have developed a new service, BioModels Parameters, to facilitate search and retrieval of parameter values from the Systems Biology Markup Language models stored in BioModels. Modellers can now directly search for a model entity (e.g. a protein or drug) to retrieve the rate equations describing it; the associated parameter values (e.g. degradation rate, production rate, Kcat, Michaelis-Menten constant, etc.) and the initial concentrations. Currently, BioModels Parameters contains entries from over 84,000 reactions and 60 different taxa with cross-references. The retrieved rate equations and parameters can be used for scanning parameter ranges, model fitting and model extension. Thus, BioModels Parameters will be a valuable service for systems biology modellers. AVAILABILITY AND IMPLEMENTATION:The data are accessible via web interface and API. BioModels Parameters is free to use and is publicly available at https://www.ebi.ac.uk/biomodels/parameterSearch. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.

SUBMITTER: Glont M

PROVIDER: S-EPMC7653554 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

BioModels Parameters: a treasure trove of parameter values from published systems biology models.

Glont Mihai M Arankalle Chinmay C Tiwari Krishna K Nguyen Tung V N TVN Hermjakob Henning H Malik-Sheriff Rahuman S RS

Bioinformatics (Oxford, England) 20201101 17

<h4>Motivation</h4>One of the major bottlenecks in building systems biology models is identification and estimation of model parameters for model calibration. Searching for model parameters from published literature and models is an essential, yet laborious task.<h4>Results</h4>We have developed a new service, BioModels Parameters, to facilitate search and retrieval of parameter values from the Systems Biology Markup Language models stored in BioModels. Modellers can now directly search for a mo ...[more]

PMID: 32573648

Similar Datasets

Project description:Quantitative models of biochemical and cellular systems are used to answer a variety of questions in the biological sciences. The number of published quantitative models is growing steadily thanks to increasing interest in the use of models as well as the development of improved software systems and the availability of better, cheaper computer hardware. To maximise the benefits of this growing body of models, the field needs centralised model repositories that will encourage, facilitate and promote model dissemination and reuse. Ideally, the models stored in these repositories should be extensively tested and encoded in community-supported and standardised formats. In addition, the models and their components should be cross-referenced with other resources in order to allow their unambiguous identification.BioModels Database http://www.ebi.ac.uk/biomodels/ is aimed at addressing exactly these needs. It is a freely-accessible online resource for storing, viewing, retrieving, and analysing published, peer-reviewed quantitative models of biochemical and cellular systems. The structure and behaviour of each simulation model distributed by BioModels Database are thoroughly checked; in addition, model elements are annotated with terms from controlled vocabularies as well as linked to relevant data resources. Models can be examined online or downloaded in various formats. Reaction network diagrams generated from the models are also available in several formats. BioModels Database also provides features such as online simulation and the extraction of components from large scale models into smaller submodels. Finally, the system provides a range of web services that external software systems can use to access up-to-date data from the database.BioModels Database has become a recognised reference resource for systems biology. It is being used by the community in a variety of ways; for example, it is used to benchmark different simulation systems, and to study the clustering of models based upon their annotations. Model deposition to the database today is advised by several publishers of scientific journals. The models in BioModels Database are freely distributed and reusable; the underlying software infrastructure is also available from SourceForge https://sourceforge.net/projects/biomodels/ under the GNU General Public License.

Project description:BackgroundMathematical models play a key role in systems biology: they summarize the currently available knowledge in a way that allows to make experimentally verifiable predictions. Model calibration consists of finding the parameters that give the best fit to a set of experimental data, which entails minimizing a cost function that measures the goodness of this fit. Most mathematical models in systems biology present three characteristics which make this problem very difficult to solve: they are highly non-linear, they have a large number of parameters to be estimated, and the information content of the available experimental data is frequently scarce. Hence, there is a need for global optimization methods capable of solving this problem efficiently.ResultsA new approach for parameter estimation of large scale models, called Cooperative Enhanced Scatter Search (CeSS), is presented. Its key feature is the cooperation between different programs ("threads") that run in parallel in different processors. Each thread implements a state of the art metaheuristic, the enhanced Scatter Search algorithm (eSS). Cooperation, meaning information sharing between threads, modifies the systemic properties of the algorithm and allows to speed up performance. Two parameter estimation problems involving models related with the central carbon metabolism of E. coli which include different regulatory levels (metabolic and transcriptional) are used as case studies. The performance and capabilities of the method are also evaluated using benchmark problems of large-scale global optimization, with excellent results.ConclusionsThe cooperative CeSS strategy is a general purpose technique that can be applied to any model calibration problem. Its capability has been demonstrated by calibrating two large-scale models of different characteristics, improving the performance of previously existing methods in both cases. The cooperative metaheuristic presented here can be easily extended to incorporate other global and local search solvers and specific structural information for particular classes of problems.

Dataset Information

BioModels Parameters: a treasure trove of parameter values from published systems biology models.

Publications

BioModels Parameters: a treasure trove of parameter values from published systems biology models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets